Dietary management method and system based on digital twinning

Abstract

The invention discloses a diet management method and system based on digital twinning, and relates to the technical field of diet management, and the method comprises the following steps: collecting basic information data of a user, including personal information, personal disease history, family disease history, diet habit data and recent physical examination data, and sorting and classifying the basic information data, establishing a human hand geometric model according to the basic information data, identifying and tracking hand actions of a user, judging food intake, collecting and preprocessing food intake data, and performing feature extraction on the food intake data to obtain food features and container features; and based on the extracted food features and container features and in combination with a human hand geometric model, evaluating the food capacity of the container, and calculating the nutrient content of the current food material. By monitoring the nutrition intake and the physical state of an individual in real time, abnormal or potential health risks are found in time, the early warning signal is recognized and sent out, and the individual is reminded to take measures in time for adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of dietary management, and in particular to a dietary management method and system based on digital twins. Background Art

[0002] Dietary management plays a vital role in physical health. Reasonable dietary matching can not only provide various nutrients needed by the human body, such as protein, carbohydrates, fat, vitamins and minerals, but also effectively prevent a variety of chronic diseases, such as cardiovascular disease, diabetes and obesity. In addition, dietary management can also help control weight and maintain a suitable body shape, thereby enhancing personal self-confidence and quality of life. However, in real life, many people face challenges in dietary management.

[0003] In the prior art, due to the wide variety of food and different forms, and the profound influence of various external factors such as cooking methods and storage conditions, it is difficult for the system to make scientific choices based on the nutritional components and functions of food. In addition, each person's physical condition, health needs and eating habits are different, and the system cannot accurately assess the user's nutritional needs, which in turn affects the dynamic adjustment of the system. Therefore, a dietary management method and system based on digital twins are proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a dietary management method and system based on digital twins to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] First, the dietary management method based on digital twins includes the following steps:

[0007] Step 1: Collect basic user information data, including personal information, personal medical history, family medical history, dietary habits data, and recent physical examination data, and organize and classify the basic information data.

[0008] Personal information: including age, gender, height, weight, etc.; personal medical history: including diseases the user has suffered from and current health status; family medical history: whether there are certain hereditary diseases in the family; dietary habit data: the user's daily dietary preferences and frequency; recent physical examination data: including physiological indicators such as blood pressure, blood sugar, and blood lipids;

[0009] Step 2: Establish a human hand geometry model based on the basic information data to identify and track the user's hand movements and determine the food intake. At the same time, collect and pre-process the food intake data, and extract features of the food intake data, namely food features and container features. Food features include the type and nutritional ingredients of food, and container features include the shape, size, material, etc. of the container.

[0010] Step 3, based on the extracted food features and container features and combined with the human hand geometry model, the food capacity of the container is evaluated and the nutrient content of the current food is calculated;

[0011] Step 4, extracting nutrition-related factors based on the user's basic information data and establishing a nutrition requirement table, wherein the nutrition-related factors include a food intake factor, an intake nutrient content factor and a body condition factor, specifically: the food intake factor is the amount of food that the user should consume daily, the body condition factor is the user's health status, weight change, etc., and the intake food nutrient content factor is the amount of nutrients that the user should consume daily;

[0012] Step 5: Analyze the correlation between dietary habit data and nutrition-related factors, and construct a nutrition requirement content assessment model to obtain a nutrition requirement content assessment coefficient. At the same time, use the calculated nutrition requirement content assessment coefficient and combine it with the nutrient content of the current ingredients to output dietary management suggestions and plans, such as increasing or decreasing the intake of a certain food, recommending certain nutrient-rich ingredients, etc.

[0013] A further improvement of the technical solution of the present invention is that in step 1, the process of collecting user basic information data is:

[0014] Step 101, establishing a database, including creating a user table, a disease history table, a family disease history table, a dietary habit table, a physical examination data table, etc., determining the association between the tables, and entering the user's personal information, personal disease history, family disease history, dietary habit data, and recent physical examination data, and associating the entered data to obtain the user's complete basic information data;

[0015] Step 102: remove duplicates from the entered data, complete missing information, correct erroneous data, and classify the data according to its characteristics and purpose. At the same time, set a data update cycle, such as updating the user's physical examination data and eating habits data every month or quarter.

[0016] Regularly update users' basic information data, such as physical examination data, dietary habits data, etc., to ensure the timeliness and accuracy of the data and provide support for subsequent analysis and decision-making;

[0017] Step 103, encrypting and controlling access to the database to ensure the security and privacy of user data, and establishing a backup mechanism to regularly back up database data.

[0018] A further improvement of the technical solution of the present invention is that in step 2, the process of determining the food intake is:

[0019] Step 201, using the personal information in the collected basic information of the user, including height, weight, age, and gender, the user measures the size range of the user's hand by himself or using an application, including palm length, width, finger length, etc., and establishes a human hand geometric model;

[0020] Step 202, using a camera and a sensor to collect pictures and videos of the user's hand movements in real time, obtain hand movement information and food intake information, and perform preprocessing, including denoising, contrast enhancement, and cropping of useless areas;

[0021] Step 203, using the acquired hand motion information and food intake information, which should cover various hand postures, lighting conditions, background environments, etc., to train a human hand geometry model to extract the precise contour and shape of the hand;

[0022] Step 203, based on the preprocessed hand movement information and food intake information, extract the associated features of hand movement and food intake, which are food features and container features, and analyze the association between the features, wherein food features can be extracted based on the type, color, shape, etc. of food, and container features can be extracted based on size, shape, color, etc.

[0023] A further improvement of the technical solution of the present invention is that in step 3, the process of calculating the nutrient content of the current food is:

[0024] Step 301, based on the extracted container features, the type, shape and size of the container are analyzed, and the size of the container is determined in combination with the human hand geometric model, and the food capacity of the container is evaluated to obtain the food intake; the food capacity is mainly determined in three ways: 1) If a container is used, the volume of the container can be measured to obtain the food capacity; 2) If the container capacity is unknown, the container capacity can be estimated by taking a photo of the hand holding the container, and the geometric relative relationship between the user's fingers, palms and the length and height of the container is compared when the geometric features of the user's hand are known; 3) If the food is not placed in a container, such as apples, bread and other foods, a photo of the hand holding the food can be taken, and the size of the food can be estimated by comparing the geometric relationship between the user's fingers, palms and the contours of the real object;

[0025] Step 302, identifying the type of food based on the extracted food features, and determining the corresponding type of nutrients, such as protein, fat, carbohydrates, vitamins and minerals, according to the published food nutrition table;

[0026] Step 303: Combine the assessed food container capacity with the determined nutritional components of the food to calculate the total content of various nutritional components in the current food.

[0027] A further improvement of the technical solution of the present invention is that in step 4, the process of extracting nutrition-related factors is:

[0028] Step 401, extracting food types and intake information from the dietary habit data, including the types, quantity, weight, volume and frequency of food consumed, classifying and arranging the food types, such as grains, vegetables, fruits, meat, dairy products, oils, etc., and counting the total intake of each type of food in a fixed period such as a week or a month;

[0029] Step 402, according to the intake of each type of food, analyzing the proportion of each type of food intake to the total food intake, and obtaining a food intake factor;

[0030] Step 403, according to the type and amount of food ingested by the user, combined with the food nutrition table, obtain the ingested nutrition content factor and various nutrients ingested by the user every day;

[0031] Step 404, assessing the user's physical condition and nutritional needs based on the user's personal medical history, family medical history, and recent physical examination data, and obtaining a physical condition factor;

[0032] Step 405, based on the user's personal information, including age, gender, height and weight, as well as physical health status and eating habits data, determine the user's nutritional needs, combine the extracted nutrition-related factors with the user's nutritional needs, and construct a nutritional needs table. The nutritional needs table includes key information such as the total amount of various nutrients consumed by the user on a daily basis, the recommended intake, and the comparison between the actual intake and the recommended intake. At the same time, the food intake index, the intake nutrient content index and the physical condition index are obtained to evaluate the sufficient and excessive status of the user's nutrient intake and analyze the intake trend of the nutrient components.

[0033] A further improvement of the technical solution of the present invention is that the calculation formula of the food intake index is:

[0034]

[0035] Among them, FII is the food intake index, I is the actual amount of food consumed by the user, BI is the benchmark food intake, that is, the recommended daily intake, and SF is the standard deviation of the intake; the BI benchmark food intake can be set by crawling data of healthy people with the same basic parameters as the user's age, gender, etc. from the Internet through the big data model; it can also be set for users in a targeted manner based on expert experience by nutrition experts; or it can be set after evaluation based on the user's own historical data and combined with health status.

[0036] The calculation formula of the nutrient intake content index is:

[0037]

[0038] Among them, NII is the nutrient content intake index, N is the total amount of nutrients actually ingested by the user, BN is the benchmark nutrient intake, that is, the recommended daily nutrient intake, and SN is the standard deviation of nutrient intake; the BN benchmark nutrient intake can be set by crawling data of healthy people with the same basic parameters as the user's age, gender, etc. from the Internet through the big data model; it can also be set for users in a targeted manner based on expert experience by nutrition experts; or it can be set after evaluation based on the user's own historical data and combined with health status.

[0039] The calculation formula of the body condition index is:

[0040]

[0041] Among them, HSI is the body state index, H is the user's body state index, BH is the baseline body state index, that is, the ideal body state index, and SH is the standard deviation of the body state index. The BH baseline body state index can be set by crawling the data of healthy people with the same basic parameters as the user's age, gender, etc. from the Internet through the big data model; it can also be set by doctors based on expert experience for users; or it can be set after evaluating the user's own historical data combined with health status.

[0042] A further improvement of the technical solution of the present invention is that in step 5, the process of obtaining the nutritional requirement content assessment coefficient and the dietary management suggestions and plans is as follows:

[0043] Step 501, combining the food intake index, the intake nutrient content index and the body condition index, analyzing the correlation between the eating habit data and the nutrition-related factors, and constructing a nutrient requirement content assessment model to analyze the impact of each factor on the total nutrient intake;

[0044] Step 502: Assign different weights to the food intake index, the nutrient intake content index, and the physical condition index, and perform weighted calculations to obtain the nutritional requirement content evaluation coefficient, and generate a user's nutritional intake report, which includes the total amount of nutrients ingested, the intake ratio of various nutrients, the comparison with the ideal intake, etc.;

[0045] Step 503: Match the user's physical health status according to the eating habit data, and determine different status levels, namely the optimal status level, the good status level, the general status level, and the poor status level;

[0046] Step 504: Match the determined different status levels with the results of the nutritional requirement content evaluation coefficient, and set corresponding status evaluation thresholds for different status levels;

[0047] Step 505: Compare the user's nutritional requirement content evaluation coefficient with the corresponding status level threshold to determine the user's current status level;

[0048] Step 506: Combine the user's food ingredient intake and the food ingredient nutrition database, calculate the total amount of various nutrients actually ingested by the user, compare the user's actual nutrient intake with the nutritional requirement content evaluation coefficient, analyze whether the user's nutrient intake meets the individual nutritional requirements, obtain the analysis results, and generate personalized dietary management suggestions for the user, including: Adjust food ingredient selection: Select suitable food ingredients according to nutritional requirements to ensure sufficient nutrient intake. Optimize the dietary structure: Reasonably arrange the types and portions of food for each meal to ensure the balance and diversity of the diet. Control the cooking method: Choose healthy cooking methods such as steaming, boiling, roasting, or stir-frying, and avoid excessive frying, adding sugar or salt. Adjust the meal frequency: According to the individual's eating habits and activity level, reasonably arrange the meal time and frequency. Provide nutritional supplement suggestions: If it is difficult to meet certain nutrients through diet, appropriate nutritional supplements can be considered.

[0049] A further improvement of the technical solution of the present invention lies in that: The calculation formula of the nutritional requirement content evaluation coefficient is:

[0050] NDEI = a·FII + β·NII + γ·HSI

[0051] Wherein, NDEI is the nutritional requirement content evaluation coefficient, FII is the food intake index, NII is the nutrient intake content index, HSI is the physical condition index, α, β, and γ are weight coefficients, and are positive numbers greater than 0 and less than 1.

[0052] A further improvement of the technical solution of the present invention lies in that: Multiple said status levels correspond to multiple said status level thresholds, wherein, said status level thresholds include upper limit thresholds and lower limit thresholds;

[0053] The multiple state levels and the multiple state level thresholds satisfy the following relationship:

[0054] The best status level is NDEI ≥ A;

[0055] Good status level A>NDEI≥B;

[0056] General status level B>NDEI≥c;

[0057] Poor status level c>NDEI;

[0058] Among them, NDEI is the nutritional requirement content assessment coefficient, C is the upper threshold corresponding to the poor status level and the lower threshold corresponding to the general status level, B is the upper threshold corresponding to the general status level and the lower threshold corresponding to the good status level, and A is the upper threshold corresponding to the good status level and the lower threshold corresponding to the optimal status level.

[0059] In the second aspect, a dietary management system based on digital twins is used to implement a dietary management method based on digital twins, including a user management center, wherein the user management center is communicatively connected to a user information collection module, a digital twin model construction module, a dietary analysis module, a nutritional demand assessment module, and a dietary management module, wherein electrical signals are connected between the modules;

[0060] The user information collection module is used to collect user data, including the user's basic information, personal medical history, family medical history, dietary habits data and recent physical examination data, to build a user portrait, so that the system can deeply understand the user's physical condition and dietary behavior pattern, laying the foundation for personalized dietary management;

[0061] The digital twin model building module is used to build a digital twin model of the user based on the collected user data, including a hand motion model and a dietary model, and monitor and analyze food intake, such as the amount and volume of food;

[0062] The dietary analysis module is used to analyze the dietary intake of the user, extract nutrition-related factors, including food intake factor, intake nutrient content factor and body condition factor, analyze and evaluate the dietary nutritional status of the user; use image recognition technology to identify food types, and analyze the nutritional components of food in combination with the food nutritional component database to provide users with detailed dietary nutritional analysis, including the intake of various nutrients;

[0063] The nutritional requirement assessment module is used to calculate the nutritional requirement content assessment coefficient based on the user's food intake factor, intake nutritional content factor, and physical condition factor, analyze the degree of correlation between dietary habit data and nutritional correlation factors, generate a personalized nutritional requirement table, assess the user's health status and nutritional requirements, and output an assessment report;

[0064] The dietary management module is used to formulate a personalized dietary recommendation plan for the user based on the evaluation report of the nutritional needs evaluation module, combined with the user's dietary preferences and actual life situation, to ensure that the user's dietary intake meets nutritional needs and improve the user's health level and quality of life.

[0065] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0066] 1. The present invention provides a dietary management method and system based on digital twins, which monitors the nutritional intake and physical condition of individuals in real time, promptly detects abnormal or potential health risks, identifies and issues early warning signals, and reminds individuals to take timely measures to make adjustments, which helps individuals to promptly detect health problems, take preventive measures, and avoid the occurrence and development of diseases.

[0067] 2. The present invention provides a dietary management method and system based on digital twins. By constructing an individual digital twin model, it can comprehensively and accurately reflect the individual's nutritional needs, eating habits and physical condition. The digital twin model updates the individual's nutritional intake data in real time and dynamically adjusts it according to the individual's physical condition and nutritional needs.

[0068] 3. The present invention provides a dietary management method and system based on digital twins, which combines the individual's physical condition, nutritional needs and living habits to provide individuals with a precise nutritional management plan. It not only helps individuals to reasonably match their diet and ensure a balanced intake of nutrients, but also can promptly detect and correct bad eating habits, thereby effectively preventing the occurrence of problems such as overnutrition or malnutrition.

[0069] 4. The present invention provides a dietary management method and system based on digital twins, which monitor the individual's dietary intake data and physical condition data in real time. Once an abnormality or potential health risk is found, the system immediately sends out a warning signal, which helps the individual to promptly discover and deal with health problems and avoid worsening of the disease or the occurrence of complications. At the same time, the system provides targeted dietary adjustment suggestions based on the warning results to help the individual quickly restore a healthy state. By reducing health risks, the system provides strong protection for the long-term health of the individual. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0071] Figure 1 is a flow chart of the method of the present invention;

[0072] Figure 2 A flowchart for obtaining the nutrition-related factors and the nutrition requirement table of the present invention;

[0073] Figure 3 A flowchart for obtaining the nutritional requirement content assessment coefficient and dietary management suggestions and plans of the present invention;

[0074] Figure 4 It is a module structure diagram of the present invention. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Embodiment 1, as Figures 1 to 3 As shown, the present invention provides a dietary management method based on digital twins, comprising the following steps:

[0077] Step 1, collect user basic information data, including personal information, personal medical history, family medical history, dietary habits data and recent physical examination data, and organize and classify the basic information data, including personal information: including age, gender, height, weight, etc., personal medical history: including diseases the user has suffered from and current health status, family medical history: whether there are certain hereditary diseases in the family, dietary habits data: user's daily dietary preferences, frequency, etc., recent physical examination data: including physiological indicators such as blood pressure, blood sugar, and blood lipids; the process of collecting user basic information data is: establishing a database, including creating user tables, disease history tables, family disease history tables, dietary habits tables, physical examination data tables, etc., determining the association between the tables, The user's personal information, personal medical history, family medical history, dietary habits data and recent physical examination data are entered, and the entered data are associated to obtain the user's complete basic information data, and the entered data is deduplicated, missing information is supplemented and erroneous data is corrected. The data is classified according to its characteristics and purpose, and a data update cycle is set, such as updating the user's physical examination data and dietary habits data monthly or quarterly. The user's basic information data, such as physical examination data and dietary habits data, is regularly updated to ensure the timeliness and accuracy of the data, to provide support for subsequent analysis and decision-making, to encrypt data and control access to the database, to ensure the security and privacy of user data, and to establish a backup mechanism to regularly back up database data;

[0078] Step 2: A human hand geometry model is established based on the basic information data to identify and track the user's hand movements and determine the amount of food intake. At the same time, the food intake data is collected and preprocessed, and feature extraction is performed on the food intake data, which includes food features and container features. The food features include the type and nutritional ingredients of the food, and the container features include the shape, size, and material of the container. The process of determining the amount of food intake is to use the personal information collected from the basic information of the user, including height, weight, age, and gender, and the user self-measures or uses an application to measure the size range of the user's hand, including palm length, width, finger length, etc., and establish a human hand geometry model. The camera and sensor are used to collect the user's hand movements in real time. The hand motion information and food intake information are obtained by using pictures and videos of the hand movements, and preprocessing is performed, including denoising, contrast enhancement, and cropping of useless areas. The obtained hand motion information and food intake information are used, and these data should cover various hand postures, lighting conditions, background environments, etc. The human hand geometry model is trained to extract the precise contour and shape of the hand. Based on the preprocessed hand motion information and food intake information, the associated features of hand motion and food intake are extracted, which are food features and container features, and the association between the features is analyzed, wherein the food features can be extracted based on the type, color, shape, etc. of the food, and the container features can be extracted based on the size, shape, color, etc.;

[0079] Step 3, based on the extracted food features and container features combined with the human hand geometry model, evaluate the food capacity of the container and calculate the nutritional content of the current food; the process of calculating the nutritional content of the current food is: based on the extracted container features, analyze the type, shape and size of the container, and combine the human hand geometry model to determine the size of the container, and evaluate the food capacity of the container. The food capacity is mainly determined in three ways: 1) If a container is used, the volume of the container can be measured to obtain the food capacity; 2) If the container capacity is unknown, a photo of the hand holding the container can be taken, and the user's finger can be compared when the user's hand geometry is known. , the geometric relative relationship between the palm and the length and height of the container to estimate the capacity of the container; 3) If the food is not placed in a container, such as apples, bread and other foods, you can take a photo of the food in your hand, and estimate the size of the food by comparing the geometric relationship between the user's fingers, palm and the outline of the real object to obtain the food intake, identify the type of food based on the extracted food features, and determine the corresponding type of nutrients according to the published food nutrition table, such as protein, fat, carbohydrates, vitamins and minerals, etc., combine the assessed food capacity of the container with the determined nutritional components of the food, and calculate the total content of various nutrients in the current food;

[0080] Step 4, extracting nutrition-related factors according to the user's basic information data, and establishing a nutrition requirement table, wherein the nutrition-related factors include food intake factors, intake nutrient content factors and body condition factors, specifically: the food intake factor is the amount of food that the user should ingest daily, the body condition factor is the user's health status, weight change, etc., and the intake food nutrient content factor is the amount of nutrients that the user should ingest daily; the process of extracting nutrition-related factors is: extracting food types and intake information from the dietary habit data, including the types, quantities, weights, volumes and meal frequencies of the consumed foods, classifying and arranging the food types, such as cereals, vegetables, fruits, meats, dairy products, oils, etc., counting the total intake of each type of food in a fixed period such as a week or a month, and analyzing the proportion of each type of food intake to the total food intake based on the intake of each type of food, to obtain the food intake. Intake factor: Based on the types and quantities of food consumed by the user, combined with the food nutrition table, the intake nutrition content factor and the various nutrients consumed by the user on a daily basis are obtained. Based on the user's personal medical history, family medical history and recent physical examination data, the user's physical condition and nutritional needs are evaluated. The physical condition factor is obtained. Based on the user's personal information, including age, gender, height and weight, as well as physical health status and eating habits data, the user's nutritional needs are determined. The extracted nutrition-related factors are combined with the user's nutritional needs to construct a nutritional needs table. The nutritional needs table includes key information such as the total amount of various nutrients consumed by the user on a daily basis, the recommended intake, and the comparison between the actual intake and the recommended intake. At the same time, the food intake index, the intake nutrition content index and the physical condition index are obtained to evaluate the sufficient and excessive state of the user's nutrient intake and analyze the intake trend of nutrients.

[0081] Step 5, analyzing the correlation between the dietary habit data and the nutrition-related factors, and constructing a nutrition requirement content assessment model to obtain the nutrition requirement content assessment coefficient, and at the same time using the calculated nutrition requirement content assessment coefficient and combining it with the nutrient content of the current food, output dietary management suggestions and plans, such as increasing or decreasing the intake of a certain food, recommending certain nutrient-rich ingredients, etc.; the process of obtaining the nutrition requirement content assessment coefficient and the dietary management suggestions and plans is as follows: combining the food intake index, the intake nutrient content index and the body condition index, analyzing the correlation between the dietary habit data and the nutrition-related factors, and at the same time constructing a nutrition requirement content assessment model, analyzing the impact of each factor on the total nutrient intake, and obtaining the food intake index. 、Different weights are assigned to the nutrient content index and the body state index, and weighted calculation is performed to obtain the nutrient requirement content assessment coefficient, and a nutrient intake report for the user is generated. The report includes the total amount of nutrient intake, the intake ratio of various nutrients, the comparison with the ideal intake, etc. The user's physical health status is matched according to the dietary habit data to determine different status levels, namely, the best status level, the good status level, the general status level, and the poor status level. The determined different status levels are matched with the results of the nutrient requirement content assessment coefficient, and corresponding status assessment thresholds are set for different status levels. The user's nutrient requirement content assessment coefficient is compared with the corresponding status level threshold to determine the user's current status level. Combined with the user's food intake and the food nutrient composition database, the user's actual The total amount of various nutrients actually consumed by the user is compared with the nutritional requirement content assessment coefficient, and the user's actual nutrient intake is analyzed to see whether the user's nutrient intake meets personal nutritional needs. The analysis results are obtained and personalized dietary management suggestions are generated for the user, including: Adjusting ingredient selection: Select appropriate ingredients according to nutritional needs to ensure adequate intake of nutrients, Optimizing dietary structure: Reasonably arrange the types and amounts of food for each meal to ensure a balanced and diverse diet, Controlling cooking methods: Choose healthy cooking methods such as steaming, boiling, baking or stir-frying, and avoid excessive frying or adding sugar and salt, Adjusting meal frequency: Reasonably arrange meal time and frequency according to personal eating habits and activity levels, and Providing nutritional supplement suggestions: If certain nutrients are difficult to meet through diet, appropriate nutritional supplements can be considered.

[0082] Embodiment 2, as Figures 1 to 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, the calculation formula of the food intake index is:

[0083]

[0084] Among them, FII is the food intake index, I is the actual amount of food consumed by the user, BI is the benchmark food intake, that is, the recommended daily intake, and SF is the standard deviation of the intake; the BI benchmark food intake can be set by crawling data of healthy people with the same basic parameters as the user's age, gender, etc. from the Internet through the big data model; it can also be set for users in a targeted manner based on expert experience by nutrition experts; or it can be set after evaluation based on the user's own historical data and combined with health status.

[0085] The calculation formula for the nutrient content index is:

[0086]

[0087] Among them, NII is the nutrient content intake index, N is the total amount of nutrients actually ingested by the user, BN is the benchmark nutrient intake, that is, the recommended daily nutrient intake, and SN is the standard deviation of nutrient intake; the BN benchmark nutrient intake can be set by crawling data of healthy people with the same basic parameters as the user's age, gender, etc. from the Internet through the big data model; it can also be set for users in a targeted manner based on expert experience by nutrition experts; or it can be set after evaluation based on the user's own historical data and combined with health status.

[0088] The calculation formula of body condition index is:

[0089]

[0090] Among them, HSI is the body state index, H is the user's body state index, BH is the baseline body state index, that is, the ideal body state index, and SH is the standard deviation of the body state index. The BH baseline body state index can be set by crawling the data of healthy people with the same basic parameters as the user's age, gender, etc. from the Internet through the big data model; it can also be set by doctors based on expert experience for users; or it can be set after evaluating the user's own historical data combined with health status;

[0091] The calculation formula for the nutritional requirement content assessment coefficient is:

[0092] NDEI=α·FII+β·NII+γ·HSI

[0093] Among them, NDEI is the nutritional requirement content assessment coefficient, FII is the food intake index, NII is the intake nutritional content index, HSI is the body state index, α, β, γ are weight coefficients, which are positive numbers greater than 0 and less than 1;

[0094] The multiple status levels correspond to multiple status level thresholds, wherein the status level threshold includes an upper threshold and a lower threshold;

[0095] The multiple status levels and the multiple status level thresholds satisfy the following relationship:

[0096] The best status level is NDEI ≥ A;

[0097] Good status level A>NDEI≥B;

[0098] General status level B>NDEI≥C;

[0099] Poor status level C>NDEI;

[0100] Among them, NDEI is the nutritional requirement content assessment coefficient, C is the upper threshold corresponding to the poor status level and the lower threshold corresponding to the general status level, B is the upper threshold corresponding to the general status level and the lower threshold corresponding to the good status level, and A is the upper threshold corresponding to the good status level and the lower threshold corresponding to the optimal status level.

[0101] Embodiment 3, as Figure 4 As shown, on the basis of Embodiment 1-2, the present invention provides a dietary management system based on digital twins, including a user management center, the user management center is communicatively connected with a user information collection module, a digital twin model construction module, a dietary analysis module, a nutritional demand assessment module and a dietary management module, wherein the modules are connected by electrical signals;

[0102] The user information collection module is used to collect user data, including the user's basic information, personal medical history, family medical history, dietary habits data, and recent physical examination data, to build a user portrait, so that the system can deeply understand the user's physical condition and dietary behavior patterns, laying the foundation for personalized dietary management;

[0103] The digital twin model building module is used to build a digital twin model of the user based on the collected user data, including a hand motion model and a dietary model, and monitor and analyze food intake, such as the amount and volume of food;

[0104] The dietary analysis module is used to analyze the user's dietary intake, extract nutrition-related factors, including food intake factor, intake nutrient content factor and physical condition factor, and analyze and evaluate the user's dietary nutritional status; use image recognition technology to identify food types, and analyze the nutritional components of food in combination with the food nutritional component database to provide users with detailed dietary nutritional analysis, including the intake of various nutrients;

[0105] The nutritional requirement assessment module is used to calculate the nutritional requirement content assessment coefficient based on the user's food intake factor, nutritional content factor, and physical condition factor, analyze the correlation between dietary habit data and nutritional correlation factors, generate a personalized nutritional requirement table, assess the user's health status and nutritional requirements, and output an assessment report;

[0106] The dietary management module is used to formulate personalized dietary recommendation plans for users based on the assessment report of the nutritional needs assessment module, combined with the user's dietary preferences and actual life situation, to ensure that the user's dietary intake meets nutritional needs and improve the user's health level and quality of life.

[0107] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A dietary management method based on digital twins, characterized by: The following steps are involved: Step 1: Collect basic user information data, including personal information, personal medical history, family medical history, dietary habits data, and recent physical examination data, and organize and classify the basic information data; Step 2: Establish a human hand geometry model based on the basic information data, identify and track the user's hand movements, and determine the food intake. At the same time, collect and pre-process the food intake data, and extract features of the food intake data, namely food features and container features; Step 3, based on the extracted food features and container features and combined with the human hand geometry model, the food capacity of the container is evaluated and the nutrient content of the current food is calculated; Step 4, extracting nutrition-related factors based on the user's basic information data and establishing a nutrition requirement table, wherein the nutrition-related factors include food intake factors, intake nutrient content factors and physical condition factors; Step 5: Analyze the correlation between dietary habit data and nutrition-related factors, and construct a nutrition requirement content assessment model to obtain a nutrition requirement content assessment coefficient. At the same time, use the calculated nutrition requirement content assessment coefficient and combine it with the nutritional content of the current ingredients to output dietary management suggestions and plans.

2. The dietary management method based on digital twin according to claim 1, characterized in that: In step 1, the process of collecting user basic information data is as follows: Step 101, establishing a database, including creating a user table, a disease history table, a family disease history table, a dietary habit table, a physical examination data table, etc., determining the association relationship between the tables, and entering the user's personal information, personal disease history, family disease history, dietary habit data and recent physical examination data, and associating the entered data; Step 102, de-duplicate the entered data, complete missing information, correct erroneous data, and classify the data according to its characteristics and purpose. At the same time, set a data update cycle and regularly update the user's basic information data; Step 103, encrypting and controlling access to the database to ensure the security and privacy of user data, and establishing a backup mechanism to regularly back up database data.

3. The dietary management method based on digital twin according to claim 2, characterized in that: In step 2, the process of determining food intake is: Step 201, using the personal information in the collected basic information of the user, including height, weight, age, and gender, the user measures the size range of the user's hand by himself or by using an application, and establishes a human hand geometric model; Step 202, using a camera and a sensor to collect pictures and videos of the user's hand movements in real time, obtain hand movement information and food intake information, and perform preprocessing, including denoising, contrast enhancement, and cropping of useless areas; Step 203, using the acquired hand motion information and food intake information, training a human hand geometric model to extract the precise contour and shape of the hand; Step 203 , based on the pre-processed hand motion information and food intake information, extract the associated features of the hand motion and food intake, which are food features and container features, and analyze the association relationship between the features.

4. The dietary management method based on digital twin according to claim 3, characterized in that: In step 3, the process of calculating the nutrient content of the current food is as follows: Step 301, analyzing the type, shape and size of the container based on the extracted container features, and judging the size of the container in combination with the human hand geometry model, and evaluating the food capacity of the container to obtain the food intake; Step 302, identifying the type of food based on the extracted food features, and determining the nutritional components of the corresponding types according to the published food nutritional composition table; Step 303: Combine the assessed food container capacity with the determined nutritional components of the food to calculate the total content of various nutritional components in the current food.

5. The dietary management method based on digital twin according to claim 4, characterized in that: In step 4, the process of extracting nutrition-related factors is as follows: Step 401, extracting food types and intake information from the dietary habit data, including the types, quantity, weight, volume and frequency of food consumed, classifying and arranging the food types, and calculating the total intake of each type of food in a fixed period; Step 402, according to the intake of each type of food, analyzing the proportion of each type of food intake to the total food intake, and obtaining a food intake factor; Step 403, obtaining an intake nutrient content factor based on the type and amount of food ingested by the user and in combination with a food nutrient composition table; Step 404, assessing the user's physical condition and nutritional needs based on the user's personal medical history, family medical history, and recent physical examination data, and obtaining a physical condition factor; Step 405, based on the user's personal information, including age, gender, height and weight, as well as physical health status and eating habits data, determine the user's nutritional needs, combine the extracted nutrition-related factors with the user's nutritional needs, construct a nutritional needs table, and simultaneously obtain the food intake index, intake nutrient content index and body status index, evaluate the user's nutrient intake status, and analyze the nutrient intake trend.

6. The dietary management method based on digital twin according to claim 5, characterized in that: The calculation formula of the food intake index is: Among them, FII is the food intake index, I is the actual amount of food consumed by the user, BI is the benchmark food intake, that is, the recommended daily intake, and SF is the standard deviation of the intake; The calculation formula of the nutrient intake content index is: Among them, NII is the nutrient content index, N is the total amount of nutrients actually ingested by the user, BN is the benchmark nutrient intake, that is, the recommended daily nutrient intake, and SN is the standard deviation of nutrient intake; The calculation formula of the body condition index is: Among them, HSI is the body state index, H is the user's body state index, BH is the benchmark body state index, that is, the ideal body state index, and SH is the standard deviation of the body state index.

7. The dietary management method based on digital twin according to claim 6, characterized in that: In step 5, the process of obtaining the nutritional requirement content assessment coefficient and the dietary management suggestions and plans is as follows: Step 501, combining the food intake index, the intake nutrient content index and the body condition index, analyzing the correlation between the eating habit data and the nutrition-related factors, and constructing a nutrient requirement content assessment model to analyze the impact of each factor on the total nutrient intake; Step 502, assigning different weights to the food intake index, the intake nutrient content index and the body condition index and performing weighted calculation to obtain a nutrient requirement content assessment coefficient, and generating a nutrient intake report for the user; Step 503, matching the user's physical health status according to the eating habit data, and determining different status levels, namely, the best status level, the good status level, the average status level, and the poor status level; Step 504, matching the determined different state levels with the results of the nutritional requirement content assessment coefficient, and setting corresponding state assessment thresholds for the different state levels; Step 505, comparing the user's nutritional requirement content assessment coefficient with the corresponding state level threshold to determine the user's current state level; Step 506, combining the user's food intake and the food nutrient database, calculates the total amount of various nutrients actually ingested by the user, compares the user's actual nutrient intake with the nutritional requirement content assessment coefficient, analyzes whether the user's nutrient intake meets personal nutritional requirements, obtains analysis results, and generates personalized dietary management recommendations for the user.

8. The dietary management method based on digital twin according to claim 7, characterized in that: The calculation formula of the nutritional requirement content assessment coefficient is: NDEI=α·FII+β·NII+γ·HSI Among them, NDEI is the nutritional requirement content assessment coefficient, FII is the food intake index, NII is the nutritional content index, HSI is the body state index, α, β, γ are weight coefficients, which are positive numbers greater than 0 and less than 1.

9. The dietary management method based on digital twin according to claim 8, characterized in that: The plurality of state levels correspond to the plurality of state level thresholds, wherein the state level thresholds include an upper threshold and a lower threshold; The multiple state levels and the multiple state level thresholds satisfy the following relationship: The best status level is NDEI ≥ A; Good status level A>NDEI≥B; General status level B>NDEI≥C; Poor status level C>NDEI; Among them, NDEI is the nutritional requirement content assessment coefficient, C is the upper threshold corresponding to the poor status level and the lower threshold corresponding to the general status level, B is the upper threshold corresponding to the general status level and the lower threshold corresponding to the good status level, and A is the upper threshold corresponding to the good status level and the lower threshold corresponding to the optimal status level.

10. A dietary management system based on digital twins, used to implement the dietary management method based on digital twins according to any one of claims 1 to 9, comprising a user management center, characterized in that: The user management center is communicatively connected to a user information collection module, a digital twin model construction module, a dietary analysis module, a nutritional demand assessment module, and a dietary management module, wherein electrical signals are connected between the modules; The user information collection module is used to collect user data, including the user's basic information, personal medical history, family medical history, dietary habit data, and recent physical examination data, to build a user portrait; The digital twin model construction module is used to construct a digital twin model of the user based on the collected user data, including a hand motion model and a dietary model, and monitor and analyze food intake; The dietary analysis module is used to analyze the dietary intake of the user, extract nutrition-related factors, including food intake factor, intake nutrient content factor and body condition factor, and analyze and evaluate the dietary nutritional status of the user; The nutritional requirement assessment module is used to calculate the nutritional requirement content assessment coefficient based on the user's food intake factor, intake nutritional content factor, and physical condition factor, analyze the degree of correlation between dietary habit data and nutritional correlation factors, generate a personalized nutritional requirement table, assess the user's health status and nutritional requirements, and output an assessment report; The dietary management module is used to formulate a personalized dietary recommendation plan for the user based on the evaluation report of the nutritional needs evaluation module and in combination with the user's dietary preferences and actual living conditions.

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