Nutritional diet management method, device and equipment and storage medium

Abstract

The invention provides a nutritional diet management method and device, equipment and a storage medium, and the method comprises the steps: obtaining whole genome information of a user, the whole genome information of the user being used for obtaining a gene related to nutrient substance metabolism of the user, and obtaining metabolism data of the user; acquiring daily life style data of the user; on the basis of the health behavior mode of the user and the influence of the health behavior mode on nutritional requirements, energy, macro nutrient and micronutrient intake required by the user every day are obtained through a preset nutrition calculation model, and the nutrient proportion is dynamically adjusted according to the health target of the user; screening a food recommendation list suitable for the user based on the food supply condition, the seasonal change and the personal taste preference of the region where the user is located and the nutrient proportion required by the user; a visual nutrition diet plan is provided for the user, the user performs interactive adjustment on the nutrition diet plan according to requirements, and the method and the device can provide a comprehensive, personalized and efficient nutrition diet management scheme for the user.

Description

Technical Field

[0001] The present application relates to the technical field of nutritional diet management, and specifically relates to a nutritional diet management method, device, equipment, and storage medium. Background Art

[0002] A nutritional diet is a dietary planning model aimed at meeting the energy requirements of the human body's normal physiology and biochemistry, maintaining health, and preventing diseases. The core points focus on the scientific and reasonable configuration of various foods in terms of types, quantities, proportions, cooking methods, and other dimensions.

[0003] Traditional nutritional diet management models have significant drawbacks. The suggestions given are often relatively broad and general, and do not deeply consider the unique differences of individuals at the genetic level and in terms of metabolic functions. For example, for two people with similar ages, genders, and exercise levels, even though their external conditions are similar, due to differences in genes, their absorption and utilization efficiencies of specific nutrients may be very different. Moreover, the health status of users is constantly evolving, and lifestyle, especially exercise level, is not static. However, traditional models are difficult to quickly and accurately adjust the nutritional diet plan adaptively in the face of these dynamic changes. Furthermore, in the past, in the absence of effective nutritional trend analysis means, neither ordinary individuals in self-diet planning nor professional nutritional diet practitioners in formulating plans could accurately capture the forefront trends in the field of nutritional diets, thereby greatly reducing the scientific nature and timeliness of diet management. Therefore, how to overcome the above-mentioned technical problems and defects has become a problem that needs to be solved. Summary of the Invention

[0004] In order to overcome the above problems existing in the prior art, the present application provides a nutritional diet management method, device, equipment, and storage medium, and adopts the following technical solutions:

[0005] In the first aspect, the present application provides a nutritional diet management method, including:

[0006] Obtain the user's whole genome information, where the user's whole genome information is used to obtain the genes related to the user's nutrient metabolism;

[0007] Obtain the user's metabolic data;

[0008] Obtain the user's daily lifestyle data;

[0009] Based on the user's health behavior pattern and its impact on nutritional needs, through a preset nutritional calculation model, obtain the daily required energy, macronutrient, and micronutrient intakes of the user, and dynamically adjust the nutrient ratio according to the user's health goals;

[0010] Filter a list of food recommendations suitable for the user based on the food supply situation in the user's region, seasonal changes, personal taste preferences, and the proportion of nutrients required by the user.

[0011] Provide the user with a visual nutritional diet plan, and the user can interactively adjust the nutritional diet plan according to their needs.

[0012] Furthermore, obtain the user's whole genome information, which is used to obtain genes related to the user's nutrient metabolism, including:

[0013] Based on the user's whole genome information, establish a gene-nutrient association database to predict the user's absorption, metabolism, and utilization abilities of different nutrients.

[0014] Furthermore, obtain the user's metabolic data, including:

[0015] Analyze the user's metabolic data to construct an individual metabolic dynamic model; based on the individual metabolic dynamic model, when the metabolic index fluctuates abnormally, immediately trigger an early warning mechanism, and provide immediate dietary adjustment suggestions according to the user's individual metabolic characteristics.

[0016] Furthermore, analyze the user's metabolic data to construct an individual metabolic dynamic model, including:

[0017] Select key metabolic data indicators, and collect key metabolic data indicators at preset time intervals;

[0018] Sort the collected various metabolic data in chronological order, and label the collection time and the corresponding user identifier for each data point;

[0019] Analyze the various metabolic data labeled with the collection time and the corresponding user identifier to obtain the data analysis results of the various metabolic data;

[0020] Based on the data analysis results of the various metabolic data, construct characteristic variables;

[0021] Use a long short-term memory network to construct an individual metabolic dynamic model, and use the constructed characteristic variables and historical metabolic data to train the long short-term memory network model;

[0022] Apply the trained long short-term memory network model to actual metabolic data prediction to monitor the user's metabolic status in real time.

[0023] Furthermore, obtain the user's daily lifestyle data, including:

[0024] Conduct an association analysis of daily lifestyle, genes related to nutrient metabolism, and metabolic data to obtain the user's healthy behavior pattern and its impact on nutritional needs.

[0025] Further, based on the food supply situation in the user's location, seasonal changes, personal taste preferences, and the proportion of nutrients required by the user, a food recommendation list suitable for the user is screened, including:

[0026] Based on the synergistic effect of nutrients and the characteristics of food digestion and absorption, a food combination plan is formulated for the user.

[0027] Further, based on the food supply situation in the user's location, seasonal changes, personal taste preferences, and the proportion of nutrients required by the user, the screened food recommendation list suitable for the user further includes:

[0028] Based on the user's dietary restrictions and personal preferences, personalized customization is performed according to the food combination plan to complete the user's nutritional diet management.

[0029] Further, the user interactively adjusts the dietary plan according to needs, including:

[0030] The user adjusts the food portion by dragging the food icon, or replaces the food that the user does not like or is not easily obtained from the personalized customized food list.

[0031] In a second aspect, the present application further provides a nutritional diet management device, including:

[0032] A whole genome information acquisition module, configured to acquire the user's whole genome information, where the user's whole genome information is used to obtain genes related to the user's nutrient metabolism;

[0033] A metabolic data acquisition module, configured to acquire the user's metabolic data;

[0034] A daily lifestyle data acquisition module, configured to acquire the user's daily lifestyle data;

[0035] A nutrient ratio dynamic adjustment module, configured to, based on the user's healthy behavior pattern and its impact on nutritional needs, obtain the daily required energy, macronutrient, and micronutrient intakes of the user through a preset nutritional calculation model, and dynamically adjust the nutrient ratio according to the user's health goals;

[0036] A user food recommendation list screening module, configured to screen a food recommendation list suitable for the user based on the food supply situation in the user's location, seasonal changes, personal taste preferences, and the proportion of nutrients required by the user;

[0037] A user interactive adjustment module, configured to provide a visual nutritional diet plan for the user, and the user interactively adjusts the nutritional diet plan according to needs.

[0038] In a third aspect, the present application provides an electronic device, including:

[0039] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory and the one or more computer programs include instructions which, when executed by the device, cause the device to perform the method as described in the first aspect.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium having stored therein a computer program which, when run on a computer, causes the computer to perform the method as described in the first aspect.

[0041] In a fifth aspect, the present application provides a computer program which, when executed by a computer, is used to perform the method as described in the first aspect.

[0042] In a possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged together with the processor, or may be stored in whole or in part on a memory not packaged together with the processor.

[0043] The present application has the following beneficial effects:

[0044] 1. Based on the user's health behavior pattern and its impact on nutritional needs, the present application obtains the daily required energy, macronutrient, and micronutrient intakes of the user through a preset nutritional calculation model, and dynamically adjusts the nutrient ratio according to the user's health goals, so that in the process of formulating a nutritional diet management plan for the user, the user's health behavior pattern is fully considered and the nutrient ratio is dynamically adjusted to help the user eat scientifically and efficiently achieve nutritional diet goals.

[0045] 2. Based on the food supply situation in the user's region, seasonal changes, personal taste preferences, and the nutrient ratio required by the user, the present application screens a food recommendation list suitable for the user. The present application fully considers the actual situation of food supply in the user's region, combines seasonal changes to ensure fresh and diverse food ingredients, respects personal taste preferences to improve diet compliance, and then screens foods according to the precise nutrient ratio to create a practical food recommendation list for the user, promoting a balanced and personalized diet plan.

[0046] 3. By analyzing the user's metabolic data, the present application constructs a personal metabolic dynamic model; based on the personal metabolic dynamic model, when the metabolic index fluctuates abnormally, an early warning mechanism is immediately triggered, and instant dietary adjustment suggestions are provided according to the user's individual metabolic characteristics. In the process of constructing a personal metabolic dynamic model based on the user's metabolic data, the present application utilizes the advantages of long short-term memory networks in processing time series data to provide a scientific basis for personalized nutritional recommendations.

[0047] 4. This application provides users with a visual nutritional diet plan. Users can interactively adjust the nutritional diet plan according to their needs. This application intuitively presents the diet arrangement through the visual nutritional diet plan, facilitating users' understanding and compliance. The interactive adjustment meets users' personalized needs, enabling them to flexibly modify according to their own situations, enhancing the practicality and adaptability of the plan, and effectively improving users' willingness and effectiveness in implementing nutrition. This application can provide users with a comprehensive, personalized, and efficient nutritional diet management solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is an exemplary system architecture diagram to which the embodiments of this application can be applied;

[0049] Figure 2 It is a flowchart of the nutritional diet management method according to the embodiments of this application;

[0050] Figure 3 It is a flowchart of constructing an individual metabolic dynamic model according to the embodiments of this application;

[0051] Figure 4 It is a flowchart of the correlation analysis according to the embodiments of this application;

[0052] Figure 5 It is a flowchart of the device according to the embodiments of this application;

[0053] Figure 6 It is a schematic diagram of a computer device according to the embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application or the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0055] Referring to "embodiments" herein means that specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0056] To enable those skilled in the art of this technology to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings.

[0057] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0058] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0059] The terminal devices 101, 102, 103 may be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop portable computers, and desktop computers, etc.

[0060] The server 105 may be a server providing various services, such as a background server supporting the pages displayed on the terminal devices 101, 102, 103.

[0061] It should be noted that the nutritional diet management method provided by the embodiments of this application is generally executed by the server / terminal device. Correspondingly, the nutritional diet management device is generally set in the server / terminal device.

[0062] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0063] are merely illustrative. According to the implementation requirements, there may be any number of terminal devices, networks, and servers. Figure 2 Continuing to refer to

[0064] Step 201: Obtain the user's whole-genome information, which is used to obtain genes related to the user's nutrient metabolism.

[0065] In a possible implementation, to obtain the user's whole-genome information, it is possible to collect the user's blood, saliva, or other suitable biological samples, perform whole-genome sequencing on the biological samples to obtain the user's whole-genome information. Using bioinformatics techniques, screen out genes related to nutrient metabolism from the whole-genome information, such as genes involved in the metabolism of carbohydrates, fats, proteins, vitamins (such as vitamins A, C, D, E, K, B vitamins, etc.), and minerals (such as calcium, iron, zinc, magnesium, potassium, etc.).

[0066] In a possible implementation, based on the user's whole-genome information, establish a large-scale gene-nutrient association database to predict the user's absorption, metabolism, and utilization abilities of different nutrients. For example, for users carrying specific folate metabolism gene variations, it is possible to accurately judge the differences in the utilization efficiency of natural folate and synthetic folate, so as to customize a folate supplementation plan for the user, including the dosage, source (food or supplement), and time node of supplementation. By establishing a gene-nutrient association database, analyze the polymorphism of genes and their impact mechanisms on the absorption, metabolism, and utilization of nutrients, and determine the user's nutritional metabolism characteristics and potential risks at the gene level.

[0067] Step 202: Obtain the user's metabolic data.

[0068] The user's metabolic data includes at least one of the following: real-time blood glucose, ambulatory blood pressure, blood lipid change curve, body composition (body fat percentage, muscle mass, water content, etc.), respiratory quotient (reflecting the utilization of energy metabolism substrates), and metabolite concentration in urine.

[0069] In a possible implementation, analyze the user's metabolic data to construct a personal metabolic dynamic model. Please refer to Figure 3 , and the specific steps are as follows:

[0070] Step 31: Select key metabolic data indicators, and collect the key metabolic data indicators at preset time intervals. The key metabolic data indicators include blood glucose values (fasting blood glucose, postprandial blood glucose, etc.), blood pressure (systolic blood pressure, diastolic blood pressure), blood lipid indicators (total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol), weight change, body composition data (body fat percentage, muscle mass, etc., which can be obtained through a body fat scale), and basal metabolic rate (which can be estimated based on height, weight, age, etc.).

[0071] Step 32: Organize the collected various metabolic data in chronological order, and label the collection time and the corresponding user identifier for each data point. For example, for blood glucose data, record it in the format of "[user ID, collection time, blood glucose value]".

[0072] Step 33: Conduct data analysis on the various metabolic data labeled with the collection time and the corresponding user identifier to obtain the data analysis results of the various metabolic data. Among them, the data analysis of the various metabolic data includes:

[0073] Draw trend charts of the various metabolic data changing over time to obtain the change patterns of the various metabolic data (such as blood glucose fluctuation curves, blood pressure change curves, weight change trends, etc., and visually observe the change patterns of the data. For example, observe the fluctuation of blood glucose within a day, whether there are postprandial peaks, dawn phenomena, etc.), calculate the statistical characteristics of the various metabolic data to obtain the central tendency and dispersion degree of the various metabolic data (for example, calculate the average blood glucose value and the standard deviation of blood pressure over a period of time, and these characteristics can initially reflect the metabolic stability of the user); use the Fourier transform method to convert the time series data into the frequency domain to obtain the spectral characteristics of the various metabolic data and determine whether there are periodic components in the various metabolic data.

[0074] Step 34: Based on the data analysis results of the various metabolic data, construct characteristic variables. For example, calculate the blood glucose fluctuation amplitude (such as the difference between the postprandial blood glucose peak and the preprandial blood glucose value), the circadian rhythm change of blood pressure (such as the ratio of the average blood pressure at night to the average blood pressure during the day), and the weight change rate (the average value of the weekly weight increase or decrease). Encode the categorical variables, such as converting the food types in dietary intake into numerical codes (such as vegetable type = 1, meat type = 2, etc.) for processing in the model. At the same time, multiple related characteristics can be considered to be combined into new comprehensive characteristics. For example, combine blood glucose, insulin level, and physical activity into a "metabolic comprehensive index" to more comprehensively reflect the user's metabolic status.

[0075] Step 35: Use a long short-term memory network to construct an individual metabolic dynamic model, and use the constructed characteristic variables and historical metabolic data to train the long short-term memory network model. During the training process, adjust the parameters of the long short-term memory network, such as the number of neurons in the hidden layer, the learning rate, etc., to optimize the model performance. Evaluate the performance of the model on an independent validation set, including indicators such as accuracy and recall rate. Optimize the long short-term memory network model according to the validation results, which may include adjusting the model structure, increasing or decreasing characteristic variables, etc.

[0076] Step 36: Apply the trained long short-term memory network model to the actual metabolic data prediction to monitor the user's metabolic status in real time.

[0077] In a possible implementation, based on the individual metabolic dynamic model, when there are abnormal fluctuations in metabolic indicators, such as a rapid increase in postprandial blood glucose or abnormal changes in nocturnal blood pressure, the warning mechanism is immediately triggered, and according to the individual metabolic characteristics of the user, immediate dietary adjustment suggestions are provided, such as adjusting the carbohydrate intake of the next meal, the type of food (selecting foods with a low glycemic index), or increasing the intake of nutrients that help stabilize blood pressure (such as potassium, magnesium, etc.).

[0078] Step 203, obtain the user's daily lifestyle data.

[0079] In a possible implementation, the obtaining of the user's daily lifestyle data collects the user's daily lifestyle data through a mobile application, a questionnaire survey, and smart device sensors.

[0080] In a possible implementation, the user's daily lifestyle data includes at least one of the following: daily exercise type (aerobic exercise, strength training, yoga, etc.), exercise intensity (low, medium, high intensity), exercise frequency (number of exercises per week), sleep pattern (sleep duration, sleep quality, sleep cycle, etc.), work nature (sedentary office work, physical labor, shift work, etc.), stress level (evaluated by methods such as psychological assessment scales or heart rate variability analysis), and social activities (frequency of dining out, eating behaviors in social gatherings, etc.).

[0081] In a possible implementation, correlation analysis is performed on daily lifestyle, genes related to nutrient metabolism, and metabolic data to obtain the user's healthy behavior pattern and its impact on nutritional needs. For example, for users who often engage in high-intensity exercise and have good sleep quality, they may need to increase the intake of protein and carbohydrates to support muscle repair and energy replenishment; while for users who are in a high-stress state for a long time and have insufficient sleep, they may need to adjust their dietary structure and increase the intake of nutrients such as B vitamins and magnesium that help relieve stress and improve sleep.

[0082] In a possible implementation, correlation analysis is performed on daily lifestyle, genes related to nutrient metabolism, and metabolic data to obtain the user's healthy behavior pattern and its impact on nutritional needs. Please refer to Figure 4 , and the specific content includes:

[0083] Step 41, construct a multiple linear regression model, using metabolic indicators (such as blood glucose, blood lipids, weight changes, etc.) as the dependent variable, and daily lifestyle factors (such as daily exercise duration, carbohydrate intake in diet, sleep quality score, etc.) and gene polymorphisms related to nutrient metabolism (such as genotypes of gene loci, which can be converted into numerical variables through coding, such as 0, 1, 2 representing different genotypes) as independent variables.

[0084] Step 42: Estimate the parameters of the multiple linear regression model by the least squares method to obtain the regression coefficients of each independent variable and their significance levels (p-values). The regression coefficient represents the average change in the dependent variable when this independent variable changes by one unit while other independent variables remain unchanged. For example, when analyzing the relationship between blood glucose levels and exercise, diet, and specific gene polymorphisms, the regression coefficient can reveal how much the blood glucose level decreases on average when the exercise duration increases by one hour, how much the blood glucose level rises when the carbohydrate intake in the diet increases by 10 grams, and the degree of influence of specific gene polymorphisms on blood glucose levels.

[0085] Step 43: Determine whether the linear relationship between each independent variable and the dependent variable is statistically significant through the p-value (usually p < 0.05 is considered significant), so as to screen out the lifestyle factors and gene polymorphisms that have a significant impact on metabolic indicators.

[0086] Step 44: For the lifestyle factors that have been screened out and have a significant impact on metabolic indicators, use the clustering analysis method to classify users' health behaviors.

[0087] Step 45: Describe the characteristics of each cluster of health behavior pattern groups, obtain the statistical indicators of each group on various lifestyle factors, and the correlations between different lifestyle factors. Statistical indicators such as mean, median, standard deviation, etc. For example, for the group with a good health behavior pattern, the mean exercise duration may be more than 150 minutes per week, the proportions of carbohydrates, proteins, and fats in the diet are relatively reasonable (such as carbohydrates accounting for 50%-60%, proteins accounting for 15%-20%, and fats accounting for 20%-30%), the sleep quality score is relatively high (such as the Pittsburgh Sleep Quality Index PSQI being less than 5), and there may be a positive correlation between the exercise duration and the diet structure (such as users with more exercise are more inclined to choose a healthy diet). Based on these characteristics, name each health behavior pattern group to facilitate intuitive understanding and distinction of different patterns. For example, name the above-mentioned group with a good health behavior pattern as "actively healthy type", and name the group with a poor health behavior pattern as "sedentary and unhealthy type", etc. At the same time, analyze the differences in the distribution of gene polymorphisms among different health behavior pattern groups. Although gene polymorphisms themselves are not direct characteristics of behavior patterns, they may interact with lifestyle factors to affect metabolic indicators. Therefore, understanding the gene characteristics in different pattern groups helps to deeply understand the formation mechanism of health behavior patterns.

[0088] Step 46: Based on the lifestyle factors and gene polymorphisms that have a significant impact on metabolic indicators, construct a nutritional requirement prediction model. Use a multiple linear regression model with nutritional requirement indicators (such as the intakes of energy, protein, carbohydrates, fats, vitamins, minerals, etc. required per day) as the dependent variable, and significant lifestyle factors (such as exercise duration, intakes of various nutrients in the diet, sleep quality, etc.) and gene polymorphisms (encoded genotype variables) as the independent variables. By collecting a large amount of user sample data with different lifestyle and gene characteristics, as well as their corresponding nutritional requirement assessment data (which can be comprehensively determined through dietary surveys, body composition analysis, blood biochemical indicators, etc.), use this data to train the nutritional requirement prediction model and estimate the parameters of the nutritional requirement prediction model (such as regression coefficients, etc.). For example, through training the model, obtain how many kilocalories the daily required energy intake increases when the exercise duration increases by one hour; whether the requirement for a certain vitamin (such as vitamin D) increases when a specific gene polymorphism exists, etc. model relationships.

[0089] Step 47: Use the trained nutritional requirement prediction model to analyze the differences in nutritional requirements between different groups with healthy behavior patterns (such as "actively healthy type" and "sedentary and unhealthy type"). Substitute the average lifestyle factor values and gene polymorphism distributions of each group into the nutritional requirement prediction model to calculate the predicted nutritional requirement values of different groups.

[0090] Use the trained nutritional requirement prediction model to analyze the differences in nutritional requirements between different groups with healthy behavior patterns; substitute the average lifestyle factor values and gene polymorphism distributions of each group into the nutritional requirement prediction model to calculate the predicted nutritional requirement values of different groups.

[0091] Step 204: Based on the user's healthy behavior pattern and its impact on nutritional requirements, obtain the intakes of energy, macronutrients, and micronutrients required by the user per day through a preset nutritional calculation model, and dynamically adjust the nutrient ratio according to the user's health goals.

[0092] In a possible implementation manner, macronutrients include carbohydrates, proteins, and fats, and micronutrients include vitamins and minerals; the user's health goals include weight loss, muscle gain, maintaining a healthy weight, and preventing chronic diseases.

[0093] For example, during the weight loss stage, appropriately reduce the energy supply ratios of carbohydrates and fats, and increase protein intake to increase satiety, reduce appetite, and promote fat burning; for users with diabetes or cardiovascular diseases, accurately adjust the types and intakes of carbohydrates and fats according to their disease control conditions and changes in metabolic indicators, and strictly control blood sugar and blood lipid levels.

[0094] Step 205: Based on the food supply situation, seasonal changes, personal taste preferences, and the proportion of nutrients required by the user in the region where the user is located, screen a list of food recommendations suitable for the user.

[0095] In a possible implementation manner, in terms of food combination, based on the synergistic effect of nutrients and the characteristics of food digestion and absorption, formulate a food combination plan for the user.

[0096] For example, combine fruits rich in vitamin C (such as oranges, strawberries, etc.) with foods rich in iron (such as lean meat, beans, etc.) for consumption to promote iron absorption; combine high-fiber foods (such as whole wheat bread, vegetables, etc.) with protein foods (such as eggs, milk, etc.) to slow down the digestion and absorption rate of carbohydrates and stabilize blood sugar levels.

[0097] In a possible implementation manner, based on the user's dietary restrictions and personal preferences, perform personalized customization according to the food combination plan to complete the user's nutritional diet management.

[0098] According to the user's dietary restrictions (such as food allergies, religious beliefs, etc.) and personal preferences (such as vegetarianism, low-sugar diet, etc.), perform personalized customization on food recommendations to ensure that the dietary plan is both nutritious and meets the actual needs of the user.

[0099] Step 206: Provide the user with a visual nutritional diet plan, and the user can interactively adjust the nutritional diet plan according to needs.

[0100] In a possible implementation manner, providing the user with a visual diet plan includes showing the food arrangements for three meals a day and snacks in the form of charts (such as nutrient composition pie charts, food pyramid charts), menu lists, and recipe pictures.

[0101] In a possible implementation manner, the user interactively adjusts the nutritional diet plan according to needs, including the user adjusting the food portion by dragging the food icon, or replacing the foods that the user does not like or are not easily obtained from the personalized customized food list. The charts include nutrient composition pie charts and food pyramid charts.

[0102] In a possible implementation manner, calculate the nutrient intake in real time according to the user's adjustment, and provide feedback and suggestions to ensure that the adjusted dietary plan still meets the user's nutritional needs and health goals.

[0103] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disc, a Read-Only Memory (ROM), or a Random Access Memory (RAM), etc.

[0104] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0105] Continuing to refer to Figure 5 , the nutritional diet management device described in this embodiment includes:

[0106] A whole genome information acquisition module 501, configured to acquire the whole genome information of a user, where the whole genome information of the user is used to acquire genes related to nutrient metabolism of the user;

[0107] A metabolic data acquisition module 502, configured to acquire the metabolic data of the user;

[0108] A daily lifestyle data acquisition module 503, configured to acquire the daily lifestyle data of the user;

[0109] A nutrient ratio dynamic adjustment module 504, configured to obtain the daily required energy, macronutrient, and micronutrient intake of the user through a preset nutrition calculation model based on the user's healthy behavior pattern and its impact on nutritional needs, and dynamically adjust the nutrient ratio according to the user's health goals;

[0110] A user food recommendation list screening module 505, configured to screen a food recommendation list suitable for the user based on the food supply situation in the user's area, seasonal changes, personal taste preferences, and the nutrient ratio required by the user;

[0111] A user interaction adjustment module 506, configured to provide a visual nutritional diet plan for the user, and the user interactively adjusts the nutritional diet plan according to needs.

[0112] To solve the above technical problems, an embodiment of the present application further provides a computer device. For details, please refer to Figure 6 , Figure 6 , which is a basic structural block diagram of the computer device in this embodiment.

[0113] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are communicatively connected to each other through a system bus. It should be noted that only the computer device 6 with components 6a - 6c is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of the present technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0114] The computer device can be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The computer device can perform human-computer interaction with users through a keyboard, a mouse, a remote control, a touchpad, a voice control device, or other means.

[0115] The memory 6a at least includes one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 6a may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 6a may also be an external storage device of the computer device 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc. equipped on the computer device 6. Of course, the memory 6a may also include both the internal storage unit and the external storage device of the computer device 6. In this embodiment, the memory 6a is generally used to store the operating system installed on the computer device 6 and various application software, such as the program code of the nutritional diet management method. In addition, the memory 6a may also be used to temporarily store various data that have been output or will be output.

[0116] In some embodiments, the processor 6b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 6b is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to run the program code stored in the memory 6a or process data, such as running the program code of the nutritional diet management method.

[0117] The network interface 6c may include a wireless network interface or a wired network interface, and the network interface 6c is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0118] This application also provides another implementation manner, that is, to provide a non-volatile computer-readable storage medium storing a program of a nutritional diet management method, and the nutritional diet management can be executed by at least one processor, so that the at least one processor executes the steps of the nutritional diet management method as described above.

[0119] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0120] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields shall be within the scope of the patent protection of the present application by the same token.

Claims

1. A nutritional dietary management method, characterized in that: include: Obtaining the user's whole genome information, wherein the user's whole genome information is used to obtain the user's genes related to nutrient metabolism; Obtain the user's metabolic data; Obtaining data on users’ daily lifestyle; Based on the user's health behavior patterns and their impact on nutritional needs, the preset nutrition calculation model is used to obtain the user's daily energy, macronutrient and micronutrient intake, and dynamically adjust the nutrient ratio according to the user's health goals; Filter the recommended food list suitable for the user based on the food supply in the user's area, seasonal changes, personal taste preferences and the nutrient ratio required by the user; Provide users with visual nutritional meal plans, and users can interactively adjust the nutritional meal plans according to their needs.

2. The nutritional dietary management method according to claim 1, characterized in that: Obtain the user's whole genome information, where the user's whole genome information is used to obtain the user's genes related to nutrient metabolism, including: A gene-nutrient association database is established based on the user's whole genome information to predict the user's ability to absorb, metabolize and utilize different nutrients.

3. The nutritional dietary management method according to claim 1, characterized in that: Get the user's metabolic data, including: Analyze the user's metabolic data and build a personal metabolic dynamic model. Based on the personal metabolic dynamic model, when metabolic indicators fluctuate abnormally, the early warning mechanism is immediately triggered, and instant dietary adjustment suggestions are provided based on the user's individual metabolic characteristics.

4. The nutritional dietary management method according to claim 3, characterized in that: Analyze the user's metabolic data and build a personal metabolic dynamic model, including: Select key metabolic data indicators and collect the key metabolic data indicators based on preset time intervals; Arrange the collected metabolic data in chronological order, and mark the collection time and corresponding user ID for each data point; Perform data analysis on various metabolic data with marked collection time and corresponding user identification, and obtain data analysis results of various metabolic data; Based on the data analysis results of various metabolic data, characteristic variables are constructed; Use long short-term memory networks to build a personal metabolic dynamic model, and use the constructed characteristic variables and historical metabolic data to train the long short-term memory network model; The trained long short-term memory network model is applied to the actual metabolic data prediction to monitor the user's metabolic status in real time.

5. The nutritional dietary management method according to claim 1, characterized in that: Obtain users’ daily lifestyle data, including: Correlation analysis is performed on daily lifestyle, genes related to nutrient metabolism, and metabolic data to obtain users' health behavior patterns and their impact on nutritional needs.

6. The nutritional dietary management method according to claim 1, characterized in that: Based on the food availability in the user's area, seasonal changes, personal taste preferences and the nutrient ratio required by the user, a list of food recommendations suitable for the user is screened, including: Based on the synergistic effect of nutrients and the digestion and absorption characteristics of food, food combination plans are formulated for users.

7. The nutritional dietary management method according to claim 6, characterized in that: Based on the food availability in the user's area, seasonal changes, personal taste preferences and the nutrient ratio required by the user, a list of food recommendations suitable for the user is screened, including: Based on the user's dietary restrictions and personal preferences, food combination plans are personalized to complete the user's nutritional diet management.

8. The nutritional dietary management method according to claim 1, characterized in that: Users can interactively adjust meal plans based on their needs, including: Users can adjust food portions by dragging food icons, or replace foods they don't like or are not easily available from a personalized food list.

9. A nutritional diet management device, used to implement the nutritional diet management method of claims 1-8, characterized in that: include: A whole genome information acquisition module is used to acquire the whole genome information of the user, wherein the whole genome information of the user is used to acquire genes of the user related to nutrient metabolism; A metabolic data acquisition module, used to acquire the user's metabolic data; A daily lifestyle data acquisition module is used to acquire the user's daily lifestyle data; The dynamic adjustment module of nutrient ratio is used to obtain the user's daily energy, macronutrient and micronutrient intake based on the user's health behavior pattern and its impact on nutritional needs through a preset nutrition calculation model, and dynamically adjust the nutrient ratio according to the user's health goals; A user food recommendation list screening module is used to screen a food recommendation list suitable for the user based on the food supply situation in the user's area, seasonal changes, personal taste preferences and the nutrient ratio required by the user; The user interactive adjustment module is used to provide users with a visual nutritional meal plan, and users can interactively adjust the nutritional meal plan according to their needs.

10. An electronic device, characterized in that: include: one or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, enable the device to perform the steps of the nutritional dietary management method as described in any one of claims 1 to 8.

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