Nutrition and health recommendation method and device

Abstract

The invention relates to the technical field of nutrition and health management, and discloses a nutrition and health recommendation method and device, and the method comprises the steps: determining the body index data of a user; according to the body index data of the user and a predetermined health state range, analyzing the current health state of the user to obtain a health state analysis result of the user; when the health state analysis result is that the user is not in the health state range, historical activity related data of the user is acquired; and generating nutrition and health suggestion data of the user according to the body index data and the historical activity related data. Visibly, the accurate nutrition and health suggestions can be generated for the user based on the body index data of the user and the historical activity related parameters under the condition that the user is not in the health state currently through analysis, more appropriate nutrition and health suggestions can be recommended for the user, and therefore the method is beneficial to the body health of the user, and the user experience is improved. And the life quality of the user can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nutritional health management, and in particular to a nutritional health recommendation method and device. BACKGROUND

[0002] With the improvement of life quality and the enhancement of health awareness, formulating a nutritional health recommendation scheme for a user has become an important means of nutritional health management, and the generation of the nutritional health recommendation scheme is mainly achieved by analyzing the user's diet behavior record, specifically: analyzing the food material heat by analyzing the user's diet behavior record, and then generating the nutritional health recommendation scheme according to the food material heat.

[0003] However, it is found in practice that the existing generation method of the nutritional health recommendation scheme only simply analyzes the food material heat from the user's diet behavior record, and simply generates the nutritional health recommendation scheme based on the food material heat, such as: if the heat is high, it is recommended to adjust the dish heat or exercise more, and if the heat is too low, it is recommended to adjust the dish heat to the minimum required heat, which cannot achieve accurate recommendation for the user's nutritional health through analysis of more complex user-related data. It can be seen that it is particularly important to propose a new nutritional health recommendation scheme to achieve accurate recommendation for the user's nutritional health. SUMMARY

[0004] The present application provides a nutritional health recommendation method and device, which can generate accurate nutritional health recommendations for users and improve the user's life quality.

[0005] To solve the above technical problems, the first aspect of the present application discloses a nutritional health recommendation method, which comprises:

[0006] determining the user's physical index data;

[0007] analyzing the user's current health status according to the user's physical index data and a pre-determined health status range, to obtain a health status analysis result of the user;

[0008] when the health status analysis result is that the user is not in the health status range, obtaining the user's historical activity-related data;

[0009] generating the user's nutritional health recommendation data according to the physical index data and the historical activity-related data.

[0010] As an optional implementation manner, in the first aspect of the present application, the historical activity-related data includes historical nutritional intake data, historical sleep data and historical exercise data.

[0011] and generating the nutrition health suggestion data of the user according to the body index data and the historical activity related data, comprises:

[0012] generating the target nutrition intake data of the user according to the body index data and the historical nutrition intake data;

[0013] generating the nutrition health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data.

[0014] As an optional implementation, in the first aspect of the present application, the generating the nutrition health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data, comprises:

[0015] adjusting the target nutrition intake data of the user according to the historical sleep data and the historical exercise data to obtain adjusted target nutrition intake data; and generating the dish consumption suggestion data of the user according to the adjusted target nutrition intake data;

[0016] generating the sleep suggestion data of the user according to the historical sleep data;

[0017] determining the exercise suggestion data of the user according to the historical exercise data;

[0018] integrating the dish consumption suggestion data, the sleep suggestion data and the exercise suggestion data to obtain the nutrition health suggestion data of the user.

[0019] As an optional implementation, in the first aspect of the present application, the adjusting the target nutrition intake data of the user according to the historical sleep data and the historical exercise data to obtain adjusted target nutrition intake data, comprises:

[0020] analyzing first nutrition intake adjustment data of the user about the historical sleep data according to the target nutrition intake data of the user and the historical sleep data;

[0021] analyzing exercise consumption data of the user about the target nutrition intake data according to the target nutrition intake data of the user and the historical exercise data; and analyzing second nutrition intake adjustment data of the user about the historical exercise data according to the exercise consumption data;

[0022] determining target nutrition intake adjustment data of the user about the historical sleep data and the historical exercise data according to the first nutrition intake adjustment data and the second nutrition intake adjustment data;

[0023] According to the target nutrition intake adjustment data, the target nutrition intake data is adjusted to obtain adjusted target nutrition intake data.

[0024] As an optional implementation, in the first aspect of the present application, the determining the body index data of the user comprises:

[0025] The current attribute information of the user is determined, and the current attribute information comprises one or more combinations of age, gender, height and identity;

[0026] The weight information of the user is obtained, and the weight information comprises current weight information and historical weight information in a preset time period in the past;

[0027] According to the current attribute information and the weight information, the body index data of the user is determined.

[0028] As an optional implementation, in the first aspect of the present application, the body index data at least comprises a body fat rate, or the body index data at least comprises the body fat rate and further comprises a BMI value;

[0029] When the body index data comprises the body fat rate, the health state analysis result of the user comprises a result that the user is currently in a healthy state, or a result that the user is not currently in a healthy state;

[0030] When the body index data comprises the body fat rate and the BMI value, the health state analysis result of the user comprises a result that the user is currently in a healthy state, or a result that the user is not currently in a healthy state, and when the user is not currently in the healthy state, the health state analysis result of the user further comprises a non-healthy state analysis result of the user.

[0031] As an optional implementation, in the first aspect of the present application, the analyzing the current health state of the user according to the body index data of the user and a pre-determined health state range to obtain a health state analysis result of the user comprises:

[0032] According to the body fat rate of the user, it is judged whether the body fat rate of the user is in a pre-determined normal body fat rate range corresponding to the current attribute information of the user;

[0033] When it is judged that the body fat rate of the user is in the normal body fat rate range corresponding to the current attribute information of the user, it is determined that the user is currently in a healthy state;

[0034] determining that the user is not in a healthy state when it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user; or

[0035] determining a BMI judgment result according to the BMI value of the user when it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, and determining a non-healthy state analysis result of the user according to the BMI judgment result.

[0036] The second aspect of the present application discloses a device for nutritional health recommendation, which comprises:

[0037] a determining module for determining body index data of a user;

[0038] an analysis module for analyzing a current health state of the user according to the body index data of the user and a pre-determined health state range, and obtaining a health state analysis result of the user;

[0039] an obtaining module for obtaining historical activity-related data of the user when the health state analysis result is that the user is not in the health state range;

[0040] a generating module for generating nutritional health suggestion data of the user according to the body index data and the historical activity-related data.

[0041] As an optional implementation, in the second aspect of the present application, the historical activity-related data comprises historical nutrition intake data, historical sleep data and historical exercise data.

[0042] Moreover, the generating module generates the nutritional health suggestion data of the user according to the body index data and the historical activity-related data in the following manner:

[0043] generates target nutrition intake data of the user according to the body index data and the historical nutrition intake data;

[0044] generates the nutritional health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data.

[0045] As an optional implementation, in the second aspect of the present application, the generating module generates the nutritional health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data in the following manner:

[0046] adjust the target nutrition intake data of the user according to the historical sleep data and the historical exercise data to obtain adjusted target nutrition intake data; and generate dish consumption suggestion data of the user according to the adjusted target nutrition intake data;

[0047] generate sleep suggestion data of the user according to the historical sleep data;

[0048] determine exercise suggestion data of the user according to the historical exercise data;

[0049] integrate the dish consumption suggestion data, the sleep suggestion data and the exercise suggestion data to obtain nutrition and health suggestion data of the user.

[0050] As an optional implementation, in the second aspect of the present application, the manner in which the generation module adjusts the target nutrition intake data of the user according to the historical sleep data and the historical exercise data to obtain adjusted target nutrition intake data specifically comprises:

[0051] analyze first nutrition intake adjustment data of the user with respect to the historical sleep data according to the target nutrition intake data of the user and the historical sleep data;

[0052] analyze exercise consumption data of the user with respect to the target nutrition intake data according to the target nutrition intake data of the user and the historical exercise data; and analyze second nutrition intake adjustment data of the user with respect to the historical exercise data according to the exercise consumption data;

[0053] determine target nutrition intake adjustment data of the user with respect to the historical sleep data and the historical exercise data according to the first nutrition intake adjustment data and the second nutrition intake adjustment data;

[0054] adjust the target nutrition intake data according to the target nutrition intake adjustment data to obtain adjusted target nutrition intake data.

[0055] As an optional implementation, in the second aspect of the present application, the manner in which the determination module determines the body index data of the user specifically comprises:

[0056] determine current attribute information of the user, the current attribute information comprising one or more combinations of age, gender, height and identity;

[0057] obtain weight information of the user, the weight information comprising current weight information and historical weight information in a preset time period in the past;

[0058] determine the body index data of the user according to the current attribute information and the weight information.

[0059] As an optional implementation, in the second aspect of the present application, the body index data at least includes a body fat rate, or the body index data at least includes the body fat rate and further includes a BMI value;

[0060] When the body index data includes the body fat rate, the health state analysis result of the user includes a result that the user is currently in a healthy state, or a result that the user is not currently in a healthy state;

[0061] When the body index data includes the body fat rate and the BMI value, the health state analysis result of the user includes a result that the user is currently in a healthy state, or a result that the user is not currently in a healthy state, and when the user is not currently in the healthy state, the health state analysis result of the user further includes a non-healthy state analysis result of the user.

[0062] As an optional implementation, in the second aspect of the present application, the manner that the analysis module analyzes the current health state of the user according to the body index data of the user and the pre-determined health state range to obtain the health state analysis result of the user specifically includes:

[0063] According to the body fat rate of the user, it is judged whether the body fat rate of the user is in a pre-determined normal body fat rate range corresponding to the current attribute information of the user;

[0064] When it is judged that the body fat rate of the user is in the normal body fat rate range corresponding to the current attribute information of the user, it is determined that the user is currently in a healthy state;

[0065] When it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, it is determined that the user is not currently in a healthy state; or,

[0066] When it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, according to the BMI value of the user, it is judged whether the BMI value of the user is in a pre-determined normal BMI value range to obtain a BMI judgment result, and according to the BMI judgment result, a non-healthy state analysis result of the user is determined.

[0067] The third aspect of the present application discloses another device for nutrition and health recommendation, which comprises:

[0068] a memory storing executable program codes;

[0069] a processor coupled with the memory;

[0070] The processor invokes the executable program code stored in the memory to perform part or all of the steps in the method for recommending nutritional health according to any one of the first aspect of the present application.

[0071] The fourth aspect of the present application discloses a computer storage medium, which stores computer instructions, and when the computer instructions are invoked, part or all of the steps in the method for recommending nutritional health according to any one of the first aspect of the present application are performed.

[0072] Compared with the prior art, the present application has the following beneficial effects:

[0073] In the embodiment of the present application, the body index data of the user is determined, the current health status of the user is analyzed according to the body index data of the user and the pre-determined health status range, and the health status analysis result of the user is obtained, when the health status analysis result is that the user is not in the health status range, the historical activity related data of the user is obtained, and the nutritional health suggestion data of the user is generated according to the body index data and the historical activity related data. It can be seen that the present application can determine the body index data of the user, analyze the current health status of the user according to the body index data of the user and the pre-determined health status range, and obtain the health status analysis result of the user, which can improve the analysis accuracy and efficiency of the current health status of the user. When the health status analysis result is that the user is not in the health status range, the nutritional health suggestion data of the user is generated according to the body index data and the historical activity related data of the user obtained, which can generate accurate nutritional health suggestions for the user based on the body index data and the historical activity related parameters of the user in the case that the user is not in the health status, which is beneficial to recommend more suitable nutritional health suggestions for the user, thereby helping the physical health of the user and improving the quality of life of the user. BRIEF DESCRIPTION OF DRAWINGS

[0074] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0075] Figure 1 is a flowchart of a method for recommending nutritional health disclosed by the embodiment of the present application;

[0076] Figure 2 is a flowchart of another method for recommending nutritional health disclosed by the embodiment of the present application;

[0077] Figure 3is a structural schematic view of a device for nutrition and health recommendation disclosed by an embodiment of the present application.

[0078] Figure 4 is a structural schematic view of another device for nutrition and health recommendation disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely in the following by combining the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all the embodiments. Based on the embodiment in the present application, all other embodiments obtained by the person skilled in the art without creative labor belong to the scope of protection of the present application.

[0080] The terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or end.

[0081] In this paper, the phrase "embodiment" means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. The person skilled in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0082] The present application discloses a nutrition and health recommendation method and device, which can determine the body index data of a user, analyze the current health status of the user according to the body index data of the user and a predetermined health status range, obtain a health status analysis result of the user, improve the analysis accuracy and efficiency of the current health status of the user, generate nutrition and health suggestion data of the user according to the body index data and the historical activity related data of the user obtained when the health status analysis result is that the user is not in the health status range, generate accurate nutrition and health suggestions for the user based on the body index data and the historical activity related parameters of the user under the condition that the user is analyzed to be not in the health status, recommend more suitable nutrition and health suggestions for the user, thereby helping the user's physical health and improving the user's quality of life. The following will be described in detail.

[0083] Embodiment one

[0084] Please refer to Figure 1 , Figure 1 is a flowchart of a method for nutritional health recommendation according to an embodiment of the present application. In the method, Figure 1 The method for nutritional health recommendation can be applied to a device for nutritional health recommendation, wherein the device can include a server, and the server can include a cloud server or a local server, which is not limited in the embodiments of the present application. As Figure 1 The method for nutritional health recommendation can include the following operations:

[0085] 101, determining body index data of a user.

[0086] In the embodiments of the present application, the body index data at least includes a body fat rate, or the body index data at least includes a body fat rate and further includes a BMI value, wherein the BMI represents a body mass index.

[0087] 102, analyzing a current health state of the user according to the body index data of the user and a predetermined health state range, to obtain a health state analysis result of the user.

[0088] Optionally, when the body index data includes the body fat rate, the health state analysis result of the user includes a result that the user is currently in a health state, or a result that the user is not currently in the health state, which is not limited in the embodiments of the present application.

[0089] Optionally, when the body index data includes the body fat rate and the BMI value, the health state analysis result of the user includes a result that the user is currently in a health state, or a result that the user is not currently in the health state, and when the user is not currently in the health state, the health state analysis result of the user further includes a non-health state analysis result of the user, wherein the non-health state analysis result can include an analysis result of a non-health state type in which the user is located, and optionally, the non-health state type can be divided into an implicit non-health state type or an explicit non-health state type, for example, can be an implicit obesity type or an explicit obesity type, which is not limited in the embodiments of the present application.

[0090] Optionally, when the health state analysis result is that the user is in the health state range, the flowchart can be ended, or a prompt information for maintaining the current nutritional health data can be generated to prompt the user to maintain the current nutritional health data, which is not limited in the embodiments of the present application.

[0091] 103, when the health state analysis result is that the user is not in the health state range, obtaining historical activity related data of the user.

[0092] In an embodiment of the present invention, historical activity-related data can be used to represent activity-related data of a user in a recent period of time, such as data related to nutritional intake, sleep, exercise and other activities. Specifically, historical activity-related data may include historical nutritional intake data, historical sleep data and historical exercise data. Furthermore, the historical nutritional intake data may be historical nutritional intake data for three meals a day.

[0093] 104. Generate nutritional health advice data for users based on physical indicator data and historical activity-related data.

[0094] In an embodiment of the present invention, optionally, the nutritional health advice data may include one or more combinations of sleep advice data, dish consumption advice data, and exercise advice data, such as: sleep duration, types and weights of dishes to be consumed, how long to exercise a day, what kind of exercise is suitable, etc., which is not limited in the embodiment of the present invention.

[0095] It can be seen that implementation Figure 1 The described method of nutritional and health recommendation can determine the user's physical indicator data, and analyze the user's current health status based on the user's physical indicator data and a predetermined health status range to obtain the user's health status analysis result, which can improve the accuracy and efficiency of the analysis of the user's current health status. When the health status analysis result shows that the user is not within the health status range, the user's nutritional and health recommendation data is generated based on the physical indicator data and the user's historical activity-related data obtained. When the analysis shows that the user is not currently in a healthy state, accurate nutritional and health recommendations can be generated for the user based on the user's physical indicator data and historical activity-related parameters, which is conducive to recommending more appropriate nutritional and health recommendations to the user, thereby contributing to the user's physical health and improving the user's quality of life.

[0096] In an optional embodiment, the above step 104 of generating nutritional health advice data for the user based on the physical indicator data and historical activity-related data may include:

[0097] Generate target nutritional intake data for users based on body index data and historical nutritional intake data;

[0098] Generate nutritional health advice data for the user based on the user's target nutritional intake data, historical sleep data, and historical exercise data.

[0099] In the embodiment of the present invention, the user's target nutritional intake data may be used to represent the nutritional intake data that the user needs to achieve, such as calorie content, nutritional component (such as protein, vitamins, etc.) content, and the like.

[0100] It can be seen that the optional embodiment can generate target nutrition intake data of the user according to the physical index data and the historical nutrition intake data, and then generate nutrition health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data, and can comprehensively analyze the physical index data, the historical nutrition intake data, the historical exercise data and the historical sleep data, so as to generate accurate nutrition health suggestions for the user.

[0101] In the optional embodiment, as an optional implementation, generating the nutrition health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data can include:

[0102] Adjusting the target nutrition intake data of the user according to the historical sleep data and the historical exercise data to obtain adjusted target nutrition intake data, and generating dish consumption suggestion data of the user according to the adjusted target nutrition intake data;

[0103] Generating sleep suggestion data of the user according to the historical sleep data;

[0104] Determining exercise suggestion data of the user according to the historical exercise data;

[0105] Integrating the dish consumption suggestion data, the sleep suggestion data and the exercise suggestion data to obtain the nutrition health suggestion data of the user.

[0106] In the embodiment of the application, specifically, target nutrition intake adjustment data is generated according to the historical sleep data and the historical exercise data, and the target nutrition intake data of the user is adjusted according to the target nutrition intake adjustment data to obtain adjusted target nutrition intake data. In this way, the target nutrition intake data is accurately adjusted through the historical sleep data and the historical exercise data, which is beneficial to recommending more accurate nutrition intake requirement related data to the user.

[0107] For example, when the historical sleep duration of the user is long, the user can be suggested to gradually reduce the sleep duration in stages until a preset reasonable sleep duration (such as 8 hours) is reached; when the historical sleep quality of the user is poor (such as the duration of deep sleep is short), the user can be suggested to take a sleep-aiding exercise (such as stretching, adjusting the breathing mode to abdominal breathing, etc.) before sleep.

[0108] It can be seen that the optional embodiment can adjust the target nutrition intake data of the user according to the historical sleep data and the historical exercise data, obtain the adjusted target nutrition intake data, improve the adjustment accuracy and efficiency of the target nutrition intake data, and generate the dish consumption suggestion data of the user according to the adjusted target nutrition intake data, so as to generate accurate dish consumption suggestion data for the user based on the accurately adjusted target nutrition intake data, generate the sleep suggestion data of the user according to the historical sleep data, generate accurate sleep suggestion data for the user through analysis of the historical sleep data, generate the exercise suggestion data of the user according to the historical exercise data, generate accurate exercise suggestion data for the user through analysis of the historical exercise data, integrate the dish consumption suggestion data, the sleep suggestion data and the exercise suggestion data to obtain the nutrition and health suggestion data of the user, and improve the generation accuracy and reliability of the nutrition and health suggestion data through comprehensive analysis of the sleep, exercise, nutrition intake and other related data.

[0109] In the optional embodiment, optionally, adjusting the target nutrition intake data of the user according to the historical sleep data and the historical exercise data to obtain the adjusted target nutrition intake data can include:

[0110] analyzing first nutrition intake adjustment data of the user about the historical sleep data according to the target nutrition intake data and the historical sleep data of the user;

[0111] analyzing exercise consumption data of the user about the target nutrition intake data according to the target nutrition intake data and the historical exercise data of the user, and analyzing second nutrition intake adjustment data of the user about the historical exercise data according to the exercise consumption data;

[0112] determining the target nutrition intake adjustment data of the user about the historical sleep data and the historical exercise data according to the first nutrition intake adjustment data and the second nutrition intake adjustment data;

[0113] adjusting the target nutrition intake data according to the target nutrition intake adjustment data to obtain the adjusted target nutrition intake data.

[0114] Specifically, according to the historical sleep data, the historical deep sleep duration of the user is analyzed; when the historical deep sleep duration is greater than a preset deep sleep duration threshold, a difference value between the historical deep sleep duration and the deep sleep duration threshold is calculated to obtain a deep sleep duration difference value; according to the deep sleep duration difference value and a preset correlation between sleep duration and nutrient absorption, the historical nutrient absorption of the user with respect to the historical sleep data is analyzed; and according to the historical nutrient absorption and target nutrient intake data of the user, first nutrient intake adjustment data of the user with respect to the historical sleep data is generated. For example, when the deep sleep duration exceeds the preset deep sleep duration threshold, 40% of high-sugar intake is reduced for each additional 1 hour of deep sleep. In this way, the nutrient intake data of the user is adjusted based on the sleep data of the user, which is conducive to improving the adjustment accuracy and reliability of the nutrient intake data with respect to the sleep data.

[0115] Optionally, the exercise consumption data can include calories consumed by exercise. Specifically, according to the target nutrient intake data of the user, nutrient intake type information (such as carbohydrates, proteins, taurine, etc.) required to be ingested by the user is analyzed, and according to the nutrient intake type information and the exercise consumption data, the amount of nutrient intake required to be supplemented by the user for the nutrient intake type information is analyzed as second nutrient intake adjustment data of the user with respect to the historical exercise data. In this way, the nutrient intake data of the user is adjusted based on the exercise data of the user, which is conducive to improving the adjustment accuracy and reliability of the nutrient intake data with respect to the exercise data.

[0116] It can be seen that the optional implementation further enables the first nutrient intake adjustment data of the user with respect to the historical sleep data to be analyzed according to the target nutrient intake data and the historical sleep data of the user, thereby improving the accuracy and reliability of the nutrient intake adjustment data with respect to the sleep data obtained by analysis, and the exercise consumption data of the user with respect to the target nutrient intake data to be analyzed according to the target nutrient intake data and the historical exercise data of the user, and the second nutrient intake adjustment data of the user with respect to the historical exercise data to be analyzed according to the exercise consumption data, thereby improving the accuracy and reliability of the nutrient intake adjustment data with respect to the exercise data obtained by analysis, and then the target nutrient intake adjustment data of the user with respect to the historical sleep data and the historical exercise data to be determined according to the first nutrient intake adjustment data and the second nutrient intake adjustment data, thereby improving the analysis accuracy and reliability of the nutrient intake adjustment data with respect to the sleep data and the exercise data, and finally the target nutrient intake data to be adjusted according to the target nutrient intake adjustment data to obtain adjusted target nutrient intake data, which is conducive to improving the adjustment accuracy and reliability of the target nutrient intake data by comprehensive analysis of the relevant data on the sleep data level and the exercise data level, thereby being conducive to improving the generation accuracy of the relevant suggestion data based on the accurately adjusted target nutrient intake data.

[0117] Embodiment two

[0118] Please refer to Figure 2 , Figure 2 is a flowchart of a method for nutritional health recommendation according to an embodiment of the present application. In the method, Figure 2 The method for nutritional health recommendation can be applied to a device for nutritional health recommendation, wherein the device can include a server, and the server can include a cloud server or a local server, which is not limited in the embodiments of the present application. As shown in Figure 2 The method for nutritional health recommendation can include the following operations:

[0119] 201, determining current attribute information of a user.

[0120] In the embodiments of the present application, the current attribute information can include one or more combinations of age, gender, height, and identity (such as an athlete or a teacher), and the current attribute information of the user can be obtained by analyzing relevant information input by the user, such as age, gender, height, and identity information input by the user in a nutritional and health detector, which is not limited in the embodiments of the present application.

[0121] 202, obtaining body weight information of the user.

[0122] In the embodiments of the present application, the body weight information includes current body weight information and historical body weight information in a preset time period in the past, such as historical body weight information of the user in the past month.

[0123] In the embodiments of the present application, the step 202 and the step 201 have no sequence, that is, the step 202 can occur before the step 201, or after the step 201, or simultaneously with the step 201, which is not limited in the embodiments of the present application.

[0124] 203, determining body index data of the user according to the current attribute information and the body weight information.

[0125] 204, analyzing a current health state of the user according to the body index data of the user and a health state range determined in advance to obtain a health state analysis result of the user.

[0126] 205, when the health state analysis result is that the user is not in the health state range, obtaining historical activity related data of the user.

[0127] 206, generating nutritional health suggestion data of the user according to the body index data and the historical activity related data.

[0128] In the embodiment of the present application, for other descriptions of steps 203-206, please refer to the detailed description of steps 101-104 in Embodiment 1. The present application will not be repeated here.

[0129] It can be seen that the implementation Figure 2 The method for describing nutritional health recommendations can determine the body index data of the user, analyze the current health status of the user according to the body index data of the user and the health status range determined in advance, obtain the health status analysis result of the user, improve the analysis accuracy and efficiency of the current health status of the user, generate the nutritional health suggestion data of the user according to the body index data and the historical activity related data of the user obtained when the health status analysis result is that the user is not in the health status range, generate accurate nutritional health suggestions for the user based on the body index data and the historical activity related parameters of the user when it is analyzed that the user is not in the health status, help to recommend more suitable nutritional health suggestions for the user, thereby helping the physical health of the user, and helping to improve the life quality of the user. In addition, the body index data of the user can be more accurately analyzed through the current attribute information, the current weight information and the historical weight information in the past period of time of the user, which is helpful to the comprehensive analysis of the height, age, gender, historical nutrition intake data, historical exercise data, historical sleep data and the like of the user, and the generation of accurate nutritional health suggestions for the user.

[0130] In an optional embodiment, the step 204 of analyzing the current health status of the user according to the body index data of the user and the health status range determined in advance to obtain the health status analysis result of the user can include:

[0131] According to the body fat rate of the user, it is judged whether the body fat rate of the user is in the normal body fat rate range corresponding to the current attribute information of the user determined in advance;

[0132] When it is judged that the body fat rate of the user is in the normal body fat rate range corresponding to the current attribute information of the user, it is determined that the user is currently in the health status;

[0133] When it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, it is determined that the user is not currently in the health status; or,

[0134] When it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, it is judged according to the BMI value of the user whether the BMI value of the user is in the normal BMI value range determined in advance to obtain the BMI judgment result; and according to the BMI judgment result, the non-health status analysis result of the user is determined.

[0135] For example, when the gender in the current attribute information of the user is male, the normal body fat rate range corresponding to the current attribute information of the user can be 15%-18%; when the gender in the current attribute information of the user is female, the normal body fat rate range corresponding to the current attribute information of the user can be 20%-25%.

[0136] Specifically, in the case where the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, when the BMI judgment result is that the BMI value of the user is in the normal BMI value range (for example, 18.5≤BMI≤23.9), it is determined that the non-healthy state type in which the user is located is the implicit non-healthy state type; when the BMI judgment result is that the BMI value of the user is not in the normal BMI value range (for example, BMI≥24), it is determined that the non-healthy state type in which the user is located is the explicit non-healthy state type.

[0137] It can be seen that the optional embodiment can judge whether the body fat rate of the user is in the pre-determined normal body fat rate range corresponding to the current attribute information of the user according to the body fat rate of the user, when it is judged that the body fat rate of the user is in the normal body fat rate range corresponding to the current attribute information of the user, it is determined that the user is currently in a healthy state, which improves the determination accuracy and efficiency of the user currently being in a healthy state, when it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, it is determined that the user is not currently in a healthy state, which improves the determination accuracy and efficiency of the user not being in a healthy state, in the case where it is judged that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, the BMI value of the user is further judged whether it is in the pre-determined normal BMI value range according to the BMI value of the user, to obtain a BMI judgment result; and according to the BMI judgment result, a non-healthy state analysis result of the user is determined, which can accurately analyze the non-healthy state in which the user is located, and the comprehensive judgment of the body fat rate and the BMI of the user is conducive to improving the analysis accuracy and reliability of the current health state of the user.

[0138] Embodiment Three

[0139] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a nutrition and health recommendation device disclosed by the embodiment of the present application. Among them, Figure 3 The nutrition and health recommendation device described can include a server, and the server can include a cloud server or a local server, which is not limited by the embodiment of the present application. As Figure 3 indicated, the nutrition and health recommendation device can include:

[0140] The determining module 301 is configured to determine the body index data of the user.

[0141] The analysis module 302 is configured to analyze the current health state of the user according to the body index data of the user and the predetermined health state range, to obtain a health state analysis result of the user.

[0142] In the embodiment of the present application, the body index data at least includes the body fat rate, or the body index data at least includes the body fat rate and further includes the BMI value.

[0143] Optionally, when the body index data includes the body fat rate, the health state analysis result of the user includes a result that the user is currently in the health state, or a result that the user is not currently in the health state, which is not limited in the embodiment of the present application.

[0144] Optionally, when the body index data includes the body fat rate and the BMI value, the health state analysis result of the user includes a result that the user is currently in the health state, or a result that the user is not currently in the health state, and when the user is not currently in the health state, the health state analysis result of the user further includes a non-health state analysis result of the user, which is not limited in the embodiment of the present application.

[0145] The acquisition module 303 is configured to acquire historical activity related data of the user when the health state analysis result is that the user is not in the health state range.

[0146] The generation module 304 is configured to generate nutrition and health suggestion data of the user according to the body index data and the historical activity related data.

[0147] It can be seen that the embodiment of the present application has the following beneficial effects: Figure 3 The device for recommending nutrition and health described in the present application can determine the body index data of the user, analyze the current health state of the user according to the body index data of the user and the predetermined health state range, to obtain a health state analysis result of the user, which can improve the analysis accuracy and efficiency of the current health state of the user, and when the health state analysis result is that the user is not in the health state range, generate nutrition and health suggestion data of the user according to the body index data and the historical activity related data of the user, which can generate precise nutrition and health suggestion for the user based on the body index data and the historical activity related data of the user when it is analyzed that the user is not in the health state, is beneficial to recommend more suitable nutrition and health suggestion for the user, thereby helping the physical health of the user and improving the life quality of the user.

[0148] In an optional embodiment, the historical activity related data includes historical nutrition intake data, historical sleep data and historical exercise data, and the generation module 304 generates the nutrition and health suggestion data of the user according to the body index data and the historical activity related data in the following specific manner:

[0149] According to the body index data and the historical nutrition intake data, target nutrition intake data of the user is generated;

[0150] According to the target nutrition intake data of the user, the historical sleep data and the historical exercise data, nutrition health suggestion data of the user is generated.

[0151] It can be seen that the optional embodiment can generate target nutrition intake data of the user according to the body index data and the historical nutrition intake data, and then generate nutrition health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data, so as to comprehensively analyze the body index data, the historical nutrition intake data, the historical exercise data and the historical sleep data, and generate accurate nutrition health suggestion for the user.

[0152] In the optional embodiment, as an optional implementation, the manner in which the generating module 304 generates the nutrition health suggestion data of the user according to the target nutrition intake data of the user, the historical sleep data and the historical exercise data can specifically include:

[0153] According to the historical sleep data and the historical exercise data, the target nutrition intake data of the user is adjusted to obtain adjusted target nutrition intake data; and according to the adjusted target nutrition intake data, dish eating suggestion data of the user is generated;

[0154] According to the historical sleep data, sleep suggestion data of the user is generated;

[0155] According to the historical exercise data, exercise suggestion data of the user is determined;

[0156] The dish eating suggestion data, the sleep suggestion data and the exercise suggestion data are integrated to obtain the nutrition health suggestion data of the user.

[0157] It can be seen that the optional implementation can adjust the target nutritional intake data of the user according to the historical sleep data and the historical exercise data, obtain the adjusted target nutritional intake data, improve the adjustment accuracy and efficiency of the target nutritional intake data, and generate the dish consumption suggestion data of the user according to the adjusted target nutritional intake data, so as to generate accurate dish consumption suggestion data for the user based on the accurately adjusted target nutritional intake data, generate the sleep suggestion data of the user according to the historical sleep data, generate accurate sleep suggestion data for the user through analysis of the historical sleep data, generate the exercise suggestion data of the user according to the historical exercise data, generate accurate exercise suggestion data for the user through analysis of the historical exercise data, integrate the dish consumption suggestion data, the sleep suggestion data and the exercise suggestion data to obtain the nutritional health suggestion data of the user, and improve the generation accuracy and reliability of the nutritional health suggestion data through comprehensive analysis of the sleep, exercise, nutritional intake and other related data.

[0158] In the optional implementation, optionally, the generation module 304 can adjust the target nutritional intake data of the user according to the historical sleep data and the historical exercise data to obtain the adjusted target nutritional intake data in the following manner:

[0159] According to the target nutritional intake data of the user and the historical sleep data, analyze first nutritional intake adjustment data of the user about the historical sleep data;

[0160] According to the target nutritional intake data of the user and the historical exercise data, analyze exercise consumption data of the user about the target nutritional intake data; and according to the exercise consumption data, analyze second nutritional intake adjustment data of the user about the historical exercise data;

[0161] According to the first nutritional intake adjustment data and the second nutritional intake adjustment data, determine target nutritional intake adjustment data of the user about the historical sleep data and the historical exercise data;

[0162] According to the target nutritional intake adjustment data, adjust the target nutritional intake data to obtain the adjusted target nutritional intake data.

[0163] It can be seen that the optional embodiment can also analyze the first nutrition intake adjustment data of the user with respect to the historical sleep data according to the target nutrition intake data and the historical sleep data of the user, improve the accuracy and reliability of the nutrition intake adjustment data obtained by analyzing the sleep data, and analyze the exercise consumption data of the user with respect to the target nutrition intake data according to the target nutrition intake data and the historical exercise data of the user, and analyze the second nutrition intake adjustment data of the user with respect to the historical exercise data according to the exercise consumption data, improve the accuracy and reliability of the nutrition intake adjustment data obtained by analyzing the exercise data, and then determine the target nutrition intake adjustment data of the user with respect to the historical sleep data and the historical exercise data according to the first nutrition intake adjustment data and the second nutrition intake adjustment data, improve the analysis accuracy and reliability of the nutrition intake adjustment data with respect to the sleep data and the exercise data, and then adjust the target nutrition intake data according to the target nutrition intake adjustment data to obtain the adjusted target nutrition intake data, which is beneficial to improving the adjustment accuracy and reliability of the target nutrition intake data by comprehensively analyzing the related data of the sleep data and the exercise data, thereby being beneficial to improving the generation accuracy of the related suggestion data based on the accurately adjusted target nutrition intake data.

[0164] In another optional embodiment, the determination module 301 determines the body index data of the user in the following manner:

[0165] The current attribute information of the user is determined, and the current attribute information includes one or more combinations of age, gender, height, and identity;

[0166] The weight information of the user is obtained, and the weight information includes current weight information and historical weight information in a past preset time period;

[0167] The body index data of the user is determined according to the current attribute information and the weight information.

[0168] It can be seen that the optional embodiment can more accurately analyze the body index data of the user by using the current attribute information, the current weight information, and the historical weight information in a past period of time of the user, which is beneficial to comprehensively analyzing the height, age, gender, historical nutrition intake data, historical exercise data, historical sleep data, and the like of the user, and generating accurate nutrition and health suggestions for the user.

[0169] In this optional embodiment, as an optional implementation, the analysis module 302 analyzes the current health status of the user according to the body index data of the user and the pre-determined health status range to obtain the health status analysis result of the user in the following manner:

[0170] determine whether the body fat rate of the user is in a normal body fat rate range corresponding to the current attribute information of the user according to the body fat rate of the user;

[0171] determine that the user is currently in a healthy state when it is determined that the body fat rate of the user is in the normal body fat rate range corresponding to the current attribute information of the user;

[0172] determine that the user is not currently in a healthy state when it is determined that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user; or

[0173] determine whether the BMI value of the user is in a normal BMI value range according to the BMI value of the user when it is determined that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, obtain a BMI determination result; and determine a non-healthy state analysis result of the user according to the BMI determination result.

[0174] It can be seen that the optional implementation can determine whether the body fat rate of the user is in a normal body fat rate range corresponding to the current attribute information of the user according to the body fat rate of the user, determine that the user is currently in a healthy state when it is determined that the body fat rate of the user is in the normal body fat rate range corresponding to the current attribute information of the user, improve the determination accuracy and efficiency of the user currently in a healthy state, determine that the user is not currently in a healthy state when it is determined that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, improve the determination accuracy and efficiency of the user currently not in a healthy state, further determine whether the BMI value of the user is in a normal BMI value range according to the BMI value of the user when it is determined that the body fat rate of the user is not in the normal body fat rate range corresponding to the current attribute information of the user, obtain a BMI determination result; and determine a non-healthy state analysis result of the user according to the BMI determination result, which can accurately analyze the non-healthy state of the user, and the comprehensive judgment of the body fat rate and the BMI is beneficial to improve the analysis accuracy and reliability of the current healthy state of the user.

[0175] Embodiment Four

[0176] Please refer to Figure 4 , Figure 4 is a structural schematic diagram of another nutrition and health recommendation device disclosed by the embodiments of the present application. As Figure 4 shown, the nutrition and health recommendation device can include:

[0177] a memory 401 storing executable program codes;

[0178] a processor 402 coupled with the memory 401;

[0179] The processor 402 invokes the executable program code stored in the memory 401 to execute part or all of the steps in any of the nutritional health recommendation methods disclosed in Embodiment One or Embodiment Two.

[0180] Embodiment Five

[0181] The computer storage medium disclosed in the embodiments of the present application stores computer instructions, which, when invoked, are used to execute part or all of the steps in any of the nutritional health recommendation methods disclosed in Embodiment One or Embodiment Two.

[0182] Embodiment Six

[0183] The computer program product disclosed in the embodiments of the present application comprises a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in any of the nutritional health recommendation methods disclosed in Embodiment One or Embodiment Two.

[0184] The device embodiments described above are only schematic, wherein the modules illustrated as separate components can or can not be physically separate, and the components illustrated as modules can or can not be physical modules, i.e., can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0185] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above specific description of the embodiments, and the various embodiments can be realized by means of software and necessary universal hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in terms of contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, which includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically-Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM), or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other medium that can be used to carry or store computer readable instructions.

[0186] Finally, it should be noted that: the above-mentioned embodiments disclosed only the preferred embodiments of the present application, only for the description of the technical solutions of the present application, and not limited; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for nutritional health recommendation, characterized in that: The method comprises: Determine the user's physical indicator data; Analyzing the user's current health status based on the user's physical indicator data and a predetermined health status range to obtain a health status analysis result of the user; When the health status analysis result shows that the user is not in the health status range, obtaining historical activity related data of the user; Generate nutritional health advice data for the user based on the physical indicator data and the historical activity-related data.

2. The method for nutritional health recommendation according to claim 1, characterized in that: The historical activity-related data includes historical nutrition intake data, historical sleep data, and historical exercise data; Furthermore, generating nutritional health advice data for the user based on the physical indicator data and the historical activity-related data includes: generating target nutritional intake data of the user according to the physical indicator data and the historical nutritional intake data; Generate nutritional health advice data for the user based on the user's target nutritional intake data, the historical sleep data, and the historical exercise data.

3. The method for nutritional health recommendation according to claim 2, characterized in that: Generating nutritional health advice data for the user based on the user's target nutritional intake data, the historical sleep data, and the historical exercise data includes: adjusting the user's target nutritional intake data based on the historical sleep data and the historical exercise data to obtain adjusted target nutritional intake data; and generating dish consumption recommendation data for the user based on the adjusted target nutritional intake data; generating sleep suggestion data for the user based on the historical sleep data; Determining exercise recommendation data for the user based on the historical exercise data; The dish consumption recommendation data, the sleep recommendation data and the exercise recommendation data are integrated to obtain the nutritional health recommendation data of the user.

4. The method for nutritional health recommendation according to claim 3, characterized in that: The adjusting the user's target nutritional intake data according to the historical sleep data and the historical exercise data to obtain the adjusted target nutritional intake data includes: analyzing the user's first nutritional intake adjustment data related to the historical sleep data according to the user's target nutritional intake data and the historical sleep data; analyzing the user's exercise consumption data related to the target nutritional intake data based on the user's target nutritional intake data and the historical exercise data; and analyzing the user's second nutritional intake adjustment data related to the historical exercise data based on the exercise consumption data; determining target nutritional intake adjustment data of the user with respect to the historical sleep data and the historical exercise data according to the first nutritional intake adjustment data and the second nutritional intake adjustment data; The target nutritional intake data is adjusted according to the target nutritional intake adjustment data to obtain adjusted target nutritional intake data.

5. The method for nutritional health recommendation according to any one of claims 1 to 4, characterized in that: Determining the user's physical indicator data includes: Determine the user's current attribute information, wherein the current attribute information includes one or more combinations of age, gender, height, and identity; Obtaining the user's weight information, the weight information including current weight information and historical weight information within a preset time period; The user's body index data is determined according to the current attribute information and the weight information.

6. The method for nutritional health recommendation according to claim 5, characterized in that: The body index data includes at least a body fat percentage, or the body index data includes at least a body fat percentage and a BMI value; When the body index data includes the body fat percentage, the user's health status analysis result includes a result that the user is currently in a healthy state, or a result that the user is currently not in a healthy state; When the body indicator data includes the body fat percentage and the BMI value, the user's health status analysis result includes the result that the user is currently in a healthy state, or the result that the user is currently not in a healthy state, and when the user is not currently in the healthy state, the user's health status analysis result also includes the user's unhealthy state analysis result.

7. The method for nutritional health recommendation according to claim 6, characterized in that: Analyzing the user's current health status based on the user's physical indicator data and a predetermined health status range to obtain a health status analysis result of the user includes: determining, based on the user's body fat percentage, whether the user's body fat percentage is within a predetermined normal body fat percentage range corresponding to the user's current attribute information; When it is determined that the body fat percentage of the user is within a normal body fat percentage range corresponding to the current attribute information of the user, determining that the user is currently in a healthy state; When it is determined that the body fat percentage of the user is not within the normal body fat percentage range corresponding to the current attribute information of the user, it is determined that the user is not currently in a healthy state; or When it is determined that the user's body fat percentage is not within the normal body fat percentage range corresponding to the user's current attribute information, the user's BMI value is determined based on the user's BMI value to determine whether the user's BMI value is within a predetermined normal BMI value range to obtain a BMI judgment result; and based on the BMI judgment result, the user's unhealthy status analysis result is determined.

8. A device for nutritional health recommendations, characterized in that: The device comprises: A determination module, used to determine the user's physical indicator data; An analysis module, configured to analyze the user's current health status based on the user's physical indicator data and a predetermined health status range, and obtain a health status analysis result of the user; an acquisition module, configured to acquire historical activity-related data of the user when the health status analysis result indicates that the user is not within the health status range; A generation module is used to generate nutritional health advice data for the user based on the physical indicator data and the historical activity-related data.

9. A device for nutritional health recommendations, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method for nutritional health recommendations as described in any one of claims 1-7.

10. A computer storage medium, characterized in that The computer storage medium stores computer instructions, which, when called, are used to execute the method for nutritional health recommendation according to any one of claims 1 to 7.

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