Suggested data generation apparatus, method, and computer program product

Abstract

The invention discloses a suggestion data generation device, a suggestion data generation method and a computer program product. The device comprises a data collection module, a health index determination module and a suggestion data generation module, the data collection module is used for collecting first data and second data corresponding to the target object; wherein the first data is nutritional ingredient data in the food; the second data is physiological index data associated with the target object; the health index determination module is used for determining a health index corresponding to the target object according to the first data and the second data; and the suggestion data generation module is used for generating suggestion data corresponding to the target object according to the health index and displaying the suggestion data to the target object. Based on the technical scheme, the first data and the second data associated with the target object are automatically obtained, and the suggestion data corresponding to the target object are generated, so that the generation efficiency of the suggestion data is improved, and the reasonability of the suggestion data is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a device and method for generating suggestion data, and a computer program product. Background Art

[0002] With the growing demand for health management, various health management applications are becoming increasingly popular. Current health management tools on the market primarily rely on users manually recording data such as diet and exercise, and then provide health recommendations based on general nutritional standards.

[0003] However, existing technical solutions rely on users to actively input diet and exercise data and lack automated collection methods. Manual input is not only time-consuming, but also prone to inaccurate data due to memory bias or estimation errors, which in turn reduces the credibility of health recommendations and fails to provide users with more reasonable health recommendations. Summary of the Invention

[0004] The present invention provides a device, method, and computer program product for generating suggestion data to automatically obtain first data and second data associated with a target object and generate suggestion data corresponding to the target object, thereby improving the efficiency of generating suggestion data and enhancing the rationality of the suggestion data.

[0005] According to one aspect of the present invention, a device for generating recommended data is provided, the device comprising: a data collection module, a health indicator determination module, and a recommended data generation module; wherein,

[0006] The data collection module is used to collect first data and second data corresponding to the target object; wherein the first data is the nutritional component data in the food; and the second data is the physiological indicator data associated with the target object;

[0007] The health indicator determination module is configured to determine a health indicator corresponding to the target object based on the first data and the second data;

[0008] The recommendation data generation module is used to generate recommendation data corresponding to the target object based on the health indicators, and present the recommendation data to the target object, wherein the recommendation data includes diet recommendation data and exercise recommendation data.

[0009] According to another aspect of the present invention, a method for generating suggestion data is provided, which is applied to the device for generating suggestion data according to any embodiment of the present invention. The method includes:

[0010] Collecting first data and second data corresponding to a target object; wherein the first data is nutritional component data in food; and the second data is physiological indicator data associated with the target object;

[0011] determining a health indicator corresponding to the target object based on the first data and the second data;

[0012] Recommendation data corresponding to the target object is generated according to the health indicator, and the recommendation data is presented to the target object, wherein the recommendation data includes diet recommendation data and exercise recommendation data.

[0013] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for generating suggestion data according to any embodiment of the present invention is implemented.

[0014] The technical solution of the embodiment of the present invention is to collect first data and second data corresponding to the target object, wherein the first data is the nutritional component data in the food; the second data is the physiological indicator data associated with the target object, and then determine the health indicator corresponding to the target object based on the first data and the second data, and finally generate the recommendation data corresponding to the target object based on the health indicator, and display the recommendation data to the target object, wherein the recommendation data includes diet recommendation data and exercise recommendation data. Based on the above technical solution, by automatically acquiring the first data and the second data associated with the target object and generating the recommendation data corresponding to the target object, the efficiency of generating the recommendation data is improved, and the rationality of the recommendation data is improved.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 is a structural diagram of a suggestion data generating device provided by an embodiment of the present invention;

[0018] Figure 2 1 is a flow chart of a method for generating suggestion data provided by an embodiment of the present invention;

[0019] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of national laws and regulations.

[0023] Example 1

[0024] Figure 1 This is a structural diagram of a device for generating suggestion data provided by an embodiment of the present invention. Figure 1 As shown, the device includes: a data collection module 110, a health indicator determination module 120 and a recommendation data generation module 130; wherein,

[0025] The data collection module is used to collect first data and second data corresponding to the target object; the health indicator determination module is used to determine the health indicator corresponding to the target object based on the first data and the second data; the recommended data generation module is used to generate recommended data corresponding to the target object based on the health indicator, and display the recommended data to the target object.

[0026] Among them, the first data is the nutritional content data in the food; the second data is the physiological indicator data associated with the target object. The target object can be understood as the user who needs to obtain the recommended data. The health index can be an indicator used to evaluate the health status of the target object. For example, it can be a health index obtained by evaluating the health status of the target object based on the collected first data and second data. It can be a text indicator such as excellent, good, medium, poor, etc., or it can be a numerical score indicator obtained after scoring. The recommended data can be data used to prompt the target object to improve its health level. The recommended data includes diet recommendation data and exercise recommendation data. It can be understood that the diet recommendation data can be used to prompt the user to improve his diet, and the exercise recommendation data can be used to prompt the user to improve his exercise.

[0027] Specifically, by collecting first data and second data corresponding to the target object, and comprehensively determining the health index corresponding to the target object based on the first data and the second data, and generating recommendation data corresponding to the target object based on the health index, and displaying the recommendation data to the target object, so that the target object can adjust the diet recipe and / or adjust the amount of exercise according to the recommendation data, thereby improving the health of the target object. Exemplarily, a combination of automatic acquisition and manual input can be used to improve data accuracy and user experience. The automatically collected data includes the number of steps, heart rate, blood sugar, sleep quality, etc., and the manually input data includes age, height, weight, dietary preferences, allergy history, and subjective health status self-report.

[0028] It should be noted that based on the user's health data and goals, customized health advice is also provided, including: 1) Health index calculation

[0029] Comprehensive Assessment: Calculates a daily health index based on a user's diet, exercise, and sleep data to reflect their overall health. Trend Analysis: Analyzes health index trends using a sliding time window, such as 7 or 30 days, to help users understand their long-term health trends. It should be noted that the daily health index is a comprehensive metric used to assess a user's overall health status on a daily basis. This index integrates a user's diet, exercise, and sleep data to reflect their health level. The weighting of each data element in the health index can be configured based on the user's specific needs. For example, if a user configures diet, exercise, and sleep data with weights of 0.5, 0.3, and 0.2, respectively, the daily health index is calculated as: Health Index = 0.5 × Diet Score + 0.3 × Exercise Score + 0.2 × Sleep Score. The Diet Score, Exercise Score, and Sleep Score represent the user's performance in these three areas, respectively, and typically range from 0 to 1, with 1 representing optimal performance. These scores are calculated based on relevant health guidelines or standards. Personalized Assessment: Nutritional Adjustment: Provides specific recommendations based on the user's nutritional intake. For example, if protein intake is insufficient, the system will recommend increasing protein-rich foods, such as eggs and soy products. Customized recipe recommendations: Based on the user's health goals and dietary preferences, daily or weekly recipes are recommended, and detailed preparation methods are provided. Exercise plan optimization: Based on the user's exercise data and health goals, appropriate exercise type, intensity, and frequency are recommended. For example, for users who need to lose fat, it is recommended to increase the frequency and duration of aerobic exercise.

[0030] Based on the above technical solution, the health indicator determination module includes: a basic health indicator determination unit and a personalized health indicator determination unit; wherein, the basic health indicator determination unit is used to obtain the recommended nutrient intake of the target object, and determine the basic health indicator of the target object based on the recommended nutrient intake and the first data; the personalized health indicator determination unit is used to determine the personalized health indicator corresponding to at least one health goal corresponding to the target object according to the first data and the second data.

[0031] Among them, the recommended nutrient intake can be the amount of various nutrients that should be consumed daily to meet the normal physiological functions and health needs of the human body, and can be formulated based on nutritional research and health guidelines. Basic health indicators can be understood as basic indicators used to evaluate the dietary health of the target object. Health goals can be goals set by the target object themselves. Personalized health indicators are evaluation indicators corresponding to health goals, which are used to quantify the target object's completion status under the current health goals. Health goals include at least one of sugar control goals, fat control goals and muscle gain goals.

[0032] Specifically, the recommended nutrient intake for the target subject is obtained, and the target subject's basic health indicators are determined based on the recommended nutrient intake and the first data. It should be noted that when calculating the food score, the nutritional components of each food, including the food's calories, protein, carbohydrates, fat, trace elements, etc., are extracted from the database based on nutritional knowledge and the food database. The target subject's basic health indicators are then determined based on the first data and the target subject's recommended nutrient intake. Furthermore, based on at least one health goal corresponding to the target subject, personalized health indicators corresponding to the health goal are determined based on the first and second data, and foods are scored and evaluated from multiple dimensions based on the user's specific health data and health goals.

[0033] Based on the above technical solution, the personalized health index determination unit includes: a sugar control index determination subunit, a fat control index determination subunit and a muscle gain index determination subunit; wherein, the sugar control index determination subunit is used to determine the dynamic adjustment factor of the sugar control target according to the second data, and determine the sugar control index based on the dynamic adjustment factor of the sugar control target and the first data; the fat control index determination subunit is used to determine the dynamic adjustment factor of the fat control target according to the second data, and determine the fat control index based on the dynamic adjustment factor of the fat control target and the first data; the muscle gain index determination subunit is used to determine the dynamic adjustment factor of the muscle gain target according to the second data, and determine the muscle gain index based on the dynamic adjustment factor of the muscle gain target and the first data.

[0034] The dynamic adjustment factor may be a base factor for dynamically adjusting the score according to the user's health dynamics.

[0035] Specifically, users can set personalized health goals, such as controlling sugar, controlling fat, and building muscle, and dynamically adjust them based on health data such as blood sugar, weight, and exercise volume. Each goal is scored, and the scores for each goal are weighted and combined to create a comprehensive score.

[0036] For the goals of sugar control, fat control, and muscle gain, scores are given according to the following formula.

[0037] Sugar Control Goal: If the user wants to control sugar intake, they can calculate the score based on the sugar content of the food. Low-sugar foods will get higher scores, and vice versa. Scoring formula:

[0038] S = (1-food sugar content / target sugar intake) * f(blood sugar fluctuation); where f(blood sugar fluctuation) is a dynamic adjustment factor based on the user's blood sugar fluctuation, defined as:

[0039]

[0040] When the user's current blood sugar is close to the target blood sugar, the factor value is close to 1; when the deviation is large, the factor value decreases. This will encourage users to choose foods that are more conducive to blood sugar control.

[0041] Fat Control Goal: Dynamic scoring is performed based on the user's weight data and fat intake. If the user is overweight or has a high fat percentage, the fat control goal score penalty will increase. Scoring formula:

[0042] S = (1-dietary fat / target fat intake)*f(weight change);

[0043] Where f(weight change) is a dynamic adjustment factor based on the user's weight change and is defined as:

[0044]

[0045] When the user's current weight is close to the target weight, the factor value is close to 1; when the deviation is large, the factor value decreases. This can encourage users to choose foods that are more conducive to weight control.

[0046] Muscle Gain Goal: Dynamic scoring is performed based on the user's exercise volume and protein intake. If the user performs more strength training, the muscle gain goal score will increase, and more high-protein foods will be recommended. Scoring formula: S = food protein / recommended protein intake * f (exercise volume); where f (exercise volume) is a dynamic adjustment factor based on the user's exercise volume. It is defined as:

[0047]

[0048] When the user's actual exercise volume exceeds the basic exercise volume, the factor value is greater than 1, encouraging more protein intake; when it is lower than the basic exercise volume, the factor value is less than 1, reducing protein intake.

[0049] Based on the above technical solution, the data collection module includes: a first data determination unit and a second data acquisition unit; wherein, the first data determination unit is used to obtain the food image uploaded by the target object, and determine the first data based on the food image and a pre-established food database; the second data acquisition unit is used to collect second data corresponding to the target object through a sensor set on the smart wearable device.

[0050] The food image may be a food image obtained by photographing the food. It should be noted that before consuming the food, the user may capture images related to the food using an image acquisition device, such as a smart phone. The food database may be a pre-established database for storing food data.

[0051] Specifically, a food image uploaded by a target user is obtained and first data is determined based on the food image and a pre-established food database. It should be noted that the first data includes information such as the food type, nutritional composition, and intake amount. Furthermore, second data corresponding to the target user is collected via sensors on a smart wearable device. The second data includes physiological data such as the user's step count, heart rate, blood sugar, and sleep quality. For example, the user can manually enter the food name or upload a photo of the food, and the application automatically identifies the food and calculates a personalized score. Combining OCR text recognition with AI visual recognition, the application automatically matches food information in the database and uses historical data to estimate food intake, eliminating the need for the user to manually enter the number of grams each time. The user's physiological data is collected via smart devices such as smart bracelets, mobile phone sensors, and blood glucose meters. This physiological data may include step count, heart rate, blood sugar, sleep quality, and other data. It should be noted that different physiological data collection devices may correspond to different applications. Therefore, when acquiring the second data corresponding to the target user, the second data corresponding to each physiological data collection device can be retrieved through the API of each application. The user can also manually enter additional personalized information, including objective basic information such as age, height, weight, dietary preferences, and allergy history. Self-report of subjective health status: such as "feeling tired today" and "stomach discomfort", etc., is used to optimize health recommendations.

[0052] Based on the above technical solution, the first data determination unit includes: a food type determination subunit and a food weight determination subunit; wherein, the food type determination subunit is used to identify the food image and determine the food type corresponding to each food ingredient in the food image; the food weight determination subunit is used to determine the food volume corresponding to each food ingredient based on the food image, and determine the food weight corresponding to each food ingredient based on the food type and the food volume.

[0053] The food ingredients may be the components of the food corresponding to the food image. For example, in the case of scrambled eggs with tomatoes, the food ingredients may be tomatoes and eggs. The food type may be the name of the food corresponding to the food ingredients.

[0054] Specifically, the input food image is preprocessed, including cropping, scaling, denoising, etc., to improve recognition accuracy, and then a deep learning model is used to extract features in the image. The features include color, texture, shape, etc. The extracted features are matched with a pre-trained food type classification model to determine the food type in the image. The output of the food type determination subunit is the name of the food type, which corresponds to each food ingredient in the image; deep learning technology is then used to estimate the three-dimensional volume of the food in the image, and based on the food type and volume, a pre-established food density database is used to calculate the weight of the food.

[0055] It should be noted that different types of food may have different densities, so weight calculations need to be adjusted based on the food type. Using deep learning techniques to estimate the 3D volume of food in images can involve collecting a large dataset containing food images and their corresponding 3D volume annotations. The images are then preprocessed, such as cropping, scaling, and normalization, to improve model training. The model is then trained based on the processed dataset. During training, the model's output is calculated through forward propagation, and the model's parameters are updated through backpropagation. An appropriate loss function is used to evaluate the model's prediction error, and optimization algorithms, such as stochastic gradient descent, are used to minimize the prediction error. Once the model is trained, the model can be used to estimate the 3D volume of new food images.

[0056] Based on the above technical solution, the first data determination unit includes: a first data determination subunit; wherein, the first data determination subunit is used to determine the nutrient composition corresponding to the food type from the food database based on the food type, and determine the first data according to the nutrient composition and the food weight corresponding to each food component.

[0057] Among them, the nutrient composition can be the composition of nutrients in food, for example, it can be the amount of various nutrients contained in every 100g of food. For example, taking tomatoes as an example, its nutrient composition is about 17 kcal per 100g of raw tomatoes; about 3.3g of carbohydrates per 100g of raw tomatoes; about 0.9g of protein per 100g of raw tomatoes; the fat content in every 100g of raw tomatoes is very low, only about 0.2g; and about 1g of fiber per 100g of raw tomatoes.

[0058] Specifically, food type is used as a query keyword for searching in a food database. It should be noted that the food database is a database containing various food types and their corresponding nutrient compositions. According to the food type, the corresponding nutrient composition is retrieved from the database, including but not limited to energy, protein, fat, carbohydrates, vitamins, minerals, etc. The total intake of each nutrient is calculated based on the retrieved nutrient composition and the weight of each food ingredient. For example, if a certain food contains 10 grams of protein and 20 grams of carbohydrates, and its weight is 100 grams, then when 100 grams of the food is consumed, the protein intake is 10 grams and the carbohydrate intake is 20 grams.

[0059] Based on the above technical solution, the recommendation data generation module includes: a long-term recommendation data generation unit and a personalized recommendation data generation unit; wherein, the long-term recommendation data generation unit is used to determine the long-term health status corresponding to the target object based on the time sliding window and the health indicators, and generate long-term recommendation data based on the long-term health status; the personalized recommendation data generation unit is used to generate recommendation data corresponding to the health goal based on the personalized indicators corresponding to the health goal when a health goal corresponding to the target object is detected.

[0060] The time sliding window can be a pre-set time range, such as a month or a week. Users can customize the time sliding window as needed. Long-term health status can be understood as a health status assessment corresponding to the target subject. Long-term recommendation data can be used to provide recommendations for the target subject to improve their long-term health.

[0061] Specifically, a time sliding window is pre-set, such as the past month, three months, or one year, and the health data of the target object is collected within the time sliding window. Health indicators are calculated based on the collected health data to reflect the overall health status of the target object. The long-term health status of the target object is determined based on the changing trend of the health indicators within the time sliding window, such as stable, improved, deteriorated, etc. Based on the long-term health status, corresponding long-term health recommendations are generated, such as maintaining current health habits, strengthening exercise, improving diet, etc. The personalized recommendation data generation unit is used to determine the corresponding personalized indicators such as weight, body fat percentage, muscle mass, sleep duration, etc. according to the health goals, and generate corresponding recommendation data such as diet plans, exercise plans, and work and rest adjustments based on the personalized indicators and the current status of the target object.

[0062] For example, this system monitors users' physiological data in real time, such as blood sugar, weight, and activity levels, and instantly adjusts food ratings based on this data. For example, if the system detects a recent increase in a user's blood sugar level, it will lower the rating of high-sugar foods, reminding the user to be mindful of their intake. Periodic Assessment: Using a 7-day sliding window approach, the system calculates the mean and standard deviation of health indicators such as blood sugar and weight within each sliding window to establish a personalized baseline health status for the user. This baseline value measures whether the user's current health status is within a reasonable range. If a metric exceeds 1.5-2 standard deviations of the baseline mean, it is considered abnormal. Based on identified anomalies or trends, the system automatically adjusts the rating of the relevant food. Habit Analysis: The system continuously tracks users' eating and lifestyle habits, identifying long-term preferences, such as a preference for high-fat foods. Based on these habits, it adjusts food ratings and recommendation strategies, gradually guiding users towards a healthier diet. Goal Achievement Assessment: The system regularly assesses the completion of users' health goals, such as weight loss and muscle gain, and adjusts food rating strategies based on achievement, providing more targeted recommendations. Tolerance mechanism: Considering that users may occasionally consume high-calorie or unhealthy foods, a tolerance mechanism should be set up. While not punishing users harshly for occasional consumption, subsequent recommendations will remind users to strike a balance. Compensatory suggestions: After users occasionally consume high-calorie foods, the system can recommend increasing exercise or choosing low-calorie foods for subsequent meals to balance overall intake.

[0063] Based on the above technical solution, the data collection module is further used to: collect motion data corresponding to the target object; and the recommendation data generation module is used to determine motion recommendation data corresponding to the target object based on the motion data.

[0064] The motion data may be data generated by the target object during the motion process, and may include the motion type, motion duration, etc.

[0065] Specifically, wearable devices, such as sensors built into smart watches or smartphones, are used to collect the target object's motion data in real time. The motion data includes but is not limited to activity volume, exercise type, exercise duration, exercise intensity, heart rate, number of steps, etc. The motion data is deeply analyzed to identify the target object's motion patterns, preferences, intensity and other characteristics. Reasonable exercise goals are set based on the target object's health status and exercise goals, such as weight loss, muscle gain, and endurance improvement. Combined with the motion data and goal setting, personalized exercise recommendation data is generated, including exercise type, duration, intensity, frequency, etc.

[0066] The technical solution of the embodiment of the present invention is to collect first data and second data corresponding to the target object, wherein the first data is the nutritional component data in the food; the second data is the physiological indicator data associated with the target object, and then determine the health indicator corresponding to the target object based on the first data and the second data, and finally generate the recommendation data corresponding to the target object based on the health indicator, and display the recommendation data to the target object, wherein the recommendation data includes diet recommendation data and exercise recommendation data. Based on the above technical solution, by automatically acquiring the first data and the second data associated with the target object and generating the recommendation data corresponding to the target object, the efficiency of generating the recommendation data is improved, and the rationality of the recommendation data is improved.

[0067] Example 2

[0068] Figure 2 The flowchart of a method for generating suggestion data provided by an embodiment of the present invention is applied to a device for generating suggestion data as described in any one of the embodiments of the present invention. Figure 2 As shown, the method includes:

[0069] S210, collecting first data and second data corresponding to the target object; wherein the first data is nutritional component data in food; and the second data is physiological indicator data associated with the target object;

[0070] S220, determining a health indicator corresponding to the target object based on the first data and the second data;

[0071] S230: Generate suggestion data corresponding to the target object based on the health indicator, and present the suggestion data to the target object, wherein the suggestion data includes diet suggestion data and exercise suggestion data.

[0072] On the basis of the above technical solution, the determining of the health indicators corresponding to the target object based on the first data and the second data includes: obtaining the recommended nutrient intake of the target object, and determining the basic health indicators of the target object based on the recommended nutrient intake and the first data; based on at least one health goal corresponding to the target object, determining the personalized health indicators corresponding to the health goal based on the first data and the second data, wherein the health goals include at least one of a sugar control goal, a fat control goal and a muscle gain goal.

[0073] On the basis of the above technical solution, the personalized health indicator corresponding to the health goal is determined according to the first data and the second data, including: determining the dynamic adjustment factor of the sugar control target according to the second data, and determining the sugar control indicator based on the dynamic adjustment factor of the sugar control target and the first data; determining the dynamic adjustment factor of the fat control target according to the second data, and determining the fat control indicator based on the dynamic adjustment factor of the fat control target and the first data; determining the dynamic adjustment factor of the muscle gain target according to the second data, and determining the muscle gain indicator based on the dynamic adjustment factor of the muscle gain target and the first data.

[0074] Based on the above technical solution, the collection of first data and second data corresponding to the target object includes: obtaining a food image uploaded by the target object, determining the first data based on the food image and a pre-established food database; and collecting the second data corresponding to the target object through a sensor set on a smart wearable device.

[0075] Based on the above technical solution, the determining of the first data based on the food image and a pre-established food database includes: identifying the food image to determine the food type corresponding to each food ingredient in the food image; determining the food volume corresponding to each food ingredient based on the food image, and determining the food weight corresponding to each food ingredient based on the food type and the food volume.

[0076] Based on the above technical solution, the first data is determined according to the food image and a pre-established food database, including: determining the nutrient composition corresponding to the food type from the food database based on the food type, and determining the first data according to the nutrient composition and the food weight corresponding to each food ingredient.

[0077] Based on the above technical solution, the generation of recommendation data corresponding to the target object based on the health indicators includes: determining the long-term health status corresponding to the target object based on the time sliding window and the health indicators, and generating long-term recommendation data based on the long-term health status; when a health goal corresponding to the target object is detected, generating recommendation data corresponding to the health goal based on the personalized indicators corresponding to the health goal.

[0078] On the basis of the above technical solution, it also includes collecting motion data corresponding to the target object; and determining motion suggestion data corresponding to the target object based on the motion data.

[0079] The technical solution of the embodiment of the present invention is to collect first data and second data corresponding to the target object, wherein the first data is the nutritional component data in the food; the second data is the physiological indicator data associated with the target object, and then determine the health indicator corresponding to the target object based on the first data and the second data, and finally generate the recommendation data corresponding to the target object based on the health indicator, and display the recommendation data to the target object, wherein the recommendation data includes diet recommendation data and exercise recommendation data. Based on the above technical solution, by automatically acquiring the first data and the second data associated with the target object and generating the recommendation data corresponding to the target object, the efficiency of generating the recommendation data is improved, and the rationality of the recommendation data is improved.

[0080] Example 3

[0081] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0082] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12 and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0083] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0084] The processor 11 may be any general-purpose and / or specialized processing component with processing and computing capabilities. Examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors for running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for generating recommendation data.

[0085] In some embodiments, the suggestion data generation method may be implemented as a computer program that is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the suggestion data generation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the suggestion data generation method in any other appropriate manner (e.g., by means of firmware).

[0086] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0090] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0091] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0092] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0093] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A suggestion data generating device, characterized in that: The device includes: a data collection module, a health index determination module and a suggestion data generation module; wherein, The data collection module is configured to collect first data and second data corresponding to a target object; wherein the first data is nutritional data in food; and the second data is physiological indicator data associated with the target object; The health indicator determination module is configured to determine a health indicator corresponding to the target object based on the first data and the second data; The recommendation data generation module is used to generate recommendation data corresponding to the target object based on the health indicators, and present the recommendation data to the target object, wherein the recommendation data includes diet recommendation data and exercise recommendation data.

2. The device according to claim 1, characterized in that The health index determination module includes: a basic health index determination unit and a personalized health index determination unit; wherein, The basic health index determination unit is configured to obtain the recommended nutrient intake of the target subject and determine the basic health index of the target subject based on the recommended nutrient intake and the first data; The personalized health indicator determination unit is used to determine a personalized health indicator corresponding to at least one health goal corresponding to the target object based on the first data and the second data, wherein the health goal includes at least one of a sugar control goal, a fat control goal, and a muscle gain goal.

3. The device according to claim 2, characterized in that The personalized health index determination unit includes: a sugar control index determination subunit, a fat control index determination subunit and a muscle gain index determination subunit; wherein, The blood sugar control index determination subunit is configured to determine a dynamic adjustment factor of the blood sugar control target according to the second data, and determine a blood sugar control index based on the dynamic adjustment factor of the blood sugar control target and the first data; The fat control index determination subunit is configured to determine a dynamic adjustment factor of the fat control target according to the second data, and determine a fat control index based on the dynamic adjustment factor of the fat control target and the first data; The muscle-building index determination subunit is configured to determine the dynamic adjustment factor of the muscle-building target according to the second data, and to determine the muscle-building index based on the dynamic adjustment factor of the muscle-building target and the first data.

4. The device according to claim 1, characterized in that The data collection module includes: a first data determination unit and a second data acquisition unit; wherein, The first data determining unit is configured to obtain a food image uploaded by the target object and determine the first data based on the food image and a pre-established food database; The second data acquisition unit is configured to acquire second data corresponding to the target object through a sensor provided on the smart wearable device.

5. The device according to claim 4, characterized in that The first data determination unit includes: a food type determination subunit and a food weight determination subunit; wherein, The food type determination subunit is configured to identify the food image and determine the food type corresponding to each food ingredient in the food image; The food weight determination subunit is configured to determine the food volume corresponding to each food component according to the food image, and determine the food weight corresponding to each food component according to the food type and the food volume.

6. The device according to claim 5, characterized in that The first data determination unit includes: a first data determination subunit; wherein, The first data determination subunit is configured to determine the nutrient composition corresponding to the food type from the food database based on the food type, and determine the first data according to the nutrient composition and the food weight corresponding to each food component.

7. The device according to claim 1, characterized in that The suggestion data generation module includes: a long-term suggestion data generation unit and a personalized suggestion data generation unit; wherein, The long-term recommendation data generating unit is configured to determine a long-term health status corresponding to the target object based on the time sliding window and the health indicator, and generate long-term recommendation data based on the long-term health status; The personalized recommendation data generating unit is configured to generate recommendation data corresponding to the health goal according to the personalized indicator corresponding to the health goal when a health goal corresponding to the target object is detected.

8. The device according to claim 1, characterized in that The data collection module is further configured to: collect motion data corresponding to the target object; The suggestion data generating module is configured to determine the motion suggestion data corresponding to the target object according to the motion data.

9. A method for generating suggestion data, characterized in that: Applied to the suggestion data generating device according to any one of claims 1 to 8, the method comprises: Collecting first data and second data corresponding to a target object; wherein the first data is nutritional component data in food; and the second data is physiological indicator data associated with the target object; determining a health indicator corresponding to the target object based on the first data and the second data; Recommendation data corresponding to the target object is generated according to the health index, and the recommendation data is presented to the target object, wherein the recommendation data includes diet recommendation data and exercise recommendation data.

10. A computer program product comprising a computer program, which implements the suggestion data generating method according to claim 9 when the computer program is executed by a processor.

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