A method of health management and electronic device

Abstract

This application provides a health management method and electronic device, relating to the field of data processing technology. The health management method provided by this application includes: acquiring a user's first type of health data and second type of health data at a first moment; determining the user's health risk score based on the first type of health data; determining the user's current state value based on the second type of health data; the health risk score reflects the probability of the user developing a target disease in the future; the current state value evaluates the user's physical condition; and providing the user with matched health management suggestions based on the user's health risk score and current state value. Using the method in this application's embodiments makes the health management suggestions provided to the user more consistent with the user's current health status, helping the user improve their health as much as possible.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and electronic device for health management. Background Technology

[0002] With societal development and an increasingly fast-paced lifestyle, people are paying more and more attention to their personal health. Users can monitor various bodily parameters by wearing smart wearable devices (such as smartwatches and smart bracelets). These smart wearable devices are typically connected to a mobile phone, allowing the phone to access the bodily parameters monitored by the device.

[0003] However, the health management advice currently provided by mobile phones or smart wearable devices based on monitored body parameters may be unreasonable, such as not being applicable to the user's current health status. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a health management method and electronic device that makes the health management advice provided to users more aligned with their current health status, thereby helping users improve their health as much as possible.

[0005] In a first aspect, embodiments of this application provide a health management method applied to a first electronic device, comprising: responding to a user's query operation at a first moment, acquiring first-type health data and second-type health data at the first moment, wherein the first-type health data consists of current body parameters related to the probability of the user developing a target disease in the future, and the second-type health data consists of body parameters related to the user's current physical state, and the target disease includes at least two diseases; determining a health risk score for the user at the first moment based on the first-type health data at the first moment, the health risk score reflecting the probability of the user developing a target disease in the future; determining a state value for the user at the first moment based on the second-type health data at the first moment, the state value evaluating the user's physical state; determining health management recommendations for the user at the first moment based on the user's health risk score and the user's state value at the first moment, the health management recommendations including at least one of exercise recommendations, dietary recommendations, or rest recommendations; and displaying the health management recommendations for the user at the first moment.

[0006] For example, the first electronic device may be a smart wearable device (such as a bracelet or watch) or a mobile terminal, such as a mobile phone, that is wirelessly connected to the smart wearable device.

[0007] For example, the first type of health data is used to determine the probability that a user will develop a target disease in the future. In this example, the target disease may include chronic diseases such as hypertension, hyperlipidemia, diabetes, cervical spondylosis, coronary heart disease, etc., as well as acute diseases such as acute cerebral infarction, and tumor diseases such as cancer.

[0008] For example, physical status includes physical strength status and daily health status. Physical strength status reflects a user's remaining energy level throughout the day, determining the duration of daily activities such as work, study, and exercise. This physical strength status is a dynamically changing indicator, influenced by factors such as the user's activity level, sleep quality, and rest time from the previous day. For instance, good sleep and adequate rest the previous day help restore a user's physical strength, thus supporting the duration and intensity of daily activities. The better the physical strength status, the more energy the user has remaining throughout the day, and the longer they can support daily activities. Daily health status can be understood as the user's health condition on that day, including whether the user has short-term health problems, such as colds or headaches, or the impact of other unhealthy or sub-healthy behaviors on the user's physiological indicators (or body parameters). Unhealthy or sub-healthy behaviors include excessive fatigue and prolonged lack of rest, while body parameters include resting heart rate and heart rate variability. This daily health status reflects whether the user will experience physical discomfort or poor condition on that day, affecting their performance in daily activities. In this example, the body's state can be quantified through a status value, allowing users to intuitively understand their own physical condition. The higher the user's status value at the initial moment, the longer the user can perform daily activities, and the lower the probability of experiencing physical discomfort.

[0009] For example, a query operation could be a user querying for health management suggestions, such as when a user clicks the "Health Status Assessment and Management" option after completing their health data. Figure 8a As shown in (1) of the table.

[0010] In this way, since the health risk score reflects the probability of a user developing a target disease in the future, and the state value at the first moment quantifies the user's physical condition at that moment, health management recommendations are determined by using both the user's health risk score and state value at the first moment. This enriches the data dimensions used to determine health management recommendations and improves the accuracy of the recommendations. The first electronic device uses the user's health risk score and state value at the first moment to determine the user's health management recommendations. This considers both the user's long-term health (reflected by the health risk score) and the user's short-term physical condition, making the provided health management recommendations more consistent with the user's current health level. This avoids situations where recommendations only address long-term health issues but are unsuitable for the user's current physical condition, allowing the user to improve their health problems based on more reasonable health management recommendations. For example, if a user has a low health risk score (the lower the score, the lower the probability of the user developing a target disease in the future), but a poor state value at the first moment, such as being unsuitable for high-intensity exercise, then the health management recommendation provided in this example is to suggest rest at the first moment, rather than suggesting high-intensity exercise. In addition, the first electronic device provides users with reasonable health management suggestions. After the user adopts these suggestions, their health problems can be improved. The improved health problems then provide the user with a basis for determining new management suggestions, thus forming a closed-loop system to improve the user's health status.

[0011] According to the first aspect, the method further includes: determining the user's health status score at the first moment based on the user's health risk score and the user's status value at the first moment, the health status score being used to evaluate the user's health level; and displaying the user's health status score at the first moment.

[0012] For example, health level is used to reflect, to some extent, the overall functional status of a user's various organ systems. That is, health level can be understood as including both short-term and long-term health issues. In this example, a health status score quantifies the user's health level, allowing the user to intuitively understand their own health status.

[0013] In this way, the health status score at the first moment is determined based on the health risk score and the status value at the first moment, which combines the user's long-term health and short-term health, so that the health status score at the first moment can reflect the user's current health level more comprehensively.

[0014] According to the first aspect, when the user follows the health management recommendations at the first moment, the method further includes: in response to the user's query operation at the second moment, obtaining first-type health data and second-type health data at the second moment; determining the user's health risk score at the second moment based on the first-type health data at the second moment, wherein the health risk score at the second moment is lower than the health risk score at the first moment; determining the user's status value at the second moment based on the second-type health data at the second moment; determining the user's health management recommendations and health status score at the second moment based on the user's health risk score and status value at the second moment, wherein the health management recommendations at the second moment are different from the health management recommendations at the first moment; wherein, if the status value at the second moment is equal to the status value at the first moment, the health status score at the second moment is higher than the health status score at the first moment.

[0015] In this way, after a user follows the health management recommendations in the first moment for a period of time, the probability of the user developing the target disease in the future will decrease. The status value in the second moment is related to the user's physical condition in the second moment. When the status value in the second moment is the same as that in the first moment, the health status score in the second moment is higher than that in the first moment. This allows the user to intuitively feel that their health problems have improved. This health status score provides positive feedback for the user to follow the health management recommendations in the first moment.

[0016] According to the first aspect, the second category of health data includes at least: body mass index (BMI), exercise capacity, resting heart rate, heart rate variability, sleep quality, real-time heart rate variability, real-time stress level, and external load, which includes the load values ​​generated by various sports and physical activities.

[0017] For example, heart rate variability can be the mean of a user's heart rate variability. Real-time heart rate variability can be collected during a user's rest period, with the real-time heart rate variability collection interval being longer than the real-time heart rate collection interval.

[0018] According to the first aspect, acquiring the second type of health data at a first moment includes: acquiring the height information input by the user at a first moment when the user inputs height information; acquiring the weight information from the first body monitoring device at a first moment when the first body monitoring device collects the user's weight information, wherein the first body monitoring device is wirelessly connected to the first electronic device; acquiring the weight information input by the user at a first moment when the first body monitoring device does not collect the user's weight information but detects that the user has input weight information; determining the user's BMI at a first moment based on the weight information and height information; and processing each piece of data collected by the first device in the second type of health data as follows: acquiring the data collected by the first device from the second body monitoring device at a first moment when the second body monitoring device collects the data collected by the first device, wherein the data collected by the first device includes: exercise capacity, resting heart rate, real-time heart rate, heart rate variability, sleep quality, real-time stress level, and external load, wherein the second body monitoring device is wirelessly connected to the first electronic device.

[0019] For example, some data in the second type of health data can be obtained in two ways. For instance, weight information can be input by the user or obtained from a first body monitoring device (such as a scale), and height information can be input by the user or obtained from a first body monitoring device (such as a height measuring device).

[0020] In this way, the first electronic device can calculate the user's BMI after acquiring the user's height and weight information. Once the second body monitoring device has acquired the user's data collected by the first device, the first electronic device can retrieve that data from the second body monitoring device without requiring user input, making the acquisition of data from the first device more convenient and timely.

[0021] According to the first aspect, the method further includes: when it is detected that the user's height information or weight information is not obtained, obtaining a preset first BMI as the user's BMI at the first moment; when it is detected that the user's exercise ability is not obtained, obtaining a preset first exercise ability as the user's exercise ability at the first moment; when it is detected that the user's real-time heart rate variability is not obtained, obtaining the average value of the real-time heart rate variability within a first preset duration before the first recording time as the user's real-time heart rate variability at the first moment, wherein the first recording time is the moment when the user's real-time heart rate variability was most recently obtained; when it is detected that the user's real-time stress level is not obtained, obtaining the average value of the real-time stress level within a second preset duration before the second recording time as the user's real-time stress level at the first moment, wherein the second recording time is the moment when the user's real-time stress level was most recently obtained.

[0022] For example, the preset first BMI can be a value within the normal BMI range, such as 21 kg / m². 2 The first preset duration can range from 20 minutes to 90 minutes; in this example, the first preset duration is 30 minutes. The second preset duration can also range from 20 minutes to 90 minutes; in this example, the second preset duration is 30 minutes.

[0023] In this way, when the first electronic device detects that it has failed to acquire the user's height or weight information, it acquires a preset first BMI as the user's BMI at the first moment, avoiding the problem of being unable to determine the user's status value at the first moment due to the first electronic device's failure to acquire weight or height information. Since the interval for the second body monitoring device (such as a smartwatch, wristband, etc.) to acquire heart rate variability is longer than the interval for acquiring real-time heart rate, the first electronic device may fail to acquire heart rate variability. Therefore, when it detects that heart rate variability has not been acquired, it acquires the average of heart rate variability within a first preset time period before the first recording moment as the heart rate variability at the first moment, which can avoid the problem of inaccurate status value determination due to failure to acquire heart rate variability. In addition, when the user is not wearing the second body monitoring device, it may be possible that real-time stress level is not acquired; the acquisition interval for real-time stress level is also longer than the acquisition interval for real-time heart rate, which may also result in the inability to acquire it. By acquiring the average of real-time stress level within a second preset time period before the second recording moment as the real-time stress level at the first moment, the problem of failure to acquire real-time stress level leading to failure or inaccuracy in determining the user's status value at the first moment can be avoided.

[0024] According to the first aspect, the user's state value at the first moment is determined based on the second type of health data at the first moment, including: determining the consumption value and recovery value at the first moment based on the second type of data at the first moment. The consumption value at the first moment includes the user's basic consumption state value during the first non-rest period and the state value of additional external load consumption during the first non-rest period. The first non-rest period is the duration during which the user is in a non-rest state from the moment the user last entered the query operation to the moment at the first moment. The recovery value at the first moment is the user's recovered state value from the moment the user last entered the query operation to the moment at the first moment. The consumption value at the first moment is subtracted from the first state value of the current query to obtain a first value. The first state value of the current query is the state value determined when the user last entered the query operation, wherein the first state value of the first query is a preset initial state value. The recovery value at the first moment is added to the first value to obtain the user's state value at the first moment, and the state value at the first moment is used as the first state value for the next query.

[0025] For example, the first electronic device can determine whether the user is in a resting state by comparing the user's real-time pressure level with a preset pressure threshold. When the user's real-time pressure level is greater than or equal to the pressure threshold, it is determined that the user is not in a resting state. When the user's real-time pressure level is less than the pressure threshold, it is determined that the user is in a resting state.

[0026] For example, the first state value of the first query in each update cycle is the initial state value in the current update cycle, and the update cycle can be 3 days, 7 days or more.

[0027] In this way, the user's state value can be restored when the user is in a resting state, while the user's state value will be consumed when the user is in a non-resting state. Based on this, the user's state value at the first moment = the first state value at this moment - the consumption value at the first moment + the restoration value at the first moment. This method ensures that the user can determine the corresponding state value at any time.

[0028] According to the first aspect, based on the second type of data at the first moment, the status value of the user's basic consumption during the first non-rest period is determined, including: determining the consumption rate of each first related input parameter item based on the value of each first related input parameter item and the consumption rate correspondence, wherein the first related input parameter item belongs to the second type of health data, and the consumption rate correspondence is used to indicate the correspondence between the value of each first related input parameter item and the consumption rate; determining the first consumption rate based on the consumption rate of each first related input parameter item and the preset weight of each first related input parameter item; obtaining the product of the first consumption rate, the first consumption value, and the duration of basic consumption as the status value of the user's basic consumption during the first non-rest period, wherein the first consumption value is the preset status value of the user's consumption per minute, and the duration of basic consumption is the difference between the duration of the first non-rest period and the total duration of the user's external load during the first non-rest period.

[0029] For example, the first relevant input parameters may include the user's BMI, exercise capacity, resting heart rate, heart rate variability, and real-time stress level.

[0030] For example, the consumption rate of each first relevant input parameter is multiplied by its corresponding preset weight, and the sum of these products is taken as the first consumption rate. This first consumption rate can be understood as the user's actual consumption rate. The first consumption value can be a preset consumption value, such as 0.069.

[0031] For example, if a user's first non-rest period lasts for 200 minutes, and the user generates external load for 70 minutes during the first non-rest period, then the user's base consumption duration is 130 minutes (i.e., 200 - 70).

[0032] In this way, the first relevant input parameter is related to the user's consumption status value. Different first relevant input parameters have different effects on the user's consumption status value. By assigning different weights to each first relevant input parameter, the user's actual consumption rate (i.e., the first consumption rate) can be determined more accurately, making the first consumption rate more consistent with the user's actual situation. The first electronic device obtains the product of the first consumption rate and the first consumption value to get the user's actual consumption status value per minute. The product of the user's actual consumption status value per minute and the basic consumption duration is the user's basic consumption status value during the first non-rest period. This method determines the user's consumption status value during non-rest periods, which is more consistent with the user's actual (i.e., real) situation.

[0033] According to the first aspect, based on the second type of data at the first moment, the state value of additional external load consumption during the first non-rest period is determined, including: obtaining the duration of each heart rate intensity zone experienced when generating external load during the first non-rest period, wherein the heart rate intensity zone is divided based on the user's maximum heart rate; obtaining the sum of the state values ​​of external load consumption in each heart rate intensity zone as a first external load consumption value based on the duration of each heart rate intensity zone; obtaining the total duration of generating external load; obtaining the state value of external load consumption within the total duration as a second external load consumption value; and obtaining the sum of the first external load consumption value and the second external load consumption value as the state value of external load consumption during the first non-rest period.

[0034] For example, when a user's heart rate exceeds a heart rate threshold (e.g., 50% of their maximum heart rate), the user generates an external load. The state value of external load consumption varies depending on the user's heart rate within different heart rate intensity zones. The preset heart rate intensity zones can include five: a first heart rate intensity zone (50%–60% of the user's maximum heart rate), a second heart rate intensity zone (60%–70% of the user's maximum heart rate), a third heart rate intensity zone (70%–80% of the user's maximum heart rate), a fourth heart rate intensity zone (80%–90% of the user's maximum heart rate), and a fifth heart rate intensity zone (90%–100% of the user's maximum heart rate). The state value of external load consumption is highest when the user's heart rate is in the fifth heart rate intensity zone, and lowest when the user's heart rate is in the first heart rate intensity zone.

[0035] In this way, the first external load consumption value is the state value of the user's additional external load consumption. The sum of the state value consumed by the user during the total duration of external load generation and the first external load consumption value is the state value of the additional external load consumption during the first non-rest period. This method makes the determined state value of external load consumption more accurate.

[0036] According to the first aspect, the state value of consumption for each heart rate intensity zone is obtained, including: obtaining the product of the duration of the heart rate intensity zone, the preset weight, and the first consumption value as the state value of consumption for the heart rate intensity zone, wherein the first consumption value is the preset state value of consumption per minute by the user; the state value of consumption of external load within the total duration is obtained, including: obtaining the product of the total duration and the first consumption value as the state value of consumption of external load within the total duration.

[0037] In this way, each heart rate intensity zone has a corresponding weight. The product of the duration of external load in the heart rate intensity zone, the weight of each heart rate intensity zone, and the first consumption value is used as the state value of the additional consumption of the heart rate intensity zone, instead of using the mean to determine the state value of the additional consumption of the heart rate intensity zone, making the determined state value of the additional consumption of each heart rate intensity zone more accurate.

[0038] According to the first aspect, based on the second type of data at the first moment, the recovery value at the first moment is determined, including: determining the recovery rate of each second related input parameter based on the value of each second related input parameter and the recovery rate correspondence, wherein the second related input parameter belongs to the second type of health data, and the recovery rate correspondence is used to indicate the correspondence between the value of each second related input parameter and the recovery rate; determining the first recovery rate based on the recovery rate of each second related input parameter and the preset weight of each second related input parameter; obtaining the product of the first recovery rate, the first recovery value, and the duration of the first rest period as the recovery value at the first moment, wherein the first recovery value is a preset state value of user recovery per minute, and the first rest period is the duration of the user's rest state from the moment the user last entered the query operation to the first moment.

[0039] For example, the second relevant input parameter may include real-time heart rate, real-time heart rate variability, and real-time stress level.

[0040] In this way, each second related input parameter has a different degree of influence on the user's recovered state value. Based on the weight of each second related input parameter, the corresponding recovery rate, and the first recovery value, the user's recovered state value can be determined relatively accurately.

[0041] According to the first aspect, the first type of health data includes user input data, questionnaire data, and data collected by second devices; user input data includes a first input item, a second input item, and a third input item, the first input item including at least: age information, gender information, and waist circumference information, the second input item including at least: blood pressure information and blood lipid information, and the third input item including at least: physical activity duration; questionnaire data including at least: smoking information, disease information, and family medical history information; and data collected by second devices including at least: permanent residence information.

[0042] In this way, the first electronic device can obtain user input data, obtain questionnaire data through questionnaires, and obtain data collected by the second device through body monitoring devices (such as the first body monitoring device and the second body monitoring device). Multiple ways to obtain the first type of health data can reduce the number of user input operations.

[0043] According to the first aspect, acquiring a first type of health data at a first moment includes: acquiring user input data when the user has input data; when it is detected that a first input item has not been acquired, outputting a first prompt message, the first prompt message being used to prompt the user to input the currently unacquired first input item; when it is detected that a second input item has not been acquired, outputting a first query message, the first query message being used to ask the user whether to use the value of an abnormal option as the value of the currently acquired second input item; when the user inputs a first selection operation, acquiring the value of the abnormal option as the value of the current second input item, the first selection operation being the operation in which the user selects to use the value of the abnormal option as the value of the current second input item; when the user inputs a second selection operation, outputting a second prompt message, the second prompt message being used to prompt the user to input the currently unacquired second input item; when it is detected that a third input item has not been acquired, acquiring the value of the third input item from a second body monitoring device, the second body monitoring device being wirelessly connected to the first electronic device, the second body monitoring device being used to collect the third input item.

[0044] This approach outputs the first prompt, allowing users to promptly input the first input item and avoiding the problem of incomplete first-category health data leading to an indeterminate health risk score. By using a query to determine whether the user is using the value of an abnormal option as the value for the currently acquired second input item, the complexity of inputting the second input item is reduced. The value for the third input item is obtained from the second body monitoring device, further minimizing user input.

[0045] According to the first aspect, obtaining the first type of health data at the first moment also includes: displaying a questionnaire selection page that sequentially shows the input parameters of each questionnaire, the questionnaire selection page including the candidate information for each questionnaire data; responding to the user's reply operation on each questionnaire selection page and obtaining the reply information for each questionnaire data; and obtaining the permanent residence information from the second body monitoring device. In this way, the questionnaire data is input in the form of a questionnaire, reducing the complexity of user operation.

[0046] According to the first aspect, the user's health risk score at the first moment is determined based on the first type of health data at the first moment, including: inputting the first type of health data at the first moment into a preset disease probability model to obtain the probability that the user will suffer from the target disease in the future; and determining the user's health risk score at the first moment based on the probability that the user will suffer from the target disease in the future.

[0047] According to the first aspect, the method further includes: when an update cycle reaching the initial state value is detected, obtaining the mean values ​​of various basic physiological data from the second type of health data within the update cycle, the basic physiological data including at least: height, weight, BMI, exercise capacity, resting heart rate, heart rate variability, and sleep quality; matching the mean values ​​of the user's various basic physiological data with the basic physiological data corresponding to each stored preset state value; and obtaining the successfully matched state value as the new initial state value. In this way, the initial state value is updated periodically, ensuring that a state value consistent with the user's physical condition can still be accurately determined over time.

[0048] According to the first aspect, based on the user's health risk score and status value at the first moment, the health management recommendations for the user at the first moment are determined, including: determining the user's health risk level based on the user's health risk score and a preset risk level correspondence, wherein the risk level correspondence is the correspondence between the health risk score and the health risk level; determining the user's risk group based on the user's health risk level and abnormal data in the first type of health data; obtaining the intersection between the first health recommendation corresponding to the user's risk group and the second health recommendation corresponding to the user's status value at the first moment; and using the intersection as the user's health management recommendation at the first moment.

[0049] For example, health risk levels can include low risk, medium risk, and high risk. When a user is classified as low risk, the probability of that user developing the target disease in the future is low; when a user is classified as high risk, the probability of that user developing the target disease in the future is high.

[0050] For example, abnormal data in the first type of health data may include data that reaches an abnormal threshold.

[0051] In this way, the first electronic device can store the criteria for determining different risk groups, including health risk levels and matching abnormal data. The first electronic device obtains the intersection of the first health recommendation for the user's current risk group and the second health recommendation corresponding to the current status value, so that the determined health management recommendations are consistent with the user's current health status.

[0052] Secondly, this application provides an electronic device, including: one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and when the computer programs are executed by the one or more processors, the electronic device performs the health management method corresponding to the first aspect and any implementation thereof.

[0053] The second aspect and any implementation thereof correspond to the first aspect and any implementation thereof, respectively. The technical effects of the second aspect and any implementation thereof are similar to those of the first aspect and any implementation thereof, and will not be repeated here.

[0054] Thirdly, this application provides a computer-readable medium for storing a computer program that, when run on an electronic device, causes the electronic device to perform the health management method corresponding to the first aspect and any implementation thereof. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1a This is an example of an application interface for screening cardiovascular and cerebrovascular risks;

[0057] Figure 1b This is an illustrative diagram showing how a mobile phone displays a user's current readiness score.

[0058] Figure 2 This is an exemplary hardware structure diagram of an electronic device;

[0059] Figure 3 This is an exemplary software structure diagram of an electronic device;

[0060] Figure 4 This is a schematic diagram illustrating an example of an implementation environment of this application;

[0061] Figure 5 This is a schematic diagram illustrating the interaction logic between functional modules involved in an exemplary health management method;

[0062] Figure 6 This is an exemplary schematic diagram showing the connection between a smart wearable device and a mobile phone;

[0063] Figure 7 This is an exemplary flowchart for determining a health risk score;

[0064] Figures 8a to 8d This is an illustrative diagram illustrating a user-completed first type of health data;

[0065] Figure 9This is an example flowchart illustrating how to determine a user's current daily status value;

[0066] Figure 10 This is an illustrative diagram of the second type of health data;

[0067] Figure 11 This is an example illustrating health risk levels and the user's physical condition indicated by different status values;

[0068] Figure 12 This is an example illustrating the range of health status score variations corresponding to different health risk levels;

[0069] Figure 13 This is an example diagram illustrating a user querying their health status. Detailed Implementation

[0070] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0071] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first target object" and "second target object," etc., are used to distinguish different target objects, not to describe a specific order of target objects.

[0072] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0073] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more. For example, multiple processing units means two or more processing units; multiple systems means two or more systems.

[0074] With the continuous development of technology, various types of body monitoring devices can monitor users' body parameters, such as smart wearable devices (e.g., watches, wristbands), blood lipid testing devices, smart scales, and smart blood pressure monitors. These body monitoring devices can connect to mobile phones (including wireless and wired connections) so that the phone can access the data collected by the device.

[0075] In some embodiments, the mobile phone can be pre-set with a risk assessment model for a target disease, which can be used to assess the user's risk of having the target disease. For example, the target disease is cardiovascular and cerebrovascular disease. Figure 1a An example of an application interface for screening cardiovascular and cerebrovascular risks is shown.

[0076] See Figure 1a For example, the display interface of mobile phone 100 shows a first application interface 101, which is used to provide health management functions for the user. The first application interface 101 includes a cardiovascular risk screening option 102 and a health management option 103. The health management option 103 includes various physical parameter information of the user, such as... Figure 1a The system displays information such as blood lipids, blood sugar, blood pressure, and weight. Blood sugar and blood lipids can be input by the user or obtained via a health monitoring device on the mobile phone. When the user clicks the cardiovascular risk screening option 102, the application uses a preset cardiovascular disease risk scoring model (i.e., the QRISK model) to determine the user's current risk level for cardiovascular disease (i.e., the probability of developing cardiovascular disease) and displays this risk level. However, this method can only predict the user's risk level for the target disease (such as cardiovascular disease) and cannot comprehensively reflect long-term health status. Therefore, the health management recommendations based on the user's risk level for the target disease are incomplete and cannot reasonably improve the user's health status.

[0077] In other embodiments, the smartwatch collects the user's sleep, activity, and heart rate data, and determines the user's energy score based on these data. This energy score reflects the user's physical condition for the day; a higher energy value indicates better physical condition. Furthermore, when the smartwatch is connected to a mobile phone, the user's daily energy score and collected parameters (i.e., activity, sleep, and heart rate variability) can be viewed on the phone. It is understood that different wearable devices or applications may use different names for the energy score. For example, device A determines the user's readiness score based on sleep, activity, and heart rate data; device B determines the user's daily energy score based on the same data.

[0078] Figure 1b (2) illustrates, for example, a schematic diagram of a mobile phone displaying the user’s current readiness score.

[0079] See Figure 1bIn example (2), the display interface of mobile phone 100 shows the user's readiness score for the day, interface 104. Interface 104 includes a readiness score 105 and evaluations of various physical parameters. The physical parameter information displayed by the mobile phone includes activity (exercise) status, sleep status, and heart rate variability. Figure 1b As shown in Figure (2), the user's daily activity evaluation 106, recent sleep evaluation 107, and heart rate variability evaluation 108 are displayed. The user's daily readiness score can also be viewed on a smart wearable device (such as a watch) connected to the mobile phone.

[0080] Figure 1b (2) Example shows a schematic diagram of a smartwatch 200 displaying the user's energy score for the day.

[0081] See Figure 1b In (2), for example, the display interface of the smartwatch 200 displays an energy score display interface, which includes the user's energy score for the day (i.e., Figure 1b (2) 92 points), the user energy score of the previous day ( Figure 1b (2) shows that the energy score of the previous day was 86 points), and the difference between the energy score of the current day and the energy score of the previous day is ( Figure 1b (2) shows that the energy score of the day is 6 points higher than the energy score of the previous day, and the evaluation corresponding to the energy score of the day. Figure 1b (2) shows that the energy score for the day is rated as “Excellent”. It is understandable that the user’s energy score for the day can also be viewed on a mobile phone connected to the smartwatch.

[0082] However, mobile phones or smart wearable devices determine a user's physical condition for the day based on activity (exercise), sleep patterns, and heart rate. Figure 1b The preparedness score or energy score shown only reflects the user's physical condition on that day, and cannot reflect the user's current health level in real time. The user's health level includes both short-term and long-term health issues. Therefore, the health management suggestions provided by the mobile phone based on the user's physical condition on that day may not be suitable for that user.

[0083] This application provides a health management method. The method collects a first type of health data related to long-term health risk prediction for the user, and determines the user's health risk score based on this first type of health data. Simultaneously, it collects a second type of health data related to the user's daily current status value, and determines the user's daily current status value based on this second type of health data. Based on the user's long-term risk level and daily current status value, the method provides the user with health management suggestions matching their current health status, and determines a health status score to reflect the user's health status in real time. The user's daily current status value is used to evaluate the user's current physical condition. The daily current status value changes over time, and the same user's status value differs at different times; that is, the user's daily current status value is a real-time changing value. The user's health risk score reflects the probability that the user's body will develop a target disease in the future.

[0084] The health management method in this embodiment determines health management recommendations based on the user's health risk score and daily current status value, encouraging the user to proactively improve their health through exercise and a healthy lifestyle, thereby reducing the risk of developing target diseases in the future. The electronic device can determine the user's real-time health level and provide real-time health management recommendations. The user can execute these recommendations to improve their health. After the user's health improves, the health management recommendations are dynamically adjusted based on the user's health risk score and current status value, forming a closed-loop health management model that helps users establish healthy lifestyle habits and improve their overall health. Furthermore, the user's health status score, determined by the health risk score and daily current status value, takes into account both the risk of developing target diseases in the future and the user's current health status, providing a comprehensive reflection of their current health level. The electronic device determines the health status score based on the user's risk of developing target diseases in the future and their current health status, quantifying the user's health level through this score. This allows the user to intuitively perceive changes in their health level, increasing their motivation to improve their health.

[0085] The health management method in this application embodiment can be applied to smart wearable devices, such as smartwatches and smart bracelets; it can also be applied to mobile terminals, such as smartphones. When the health management method is applied to a smart wearable device, the smart wearable device can display the user's current health status score in real time and provide the user with matching health management suggestions in real time. It can also share the health status score and health management suggestions to a mobile terminal connected to the smart wearable device for display. When the health management method is applied to a mobile terminal, the mobile terminal can display the user's real-time health status score and provide the user with suggestions suitable for the current health status score. The mobile terminal can also display detailed information on the user's first type of health data and detailed information on the second type of health data for the user to query. The mobile terminal can also share the user's real-time health status score and real-time health management suggestions to a smart wearable device connected to the mobile phone for display.

[0086] In some embodiments, this health management method can also be applied to tablets, in-vehicle computers, and smart assistants (such as smart speakers). These devices can connect to different body monitoring devices to collect first-type and second-type health data, and determine the user's real-time health status score and health management recommendations based on the first-type and second-type health data. For example, the health management method can be applied to a smart speaker, which can connect to a smartwatch. The smart speaker can obtain the user's body parameter information from the smartwatch and can also collect the user's voice input body parameter information through a microphone, thereby obtaining all the first-type and second-type health data. The smart speaker determines the user's real-time health status score and real-time health management recommendations based on the first-type and second-type health data. The smart speaker can display the user's real-time health status score and real-time health management recommendations on a display interface, or it can output the user's real-time health status score and real-time health management recommendations through a broadcast.

[0087] Figure 2 This is an exemplary hardware structure diagram of an electronic device to which embodiments of this application apply. It should be understood that... Figure 2 The electronic device shown is only one example of an electronic device, and electronic devices may have more or fewer components than those shown in the figure, may combine two or more components, or may have different component configurations. Figure 2 The various components shown can be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and / or application-specific integrated circuits. In this example, the electronic device can be a mobile phone or a smart wearable device (such as a watch or bracelet).

[0088] When the electronic device is a mobile phone or a smartwatch with calling capabilities, it may include: a processor 110, an external memory interface 120, an internal memory 121, a Universal Serial Bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, antenna 1, antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a Subscriber Identification Module (SIM) card interface 195, etc. The sensor module 180 may include pressure sensors, gyroscope sensors, barometric pressure sensors, magnetic sensors, accelerometers, proximity sensors, proximity sensors, fingerprint sensors, temperature sensors, touch sensors, ambient light sensors, bone conduction sensors, etc.

[0089] Understandable, Figure 2 The components included in the illustrated electronic device do not constitute a specific limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may include more or fewer components than those shown; for example, when the electronic device is a smartwatch without call functionality, the electronic device may not include the mobile communication module 150 or the SIM card interface 195; it may also not include a microphone and headphone jack.

[0090] Furthermore, to achieve the aforementioned functions, the electronic device includes hardware and / or software modules corresponding to the execution of each function. Based on the algorithmic steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in conjunction with the embodiments, but such implementation should not be considered beyond the scope of this application.

[0091] Figure 3 This is a software structure block diagram of an electronic device according to an embodiment of this application.

[0092] The layered architecture of electronic devices divides software into several layers, each with a clear role and function. Layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into three layers: the application layer, the application framework layer, and the kernel layer, from top to bottom.

[0093] The application layer can include a series of application packages.

[0094] like Figure 3 As shown, the application package can include applications such as Notes, Honor Health, Bluetooth, Gallery, Camera, Video, and Calling.

[0095] The Honor Health app can determine a user's risk level for developing a target disease in the future based on first-type health data related to the user's long-term health risks; it can also determine a user's real-time health status based on second-type health data related to the user's daily current status value (which can be understood as a real-time health status value); and based on the user's risk level for developing a target disease in the future and the real-time health status value, it determines the user's current health management recommendations and the user's health status score. The health status score is used to reflect the user's health status in real time. Health management recommendations include suggestions on exercise, diet, and lifestyle (i.e., rest) that match the user's current health status.

[0096] The application framework layer provides application programming interfaces (APIs) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

[0097] like Figure 3 As shown, the application framework layer may include a window manager, content provider, view system, phone manager, resource manager, notification manager, etc.

[0098] The window manager is used to manage windowed applications. It can retrieve screen size, determine the presence of a status bar, lock the screen, and capture screenshots, among other things.

[0099] Content providers store and retrieve data, making that data accessible to applications. This data may include videos, images, audio, made and received phone calls, browsing history and bookmarks, phone books, etc.

[0100] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text notification icon could include views for displaying text and views for displaying images.

[0101] A phone manager is used to provide communication functions for electronic devices. For example, it manages call status (including connection and disconnection).

[0102] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, and more.

[0103] The notification manager allows applications to display notification information in the status bar. It can be used to convey informational messages and can disappear automatically after a short time without user interaction.

[0104] The kernel layer is the layer between hardware and software. The kernel layer includes health status assessment and management systems.

[0105] The health status assessment and management system collects first-type health data related to the user's long-term health risk and second-type health data related to the user's daily current status value. Based on the first-type health data, it determines the user's future risk level of developing a target disease (i.e., the probability of developing the target disease in the future); based on the second-type health data, it determines the user's daily current status value; and based on the user's future risk level of developing the target disease and daily current status value, it determines the user's current health management recommendations and the user's current health status score. The kernel layer also includes at least display drivers, camera drivers, audio drivers, and sensor drivers (…). Figure 3 (Not shown in the image).

[0106] Understandable, Figure 3 The layers in the illustrated software structure and the components contained in each layer do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer layers than illustrated, and each layer may include more or fewer components; this application does not impose any limitations.

[0107] Figure 4 This is a schematic diagram of an implementation environment provided in an embodiment of this application. For example... Figure 4 As shown, the implementation environment includes a mobile phone 100 and a smartwatch 200. The mobile phone 100 and the smartwatch 200 establish a communication connection via a wireless network, which can be Bluetooth or Wi-Fi. In one example, both the mobile phone 100 and the smartwatch 200 have a health status assessment and management system installed. In another example, either the mobile phone 100 or the smartwatch 200 has a health status assessment and management system installed. This example uses the mobile phone 100 as an example.

[0108] Figure 5 This is a schematic diagram illustrating the interaction logic between functional modules involved in an exemplary health status assessment method. For example... Figure 5As shown, the health status assessment and management system includes a long-term health risk model, a daily current status model, a health status scoring module, and a health status management module. The long-term health risk model includes a first data collection component, which collects first-type health data related to the user's long-term health risk. The daily current status model includes a second data collection component, which collects second-type health data related to the user's daily current status value. The first and second data collection components can also be used to preprocess the collected data.

[0109] Continue to refer to Figure 5 The first data acquisition component includes a first data acquisition module, a first data input module, a health questionnaire module, and a first data processing module. When the mobile phone 100 is connected to at least one body monitoring device (such as a smartwatch 200), the first data acquisition module is used to acquire body parameters collected by the body monitoring device (i.e., data collected by the second device) and transmit the acquired data to the first data processing module. For example, if the mobile phone 100 is connected to a body fat scale, and the body fat scale collects body parameters such as the user's weight and body fat percentage, the first data acquisition module in the mobile phone 100 can obtain the user's weight, body fat percentage, and other body parameters from the body fat scale. As another example, when the mobile phone 100 is connected to a blood glucose meter, the first data acquisition module in the mobile phone 100 can obtain the user's blood glucose data from the blood glucose meter.

[0110] The first data input module is used to acquire the body parameters input by the user and transmit the acquired data to the first data processing module. The electronic device supports the following input methods: text input and voice input.

[0111] The health questionnaire module is used to obtain users' physical parameters through questionnaires and transmit the obtained data to the first data processing module. For example, the health questionnaire module in mobile phone 100 displays "Do you smoke?" on the screen and provides "yes" and "no" options. When the user selects, the module obtains information on whether the user smokes based on the user's selection.

[0112] The first data processing module acquires body parameter information collected by the first data acquisition module, the first data input module, and the health questionnaire module, and preprocesses some of the data. This preprocessing includes converting the collected body parameter information into a format required by the long-term health risk model.

[0113] Continue to refer to Figure 5 The second acquisition component includes a second data acquisition module, a second data input module, and a second data processing module. When connected to at least one body monitoring device, the second data acquisition module can obtain body parameters related to the user's daily current state from the connected body monitoring device.

[0114] The second data input module acquires the body parameters input by the user and transmits them to the second data processing module. The electronic device supports input methods including text input and voice input.

[0115] The second data processing module acquires body parameter information collected by the second data acquisition module and the second data input module, and preprocesses some of the data. The preprocessing includes one or two of the following: determining a first preset input parameter (e.g., BMI) for calculating the second type of health data based on the collected body parameter information; or converting the collected body parameter information into the form required by the daily current state model.

[0116] Continue to refer to Figure 5 Long-term health risk models also include pre-defined disease probability models ( Figure 5 (No disease probability model is shown). The disease probability model obtains the data output by the first data processing module and determines the probability that the user will develop the target disease in the future based on the first type of health data. Figure 5 The model does not depict the disease probability model, only the long-term health risk model. The data transmission value output by the first data processing module is the disease probability model within the long-term health risk model. In one example, this long-term health risk model may also include a health risk scoring module. This module stores the correspondence between the user's probability of developing the target disease in the future and their health risk score. The health risk scoring module obtains the probability of the user developing the target disease in the future, output by the disease probability model, and determines the user's health risk score.

[0117] Optionally, the daily current state model may further include a state value determination module. This module acquires the data output by the second data processing module and determines the user's daily state value (i.e., the user's current state value) based on the data output by the second data processing module (i.e., the second type of health data). Figure 5 The state value determination module in the daily current state model is not shown.

[0118] The health status scoring module determines a user's health status score based on the health risk score output by the long-term health risk model and the daily current status value output by the daily current status model. This module can then transmit the health risk score and daily current status value to the health status management module, which provides the user with health management suggestions (i.e.,...). Figure 5 (Health advice) to promote users' health.

[0119] Figure 6 This illustrates the connection between a smart wearable device and a mobile phone. Figure 6In this context, the smart wearable device may include a heart rate sensor, accelerometer and gyroscope, a second GPS module, and an ambient light sensor. It may also include other sensors to monitor the user's body parameters. The smart wearable device can transmit data requiring calculation to a second processing module for processing; for example, maximum oxygen uptake can be calculated by the smart wearable device based on light signals received by the light sensor. The second storage module can store the body parameters monitored by the smart wearable device for a period of time, such as 7 days, 15 days, etc. The mobile phone may include a camera, microphone, first GPS module, touchscreen, etc. The microphone can collect sound information, the camera can collect image information, the first GPS module can determine the user's location, and the touchscreen can display data. The components in the mobile phone transmit the collected data to a first processor, which calculates the required data. The first storage module can store the collected data. The mobile phone and the smart wearable device can share their respective acquired data or determined data such as health risk scores, daily current status values, health management suggestions, and health status scores.

[0120] The following is combined Figure 5 This section provides a detailed explanation of the health management process. In this example, a mobile phone with a data collection and processing system is used. The long-term health risk model includes a first data collection component, a disease probability model, and a health risk scoring module.

[0121] Step 1: The first acquisition component collects data related to the degree of long-term health risk and transmits it to the disease probability model in the long-term health risk model.

[0122] For example, data related to a user's long-term health risks (i.e., Category 1 health data) may include age, gender, blood pressure, blood lipids, waist circumference, smoking information, medical history, family medical history, place of residence, duration of physical activity, etc.

[0123] The collection methods for different input parameters in the first type of health data vary. For example, the first data input module acquires information such as age, gender, and waist circumference entered by the user (i.e., the first input item); the first data input module can also acquire blood pressure and blood lipid information entered by the user (i.e., the second input item). If the user does not enter the second input item, it can also determine whether to use the value of the abnormal option as the value of the second input item through a query. The first data input module can also acquire the duration of physical activity entered by the user (i.e., the third input item). If the user does not enter the third input item, the first data acquisition module can also acquire the duration of physical activity collected by a body monitoring device (such as a smartwatch). The health questionnaire module acquires information such as the user's smoking information, medical condition information, and family medical history through a questionnaire (i.e., questionnaire data). The first data acquisition module acquires the user's permanent residence information through GPS (i.e., data collected by the second device). Table 1 shows, for example, the input parameters included in the first type of health data, the acquisition method of each input parameter, and alternative acquisition methods.

[0124] Table 1

[0125]

[0126]

[0127] As shown in Table 1, the first data input module acquires the blood pressure information input by the user and transmits it to the first data processing module. Optionally, when the user selects the value of the abnormal option as the user's input blood pressure information, the first data processing module acquires the preset value of the abnormal option as the user's blood pressure information. The duration of physical activity in the first type of health data can be acquired by the first data input module or by the first data acquisition module acquiring data recorded by the smartwatch. For example, the user's family disease information is set to "no family history" by default, meaning the first data processing module retrieves the user's default family disease information from the storage module.

[0128] In some embodiments, the first type of health data may also include other information, not limited to the input parameters in Table 1 above, such as information on sleep quality, meal times, and air pollution index of the place of residence.

[0129] In this example, the health questionnaire module obtains the questionnaire results through a questionnaire format and transmits the results to the first data processing module. The first data processing module then retrieves the information matching the questionnaire results as the data for the current input parameter (i.e., the recovery information for the questionnaire input parameter). For example, in a questionnaire about "whether you smoke," if the user selects "no" on the display interface, the health questionnaire module obtains the questionnaire result for that input parameter (i.e., "smoking information") and transmits it to the first data processing module. The first data processing module uses the numerical value (e.g., "0") corresponding to the questionnaire result for that input parameter as the data for that input parameter.

[0130] The first data processing module in the first data acquisition component transmits the acquired first type of health data to the disease probability model, which then determines the probability of the user developing a disease in the future based on the first type of health data.

[0131] Step 2: The second acquisition component collects data related to the daily current status value and transmits it to the status value determination module in the corresponding daily current status model.

[0132] For example, data related to a user's daily current state (i.e., the second type of health data) may include height, weight, BMI, exercise capacity, resting heart rate, heart rate variability, sleep quality (including nighttime and daytime sleep), real-time stress level, real-time heart rate variability, and external load. Exercise capacity can be measured by the user's maximum oxygen uptake. Real-time stress level is used to assess the user's psychological stress state. Typically, smart wearable devices obtain real-time stress levels by monitoring the user's physiological indicators, including heart rate, pulse, blood pressure, blood oxygen saturation, and sleep quality. In this example, the stress level ranges from 1 to 100, with higher values ​​corresponding to greater stress. Stress levels are typically divided into four levels: 1-25 indicates a relaxed state, 26-50 indicates mild stress, 51-80 indicates moderate stress, and 81-100 indicates severe stress. External load indicates the load generated by various sports and physical activities, and is typically determined based on data such as exercise duration, intensity, frequency, and heart rate. Smart wearable devices collect users' real-time heart rate variability at preset collection intervals. The smart wearable device can obtain the average of the user's real-time heart rate variability over 24 hours as the user's heart rate variability for that day.

[0133] Table 2 shows the input parameters included in the second type of health data, the acquisition method for each input parameter, and alternative acquisition methods.

[0134] Table 2

[0135] parameter How to obtain Alternative solutions height User input none weight Weight scale User input BMI The second data processing module calculates... none athletic ability Obtained through wearable devices none resting heart rate Obtained through wearable devices none Heart rate variability Obtained through wearable devices Take the average of the nearest 30 minutes Sleep quality Obtained through wearable devices none stress level Obtained through wearable devices Take the average of the nearest 30 minutes External load Obtained through wearable devices none

[0136] As shown in Table 2, the second data acquisition module transmits body and weight information to the second data processing module, which calculates the BMI based on the user's input height and weight. Weight is obtained in two ways: either by the second data acquisition module from the scale, or by the second data input module from the user's input weight data. When the second data processing module does not acquire real-time heart rate variability, it determines the current user's real-time heart rate variability based on the average real-time heart rate acquired over the 30 minutes prior to the first recording time, where the first recording time is the most recent acquisition of the user's real-time heart rate variability. Similarly, when the second data processing module does not acquire the user's real-time stress level, it can also determine the current user's stress level based on the average real-time stress level acquired over the 30 minutes prior to the second recording time, where the second recording time is the most recent acquisition of the user's real-time stress level.

[0137] In some embodiments, the second type of health data may also include other information, not limited to the input parameters in Table 2 above, such as meal times, gender, etc.

[0138] The second data processing module transmits the acquired second type of health data to the status value determination module in the daily current status value model. The status value determination module then determines the user's current status value based on the acquired second type of health data.

[0139] Step 3: Based on the acquired first-class health data, the long-term health risk model generates a health risk score for the current user and transmits the health risk score to the health status scoring module.

[0140] In this example, the input data for the disease probability model is the user's current first-category health data, and the output of the model is the probability that the user will develop the target disease in the future. The health risk scoring module stores the correspondence between the user's probability of developing the target disease in the future and the current health risk score (referred to as the first correspondence).

[0141] The training process of this disease probability model can be as follows: Input a first training sample set into a pre-defined first model; obtain the difference between the estimated probability of a user developing the target disease in the future (output by the first model) and the actual probability of developing the target disease corresponding to the first training sample set; and adjust the parameters of the first model based on this difference. The first training sample set includes first-class health data from different users, and the number of users can be 100, 1000, 10000, etc.; the actual probability of developing the target disease is the ratio between the number of users actually suffering from the target disease in the first training sample set and the total number of users in the first training sample set. The target disease can include multiple types of diseases, such as chronic diseases, and also various acute diseases caused by long-term unhealthy behaviors, malignant tumors, etc. The target disease must include at least two diseases. The structure of the first model can adopt the network structure of deep learning, such as a CNN network.

[0142] For example, the disease probability model acquires a first type of health data, and based on this first type of health data, outputs the probability that the current user will develop a target disease in the future. The health risk scoring module determines the user's health risk score based on the probability of the user developing the target disease in the future and the first correspondence. The health risk scoring module then transmits the user's health risk score to the health status scoring module.

[0143] For example, the health risk scoring module can also transmit the current user's health risk score to the display screen of the current device to display the user's health risk score. In other words, the user can view the user's health risk score through the display screen of the current device.

[0144] This example combines Figure 7 The process of collecting Category I health data and determining a user's health risk score in the long-term health risk model is explained in detail below:

[0145] Step 701: The first data processing module checks whether age information has been obtained. If the first data processing module determines that age information has been obtained, proceed to step 703; if age information has not been obtained, proceed to step 702.

[0146] Specifically, the first data processing module can sequentially check whether the input parameters in the first list have been obtained according to the preset order. The first list contains all the input parameters in the first type of health data. For example, the input parameters in the first list may be, in order: age, gender, waist circumference, blood pressure, blood lipids, smoking information, disease information, family medical history information, physical activity duration, and place of residence. The order of the input parameters in the first list can also be other orders, such as: age, gender, blood pressure, blood lipids, waist circumference, smoking information, disease information, place of residence, family medical history information, and physical activity duration. In this example, the first data processing module checks whether the age information has been obtained. Once it is determined that the age information has been obtained, the first data processing module checks whether the gender information has been obtained. When it is determined that age information has not been obtained, the first data processing module triggers the electronic device to prompt the user to enter the currently unobtained information (such as prompting the user to enter age information).

[0147] For example, taking a mobile phone as an example, in response to the user's action of opening Honor Health, the phone jumps to the main interface of Honor Health. Please refer to... Figure 8a In (1), the phone's display screen shows the main interface 801 of Honor Health. The main interface 801 includes fat burning options 802, blood sugar options 803, and health status assessment and management options 804. The main interface 801 also displays the user's daily steps, energy consumption, and exercise duration. Figure 8a The main interface 801 shown in (1) is only an example and is not intended to limit the main interface 801. Optionally, the main interface 801 may also include other information, such as sleep quality, heart rate information, maximum oxygen uptake and other information.

[0148] This example uses a user who is using the health status assessment and management function for the first time. Continue reading... Figure 8a In (1), when the user clicks the Health Status Assessment and Management option 804, Honor Sports Health responds to the user's click operation and jumps to the first interface 8041 of Health Status Assessment and Management, referring to... Figure 8a(2). The first interface 8041 includes a first pop-up window 8042, which is used to prompt the user to complete their personal health information. The first pop-up window includes a "Cancel" control 8042-1 and a "Go to Complete" control 8042-2. When the user clicks the "Cancel" control 8042-1, Honor Health responds to the user's cancellation operation and returns to the main interface 801. When the user clicks the "Go to Complete" control 8042-2, Honor Health responds to the user's operation and jumps to the first list completion interface 8043. The first list 8044 refers to... Figure 8a (3)

[0149] Assuming the user hasn't entered age information, when the user clicks the manual input control for gender, the first data input module doesn't retrieve age information and transmits an empty age information message to the first data processing module. Upon detecting an empty age information message, the first data processing module determines that no age information has been retrieved and triggers the phone to display prompt message 8044-12, which prompts the user to enter their age information. Figure 8a As shown in (4) above. The user clicks the manual input control for age information to input age information into the phone. The first data input module in Honor Health obtains the age information input by the user. The first data input module transmits the obtained age information to the first data processing module. In addition, after obtaining the age information, the first data processing module can also fill in the user's age information in the age position of the first list completion interface, such as... Figure 8b The first item of the first list shown in (1)

[0150] In other embodiments, when Honor Health receives a user's request to activate the health status assessment and management function, the long-term health risk model triggers the first data processing module to iterate through each input parameter in the first type of health data to see if it has been obtained. If it has been obtained, each input parameter in the first type of health data is transmitted to the disease probability model. If it has not been obtained, it is processed according to the processing rules corresponding to the unobtained input parameter, until there are no unobtained input parameters.

[0151] For example, the first data input module stores the identifiers of the input parameters to be acquired and the corresponding processing rules for each input parameter. For instance, age, gender, and waist circumference identifiers all correspond to processing rule 1, which prompts the user to input information for any input parameters that have not yet been acquired. Blood pressure and blood lipid identifiers correspond to processing rule 2, which asks the user whether to replace the current input parameter value with the value of an abnormal option. When a user is using the function of recording physical activity duration in Honor Health for the first time, if the physical activity duration transmitted from the first data input module to the first data processing module is empty, and the physical activity duration transmitted from the first data acquisition module to the first data processing module is not empty, then the first data processing module determines that it has acquired the physical activity duration. If the physical activity duration transmitted from the first data input module to the first data processing module is empty, and the physical activity duration transmitted from the first data acquisition module to the first data processing module is also empty, then the first data processing module determines that it has not acquired the physical activity duration and can trigger the phone to prompt the user to input the physical activity duration information. If the physical activity duration transmitted from the first data input module to the first data processing module is not empty and the physical activity duration transmitted from the first data acquisition module to the first data processing module is also not empty, then the first data processing module obtains the physical activity duration transmitted from the first data acquisition module as the user's current physical activity duration.

[0152] Step 702: The first data processing module triggers the electronic device to prompt the user to input information.

[0153] For example, if the first data processing module detects that the user's age information has not been obtained, the phone will prompt the user to input the current input parameter. For instance, if the first data processing module detects that the user's gender information has not been obtained, the phone will display a prompt message on the screen to prompt the user to input their gender information. In this example, the prompt could be displayed on the screen, such as... Figure 8a The prompt box 8044-12 shown in (4) can also be prompted by outputting a prompt sound.

[0154] Step 703: The first data processing module checks whether gender information has been obtained. When the first data processing module determines that gender information has been obtained, step 704 is executed; when the first data processing module determines that gender information has not been obtained, the mobile phone is triggered to prompt the user to input the information of the current input parameter.

[0155] For example, the first data processing module can detect whether the gender information is empty when it receives the gender information transmitted by the first data input module. If it determines that the gender information is empty, it determines that no gender information has been obtained. For example, it can refer to... Figure 8aIn (1), the first data input module responds to the user's operation of clicking the manual input control in the waist size option. The first data input module sends empty gender information to the first data processing module. When the first data processing module detects that the gender information is empty, it determines that no gender information has been obtained and displays a prompt message to remind the user to input gender information.

[0156] Step 704: The first data processing module detects whether waist circumference information has been obtained. If it is determined that waist circumference information has been obtained, proceed to step 705; otherwise, proceed to step 702.

[0157] For example, this step is similar to the process of step 703, and will not be described again here.

[0158] Step 705: The first data processing module detects whether blood pressure information has been obtained. If it is determined that blood pressure information has been obtained, proceed to step 706. If it is determined that blood pressure information has not been obtained, proceed to step 707.

[0159] For example, when the first data processing module detects that the gender information transmitted by the first data input module is empty, it determines that no blood pressure information has been obtained and executes step 707, that is, asking the user whether to use the value of the abnormal option as the user's current blood pressure information. When the first data processing module detects that the gender information transmitted by the first data input module is not empty, it obtains the gender information transmitted by the first data input module.

[0160] For example, refer to Figure 8b In (1), the phone's display screen shows the first list completion interface 8043. After the user completes the input of waist circumference information, they click the manual input control 8044-51 for the blood lipid option. The first data input module responds to the user clicking the manual input control 8044-51 and obtains the information input by the user. When the user input is empty, the blood pressure information transmitted by the first data input module to the first data processing module is empty. When the first data processing module detects that the blood pressure information is empty, it asks the user whether to use the value of the abnormal option to replace it, such as Figure 8b As shown in (2), a first inquiry pop-up 8044-42 is displayed on the first list completion interface 8043. The first inquiry pop-up 8044-42 is used to ask the user whether to use the abnormal option as the current user's blood pressure information.

[0161] Step 706: The first data input module detects whether blood lipid information has been obtained. If it is determined that blood lipid information has been obtained, proceed to step 708. If it is determined that blood lipid information has not been obtained, proceed to step 707.

[0162] For example, this step is similar to the process of detecting whether blood pressure information has been obtained in step 705. You can refer to the relevant description in step 705, which will not be repeated here.

[0163] Step 707: The first data processing module asks the user whether to replace the value of the current input parameter with the value of the abnormal option.

[0164] For example, the first data processing module can control the display interface to show a first query pop-up, which asks the user whether to use the value of an abnormal option to replace the value of the current input parameter. When the user selects to use the value of an abnormal option as the value of the current input parameter, the value of the abnormal option is obtained and used as the value of the user's current input parameter. When the user does not select to use the value of an abnormal option as the value of the current input parameter, a prompt message can be output to remind the user to enter the value of the current input parameter.

[0165] When the user selects the value of the exception option as the value of the current input parameter, the value of the exception option for that input parameter is obtained and used as the value of the current input parameter. When the user does not select the value of the exception option as the value of the current input parameter, a prompt message can be output to remind the user to enter the value of the current input parameter.

[0166] When the first data processing module detects that the gender information transmitted by the first data input module is not empty, it obtains the information of the input parameter item transmitted by the first data input module.

[0167] For example, continuing with the previous example, refer to... Figure 8b As shown in (2), a first query pop-up 8044-42 is displayed on the first list completion interface 8043. This first query pop-up 8044-42 is used to ask the user whether to use the abnormal option as the current user's blood pressure information. The first query pop-up 8044-42 includes a "Yes" control and a "No" control. In this example, as shown in (2), Figure 8b As shown in (2), the user clicks the "Yes" control in the first query pop-up 8044-42. In response to the user's click of the "Yes" control, the first data input module obtains the data corresponding to the control (e.g., "diastolic pressure = 75 mmHg; systolic pressure = 95 mmHg") as the current user's blood pressure information. The first data input module transmits the current user's blood pressure information to the first data processing module; the first data input module can also fill in the corresponding blood pressure data in the blood pressure option of the first list completion interface 8043 and instruct the display screen to refresh the display. Figure 8bAs shown in (3), when the user selects to use the abnormal option as a substitute, the default blood pressure information is displayed in the blood pressure option 8044-4. Optionally, the first data input module can obtain the logical value corresponding to the "Yes" control (such as the logical value BP=truth corresponding to the "Yes" control), and the first data input module transmits the logical value to the first data processing module, which converts the logical value into processable information. For example, the processable information corresponding to BP=truth is "diastolic pressure = 75mmHg; systolic pressure = 95mmHg", so the first data processing module uses "diastolic pressure = 75mmHg; systolic pressure = 95mmHg" as the current user's blood pressure information.

[0168] In another example, refer to Figure 8c In (1), the first list completion interface 8043 displays the first inquiry pop-up 8044-42. When the user clicks the "No" control, the health questionnaire module responds to the user's click of the "No" control and displays the prompt box 8044-43 on the first list completion interface 8043, such as... Figure 8c As shown in (1) above. The prompt box 8044-43 is used to prompt the user to enter blood pressure information. When the user clicks the manual input control of the blood pressure option 8044-4, the prompt box 8044-43 is canceled and the blood pressure input interface (not shown) is displayed for the user to enter blood pressure information. When the user exits the blood pressure input interface, the first data input module transmits the acquired blood pressure information to the first data processing module, which then checks again whether the blood pressure information has been acquired.

[0169] Step 708: The first data processing module detects whether smoking information has been obtained. When it is determined that smoking information has been obtained, step 709 is executed.

[0170] For example, the health questionnaire module responds to the user opening a questionnaire (e.g. Figure 8d As shown in (1), clicking the "Click to Select" control (8044-61) displays a smoking questionnaire pop-up. The smoking questionnaire pop-up is used to ask the user whether they smoke in order to obtain the user's smoking information. After the user completes the questionnaire, the health questionnaire module obtains the user's smoking questionnaire result and transmits the smoking questionnaire result to the first data processing module. The first data processing module converts the smoking questionnaire result into processable information. For example, if the smoking questionnaire result is "Sm=truth", the converted processable information can be "0", and if the smoking result is "Sm=false", the converted processable information can be "1".

[0171] Optionally, the health questionnaire module can also fill in the corresponding display data in the smoking information option of the first list completion interface 8043 based on the smoking questionnaire results, and instruct the display screen to refresh the display.

[0172] Optionally, the smoking questionnaire pop-up may only include "Yes" and "No" controls. After the user completes the questionnaire, the health questionnaire module will remove the smoking questionnaire pop-up from the display; if the user has not completed the smoking questionnaire pop-up, the smoking questionnaire pop-up will remain displayed.

[0173] For example, refer to Figure 8d In (1), the phone's display shows the first complete list interface 8043. The user has completed the input of information such as age, gender, waist circumference, blood pressure, and blood lipids. The user clicks the "Click to Select" control 8044-61 in the smoking information option 8044-6. The health questionnaire module responds to the user's click control 8044-61, such as... Figure 8d As shown in (2), the smoking questionnaire pop-up 8044-62 is displayed. The smoking questionnaire pop-up 8044-62 includes two options: "Yes" and "No". In this example, as shown... Figure 8d In step (2), when the user clicks the "No" option, the health questionnaire module responds to the user's selection, obtains the user's smoking result as "Sm = false", and transmits the smoking questionnaire result to the first data processing module. The first data processing module converts the smoking questionnaire result into processable information, such as "1". The health questionnaire module fills the corresponding display data (such as "No" for "false") in the smoking information option of the first list completion interface 8043, and instructs the display screen to refresh the display, such as... Figure 8d The smoking information option 8044-6 shown in (3) displays the results of the completed smoking questionnaire.

[0174] Additionally, after obtaining the smoking results in the health questionnaire module, cancel the display of the smoking questionnaire pop-up 8044-62.

[0175] Step 709: The first data processing module checks whether disease information has been obtained. If it is determined that disease information has been obtained, proceed to step 710.

[0176] For example, in response to the user's opening of the questionnaire, the health questionnaire module displays a pop-up window for a disease questionnaire, which asks the user whether they currently have a first preset disease. The first preset disease can include multiple diseases, such as cervical spondylosis, hypertension, diabetes, asthma, fatty liver, thyroid disease, etc. The first preset diseases can be displayed in a list format, with each disease having both "yes" and "no" options. The user can select "yes" or "no" for each disease based on their own condition. In response to the user's reply to the questionnaire, the health questionnaire module obtains the questionnaire results for the disease questionnaire and transmits the results to the first data processing module. The first data processing module checks whether there are any unselected disease options in the disease questionnaire; when no unselected disease options are detected, it determines that the user has completed the disease questionnaire.

[0177] The first data processing module converts the questionnaire results into processable information. For example, the first preset diseases include cervical spondylosis, hypertension, diabetes, asthma, fatty liver, and thyroid disease. When the user selects "No" for each disease, the health questionnaire module determines that the user does not have any of the diseases in the first preset diseases, and the questionnaire result is "CD = None". The health questionnaire module then transmits the questionnaire result to the first data processing module, which converts the questionnaire result into processable information, such as "CD = 0".

[0178] When a user selects cervical spondylosis and fatty liver, the health questionnaire module obtains the disease questionnaire result as "CD = ("cervical spondylosis", "fatty liver")". The health questionnaire module transmits this disease questionnaire result to the first data processing module, which converts the questionnaire result into processable information, such as "CD = ("1", "4")", where "none" corresponds to the value 0, "cervical spondylosis" corresponds to the value "1", "hypertension" corresponds to the value "1", "diabetes" corresponds to the value "2", "asthma" corresponds to the value "3", "fatty liver" corresponds to the value "4", and "thyroid disease" corresponds to the value "5".

[0179] Optionally, the first data processing module can also populate the corresponding display data in the disease information option of the first list completion interface 8043 according to the disease questionnaire results, and instruct the display screen to refresh the display, such as... Figure 8d The disease information option shown in (4) is filled with "None". If the user selects to have multiple diseases, the names of the diseases selected by the user will be displayed in the disease information option in sequence. For example, if the user selects to have "cervical spondylosis" and "fatty liver", "cervical spondylosis" and "fatty liver" will be displayed in the corresponding position of the disease information option.

[0180] Optionally, each disease in the disease questionnaire pop-up includes a "Yes" control and a "No" control. After the user completes the questionnaire, the health questionnaire module cancels the display of the disease questionnaire pop-up. If the user has not completed the disease questionnaire, the disease questionnaire pop-up remains displayed until the user completes the disease questionnaire or the user clicks the return control to end the function of starting health status assessment and management.

[0181] Once the first data processing module determines that it has obtained the user's illness information, it executes step 710.

[0182] Step 710: The first data processing module detects whether the user's family medical history information has been obtained. If it is determined that the family medical history information has been obtained, step 712 is executed.

[0183] For example, the health questionnaire module stores a first preset family disease list, which includes diseases such as hypertension, diabetes, mental illness, cancer, and epilepsy. In response to a user opening a questionnaire, the health questionnaire module displays a family disease questionnaire pop-up, used to check whether the user currently suffers from any of the first preset family diseases. The first preset family diseases can be displayed in a list within the pop-up, with each disease having both "yes" and "no" options. Users can select "yes" or "no" for each family disease based on their own situation; when no unselected family disease is detected, the user has completed the family disease questionnaire.

[0184] Optionally, the health questionnaire module can also populate the corresponding display data in the family medical history option of the first list completion interface 8043 based on the results of the family disease questionnaire, and instruct the display screen to refresh the display, such as... Figure 8d The family medical history option shown in (4) is filled with "None". If the user selects to have multiple family diseases, the names of the diseases selected by the user will be displayed in the family medical history option in sequence. For example, if the user selects to have "diabetes" and "hypertension", "diabetes" and "hypertension" will be displayed in the corresponding position of the family medical history option.

[0185] Optionally, each disease in the family disease pop-up includes a "Yes" control and a "No" control. After the user completes the questionnaire, the health questionnaire module cancels the display of the family disease questionnaire pop-up. If the user has not completed the family disease questionnaire, the display of the family disease questionnaire pop-up remains until the user completes the family disease questionnaire or the user clicks the return control to end the function of starting health status assessment and management.

[0186] The health questionnaire module transmits the results of the family disease questionnaire to the first data processing module. The first data processing module checks whether the received family disease questionnaire results are empty. If it is determined that they are not empty, then step 710 is executed.

[0187] Step 711: The first data acquisition module detects whether the user's physical activity duration has been obtained. When it is determined that the user's physical activity duration has been obtained, step 712 is executed.

[0188] For example, the user's physical activity duration can be input by the user or acquired by the first data acquisition module through a smart wearable device. When the first data processing module detects that both the physical activity duration transmitted by the first data input module and the physical activity duration transmitted by the first data acquisition module are empty, it determines that no physical activity duration has been acquired. When the first data processing module detects that either the physical activity duration transmitted by the first data input module or the physical activity duration transmitted by the first data acquisition module is not empty, it determines that the user's physical activity duration has been acquired.

[0189] Optionally, when the first data processing module detects that the physical activity duration transmitted by the first data input module is not empty and the physical activity duration transmitted by the first data acquisition module is not empty, it can obtain the physical activity duration transmitted by the first data acquisition module as the user's physical activity duration, or it can obtain the physical activity duration transmitted by the first data processing module as the user's physical activity duration.

[0190] Optionally, when the first data processing module detects that it has acquired the physical activity duration transmitted by the first data acquisition module within a preset recording period, the first data processing module will use the physical activity duration transmitted by the first data acquisition module as the user's physical activity duration. The preset recording period can be one week, three days, etc.; this example uses a preset recording period of one week.

[0191] When the first data processing module determines that it has obtained the user's physical activity duration, it executes step 712; when the first data processing module has not obtained the user's physical activity duration, it prompts the user to manually enter the physical activity duration within the first preset duration.

[0192] In this example, the first data acquisition module in the mobile phone obtains the user's daily physical activity time from the smartwatch and transmits it to the first data processing module. The first data processing module obtains the physical activity time of the current day as the user's current physical activity time. For example, suppose the first data acquisition module obtains the physical activity time from the smartwatch from November 24th to November 18th, and supposes the current day is November 24th, with a physical activity time of 40 minutes. The first data processing module obtains the physical activity time of the current day from the first data acquisition module and populates it into the first list completion interface, such as... Figure 8d (4) in the middle.

[0193] Step 712: The first data processing module checks whether the user's permanent residence information has been obtained. When it is determined that the user's permanent residence information has been obtained, step 713 is executed.

[0194] For example, the first data acquisition module can obtain the current location information of the mobile phone through its own GPS and use the city in the location information as the user's permanent residence information. The first data acquisition module can also obtain the user's permanent residence information through the GPS of the smart wearable device. The first data acquisition module transmits the acquired GPS information to the first data processing module.

[0195] When the first data processing module detects that the GPS information transmitted by the first data acquisition module is empty, and determines that the user's permanent residence information has not been obtained, it can prompt the user to turn on the GPS positioning function of the mobile phone or prompt the user to manually enter the permanent residence information; when it detects that the GPS information transmitted by the first data acquisition module is not empty, and determines that the user's permanent residence information has been obtained, it executes step 713.

[0196] Step 713: The first data processing module transmits the first type of healthy data to the disease probability model.

[0197] For example, the first data processing module converts data in the first type of health data that does not meet the format requirements into processable information, such as converting questionnaire results into matching numerical values. The first data processing module can transmit each determined input parameter to the disease probability model when it determines an input parameter in the first type of health data. When the disease probability model detects incomplete input items, it feeds back information to the first data processing module that the first type of health data is incomplete.

[0198] Optionally, the first data processing module may also detect whether all input parameters in the first type of health data have been acquired when the third preset time is reached. When the user first starts the health status assessment and management function, the third preset time is longer than the time it takes for the user to complete the first list. After that, the third preset time is less than 1 second, such as 10 milliseconds.

[0199] When the first data processing module detects that it has obtained information on all input parameters belonging to the first type of health data, it executes step 715; when the first data processing module detects that there are still input parameters in the first type of health data that have not been obtained, it prompts the user to complete the input of all first type of health data or a questionnaire survey.

[0200] Step 714: The health risk scoring module determines the user's long-term health risk score.

[0201] For example, the disease probability model determines the probability of a user developing a target disease in the future based on the first type of health data. The health risk scoring module can determine the user's long-term health risk score based on the probability of the user developing the target disease in the future. For instance, the health risk scoring module can determine the user's health risk score based on the probability of the user developing the target disease in the future and a first correspondence relationship. The first correspondence relationship converts the probability of the user developing the target disease into a score data of 10 points. For example, if the probability of the user developing the target disease in the future is 0.9, the user's health risk score is determined to be 9 points based on the first correspondence relationship.

[0202] Step 4: Based on the acquired second type of health data, the daily current status model generates the user's daily current status value and transmits the daily current status value to the health status scoring module.

[0203] For example, the input parameters included in the second type of health data can be as shown in Table 2. The status value determination module in this daily current status model is used to determine the user's current status value in real time. The formula for the user's current status value is:

[0204] Daily current status value = previous remaining status value - consumption value + recovery value Formula (1);

[0205] The initial state value is the first state value within an update cycle of the previous balance state value (i.e., the first state value in this query). The initial state value is the user's state value obtained daily from the current state model when the health status assessment and management function is enabled. In this example, assuming the current time is the first time, determining the user's current state value is equivalent to determining the user's state value at the first time. The current consumption value (i.e., the consumption value at the first time) is the state value consumed by the user during the first non-rest period (including the user's basic consumption value during the first non-rest period and the additional consumption value due to external load generated by the user during the first non-rest period). The current recovery value (i.e., the recovery value at the first time) is the state value recovered by the current user during the first rest period. The first non-rest period is the duration during which the user is in a non-rest state from the time the user last determined the balance state value to the first time. The first rest period is the duration during which the user is in a rest state from the time the user last determined the balance state value to the first time.

[0206] For example, the second data processing module transmits the second type of health data to the state value determination module. The state value determination module can obtain basic physiological parameters from the second type of health data to determine the user's initial state value. These basic physiological parameters may include BMI, exercise capacity, resting heart rate, heart rate variability, and sleep data. The daily current state model can store the range of values ​​for each basic physiological parameter required for different initial state values. The state value determination module can determine the user's initial state value based on the value of each basic physiological parameter and a second correspondence between each initial state value and the values ​​of each basic physiological parameter. Optionally, the state value determination module can update the user's initial state value once the update cycle is reached. The update cycle can be 3 days, 7 days, etc. In this example, 7 days is used.

[0207] For example, assuming the initial state values ​​range from 5 to 100, the daily current state model stores six initial state values ​​and the corresponding ranges of basic physiological parameters for each initial state value. The six initial state values ​​are 50, 60, 70, 80, 90, and 100. An initial state value of 90 corresponds to the following range of basic physiological parameters: BMI—18.5 kg / m². 2 ~23.9kg / m 2 Exercise capacity – 40–55 ml / kg / min; Resting heart rate – 50–60 beats / min (BPM); Heart rate variability – 50–75 milliseconds; Sleep quality – Overall sleep score 85–89 points. An initial state value of 100 corresponds to the following ranges for basic physiological parameters: BMI – 18.5 kg / m² 2 ~23.9kg / m 2 Exercise capacity – 56–70 ml / kg / min; Resting heart rate – 40–50 beats / min (BPM); Heart rate variability – greater than 75 milliseconds; Sleep quality – overall sleep score 90–100. The ranges of the corresponding basic physiological parameters for the remaining four initial state values ​​are not listed individually. Assuming the basic physiological parameters obtained by the state value determination module from the second type of health data are: BMI = 22 kg / m² 2 The user's exercise capacity was 42 ml / kg / min, resting heart rate was 60 BPM, heart rate variability was 60 milliseconds, and sleep score was 87. Based on the acquired basic physiological parameters and the second correspondence, the status value determination module determined the user's initial status value to be 90.

[0208] In one example, after obtaining the second type of health data, the status value determination module can determine the user's current consumption value and current recovery value based on the second type of health data.

[0209] The user's current (time) consumption value comes from the basic consumption value generated during the non-rest period (i.e., the user's basic consumption status value during the first non-rest period) and the additional consumption value generated by the external load (i.e., the status value of the additional external load consumption during the first non-rest period, hereinafter also referred to as external load consumption). The user's current consumption value can be calculated as shown in formula (1):

[0210] Consumption value = Basic consumption value + External load consumption formula (2);

[0211] Basic consumption value = actual consumption value per minute × basic consumption duration formula (3);

[0212] Actual consumption per minute = First consumption rate × First consumption value Formula (4);

[0213] The first consumption value is a preset consumption value per minute for the state. For example, in this example, the first consumption value is set to 0.069.

[0214] The first expenditure rate is correlated with height, weight, exercise capacity, resting heart rate, heart rate variability, and real-time stress level in the second type of health data. For ease of description later, this paper refers to the input parameters in the second type of health data that are related to the first expenditure rate as the first relevant input parameters. The first expenditure rate is the sum of the products between the preset expenditure rate and the weight of each first relevant input parameter, and the specific formula is as follows:

[0215] First consumption rate = W BMI ×A1+W TR ×A2+W HRrest ×A3+W HRV ×A4+W p ×A5 formula (5);

[0216] Among them, W BMI The weights corresponding to BMI are defined by A1, where A1 is the preset consumption rate based on BMI; W TR The weights corresponding to athletic ability are defined by A2, which represents the preset expenditure rate of athletic ability; W HRrest A3 represents the weight corresponding to the resting heart rate, and W represents the preset energy expenditure rate at the resting heart rate. HRV The weights corresponding to heart rate variability are defined by A4, where A4 is the preset expenditure rate for heart rate variability; W p A5 represents the weight corresponding to the real-time pressure level, and A5 is the preset consumption rate for the real-time pressure level. The range of the first consumption rate is 0.8 to 1.4.

[0217] In this example, the weight corresponding to each first relevant input parameter is the degree of influence of each first relevant input parameter on the user's consumption status value. The degree of influence of each first relevant input parameter on the user's consumption status value can be determined based on a large amount of sample data. For example, 1000 samples are obtained, in which all parameters are the same or within a preset range except for BMI. The degree of influence of BMI on the user's consumption status value during non-rest periods is obtained from these 1000 samples. Then another 1000 samples are obtained, in which all input parameters are the same or within a preset range except for exercise ability. The degree of influence of exercise ability on the user's consumption status value during non-rest periods is obtained from these 1000 samples. Similarly, the degree of influence of other first relevant input parameters on the user's status can be determined, which will not be listed here.

[0218] In this example, BMI indirectly affects the user's exhaustion status value. Exercise capacity reflects an individual's exercise adaptability and physical fitness level, and its influence on the user's exhaustion status value is moderate to high. Resting heart rate, as an indicator of cardiovascular health, has a moderate to high influence on the user's exhaustion status value; heart rate variability, used to measure autonomic nervous system activity, also has a moderate to high influence. Real-time stress level has a high influence on the user's exhaustion status value. Based on these influence levels, it can be seen that BMI has the smallest weight, while the weights of exercise capacity, resting heart rate, and heart rate variability are all greater than the weight of BMI but less than the weight of real-time stress level. For example, BMI has a weight of 10%, exercise capacity has a weight of 15%, resting heart rate has a weight of 16%, heart rate variability has a weight of 18%, and real-time stress level has a weight of 41%.

[0219] The preset consumption rate for each first relevant input parameter is pre-set, and the preset consumption rate for each first relevant input parameter can be different. For example, the preset consumption rate for BMI is 1, the preset consumption rate for exercise capacity is 1.01, the preset consumption rate for resting heart rate is 0.9, the preset consumption rate for heart rate variability is 1.02, and the preset consumption rate for real-time stress level is 1.1.

[0220] The preset consumption rate of each first relevant input parameter can also be the same, such as the preset consumption rate of each first relevant input parameter being 1.

[0221] Optionally, the preset consumption rates for some of the first relevant input parameters can be different, while the preset consumption rates for the remaining first relevant input parameters are the same. For example, the preset consumption rates for BMI, exercise capacity, resting heart rate, and heart rate variability can all be set to 1, and the real-time stress level can be set to 1.1.

[0222] In one example, external load refers to the load value generated by a user's exercise and daily activities, determined by heart rate intensity zones. The additional energy expenditure generated by the external load is the state value at which the user needs to expend extra energy during exercise and daily activities. When the user's smart wearable device is detected to be in exercise mode or when the user's heart rate is detected to be greater than 50% of the maximum heart rate, the user's current energy expenditure equals the sum of the baseline energy expenditure and the additional energy expenditure generated by the external load. The additional energy expenditure generated by the external load is related to each heart rate intensity zone; the higher the heart rate intensity, the greater the additional energy expenditure. In this example, there can be five heart rate intensity intervals. The first heart rate intensity interval corresponds to a heart rate range of 50% to 60% of the user's maximum heart rate; the second heart rate intensity interval corresponds to a heart rate range of 60% to 70% of the user's maximum heart rate; the third heart rate intensity interval corresponds to a heart rate range of 70% to 80% of the user's maximum heart rate; the fourth heart rate intensity interval corresponds to a heart rate range of 80% to 90% of the user's maximum heart rate; and the fifth heart rate intensity interval corresponds to a heart rate range of 90% to 100% of the user's maximum heart rate. Each heart rate intensity interval has its own corresponding weight, and the stronger the heart rate intensity, the greater the weight of the corresponding consumption value. Therefore, the state value determination module can obtain the duration of each heart rate intensity interval experienced when generating external load, and determine the external load consumption value based on the state value consumed in each heart rate intensity interval, such as by using formula (5):

[0223] External load consumption = (Time0 + W) time1 ×Time1+W time2 ×Time2+W time3 ×Time3+W time4 ×Time4+W time5 ×Time5)×First Consumption Value Formula (6);

[0224] Wherein, Time0 is the total duration of the external load, Time1 is the duration of the heart rate being in the first heart rate intensity zone when the external load is generated, Time2 is the duration of the heart rate being in the second heart rate intensity zone when the external load is generated, Time3 is the duration of the heart rate being in the third heart rate intensity zone when the external load is generated, Time4 is the duration of the heart rate being in the fourth heart rate intensity zone when the external load is generated, and Time5 is the duration of the heart rate being in the fifth heart rate intensity zone when the external load is generated. W time1 W represents the weight of the energy expenditure corresponding to the first heart rate intensity zone. time2 W represents the weight of the energy expenditure corresponding to the second heart rate intensity zone. time3 W represents the weight of the energy expenditure corresponding to the third heart rate intensity zone. time4 W represents the weight of the energy expenditure corresponding to the fourth heart rate intensity zone. time5 This represents the weight of the energy expenditure corresponding to the fifth heart rate intensity zone. Where W...time1 Less than W time2 W time2 Less than W time3 W time3 Less than W time4 W time4 Less than W time5 .

[0225] In this example, during the rest period, the user does not consume any resources and begins to recover their status value. The current recovery value is equal to the product of the recovery value per minute (i.e., the first recovery value), the first recovery rate, and the recovery duration, as shown in formula (7):

[0226] Current recovery value = First recovery rate × First recovery value × Rest duration (Formula 7);

[0227] The first recovery value is a preset recovery value per minute, for example, the first recovery value can be 0.1388 / minute.

[0228] The first recovery rate is related to real-time heart rate, real-time heart rate variability, and real-time stress level in the second type of health data. For ease of description, this paper refers to the data in the second type of health data related to the first recovery rate as the second relevant input parameter. The first recovery rate is the sum of the products between the preset recovery rate and the corresponding weight of each second relevant input parameter, as shown in the following formula:

[0229] First recovery rate = W R_HR ×B1+W R_HRV ×B2+W R_P ×B3 formula (8);

[0230] Among them, W R_HR W represents the weight corresponding to the real-time heart rate. R_HRV W represents the weights corresponding to real-time heart rate variability. R_P The weights correspond to the real-time stress level. B1 is the preset recovery rate for real-time heart rate, B2 is the preset recovery rate for real-time heart rate variability, and B3 is the preset recovery rate for real-time stress level. The range of the first recovery rate is 0.72 to 1.2.

[0231] In this example, the weight of each second relevant input parameter represents the degree of influence of each second relevant input parameter on the user's restored state value. The weight of each second relevant input parameter can be determined through a large number of samples. The specific determination process is similar to that of the determination process of the weight of the first relevant input parameter, and will not be repeated here.

[0232] Generally, a lower heart rate indicates higher cardiac efficiency, and therefore, real-time heart rate has a greater impact on the user's recovery status. Heart rate variability (HRV) is associated with good user recovery ability, and HRV has a significant impact on the user's recovery status; conversely, high stress levels delay recovery, and therefore, real-time stress levels have a smaller impact on the user's recovery status than HRV. Based on this, in this example, the weights of real-time heart rate and real-time heart rate variability are greater than the weight of real-time stress level, and the weight of real-time heart rate may be the same as or different from the weight of real-time heart rate variability. For example, W... R_HR Equal to 45%, W R_HRV Equal to 40%, W R_P It equals 15%. Since the first recovery value is 0.1388 and the first recovery rate ranges from 0.72 to 1.2, the product between the first recovery rate and the first recovery value ranges from 0.0999 to 0.16656.

[0233] The status value determination module can determine the user's current status value in real time according to the above formulas (1) to (7). Optionally, the status value determination module can determine whether the user is in a resting state based on the user's real-time pressure level. Specifically, when the status value determination module detects that the real-time pressure level is greater than the pressure threshold, it determines that the user is not in a resting state; when it detects that the real-time pressure level is less than or equal to the preset pressure threshold, it determines that the user is in a resting state. The preset pressure threshold can be in the range of 20 to 30. For example, in this example, the preset pressure threshold is set to 30.

[0234] The following example illustrates the process by which the daily current state model determines a user's current state value.

[0235] For example, suppose a user has been wearing a smartwatch (for more than 7 days) before initially activating the health status assessment and management function. The user first activates the health status assessment and management function at 9:00 AM on the seventh day. After the user completes their personal health information, in response to the user activating the health status assessment and management function, the daily current status model obtains the user's basic physiological parameters for the past 7 days. These past 7 days are based on the time the user activated the health status assessment and management function, obtaining the user's basic physiological parameters for the 7 x 24 hours prior to that time. The status value determination module in the daily current status model obtains the average of the 7-day status values ​​as the initial status value based on the user's basic physiological parameters for the past 7 days. For example, based on the 7-day basic physiological parameters and the range of each basic physiological parameter corresponding to each status value, the status values ​​for the first day are determined to be 80, the second day 90, the third day 80, the fourth day 70, the fifth day 80, the sixth day 80, and the seventh day 80. The status value determination module determines the user's initial status value to be 80. If a user wears the smartwatch for the first time on the 7th day and activates the health status assessment and management function, the user can be prompted to check their real-time status value after 7 days, provided that the user has completed their personal health information.

[0236] Suppose the user enters a query at 12:00 on the 7th day (e.g., clicks...). Figure 8aWhen operating option 804 in (1), the user's status value at that moment (i.e., 12:00 on the 7th day) is calculated. Assuming that the status value determination module detects that the user's real-time pressure level (e.g., 32) is greater than the pressure threshold (e.g., 30) at 9:00 on the 7th day, it determines that the user has entered a non-rest state from 9:00 on the 7th day, and then determines that the duration of the user's non-rest state from 9:00 to the present moment is 180 minutes (i.e., the first non-rest period is 180 minutes). Assuming that the total duration of the user's external load during the first non-rest period is 60 minutes. The status value determination module obtains the data of each first relevant input parameter from the second type of health data. The status value determination module calculates the current user's first consumption rate as 1 according to formulas (2) to (5); and determines the basic consumption value generated during the non-rest period as 1 × 0.069 × (180 - 60) = 8.28 according to formulas (3) and (4). Assume that when an external load is generated, the user's heart rate is in the first heart rate intensity zone for 20 minutes, in the second heart rate intensity zone for 10 minutes, in the third heart rate intensity zone for 20 minutes, in the fourth heart rate intensity zone for 5 minutes, and in the fifth heart rate intensity zone for 5 minutes. The weights corresponding to the first heart rate intensity zone are 5%, the second heart rate intensity zone for 10%, the third heart rate intensity zone for 15%, the fourth heart rate intensity zone for 30%, and the fifth heart rate intensity zone for 40%. According to formula (6), the user's external load consumption is calculated as (60 + 20 × 5% + 10 × 10% + 20 × 15% + 5 × 30% + 5 × 40%) × 0.069 = 4.7265. According to formula (2), the user's current consumption value (i.e., 12:00 on the 7th day) is determined to be 8.28 + 4.7265 = 13.0065. Since the user was in a non-resting state from 9:00 to 12:00 on the 7th day, the user would not generate a recovery value. Therefore, it can be determined that the recovery value between 9:00 and 12:00 on the 7th day is 0. Then, the status value determination module determines the current user's status value as 80 - 13.0065 + 0, and determines the user's current status value as 66.9935.

[0237] Assuming the user enters a query operation for the second time at 7:00 on the 8th day, and the status value determination module detects at 22:00 on the 7th day that the user's real-time stress level (e.g., 25) is less than the stress threshold, the module determines that the user is in a resting state. Furthermore, the status value determination module detects that the user's real-time stress level at 7:00 on the 8th day (e.g., 31) is greater than the stress threshold, determining that the user is in a non-resting state. Based on this, the status value determination module determines that the user is in a non-resting state for 600 minutes from 12:00 on the 7th day to 7:00 on the 8th day (i.e., from 12:00 on the 7th day to 22:00). According to formulas (2) to (5), the status value determination module determines the first consumption rate to be 1. Therefore, the basic consumption value generated by the user during the non-resting period = 1 × 0.069 × 600 = 41.4. From 12:00 on day 7 to 7:00 on day 8, the user's heart rate was less than 50 when generating external load, which means that the user did not generate external load, or that the user did not generate additional consumption value. Therefore, the user's consumption value at the first moment is 41.4 + 0 = 41.4.

[0238] The status value determination module determines that the user's current rest period is 540 minutes (i.e., from 22:00 on the 7th to 7:00 on the 8th) during the period from 12:00 on the 7th to 7:00 on the 8th. Assume W... R_HR Equal to 45%, W R_HRV Equal to 40%, W R_P B1 equals 15%; B2 equals 0.75, B3 equals 1, and B3 equals 0.8. The status value determination module can determine the first recovery rate as 0.8575 according to formula (8). The first recovery value is 0.1388, so according to formula (7), the status value determination module determines the user's current recovery value as 64.27134. At this time, the current (i.e., 7:00 AM on the 8th day) user's status value = 66.9935 - 41.4 + 64.27134 = 89.86484. The status value determination module can take one decimal place of the current status value, that is, determine the current user's status value as 81.9.

[0239] In other words, the status value determination module determines when a user enters a resting state and when they enter a non-resting state based on real-time stress levels. This allows it to determine the duration of the user's non-resting state and the duration of their resting state from the last time the user entered a query to the current time. The status value determination module then subtracts the status value consumed during the non-resting period from the current initial status value (i.e., the status value determined during the last query operation) and adds the status value recovered during the resting period to determine the user's current real-time status value. It's understandable that when health status assessment and management are initially activated or when an update cycle is reached, the initial status value is the initial status value for that update cycle.

[0240] This example combines Figure 9 The process of determining a user's current state value using the daily current state model is explained in detail below:

[0241] Step 901: The second data processing module detects whether the height information has been obtained. If it is determined that the user's height information has been obtained, step 902 is executed. If it is determined that the user's height information has not been obtained, the second data processing module is triggered to determine the consumption rate corresponding to the current first relevant input parameter (BMI) as the default consumption rate.

[0242] For example, the second data input module in the second acquisition component can acquire the height information input by the user. Optionally, when the user inputs height information, both the first data input module and the second data input module can acquire the height information input by the user.

[0243] The second data input module can return empty information to the second data processing module if height information is not obtained within the first preset detection time after the health status assessment and management function is enabled. The first preset detection time can be within 10 milliseconds; in this example, 1 millisecond is used. When the second data processing module detects that the height information is empty, it determines that the first relevant input parameter corresponding to the height information is BMI, and determines that the consumption rate corresponding to BMI is the default consumption rate, where the default consumption rate can be 1.

[0244] When the second data input module obtains the height information, it returns the obtained height information to the second data processing module so that the second data processing module can determine the user's BMI based on the height information.

[0245] Step 902: The second data processing module detects whether weight information has been obtained. If it is determined that the user's weight information has been obtained, step 903 is executed. If it is determined that the user's weight information has not been obtained, the second data processing module is triggered to determine the consumption rate corresponding to the current first relevant input parameter (BMI) as the default consumption rate.

[0246] For example, a mobile phone can wirelessly connect to a weight acquisition device (such as a weight scale or body fat scale) via Bluetooth or Wi-Fi. Once the health status assessment and management function is enabled, the second data acquisition module in the mobile phone can obtain weight information from the weight acquisition device.

[0247] Optionally, after the health status assessment and management function is enabled, the second data acquisition module and the second data input module can detect whether a first preset detection time has been reached. After determining that the first preset detection time has been reached, the second data acquisition module checks whether weight information has been acquired. If the second data acquisition module detects that no weight information has been acquired, it can return empty information to the second data processing module. Additionally, when the first preset detection time has been reached, the second data input module checks whether weight information has been acquired. If it determines that weight information has been acquired, it transmits the weight information to the second data processing module. If the second data input module determines that no weight information has been acquired, it returns empty information to the second data processing module.

[0248] When the second data processing module detects that both the second data acquisition module and the second data input module return empty weight information, it determines that the second data processing module has not obtained weight information. The second data processing module then determines that the first relevant input parameter corresponding to the weight information is BMI, and determines that the consumption rate corresponding to BMI is the default consumption rate.

[0249] Step 903: The second data processing module determines that the consumption rate corresponding to the current first relevant input parameter is the default consumption rate. After this step, the consumption rate corresponding to the current first relevant input parameter is transmitted to the status value determination module, and step 910 is executed.

[0250] For example, when the second data processing module determines that weight information, height information, or maximum oxygen uptake (i.e., exercise capacity information) is not obtained, it determines the corresponding first relevant input parameter and its corresponding consumption rate. Specifically, when the second data processing module determines that weight information or height information is not obtained, it determines that the first relevant input parameter corresponding to the missing information is BMI, and determines that the consumption rate corresponding to BMI is the default consumption rate. When the second data processing module determines that maximum oxygen uptake is not obtained, it determines that the first relevant input parameter corresponding to the missing information is exercise capacity, and determines that the consumption rate corresponding to exercise capacity is the default consumption rate.

[0251] In another example, when the second data processing module determines that no weight or height information has been obtained, it identifies the first relevant input parameter corresponding to the missing information as BMI and sets the BMI value as the default value. The second data processing module then transmits the BMI value to the status value determination module. Based on the BMI value and the correspondence between the default BMI value and the default consumption rate of BMI, the status value determination module determines that the current BMI corresponds to the default consumption rate.

[0252] When the second data processing module determines that the maximum oxygen uptake has not been obtained, it identifies the first relevant input parameter corresponding to the missing information as exercise capacity and sets the value of exercise capacity as the default value. The second data processing module then transmits the information of all the first relevant input parameters to the state value determination module. Based on the value of exercise capacity and the correspondence between the value of exercise capacity and the consumption rate, the state value determination module determines the consumption value corresponding to that exercise capacity as the default consumption value.

[0253] Step 904: The second data processing module calculates the BMI.

[0254] For example, the second data processing module can calculate the user's BMI based on the height and weight information.

[0255] Step 905: The second data processing module detects whether the maximum oxygen uptake has been obtained. If it is determined that the maximum oxygen uptake has been obtained, step 906 is executed; if it is determined that the maximum oxygen uptake has not been obtained, the consumption rate corresponding to the current first relevant input parameter (i.e., exercise capacity) is determined to be the default consumption rate.

[0256] For example, a mobile phone can connect to a smart wearable device, which can collect the user's maximum oxygen uptake. After the health status assessment and management function is enabled, the second data acquisition module can obtain the user's maximum oxygen uptake collected by the smart device. If the second data acquisition module detects that a first preset detection time has been reached and the maximum oxygen uptake has not been obtained, it returns empty information to the second data processing module. When the second data acquisition module obtains the maximum oxygen uptake, it transmits the obtained maximum oxygen uptake to the second type of health data processing module.

[0257] When the second data processing module detects that the maximum oxygen uptake information returned by the second data acquisition module is empty, it determines that the maximum oxygen uptake has not been obtained. The second data processing module determines that the first relevant input parameter corresponding to the maximum oxygen uptake is exercise capacity, and determines that the corresponding consumption rate is the default consumption rate.

[0258] Step 906: The second data processing module obtains the resting heart rate.

[0259] For example, when a mobile phone is connected to a smart wearable device, and the health status assessment and management function is enabled, the second data acquisition module can obtain the resting heart rate from the smart wearable device and transmit the obtained resting heart rate to the second data processing module.

[0260] Step 907: The second data processing module obtains heart rate variability.

[0261] For example, when a mobile phone is connected to a smart wearable device, and the health status assessment and management function is enabled, the second data acquisition module can obtain heart rate variability from the smart wearable device. The second data acquisition module then transmits the obtained heart rate variability to the second data processing module.

[0262] The heart rate variability can be the average of the real-time heart rate variability collected by the second data processing module when the user is at rest for 24 hours. For example, if M real-time heart rate variabilities are collected when the user is at rest for 24 hours, then the average of the M real-time heart rate variabilities is obtained, where M is an integer greater than 1.

[0263] Step 908: The second data processing module checks whether the real-time pressure level has been acquired. If it is determined that the real-time pressure level has been acquired, proceed to step 910; if it is determined that the real-time pressure level has not been acquired, proceed to step 909.

[0264] For example, when a mobile phone is connected to a smart wearable device, and the health status assessment and management function is enabled, the second data acquisition module can obtain the real-time stress level from the smart wearable device. In some scenarios (such as when the smart device is charging), there may be situations where the user is not wearing the smart wearable device, which may cause the second data acquisition module to fail to collect the user's real-time stress level, and consequently, the second data processing module may also fail to obtain the user's real-time stress level.

[0265] When the second data processing module detects that the real-time pressure level has not been acquired, step 909 is executed.

[0266] Step 909: The second data processing module obtains the average real-time pressure level for the 30 minutes prior to the second recording time.

[0267] For example, the second data processing module obtains the average real-time pressure level of the 30 minutes prior to the second recording time from the storage module, and uses this average as the currently obtained real-time pressure level. The second recording time is the time when the real-time pressure level was most recently obtained. For instance, if the current time is T1, and the second data processing module detects that no real-time pressure level was obtained at time T1, and the most recently obtained real-time pressure level was at time T2, where T1-T2 is greater than 30 minutes, the module obtains the average real-time pressure level of the 30 minutes prior to time T2. A total of 30 real-time pressure level values ​​are collected from time T2-30 to time T2, and the average of these 30 real-time pressure levels is used as the real-time pressure level value at the current time T1.

[0268] This step is followed by step 910.

[0269] Step 910: The status value determination module obtains the current user's actual consumption rate based on the weight of each first relevant input parameter and the corresponding consumption rate.

[0270] For example, after obtaining each first relevant input parameter, the state value determination module can obtain the weight of each first relevant input parameter. Optionally, the daily current state model stores the correspondence between different values ​​of each first relevant input parameter and the consumption rate (called the first consumption rate correspondence). The state value determination module determines the consumption rate corresponding to each first relevant input parameter based on the value of each first relevant input parameter and the first consumption rate correspondence. For example, the first consumption rate correspondence indicates that when the resting heart rate is HRrest_1, the corresponding consumption rate is 1; when the resting heart rate is HRrest_2, the corresponding consumption rate is 0.9. When the daily current state model obtains a resting heart rate of HRrest_1, it determines the consumption rate of this resting heart rate to be 1 based on the first consumption rate correspondence.

[0271] Optionally, the status value determination module can also directly obtain the consumption rate of each first relevant input parameter from the second data processing module. For example, if the second data processing module determines that the consumption rate of BMI is 1, it transmits the consumption rate of BMI to the status value determination module. After obtaining the real-time pressure level, the second data processing module can obtain the consumption rate corresponding to the real-time pressure level based on the correspondence between the real-time pressure level value and the first consumption rate.

[0272] The status value determination module determines the user's actual consumption rate (i.e., the first consumption rate) based on the weight of each first relevant input parameter and the corresponding consumption rate. This can be referred to in the relevant description of formula (5), which will not be repeated here.

[0273] Step 911: The status value determination module determines the consumption value of the user's current status based on the user's actual consumption rate.

[0274] The status value determination module determines the user's current consumption value (i.e., the consumption value generated during the current non-rest period) based on the user's actual consumption rate (i.e., the first consumption rate) and the duration of the current non-rest period. The specific process can be referred to the relevant descriptions of formulas (2) to (4), which will not be repeated here.

[0275] Step 912: The second data processing module acquires the real-time heart rate.

[0276] For example, when a mobile phone is connected to a smart wearable device, and the health status assessment and management function is enabled, the second data acquisition module can obtain the real-time heart rate from the smart wearable device. The second data acquisition module then transmits the obtained real-time heart rate to the second data processing module.

[0277] Step 913: The second data processing module checks whether real-time heart rate variability has been acquired. If real-time heart rate variability is acquired, proceed to step 914; if real-time heart rate variability is not acquired, proceed to step 915.

[0278] For example, if the interval between data collection of real-time heart rate variability by the smart wearable device is longer than the interval between data collection of real-time heart rate, the current moment may not be the time when the smart wearable device collects heart rate variability, which may result in the second data processing module being unable to obtain real-time heart rate variability at the current moment; or, if the smart wearable device misses data collection of real-time heart rate variability at the current moment due to some uncontrollable factors, then step 915 is executed to obtain heart rate variability that is closer to the current moment. After obtaining the average of real-time heart rate variability over the 30 minutes prior to the most recent data collection moment as the currently obtained real-time heart rate variability, step 914 is then executed. Figure 9 (Not shown).

[0279] When the second data processing module obtains the real-time heart rate variability, it executes step 914. The process of obtaining the real-time heart rate variability can be referred to step 907, and will not be described again here.

[0280] Step 914: The second data processing module detects whether the real-time pressure level has been obtained. If it is determined that the real-time pressure level has been obtained, proceed to step 916; if it is determined that the real-time pressure level has not been obtained, proceed to step 915.

[0281] This step is largely the same as step 909, and you can refer to the relevant description of step 909. It will not be repeated here.

[0282] When a smart wearable device fails to collect real-time stress levels due to unforeseen circumstances, it uses the average of real-time stress levels from the 30 minutes preceding the most recent collection time as the current real-time stress level. This avoids the problem of being unable to calculate the daily current status value due to the smart wearable device's failure to collect the current real-time stress level.

[0283] Step 915: The second data processing module obtains the average value of the input parameters for the most recent 30 minutes.

[0284] For example, when the second data processing module detects that no real-time heart rate variability has been acquired, it acquires the average real-time heart rate variability over the 30 minutes prior to the first recording time and uses this average as the current real-time heart rate variability. The first recording time is the most recent time when real-time heart rate variability was acquired. When the second data processing module detects that no real-time stress level has been acquired, it acquires the average real-time stress level over the 30 minutes prior to the second recording time and uses this average as the current real-time stress level. The second recording time is the most recent time when real-time stress level was acquired.

[0285] It should be noted that after the second data processing module transmits all the second type of health data to the status value determination module, the status value determination module can execute step 916.

[0286] In some embodiments, after the second data processing module obtains all input parameters of the second type of health data, the numerical values ​​of the second type of health data can be displayed on the interface for the user to view. For example, Figure 8d As shown in (4), when the user clicks the first completion control 8045 in interface 8043, Honor Health responds to the user's click operation and jumps to the completion interface of the second type of health data, as shown in (4). Figure 10 The interface shown is 8046. The status value completion interface 8046 includes a second type of health data list 8048, a second completion control 8047, and a return control. The second type of health data list 8048 includes various input parameters for the second type of health data. Height can be manually entered by the user, and weight can be manually entered by the user or obtained through a scale. After completing the data in the second type of health data list, the user can click the second completion control 8047. After completing the second type of health data, the user can also query the data in the second type of health data list.

[0287] Step 916: The status value determination module obtains the actual recovery rate of the current user based on the weight of each second related input parameter and its respective recovery rate.

[0288] For example, after obtaining each first relevant input parameter, the state value determination module can obtain the weight of each first relevant input parameter. Optionally, the daily current state model stores the correspondence between different values ​​of each first relevant input parameter and the recovery rate (called the first recovery rate correspondence). The state value determination module determines the recovery rate corresponding to each first relevant input parameter based on the value of each first relevant input parameter and the first recovery rate correspondence. For example, the first recovery rate correspondence indicates that when the real-time heart rate is HR_1, the corresponding recovery rate is 0.75; when the real-time heart rate is HR_2, the corresponding recovery rate is 0.9. When the daily current state model obtains a real-time heart rate of HR_1, it determines the recovery rate of this real-time heart rate to be 0.75 based on the first recovery rate correspondence.

[0289] The status value determination module determines the user's actual recovery rate (i.e., the first recovery rate) based on the weight of each first relevant input parameter and the corresponding recovery rate. This can be referred to in the relevant description of formula (8), which will not be repeated here.

[0290] Step 917: The status value determination module determines the recovery value of the user's status based on the current user's actual recovery rate.

[0291] For example, the status value determination module can determine the user's status recovery value according to the user's actual recovery rate (i.e., the first recovery rate), the first recovery value, and the rest duration experienced by the current user, according to formula (7).

[0292] Step 918: The state value determination module obtains the initial state value.

[0293] This step is similar to the process of obtaining the initial state value in step 4, and will not be described again here.

[0294] Step 919: The status value determination module determines the current status value for each day.

[0295] For example, the status value determination module can determine the user's current status value by obtaining the initial status value, the current consumption value, and the recovery value.

[0296] It should be noted that users' rest periods and non-rest periods are mutually exclusive. During rest periods, state values ​​will not be consumed, and during non-rest periods, state values ​​will not be restored.

[0297] Step 5: The health status scoring module generates a health status score for the user based on the user's health risk score and daily current status value, and transmits it to the health status management module.

[0298] For example, the health status scoring module has three preset health levels: high risk, medium risk, and low risk. Low risk corresponds to a health risk score range of below 5 points, medium risk corresponds to a health risk score range of 5-10 points, and high risk corresponds to a health risk score range of 10-15 points. Figure 11 As shown in (1) of the table.

[0299] Daily current status values ​​are used to reflect different physical states, such as Figure 11 As shown in (2), when the state value ranges from 5 to 20, it indicates that the user's current state is severely insufficient; when the state value ranges from 20 to 40, it indicates that the user's current state is poor; when the state value ranges from 40 to 60, it indicates that the user's current state is average; when the state value ranges from 60 to 80, it indicates that the user's current state is good; and when the state value ranges from 80 to 100, it indicates that the user's current state is excellent.

[0300] For example, the health status scoring module can be based on the user's health risk score and the correspondence between health risk level and health risk score (e.g., ... Figure 11 The correspondence shown in (1) is used to determine the user's health risk level. The health status scoring module also pre-stores the health status score variation ranges corresponding to different health risk levels. In this example, three different health status score variation ranges are preset, each corresponding to a different health risk level, for example, such as Figure 12 As shown, the health status score range for high-risk levels is 20–60 points; for medium-risk levels it is 40–80 points; and for high-risk levels it is 80–100 points. The value corresponding to each health status score range is the health status score.

[0301] After the health status scoring module determines the user's health risk level, it determines the range of variation for the current user's health status score based on the correspondence between the health risk level and the range of variation for the health status score. The health status scoring module then determines the user's current health status score based on the range of variation for the user's health status score and the user's current status value.

[0302] For example, User A's health risk score is 4 points. Based on this score, the health status scoring module determines User A's health risk level as low risk. The module obtains the corresponding health status score fluctuation range for low risk (i.e., the health status score fluctuates between 60 and 100). If User A's current status value is 20 points, then User A's health status score is (20 / 100) × 40 + 60 = 68 points. Similarly, User B's health risk score is 11 points. Based on this score, the module determines User B's health risk level as high risk. The module obtains the corresponding health status score fluctuation range for high risk (i.e., the health status score fluctuates between 20 and 60). If User B's current status value is 90 points, then User A's health status score is (90 / 100) × 40 + 20 = 56 points. Finally, User C's health risk score is 6 points. Based on this score, the module determines User C's health risk level as medium risk. The health status scoring module obtains the health status score fluctuation range corresponding to medium risk (i.e., the health status score fluctuates between 40 and 80). The health status scoring module obtains that user C's current status value is 90 points, then user A's health status score is (90 / 100)×40+40=76 points.

[0303] In this example, the health status scoring module can use the user's current health status score, the user's health risk score, and the current status value as the basis for health recommendations, and send the basis for the health recommendations to the health status management module.

[0304] As can be seen, in this example, a user's health status score is related to the user's health risk level and the user's current status value. The two influence each other, so that the health status score at each moment can accurately reflect the user's current health status, thus making it convenient for users to intuitively view their own health status.

[0305] Step 6: The health status management module provides users with health advice to promote their health.

[0306] For example, the health status management module recommends health suggestions suitable for the user's current health status based on the user's health risk score and the user's current status value, and displays the user's current health status score.

[0307] In one example, the health status management module determines a user's risk level based on their health risk score. When a user's risk level is detected as high or medium risk, the module can determine which risk group the user belongs to based on preset criteria for each risk group. The criteria for each risk group are different; for example, the criteria for obese individuals are: BMI greater than or equal to 28 kg / m². 2 Men with a waist circumference greater than 90cm or women with a waist circumference greater than 80cm are considered to have dyslipidemia. The criteria for diagnosing dyslipidemia are: measured blood lipid levels less than or equal to 95%. Assuming the health status management module detects a user's risk level as medium risk, it matches the user's first-category health data with the criteria for each risk group, and identifies the successfully matched risk groups as the user's risk group. For example, if the user's BMI is 30kg / m²... 2 The user is male with a waist circumference of 90.5cm; the health status management module has determined that the user is obese.

[0308] In one example, the health status management module matches the user's first-category health data with the judgment criteria corresponding to each risk group to obtain the matching degree with each risk group. Risk groups with a matching degree greater than a matching degree threshold are identified as the user's risk groups. The matching degree threshold can be a value greater than or equal to 50%. A user can belong to multiple risk groups simultaneously. The matching degree can be the ratio of the number of successfully matched items in the judgment criteria corresponding to that risk group to the total number of items. For example, the judgment criteria for obesity are: BMI greater than or equal to 28 kg / m². 2 Men with a waist circumference greater than 90cm or women with a waist circumference greater than 80cm. Assume the user's BMI is 30kg / m². 2 The user is male with a waist circumference of 89cm; the number of successfully matched items is 1, and the total number of items is 2, so the matching degree is 50%. Optionally, after the health status management module determines the user's risk group, it can obtain that risk group as the user's risk factor and use the user's matching degree with their risk group as the percentage of the risk factor. The mobile phone can display the user's risk factors and the percentage of each risk factor.

[0309] The health status management module recommends suitable health management suggestions based on the user's risk group and current status score. It can also provide long-term health advice (including diet, exercise intensity, exercise duration, exercise type, and sleep schedule) based on the user's risk group. For example, if a user is determined to be obese and their current status score is 40, the module would suggest resting when the user's status score is 40. Long-term health advice for obese individuals includes: a low-calorie, high-protein diet combined with regular aerobic exercise and strength training for scientific fat loss; encouragement of low-impact exercises such as walking and swimming to reduce joint stress, with a suggested exercise duration of 40 minutes. The module then finds the intersection of these two recommendations: a low-calorie, high-protein diet and a recommendation to rest. It does not recommend exercise at this time.

[0310] In this example, the health status management module dynamically adjusts long-term health recommendations based on the user's status value to ensure that the user can benefit from exercise safely, while promoting long-term health improvement and creating a positive feedback loop. For example, if the user is obese and their current status value is 80, the corresponding health recommendations would typically include 50 minutes of exercise, with recommended activities such as running, skipping rope, or aerobics. The health status management module would then recommend swimming or brisk walking for 40 minutes.

[0311] The following are long-term health recommendations for different risk groups:

[0312] For example, when the health status management module determines that a user is at low risk, it can provide corresponding long-term health management recommendations based on the user's age:

[0313] For healthy individuals aged 18-64: A balanced diet is recommended, including sufficient vegetables, fruits, and whole grains, while limiting processed foods and sugary drinks. It is suggested to engage in at least 150 minutes of moderate-intensity physical activity per week, such as brisk walking, swimming, or cycling, as well as two days of strength training.

[0314] For healthy individuals aged 65 and above: Moderate physical activity, such as walking, Tai Chi, or square dancing, is encouraged to maintain flexibility and muscle strength. Pay attention to nutritional intake, ensuring sufficient protein and vitamin D intake to support bone health.

[0315] When the health status management module determines that a user is at a high risk level, it will determine which risk group the user belongs to based on the preset judgment criteria for each risk group.

[0316] For obese individuals: Health recommendations include a low-calorie, high-protein diet combined with regular aerobic exercise and strength training to achieve scientific fat loss. Low-impact exercises such as walking and swimming are encouraged to reduce stress on joints.

[0317] When a user's blood pressure exceeds the blood pressure threshold, the user is identified as having high blood pressure. Health recommendations for individuals with high blood pressure include: a low-sodium diet and increased potassium intake, such as through foods like bananas, potatoes, and spinach. Regular aerobic exercise, such as brisk walking or cycling, is also recommended to help lower blood pressure.

[0318] When a user's blood lipid levels exceed the blood lipid threshold, the user is classified as having dyslipidemia. Health recommendations for individuals with dyslipidemia include: reducing the intake of saturated and trans fats, and increasing the intake of foods rich in Omega-3 fatty acids, such as salmon and flaxseed. Regular moderate-intensity aerobic exercise is also recommended to improve blood lipid levels.

[0319] When a user's medical information is obtained and it is detected that the user has diabetes or cancer, the user is identified as belonging to the diabetic or cancer patient population. The corresponding health recommendations for diabetic and cancer patients are: encourage a low-sugar diet and pay attention to blood sugar control. It is recommended to consult with a professional medical person to develop a personalized exercise plan to safely and effectively benefit from exercise.

[0320] In addition, different exercise recommendations are provided for different risk groups of users. For example, for obese individuals: a scientific weight loss plan is offered, including dietary adjustments and customized exercise programs. For individuals with high blood pressure (normal range): blood pressure solutions are provided, including lifestyle adjustments and appropriate exercise recommendations. For individuals with insufficient physical activity: a running beginner plan is offered, encouraging a gradual increase in physical activity. For individuals with high blood sugar: blood sugar solutions are offered, including dietary management and appropriate exercise recommendations. For healthy individuals: a health / body shape maintenance plan is offered, encouraging a sustained healthy lifestyle.

[0321] Figure 13 This is an example illustration of a user querying their health status.

[0322] Taking a mobile phone as an example, in response to the user opening Honor Health, the phone will redirect to the main interface of Honor Health. Please refer to... Figure 13 In (1), the phone's display shows the main interface 1301 of Honor Health. When the user clicks the Health Status Assessment and Management option 1304 (i.e., the query operation), Honor Health responds to the user's click and redirects to the comprehensive health status assessment system interface 1305 of Health Status Assessment and Management, referring to... Figure 13(2). Interface 1305 includes the user's current health status score 13062 (80 points) and health status score animation 13061, the user's health risk level 13064 (as shown in the figure, it is low risk), the user's current status value 13063 (as shown in the figure, it is 50 points), and recommended health management suggestions for the user 13065.

[0323] Optionally, if the user's health risk level is high, the mobile phone's comprehensive health status assessment system interface 1305 can also display the user's risk factors, such as high blood pressure, high blood lipids, obesity, etc., as well as the percentage of each factor.

[0324] This application also provides a chip system including at least one processor and at least one interface circuit. The processor and the interface circuit are interconnected via lines. For example, the interface circuit can be used to receive signals from other devices (e.g., the memory of an electronic device). As another example, the interface circuit can be used to send signals to other devices (e.g., the processor). Exemplarily, the interface circuit can read instructions stored in the memory and send the instructions to the processor. When the instructions are executed by the processor, the electronic device can perform the steps in the above embodiments. Of course, the chip system may also include other discrete devices, and this application does not specifically limit this.

[0325] This embodiment also provides a computer storage medium storing computer instructions. When these computer instructions are executed on an electronic device, the electronic device performs the aforementioned method steps to implement the health management method described in the above embodiment. The storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0326] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the health management method described in the above embodiment.

[0327] In this embodiment, the electronic device, computer storage medium, computer program product or chip are all used to execute the corresponding health management method provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding method provided above, and will not be repeated here.

[0328] Any content in the various embodiments of this application, as well as any content in the same embodiment, can be freely combined. Any combination of the above content is within the scope of this application.

[0329] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0330] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for health management, characterized in that, Applied to a first electronic device, including: In response to a user's query operation at the first moment, first type of health data and second type of health data are obtained at the first moment. The first type of health data is the current body parameters related to the probability of the user developing a target disease in the future. The second type of health data is the body parameters related to the user's current physical state. The target disease includes at least two diseases. The user's health risk score is determined based on the first type of health data at the first moment, and the health risk score is used to reflect the probability that the user will develop the target disease in the future. The user's status value at the first moment is determined based on the second type of health data at the first moment, and the status value is used to evaluate the user's physical condition. Based on the user's health risk score and status value at the first moment, health management recommendations for the user at the first moment are determined, and the health management recommendations include at least one of exercise recommendations, dietary recommendations, or rest recommendations; Displays the user's health management recommendations at the first moment.

2. The method according to claim 1, characterized in that, The method further includes: Based on the user's health risk score and status value at the first moment, the user's health status score at the first moment is determined, and the health status score is used to evaluate the user's health level. Displays the user's health status score at the first moment.

3. The method according to claim 2, characterized in that, If the user follows the health management recommendations at the first moment, the method further includes: In response to the user's query operation at the second time, obtain the first type of health data and the second type of health data at the second time. The user's health risk score at the second time is determined based on the first type of health data at the second time, wherein the health risk score at the second time is lower than the health risk score at the first time. The user's status value at the second time point is determined based on the second type of health data at the second time point; Based on the user's health risk score and status value at the second time, health management suggestions and health status scores for the user at the second time are determined. The health management suggestions at the second time are different from those at the first time. In cases where the status value at the second time is equal to the status value at the first time, the health status score at the second time is higher than the health status score at the first time.

4. The method according to any one of claims 1-3, characterized in that, The second category of health data includes at least: body mass index (BMI), exercise capacity, resting heart rate, heart rate variability, sleep quality, real-time heart rate variability, real-time stress level, and external load, wherein the external load includes the load values ​​generated by various sports and physical activities.

5. The method according to claim 4, characterized in that, The acquisition of the second type of health data at the first moment includes: If the user inputs height information, the height information input by the user is obtained at the first moment; When the first body monitoring device collects the user's weight information, the weight information is obtained from the first body monitoring device at the first moment, and the first body monitoring device is wirelessly connected to the first electronic device; If the first body monitoring device does not collect the user's weight information but detects that the user has entered weight information, the weight information entered by the user is acquired at the first moment; The user's BMI at the first moment is determined based on the weight information and the height information; The data collected by each of the first devices in the second type of health data is processed as follows: when the second body monitoring device collects the data collected by the first device, the data collected by the first device is obtained from the second body monitoring device at the first moment. The data collected by the first device includes: exercise capacity, resting heart rate, real-time heart rate, heart rate variability, sleep quality, real-time stress level and external load. The second body monitoring device is wirelessly connected to the first electronic device.

6. The method according to claim 5, characterized in that, The method further includes: When it is detected that the user's height information or weight information is not obtained, a preset first BMI is obtained as the user's BMI at the first moment; When it is detected that the user's motion ability has not been acquired, a preset first motion ability is acquired as the user's motion ability at the first moment; When it is detected that the user's real-time heart rate variability has not been obtained, the average value of the real-time heart rate variability within a first preset time period before the first recording time is obtained as the user's real-time heart rate variability at the first time. The first recording time is the time when the user's real-time heart rate variability was most recently obtained. When it is detected that the user's real-time stress level has not been obtained, the average of the real-time stress levels within a second preset time period before the second recording time is obtained as the user's real-time stress level at the first time. The second recording time is the time when the user's real-time stress level was most recently obtained.

7. The method according to claim 6, characterized in that, Determining the user's status value at the first moment based on the second type of health data at the first moment includes: Based on the second type of data at the first moment, the consumption value and the recovery value at the first moment are determined. The consumption value at the first moment includes the status value of the user's basic consumption during the first non-rest period and the status value of the additional consumption of the external load during the first non-rest period. The first non-rest period is the duration during which the user is in a non-rest state from the moment the user last entered a query operation to the first moment. The recovery value at the first moment is the status value of the user's recovery from the moment the user last entered a query operation to the first moment. The first value is obtained by subtracting the consumption value at the first moment from the first state value of this query. The first state value of this query is the state value determined when the user last entered the query operation. The first state value of the first query is a preset initial state value. The user's state value at the first moment is obtained by adding the recovery value at the first moment to the first value, and the state value at the first moment is used as the first state value for the next query.

8. The method according to claim 7, characterized in that, Based on the second type of data at the first moment, determine the user's basic consumption status value during the first non-rest period, including: Based on the value of each first related input parameter and the corresponding relationship of consumption rate, the consumption rate of each first related input parameter is determined. The first related input parameter belongs to the second type of health data. The corresponding relationship of consumption rate is used to indicate the correspondence between the value of each first related input parameter and the consumption rate. The first consumption rate is determined based on the consumption rate of each first relevant input parameter and the preset weight of each first relevant input parameter. The product of the first consumption rate, the first consumption value, and the duration of basic consumption is obtained as the status value of the user's basic consumption during the first non-rest period. The first consumption value is a preset status value of the user's consumption per minute. The duration of basic consumption is the difference between the duration of the first non-rest period and the total duration of the external load generated by the user during the first non-rest period.

9. The method according to claim 7, characterized in that, Based on the second type of data at the first moment, determine the state value of the additional external load consumption during the first non-rest period, including: The duration of each heart rate intensity zone experienced during the first non-rest period when the external load is generated is obtained, the heart rate intensity zone being divided based on the user's maximum heart rate; Based on the duration of each heart rate intensity zone, the sum of the state values ​​of the external load consumed in each heart rate intensity zone is obtained as the first external load consumption value; Obtain the total duration of the external load generated; The status value of the external load consumed within the total duration is obtained as the second external load consumption value; The sum of the first external load consumption value and the second external load consumption value is obtained as the status value of the external load consumption during the first non-rest period.

10. The method according to claim 9, characterized in that, Obtain the state values ​​consumed for each heart rate intensity zone, including: The product of the duration of the heart rate intensity zone, the preset weight, and the first consumption value is obtained as the state value of the heart rate intensity zone consumption. The first consumption value is the preset state value of the user's consumption per minute. Obtaining the status values ​​of the external load consumed within the total duration includes: The product of the total duration and the first consumption value is obtained as the status value of the external load consumption within the total duration.

11. The method according to claim 7, characterized in that, Based on the second type of data at the first time point, determine the recovery value at the first time point, including: Based on the value of each second related input parameter and the corresponding relationship of recovery rate, the recovery rate of each second related input parameter is determined. The second related input parameter belongs to the second type of health data. The corresponding relationship of recovery rate is used to indicate the correspondence between the value of each second related input parameter and the recovery rate. The first recovery rate is determined based on the recovery rate of each second relevant input parameter and the preset weight of each second relevant input parameter; The product of the first recovery rate, the first recovery value, and the duration of the first rest period is obtained as the recovery value at the first moment. The first recovery value is a preset state value of the user's recovery per minute. The first rest period is the duration during which the user is in a resting state from the moment the user last entered a query operation to the first moment.

12. The method according to any one of claims 1-11, characterized in that, The first type of health data includes user input data, questionnaire data, and data collected by second devices; The user input data includes a first input item, a second input item, and a third input item. The first input item includes at least: age information, gender information, and waist circumference information. The second input item includes at least: blood pressure information and blood lipid information. The third input item includes at least: physical activity duration. The questionnaire data includes at least: smoking information, medical condition information, and family medical history information; The data collected by the second device includes at least: permanent location information.

13. The method according to claim 12, characterized in that, The acquisition of the first type of health data at the first moment includes: If the user has entered the user input data, the user input data is obtained; When it is detected that the first input item has not been obtained, a first prompt message is output, which is used to prompt the user to input the first input item that has not been obtained. When it is detected that no second input item has been obtained, the first query information is output. The first query information is used to ask the user whether to use the value of the abnormal option as the value of the currently obtained second input item. When the user inputs the first selection operation, the value of the abnormal option is obtained as the value of the current second input item. The first selection operation is the operation in which the user selects to use the value of the abnormal option as the value of the current second input item. When the user inputs a second selection operation, a second prompt message is output. The second prompt message is used to prompt the user to input a second input item that has not yet been obtained. When it is detected that the third input item has not been acquired, the value of the third input item is acquired from the second body monitoring device. The second body monitoring device is wirelessly connected to the first electronic device and is used to collect the third input item.

14. The method according to claim 13, characterized in that, The acquisition of the first type of health data at the first moment also includes: The questionnaire selection page displays each questionnaire input parameter item in sequence, and the questionnaire selection page includes each candidate information of the questionnaire data; Responding to user responses on each questionnaire selection page, retrieve response information for each questionnaire. Obtain permanent residence information from the second body monitoring device.

15. The method according to any one of claims 1-3, characterized in that, The user's health risk score at the first moment is determined based on the first type of health data at the first moment, including: Input the first type of health data at the first moment into a preset disease probability model to obtain the probability that the user will suffer from the target disease in the future; The user's health risk score at the first moment is determined based on the probability that the user will develop the target disease in the future.

16. The method according to claim 7, characterized in that, The method further includes: When an update cycle that reaches the initial state value is detected, the mean values ​​of various basic physiological data are obtained from the second type of health data within the update cycle. The basic physiological data include at least: height, weight, BMI, exercise capacity, resting heart rate, heart rate variability, and sleep quality. The mean values ​​of the user's various basic physiological data are matched with the various basic physiological data corresponding to each preset state value stored in the database. The state value of a successful match is used as the new initial state value.

17. The method according to any one of claims 1-3, characterized in that, Based on the user's health risk score and status value at the first moment, health management recommendations for the user at the first moment are determined, including: The user's health risk level is determined based on the user's health risk score at the first moment and the preset risk level correspondence, wherein the risk level correspondence is the correspondence between the health risk score and the health risk level. Based on the user's health risk level and the abnormal data in the first type of health data, the user's risk group is determined; Obtain the intersection between the first health advice corresponding to the user's risk group and the second health advice corresponding to the user's status value at the first moment; The intersection will be used as the user's health management advice at the first moment.

18. An electronic device, characterized in that, include: A memory and a processor, wherein the memory is coupled to the processor; The memory stores program instructions that, when executed by the processor, cause the electronic device to perform the health management method according to any one of claims 1 to 17.

19. A computer-readable storage medium comprising a computer program, characterized in that, When the computer program is run on an electronic device, the electronic device causes the electronic device to perform the health management method according to any one of claims 1 to 17.

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