Health early warning method and system based on smart watch

Abstract

The invention discloses a health early warning method and system based on a smart watch, and the method comprises the following steps: S1, a data collection terminal collects and uploads user data, and configures role information; s2, synchronizing user data to relatives based on role information; s3, judging whether the user has an abnormal condition or not based on the user data; and S4, if the user has an abnormal condition, health suggestions are matched based on the abnormal condition and the role information, and early warning is carried out. The method has the beneficial effects that relatives can look up the user data of the target user, whether the target user has an abnormal condition or not can be judged based on the user data, and if the abnormal condition exists, health suggestions are matched and abnormal early warning is carried out.

Description

Technical Field

[0001] This invention relates to the field of smartwatch data analysis technology, and in particular to a health early warning method and system based on a smartwatch. Background Technology

[0002] With the development of mobile technology, many traditional electronic products have begun to add mobile functions. For example, watches that used to be used only to tell time can now be connected to the Internet through smartphones or home networks. Smartwatches generally use built-in sensors to collect user data and analyze the user's health based on the data.

[0003] In existing technologies, smartwatches are usually designed for specific use scenarios, resulting in relatively simple functions. While they aim to improve the accuracy of user data collection, they also have drawbacks such as preventing relatives from accessing the target user's data and failing to provide health advice or abnormal alerts.

[0004] For example, the "Sleep Monitoring System and Method Based on Smartwatch" disclosed in Chinese patent literature, with publication number CN116327123B and application date of March 13, 2023, includes an intelligent data platform, a feature parameter processing module, a data update and replacement module, a real-time sleep monitoring module, and a comprehensive early warning module. The output end of the intelligent data platform is connected to the input end of the feature parameter processing module; the output end of the feature parameter processing module is connected to the input end of the data update and replacement module; the output ends of the data update and replacement module and the real-time sleep monitoring module are connected to the input end of the comprehensive early warning module. It can improve the monitoring level of smartwatches, divide the smartwatch usage stage into an initial stage and a long-term stage, solve the physiological or psychological impact caused by emotions and wearing habits in the initial stage, and significantly improve the monitoring accuracy in the initial stage. However, it has the problem that relatives cannot access the target user's user data, and it cannot match health advice and provide abnormal warnings. Summary of the Invention

[0005] To address the shortcomings of existing technologies that do not allow relatives to access the target user's user data, nor can they match health advice and issue abnormal warnings, this application proposes a health warning method and system based on a smartwatch. This system enables relatives to access the target user's user data, determines whether the target user has any abnormal conditions based on the user data, and if abnormal conditions are found, matches health advice and issues abnormal warnings.

[0006] The following is the technical solution of the present invention: a health warning method based on a smartwatch, comprising the following steps:

[0007] S1. The data acquisition terminal collects and uploads user data and configures role information;

[0008] S2. Synchronize user data to relatives based on role information;

[0009] S3. Determine whether there are any abnormal situations with users based on user data;

[0010] S4. If a user has an abnormal situation, health advice will be matched and an alert will be issued based on the abnormal situation and role information.

[0011] Preferably, user data includes body data, exercise data, and sleep data. Body data includes heart rate, blood pressure, blood oxygen, and temperature. Exercise data includes exercise duration, number of steps, and cadence. Sleep data includes sleep duration and single sleep duration.

[0012] As a preferred method, determining whether a user exhibits abnormal behavior based on user data includes:

[0013] Analyze abnormal physical conditions based on body data;

[0014] Analyze abnormal motion conditions based on motion data;

[0015] Analyze sleep abnormalities based on sleep data.

[0016] As a preferred approach, analyzing abnormal physical conditions based on body data includes:

[0017] Calculate the ratio of body data to standard body data. If the ratio is within the normal range, the analysis result is normal; otherwise, the analysis result is abnormal.

[0018] If the number of consecutive analysis results showing abnormality exceeds the threshold, then the user's health is abnormal; otherwise, the user's health is normal.

[0019] Among them, the threshold values ​​for heart rate, blood pressure, blood oxygen, and temperature may be the same or different.

[0020] As a preferred method, motion abnormalities are analyzed based on motion data, including:

[0021] If the exercise duration is greater than or equal to the exercise duration threshold, the number of steps is greater than or equal to the number of steps threshold, and the step frequency is within the step frequency threshold range, then the user's exercise is normal; otherwise, the user's exercise is abnormal.

[0022] Preferably, sleep abnormalities are analyzed based on sleep data, including:

[0023] If a single sleep episode lasts less than the sleep duration threshold and the heart rate is greater than the sleep heart rate threshold, the user's sleep is abnormal; otherwise, the user's sleep is normal.

[0024] As a preferred option, health recommendations are matched based on abnormal situations and role information, including:

[0025] Based on physical abnormalities, movement abnormalities, and / or sleep abnormalities as matching criteria, several corresponding health recommendations are matched, and these recommendations are compiled and output as a PDF document, which is then sent to the user and their relatives' email addresses.

[0026] As a preferred approach, early warnings are issued based on anomalies and role information, including:

[0027] Display warning images and text alerts. The text alerts include user data and its collection time. Call the SMS interface to send SMS messages about the abnormal situation to the user's and their relatives' mobile phones, and call the email interface to send emails about the abnormal situation to the user's and their relatives' email addresses.

[0028] A health alert system based on a smartwatch includes:

[0029] The data acquisition module is used to collect and upload user data;

[0030] The role login module is used to verify role account information and open the corresponding functional modules based on the role account information;

[0031] The family care module synchronizes the target user's data and suggestions to their relatives.

[0032] The database is used to store user data, health advice, and alerts.

[0033] The management planning module analyzes user data to provide health advice and issue early warnings, and connects the data collection module, database, role login module, and family care module.

[0034] The character profile module is used to set character information and connects the character login module and the family care module.

[0035] As a preferred option, the management planning module includes:

[0036] The body management module is used to display body data and analyze the body data to issue early warnings for abnormal body conditions.

[0037] The exercise management module is used to display exercise data and analyze exercise data to issue early warnings for abnormal exercise situations;

[0038] The sleep management module is used to display sleep data and analyze sleep abnormalities based on the data to issue early warnings.

[0039] The health planning module matches health recommendations based on abnormal physical conditions, exercise conditions, and sleep conditions, and connects the body management module, exercise management module, and sleep management module.

[0040] The beneficial effects of this invention are:

[0041] 1. It can collect user data of the target user, filter user data of several other users through filtering conditions, and compare the user data of the target user with the user data of other users to obtain the ranking of the target user;

[0042] 2. It can analyze health status based on body data, exercise data, and sleep data, determine whether there are any abnormalities in the target user, and issue an alert for abnormalities;

[0043] 3. Enables relatives to access the target user's user data, facilitating family care;

[0044] 4. It can match health recommendations based on abnormal situations, thereby improving the abnormal situation of the target user. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of a health warning system based on a smartwatch according to the present invention;

[0046] Figure 2 This invention provides a temperature comparison chart of a health warning system based on a smartwatch.

[0047] Figure 3 This is a flowchart of a health warning method based on a smartwatch according to the present invention;

[0048] In the picture:

[0049] 1. Data Acquisition Module; 2. Management and Planning Module; 21. Body Management Module; 22. Exercise Management Module; 23. Sleep Management Module; 24. Health Planning Module; 3. Role Login Module; 4. Role Profile Module; 5. Family Care Module; 6. Database. Detailed Implementation

[0050] To make the technical problems solved by the present invention, the technical solutions adopted, and the technical effects achieved clearer, the technical solutions of the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, a health alert system based on a smartwatch includes:

[0053] Data acquisition module 1 is used to collect and upload user data, including body data, exercise data, and sleep data, and is connected to management and planning module 2.

[0054] The role login module 3 is used to verify role account information and open the corresponding functional modules according to the role account information, connecting the management planning module 2 and the role profile module 4.

[0055] The Role Profile Module 4 is used to set role information, which includes role account information, basic role information, and family relationship information. Role account information includes username, mobile phone number, password, email, etc., while basic role information includes name, gender, age, location, etc. Family relationship information includes children's account information, parents' account information, guardians' account information, etc. It connects the Role Login Module 3 and the Family Care Module 5.

[0056] The management planning module 2 analyzes data based on body data, exercise data, and sleep data to provide suggestions. The management planning module 2 includes a body management module 21, an exercise management module 22, a sleep management module 23, and a health planning module 24, and connects to the data acquisition module 1, the database 6, the role login module 3, and the family care module 5.

[0057] The Body Management Module 21 is used to display body data and provide early warnings based on the analysis of body data for abnormal body conditions; the Exercise Management Module 22 is used to display exercise data and provide early warnings based on the analysis of exercise data for abnormal exercise conditions; the Sleep Management Module 23 is used to display sleep data and provide early warnings based on the analysis of sleep data for abnormal sleep conditions; the Health Planning Module 24 matches health suggestions based on abnormal body conditions, abnormal exercise conditions, and abnormal sleep conditions, and connects the Body Management Module 21, Exercise Management Module 22, and Sleep Management Module 23.

[0058] The Relative Care Module 5 synchronizes the target user's data and suggestions to the user's children, parents, and guardians, making it convenient for relatives to access the target user's data and suggestions. It also connects to the Role Profile Module 4 and the Management Planning Module 2.

[0059] Database 6 is used to store users' body data, exercise data, and sleep data, along with corresponding analysis suggestions. It can also filter other users' body data, exercise data, and sleep data based on filtering criteria and connects to the management and planning module 2.

[0060] Data acquisition module 1 is used to collect body data, exercise data, and sleep data, and connects to management and planning module 2. Data acquisition module 1 can be a fixed detection device (such as a blood pressure monitor, pulse oximeter, etc.) or a mobile detection device (such as a smartwatch). It periodically collects the user's body data, exercise data, and sleep data through either the fixed or mobile device. Body data includes heart rate, blood pressure, blood oxygen, and temperature; exercise data includes exercise duration, steps, and cadence; and sleep data includes sleep duration and single sleep duration. After collecting the body data, exercise data, and sleep data, data acquisition module 1 transmits the information to management and planning module 2 via a wireless network for storage.

[0061] The Role Profile Module 4 is used to set role account information, basic role information, and kinship information. Role account information includes username, mobile phone number, password, and email address. Basic role information includes name, gender, age, and location. Kinship information includes child account information, parent account information, and guardian account information. It connects the Role Login Module 3 and the Kinship Care Module 5. Users set their role account information, basic role information, and kinship information in the Role Profile Module 4. By setting the mobile phone number for the role account information, the user can call the SMS interface to receive SMS notifications; by setting the email address, the user can call the email interface to receive email notifications; and by setting the password, the user can change the password for login via username or mobile phone number. By setting gender, age, and location, the user can filter other user data in Database 6 based on gender, age, and / or location to analyze the user's situation and obtain corresponding suggestions. The kinship information module allows users to set child, parent, and guardian account information to establish kinship relationships, enabling children, parents, and guardians to view the target user's data and suggestions in the Kinship Care Module 5.

[0062] The role login module 3 verifies role account information and unlocks corresponding functional modules based on that information, connecting to the role profile module 4 and the management planning module 2. When logging into the system using an account and password, users enter their username, email address or mobile phone number, and the corresponding password, submitting this information to the server. The server retrieves the password based on the username, email address, or mobile phone number. If the passwords match, login verification is successful, and the corresponding functional modules, such as the role profile module 4 and the management planning module 2, are unlocked. Login information is also cached for future logins without requiring further verification. If the passwords differ, login verification fails, and the user is prompted to re-enter their role account information via the front-end page. When logging in using a mobile phone number and verification code, the front-end verifies the phone number format after input. If the format is correct, the "Get Verification Code" button's click event is enabled. If the format is incorrect, the user is prompted to re-enter their phone number, and the "Get Verification Code" button's click event is disabled. Clicking the "Get Verification Code" button triggers an SMS verification code sent to the phone number via an API call. If the entered verification code is correct, the user logs in to the system; otherwise, an error message is displayed and the user is not logged in. The front-end validates the phone number format, preventing users from clicking the "Get Verification Code" button if the format is incorrect, thus reducing the number of SMS API calls.

[0063] The Management Planning Module 2 analyzes body data, exercise data, and sleep data to generate recommendations. It includes a Body Management Module 21, an Exercise Management Module 22, a Sleep Management Module 23, and a Health Planning Module 24, and connects to the Data Acquisition Module 1, the Database 6, the Role Login Module 3, and the Family Care Module 5. The Management Planning Module 2 acquires user data, analyzes user conditions based on this data, and generates corresponding recommendations. Specifically, the Body Management Module 21 analyzes body data to provide health alerts; the Exercise Management Module 22 analyzes exercise data to provide exercise alerts; the Sleep Management Module 23 analyzes sleep data to provide sleep alerts; and the Health Planning Module 24 analyzes body data, exercise data, and sleep data to match health status with appropriate health recommendations.

[0064] The Body Management Module 21 is used to display body data and analyze body conditions based on this data to provide early warnings. Body data includes heart rate, blood pressure, blood oxygen, and temperature. It filters users' body data for comparison using one or more criteria such as gender, age, and / or location. Several line charts are used to display the body data, comparative body data, and standard body data. The line charts use the collection time as the X-axis and the numerical value as the Y-axis to display the heart rate, blood pressure, blood oxygen, and temperature data, respectively. Figure 2The image shows a temperature map of 26-year-old women within a 10-kilometer radius. Furthermore, the body data was analyzed based on physical data, comparative body data, and standard body data, such as... Figure 2 As shown, there are four data collection time periods. For example, in the first data collection time period, T 用户 <T 标准 <T 对比 Then the user temperature T 用户 Less than standard temperature T 标准 Comparison temperature T 对比 Greater than standard temperature T 标准 Calculate the ratio 'a' between the user temperature and the standard temperature. If ratio 'a' is within the normal range, the analysis result indicates the user's temperature is normal; otherwise, the analysis result indicates the user's temperature is abnormal. Other body data are analyzed in the same way. The analysis results, body data, and ratios for each instance are statistically analyzed. Based on the analysis results, the body condition is judged. If the number of consecutive occurrences exceeds a threshold, the body condition is determined to be abnormal. The thresholds for heart rate, blood pressure, blood oxygen, and temperature may be the same or different. When a health condition is abnormal, a warning image and text reminder are displayed in the body management module 21, along with the body data and its collection time. Simultaneously, SMS and email interfaces are used to send a message about the abnormal health condition to the user and their relatives' mobile phones and email addresses, synchronizing the relevant information to the family care module 5.

[0065] The exercise management module 22 is used to display exercise data and analyze exercise status based on the data to provide exercise alerts. Exercise data includes exercise duration, steps, and cadence. It filters the exercise data of several users using one or more filtering criteria, such as gender, age, and / or location. The module calculates the user's exercise ranking among the filtered users based on exercise duration and the comparison exercise duration. It then compares exercise duration and steps with corresponding thresholds to determine if the exercise meets the standards. If the exercise duration is greater than or equal to the exercise duration threshold and the steps are greater than or equal to the steps threshold, the user's exercise meets the standards; otherwise, the user's exercise does not meet the standards. The exercise duration threshold and steps threshold are configured. Finally, it compares cadence with a cadence threshold range to determine if the cadence is abnormal. If the cadence is within the cadence threshold range, the cadence is normal; otherwise, the cadence is abnormal. When exercise is not up to standard and / or cadence is abnormal, the exercise management module 22 displays a warning image and text reminder, and displays exercise data and its collection time. At the same time, it calls the SMS interface and email interface to send messages about exercise not up to standard and / or cadence abnormality to the mobile phones and email addresses of the user and their relatives, and synchronizes the relevant information to the relatives care module 5.

[0066] The sleep management module 23 is used to display sleep data and analyze sleep status based on the data to issue sleep warnings. Sleep data includes sleep duration, single sleep duration, etc. Several users' sleep data are compared using one or more filtering criteria, such as gender, age, and / or location. The user's sleep ranking among the selected users is calculated based on sleep duration and the compared sleep duration. Sleep quality is assessed using single sleep duration and heart rate. Sleep is considered abnormal when the single sleep duration is less than a sleep duration threshold and the heart rate is greater than a sleep heart rate threshold; otherwise, sleep is considered normal. When sleep is abnormal, the sleep management module 23 displays a warning image and text reminder, along with the sleep data and its collection time. Simultaneously, it calls SMS and email interfaces to send a sleep abnormality message to the user and their relatives' mobile phones and email addresses, and synchronizes relevant information to the family care module 5.

[0067] The health planning module 24 analyzes health status based on body data, exercise data, and sleep data to match health recommendations. Module 24 sets several health recommendations, using any deficiencies or abnormalities in body health, exercise, and sleep as matching criteria. After successfully matching several recommendations, it outputs them as a PDF document, provides a download function, and sends the PDF document to the user and their relatives' email addresses. Simultaneously, relevant information is synchronized to the family care module 5.

[0068] The Family Care Module 5 synchronizes the target user's data and suggestions with the user's children, parents, and guardians, making it easy for relatives to access the target user's data and suggestions. Within the Family Care Module 5, relevant users (e.g., parents) can be selected to access the user data and analysis results from the user's Management Planning Module 2.

[0069] Database 6 stores users' body data, exercise data, and sleep data, along with corresponding analysis suggestions. It also allows filtering of other users' body data, exercise data, and sleep data based on selection criteria. Database 6 comprises a cache database 6 and a cloud database 6. Login information is stored in the cache database 6, while user data is stored in the cloud database 6. Database 6 is stored on a server.

[0070] Example 2

[0071] like Figure 3 As shown, a health alert method based on a smartwatch includes the following steps:

[0072] S1. The data acquisition terminal collects and uploads user data and configures role information;

[0073] S2. Synchronize user data to relatives based on role information;

[0074] S3. Determine whether there are any abnormal situations with users based on user data;

[0075] S4. If a user has an abnormal situation, health advice will be matched and an alert will be issued based on the abnormal situation and role information.

[0076] In step S1, user body data, exercise data, and sleep data are periodically collected using fixed or mobile detection devices. Body data includes heart rate, blood pressure, blood oxygen, and temperature; exercise data includes exercise duration, steps, and cadence; and sleep data includes sleep duration and single sleep duration. After collecting the body, exercise, and sleep data, the data collection module 1 transmits the information to the management and planning module 2 via a wireless network for storage. Role information includes role account information, basic role information, and kinship information. Role account information includes username, mobile phone number, password, and email address; basic role information includes name, gender, age, and location; and kinship information includes child account information, parent account information, and guardian account information. Users set their role account information, basic role information, and kinship information in the role profile module 4. By setting the mobile phone number for the role account information, the user can access the SMS interface to receive SMS notifications; by setting the email address, the user can access the email interface to receive email notifications; and by setting the password, the user can change the password used to log in via username or mobile phone number. By setting gender, age, and location, the system filters other user data in database 6 based on gender, age, and / or location, so as to analyze the user's situation through other user data and obtain corresponding suggestions; in the kinship information, the system sets up account information for children, parents, and guardians to establish kinship relationships, so that children, parents, and guardians can access the target user's data and suggestions in the kinship care module 5.

[0077] In step S2, the family care module 5 synchronizes the target user's data and suggestions to the user's children, parents, and guardians, making it convenient for relatives to access the target user's data and suggestions. Within the family care module 5, relevant users (e.g., parents) can be selected to access the user data and analysis results in the user's management planning module 2.

[0078] In step S3, the user is assessed for any abnormalities based on user data, including: analyzing physical abnormalities based on body data; analyzing movement abnormalities based on exercise data; and analyzing sleep abnormalities based on sleep data.

[0079] The analysis of abnormal physical conditions based on body data includes: calculating the ratio of body data to standard body data. If the ratio is within the normal range, the analysis result is normal; otherwise, the analysis result is abnormal. If the number of consecutive abnormal analysis results exceeds the threshold, the user's body is abnormal; otherwise, the user's body is normal. Among them, the threshold values ​​for heart rate, blood pressure, blood oxygen, and temperature may be the same or different.

[0080] Based on motion data analysis, abnormal motion situations are identified, including: if the motion duration is greater than or equal to the motion duration threshold, the number of steps is greater than or equal to the number of steps threshold, and the step frequency is within the step frequency threshold range, then the user's motion is normal; otherwise, the user's motion is abnormal.

[0081] Sleep abnormalities are analyzed based on sleep data, including: if the duration of a single sleep episode is less than the sleep duration threshold and the heart rate is greater than the sleep heart rate threshold, the user's sleep is abnormal; otherwise, the user's sleep is normal.

[0082] In step S4, health recommendations are matched based on abnormal conditions and role information. This includes matching several health recommendations based on physical abnormalities, movement abnormalities, and / or sleep abnormalities as matching conditions. These recommendations are then compiled and output as a PDF document, which is sent to the user and their relatives' email addresses. Simultaneously, the relevant information is synchronized to the family care module 5.

[0083] The system provides alerts based on anomalies and role information, including: displaying alert images and text reminders, with the text reminders including user data and its collection time; sending SMS messages about the anomalies to the user's and their relatives' mobile phones via the SMS interface; and sending emails about the anomalies to the user's and their relatives' email addresses via the email interface.

[0084] It can collect user data of the target user, filter user data of several other users through filtering conditions, compare the user data of the target user with the user data of other users to obtain the target user's ranking; it can analyze health status based on body data, exercise data and sleep data, determine whether there are abnormal conditions of the target user and issue abnormal warnings; it can allow relatives to view the user data of the target user to realize relatives' care; it can match health suggestions based on abnormal conditions, thereby improving the abnormal conditions of the target user.

[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0090] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart watch-based health warning method, characterized in that, Includes the following steps: S1. The data acquisition terminal collects and uploads user data and configures role information; S2. Synchronize user data to relatives based on role information; S3. Determine whether there are any abnormal situations with users based on user data; S4. If a user has an abnormal situation, health advice will be matched and an alert will be issued based on the abnormal situation and role information. 2.The smart watch-based health warning method of claim 1, wherein, User data includes body data, exercise data, and sleep data. Body data includes heart rate, blood pressure, blood oxygen, and temperature. Exercise data includes exercise duration, steps, and cadence. Sleep data includes sleep duration and single sleep duration. 3.The smart watch-based health warning method of claim 1 or 2, wherein, Determining whether a user exhibits abnormal behavior based on user data includes: Analyze abnormal physical conditions based on body data; Analyze abnormal motion conditions based on motion data; Analyze sleep abnormalities based on sleep data. 4.The smart watch-based health warning method of claim 3, wherein, Analysis of abnormal physical conditions based on body data includes: Calculate the ratio of body data to standard body data. If the ratio is within the normal range, the analysis result is normal; otherwise, the analysis result is abnormal. If the number of consecutive analysis results showing abnormality exceeds the threshold, then the user's health is abnormal; otherwise, the user's health is normal. Among them, the threshold values ​​for heart rate, blood pressure, blood oxygen, and temperature may be the same or different. 5.The health early warning method based on smart watch of claim 3, wherein, Analysis of abnormal motion patterns based on motion data includes: If the exercise duration is greater than or equal to the exercise duration threshold, the number of steps is greater than or equal to the number of steps threshold, and the step frequency is within the step frequency threshold range, then the user's exercise is normal; otherwise, the user's exercise is abnormal. 6.The health early warning method based on smart watch of claim 3, wherein, Sleep abnormalities are analyzed based on sleep data, including: If a single sleep episode lasts less than the sleep duration threshold and the heart rate is greater than the sleep heart rate threshold, the user's sleep is abnormal; otherwise, the user's sleep is normal.

7. A health warning method based on a smartwatch according to claim 1, characterized in that, Health recommendations are matched based on abnormal situations and role information, including: Based on physical abnormalities, movement abnormalities, and / or sleep abnormalities as matching criteria, several corresponding health recommendations are matched, and these recommendations are compiled and output as a PDF document, which is then sent to the user and their relatives' email addresses.

8. A health warning method based on a smartwatch according to claim 1 or 7, characterized in that, Warnings are issued based on abnormal situations and role information, including: Display warning images and text alerts. The text alerts include user data and its collection time. Call the SMS interface to send SMS messages about the abnormal situation to the user's and their relatives' mobile phones, and call the email interface to send emails about the abnormal situation to the user's and their relatives' email addresses.

9. A health warning system based on a smartwatch, applicable to the health warning method based on a smartwatch as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect and upload user data; The role login module is used to verify role account information and open the corresponding functional modules based on the role account information; The family care module synchronizes the target user's data and suggestions to their relatives. The database is used to store user data, health advice, and alerts. The management planning module analyzes user data to provide health advice and issue early warnings, and connects the data collection module, database, role login module, and family care module. The character profile module is used to set character information and connects the character login module and the family care module.

10. A health warning system based on a smartwatch according to claim 9, characterized in that, The management planning module includes: The body management module is used to display body data and analyze the body data to issue early warnings for abnormal body conditions. The exercise management module is used to display exercise data and analyze exercise data to issue early warnings for abnormal exercise situations; The sleep management module is used to display sleep data and analyze sleep abnormalities based on the data to issue early warnings. The health planning module matches health recommendations based on abnormal physical conditions, exercise conditions, and sleep conditions, and connects the body management module, exercise management module, and sleep management module.

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