Sports data management method and system based on smart watch and medium

Abstract

The invention provides a motion data management method and system based on a smart watch and a medium, and belongs to the technical field of smart wearable devices and big data. The method comprises the following steps: acquiring user setting information and data monitoring information, extracting exercise rule data according to the user setting information, obtaining recommended exercise information, extracting health state data according to the data monitoring information, obtaining a health index, and correcting in combination with user physical examination information, so as to obtain a health correction index; judging whether the health state of the user meets the requirement or not, obtaining recommended exercise correction information according to the health correction index in combination with the recommended exercise information, obtaining exercise completion degree data corresponding to the recommended exercise correction information, and obtaining an exercise completion rating. According to the method, data of multiple dimensions are integrated, personalized exercise recommendation and health state evaluation are provided for the user, high scientificity, accuracy and practicability are achieved, and the health level and life quality of the user can be improved.

Description

Technical Field

[0001] The present application relates to the field of smart wearable devices and big data technology, and more specifically, to a sports data management method, system and medium based on a smart watch. Background Art

[0002] With the popularization of computer technology, sports data began to be recorded in digital form, such as using spreadsheets, databases and other tools to store and manage data. Digital records improve the accuracy and storability of data, but there are still difficulties in data analysis, and it is impossible to quickly and effectively analyze and process massive data. With smart watches, sports data can be obtained anytime and anywhere, and the analysis results can be displayed in a timely manner.

[0003] Although current smart watches can obtain exercise data in a timely manner, they do not reflect the current exercise status and physical health of the wearer in a timely manner in terms of data processing. There is a lack of a technical method that can highlight personalized exercise by making exercise recommendations and analyzing physical health levels according to different wearers.

[0004] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0005] The purpose of this application is to provide a method, system and medium for sports data management based on a smart watch, which can obtain user setting information and data monitoring information, extract sports law data according to the user setting information, process and obtain recommended sports information, extract health status data according to the data monitoring information, process and obtain health index and correct it in combination with user physical examination information, obtain health correction index, compare it with the preset health index threshold, judge whether the user's health status meets the requirements, obtain recommended sports correction information according to the health correction index combined with recommended sports information, obtain sports completion data corresponding to the recommended sports correction information, and obtain completed sports rating. This application integrates data from multiple dimensions to provide users with personalized sports recommendations and health status assessments, which is highly scientific, accurate and practical, and helps to improve the health level and quality of life of users.

[0006] The present application provides a method for managing sports data based on a smart watch, comprising the following steps: Acquire user setting information and data monitoring information, extract exercise pattern data according to the user setting information, and extract health status data according to the data monitoring information; Processing the motion law data through a preset motion prediction database to obtain recommended motion information; Processing the health status data through a preset health assessment model to obtain a health index and correcting it in combination with the user's physical examination information to obtain a health correction index; Comparing the health correction index with a preset health index threshold, determining whether the user's health status meets the requirements; Obtaining recommended exercise correction information according to the health correction index combined with the recommended exercise information; The motion completion data corresponding to the recommended motion correction information is acquired to obtain a completed motion rating.

[0007] Among them, in the sports data management method based on a smart watch described in the present application, the sports law data is processed through a preset sports prediction database to obtain recommended sports information, specifically: Extracting exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data according to the exercise law data; The exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data are processed through a preset exercise prediction database to obtain recommended exercise information.

[0008] Among them, in the sports data management method based on a smart watch described in the present application, the health status data is processed by a preset health assessment model to obtain a health index and corrected in combination with the user's physical examination information to obtain a health correction index, specifically: Extracting heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data according to the health status data; The health index is obtained by processing the heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data through a preset health assessment model; Obtain user physical examination information to extract critical illness characteristic data and congenital defect characteristic data; The health index is corrected according to the serious disease characteristic data and congenital defect data through a preset individual health model to obtain a health correction index.

[0009] Among them, in the sports data management method based on a smart watch described in the present application, the comparison between the health correction index and the preset health index threshold is used to determine whether the user's health status meets the requirements, specifically: Comparing the health correction index with a preset health correction index threshold to obtain a correction index deviation rate; comparing the correction index deviation rate with a preset correction index deviation rate threshold; If the correction index deviation rate is less than the correction index deviation rate threshold, the user's health status meets the requirements; If the correction index deviation rate is greater than or equal to the correction index deviation rate threshold, the user's health status does not meet the requirements.

[0010] Among them, in the sports data management method based on a smart watch described in the present application, the recommended sports correction information is obtained according to the health correction index combined with the recommended sports information, specifically: According to the health correction index and the recommended exercise information, a query is performed through a preset exercise correction database to obtain recommended exercise correction information; The recommended exercise information includes recommended exercise type data, recommended exercise amount data, recommended exercise duration data, recommended exercise intensity data and recommended exercise interval data; The recommended motion correction information includes correction motion type data, correction motion amount data, correction motion duration data, correction motion intensity data and correction motion interval data.

[0011] Among them, in the sports data management method based on a smart watch described in the present application, the acquisition of the sports completion data corresponding to the recommended sports correction information and the acquisition of the completed sports rating are specifically: Acquiring movement completion data corresponding to the recommended movement correction information; Comparing the exercise completion data with a preset first exercise completion threshold and a second exercise completion threshold respectively; The first movement completion threshold is less than the second movement completion threshold; If the exercise completion data is less than the first exercise completion threshold, an unsatisfactory rating is obtained; If the exercise completion data is greater than or equal to the first exercise completion threshold and less than the second exercise completion threshold, a compliance rating is obtained; If the exercise completion data is greater than or equal to the second exercise completion threshold, a perfect rating is obtained.

[0012] In a second aspect, the present application provides a sports data management system based on a smart watch, the system comprising: a memory and a processor, the memory comprising a program of a sports data management method based on a smart watch, the program of the sports data management method based on a smart watch being executed by the processor to implement the following steps: Acquire user setting information and data monitoring information, extract exercise pattern data according to the user setting information, and extract health status data according to the data monitoring information; Processing the motion law data through a preset motion prediction database to obtain recommended motion information; Processing the health status data through a preset health assessment model to obtain a health index and correcting it in combination with the user's physical examination information to obtain a health correction index; Comparing the health correction index with a preset health index threshold, determining whether the user's health status meets the requirements; Obtaining recommended exercise correction information according to the health correction index combined with the recommended exercise information; The motion completion data corresponding to the recommended motion correction information is acquired to obtain a completed motion rating.

[0013] Among them, in a sports data management system based on a smart watch described in the present application, the sports law data is processed through a preset sports prediction database to obtain recommended sports information, specifically: Extracting exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data according to the exercise law data; The exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data are processed through a preset exercise prediction database to obtain recommended exercise information.

[0014] Among them, in a sports data management system based on a smart watch described in the present application, the health status data is processed by a preset health assessment model to obtain a health index and corrected in combination with the user's physical examination information to obtain a health correction index, specifically: Extracting heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data according to the health status data; The health index is obtained by processing the heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data through a preset health assessment model; Obtain user physical examination information to extract critical illness characteristic data and congenital defect characteristic data; The health index is corrected according to the serious disease characteristic data and congenital defect data through a preset individual health model to obtain a health correction index.

[0015] In a third aspect, the present application also provides a computer-readable storage medium, which includes a sports data management method program based on a smart watch. When the sports data management method program based on a smart watch is executed by a processor, the steps of the sports data management method based on a smart watch as described in any one of the above items are implemented.

[0016] As can be seen from the above, the embodiment of the present application provides a sports data management method, system and medium based on a smart watch, by obtaining user setting information and data monitoring information, extracting sports law data according to the user setting information, including sports type data, sports volume data, sports duration data, sports intensity data and sports interval data, extracting health status data according to the data monitoring information, including heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data, processing the sports law data through a preset sports prediction database to obtain recommended sports information, processing the health status data through a preset health assessment model to obtain a health index, extracting serious disease feature data and congenital defect data according to the user's physical examination information, correcting the health index through a preset individual health model, obtaining a health correction index, comparing it with a preset health index threshold, judging whether the user's health status meets the requirements, and obtaining recommended sports correction information according to the health correction index combined with the recommended sports information, including corrected sports type data, corrected sports volume data, corrected sports duration data, corrected sports intensity data and corrected sports interval data, obtaining sports completion data corresponding to the recommended sports correction information, and obtaining a completed sports rating. This application provides users with scientific and accurate exercise recommendations and health assessments. By comprehensively considering multiple health factors, the accuracy of health assessments can be improved. It can promptly detect changes in users' health and provide users with timely health warnings. It can dynamically adjust recommended exercise correction information to ensure that exercise recommendations always match the user's health status, thereby achieving the purpose of personalized exercise based on individual health.

[0017] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flowchart of a sports data management method based on a smart watch provided in an embodiment of the present application; Figure 2 A flowchart of obtaining recommended exercise information in a method for managing exercise data based on a smart watch provided in an embodiment of the present application; Figure 3 A flowchart of obtaining a health correction index for a method for managing sports data based on a smart watch provided in an embodiment of the present application; Figure 4 A flowchart of a method for managing sports data based on a smart watch provided in an embodiment of the present application for determining whether a user's health status meets the requirements; Figure 5 A flowchart of obtaining recommended motion correction information in a motion data management method based on a smart watch provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0021] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0022] Please refer to Figure 1 , Figure 1 1 is a flow chart of a method for managing sports data based on a smart watch in some embodiments of the present application. The method for managing sports data based on a smart watch is used in a terminal device, such as a computer, a mobile phone terminal, etc. The method for managing sports data based on a smart watch comprises the following steps: S101, obtaining user setting information and data monitoring information, extracting exercise pattern data according to the user setting information, and extracting health status data according to the data monitoring information; S102, processing the motion law data through a preset motion prediction database to obtain recommended motion information; S103, processing the health status data through a preset health assessment model to obtain a health index and correcting it in combination with the user's physical examination information to obtain a health correction index; S104, comparing the health correction index with a preset health index threshold to determine whether the user's health status meets the requirements; S105, obtaining recommended exercise correction information according to the health correction index and the recommended exercise information; S106: Acquire motion completion data corresponding to the recommended motion correction information to obtain a completed motion rating.

[0023] Among them, the present application obtains user setting information and data monitoring information, extracts exercise pattern data according to the user setting information, including exercise type data, exercise volume data, exercise duration data, exercise intensity data and exercise interval data, extracts health status data according to the data monitoring information, including heart rate rhythm data, blood pressure range data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data, processes the exercise pattern data through a preset exercise prediction database to obtain recommended exercise information, processes the health status data through a preset health assessment model to obtain a health index, extracts serious disease characteristic data and congenital defect data according to the user's physical examination information, corrects the health index through a preset individual health model to obtain a health correction index, compares it with a preset health index threshold to determine whether the user's health status meets the requirements, obtains recommended exercise correction information according to the health correction index combined with the recommended exercise information, including corrected exercise type data, corrected exercise volume data, corrected exercise duration data, corrected exercise intensity data and corrected exercise interval data, obtains exercise completion data corresponding to the recommended exercise correction information, and obtains a completed exercise rating. This application provides users with scientific and accurate exercise recommendations and health assessments. By comprehensively considering multiple health factors, the accuracy of health assessments can be improved. It can promptly detect changes in users' health and provide users with timely health warnings. It can dynamically adjust recommended exercise correction information to ensure that exercise recommendations always match the user's health status, thereby achieving the purpose of personalized exercise based on individual health.

[0024] Please refer to Figure 2 , Figure 2 The following is a flowchart of a method for obtaining recommended exercise information based on a smartwatch exercise data management method in some embodiments of the present application. According to an embodiment of the present invention, the exercise law data is processed through a preset exercise prediction database to obtain recommended exercise information, specifically: S201, extracting exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data according to the exercise law data; S202: Process the exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data through a preset exercise prediction database to obtain recommended exercise information.

[0025] Among them, in order to provide personalized running recommendation information, exercise type data, exercise volume data, exercise duration data, exercise intensity data and exercise interval data are extracted according to the exercise law data. The exercise type is usually judged based on the user's activity pattern, movement characteristics and the built-in sensor data of the smart watch (such as accelerometer, gyroscope, etc.). The exercise volume usually refers to the total amount of exercise completed by the user in a certain period of time, which can be measured by indicators such as step number, distance, and calorie consumption. Exercise duration refers to the time the user lasts for a certain exercise. Exercise intensity can be evaluated by indicators such as heart rate, speed, and power. Exercise interval refers to the rest time or low-intensity activity time between continuous exercises of the user, and then processed through the preset exercise prediction database to obtain recommended exercise information. The information in the database can help analyze the user's exercise habits, physical condition and exercise goals, thereby providing personalized exercise recommendations.

[0026] Please refer to Figure 3 , Figure 3 This is a flow chart of obtaining a health correction index in a method for managing sports data based on a smartwatch in some embodiments of the present application. According to an embodiment of the present invention, the health status data is processed through a preset health assessment model to obtain a health index and corrected in combination with the user's physical examination information to obtain a health correction index, specifically: S301, extracting heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data according to the health status data; S302, processing the heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data through a preset health assessment model to obtain a health index; S303, obtaining the user's physical examination information to extract serious disease characteristic data and congenital defect characteristic data; S304. Correct the health index according to the serious disease characteristic data and the congenital defect data through a preset individual health model to obtain a health correction index.

[0027] Among them, in order to analyze the exercise suitable for the user, it is necessary to first understand the user's physical condition, and extract heart rate rhythm data, blood pressure range data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data based on the health status data. Heart rate rhythm data usually includes information such as heart beats per minute (BPM) and heart rate variability. Blood pressure range data refers to the values ​​of systolic and diastolic blood pressure, and classifies blood pressure into normal, high, high blood pressure and other different ranges according to the standard. Blood oxygen saturation data refers to the percentage of oxygen content in the blood. Blood sugar index data represents blood sugar concentration data. Sleep quality data includes sleep duration, Data such as deep sleep ratio, REM (rapid eye movement) sleep cycle, and body temperature data including daily body temperature changes, whether there is a fever, etc. are processed through a preset health assessment model to obtain a health index, and the user's physical examination information is obtained to extract serious disease characteristic data and congenital defect characteristic data. Serious disease characteristic data refers to characteristic data related to major diseases (such as cancer, cardiovascular disease, etc.), and congenital defect data refers to information on known congenital diseases or defects. This information can be obtained through a database or entered by the user himself, and then the health index is corrected through a preset individual health model to obtain a health correction index; The calculation formulas of the health assessment model and the individual health model are: ; ; in, is the health index, is the health correction index, They are heart rate data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data. They are serious disease characteristic data and congenital defect characteristic data, It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset sports health database).

[0028] Please refer to Figure 4 , Figure 4 The following is a flowchart of a method for managing sports data based on a smartwatch in some embodiments of the present application for determining whether the user's health status meets the requirements. According to an embodiment of the present invention, the comparison between the health correction index and a preset health index threshold is used to determine whether the user's health status meets the requirements, specifically: S401, comparing the health correction index with a preset health correction index threshold to obtain a correction index deviation rate; S402, comparing the correction index deviation rate with a preset correction index deviation rate threshold; S403: If the correction index deviation rate is less than the correction index deviation rate threshold, the user's health status meets the requirement; S404: If the correction index deviation rate is greater than or equal to the correction index deviation rate threshold, the user's health status does not meet the requirement.

[0029] Among them, in order to determine whether the user's health status meets the requirements, the health correction index is compared with the preset health correction index threshold to obtain the correction index deviation rate, and compared with the preset correction index deviation rate threshold. If the correction index deviation rate is less than the correction index deviation rate threshold, it means that the difference between the user's health status and the expected health status is within an acceptable range, and the user's health status meets the requirements. If the correction index deviation rate is greater than or equal to the correction index deviation rate threshold, it means that the difference between the user's health status and the expected health status is large, and there may be health risks or further health intervention is required. Therefore, the user's health status does not meet the requirements, and corresponding reminders are required when recommending exercise.

[0030] Please refer to Figure 5 , Figure 5 The flowchart of obtaining recommended exercise correction information of a method for managing exercise data based on a smart watch in some embodiments of the present application is as follows. According to an embodiment of the present invention, obtaining the recommended exercise correction information based on the health correction index combined with the recommended exercise information is specifically: S501, querying a preset motion correction database according to the health correction index combined with the recommended motion information to obtain recommended motion correction information; S502, the recommended exercise information includes recommended exercise type data, recommended exercise amount data, recommended exercise duration data, recommended exercise intensity data and recommended exercise interval data; S503: The recommended motion correction information includes correction motion type data, correction motion amount data, correction motion duration data, correction motion intensity data, and correction motion interval data.

[0031] Among them, in order to make exercise recommendations more accurately, the recommended exercise correction information is obtained by querying the preset exercise correction database according to the health correction index combined with the recommended exercise information. The exercise correction database is a database containing various exercise correction information. It provides personalized exercise correction suggestions based on the user's health correction index and recommended exercise information. The recommended exercise information includes recommended exercise type data, recommended exercise amount data, recommended exercise duration data, recommended exercise intensity data and recommended exercise interval data. The recommended exercise correction information includes corrected exercise type data, corrected exercise amount data, corrected exercise duration data, corrected exercise intensity data and corrected exercise interval data. The above method fully considers the user's health status to appropriately increase or decrease the amount of exercise, adjusts the exercise intensity to ensure that the user exercises within a safe range, and also adjusts the exercise duration and exercise interval considering the user's physical fitness.

[0032] According to an embodiment of the present invention, the acquiring of the motion completion data corresponding to the recommended motion correction information to obtain the completed motion rating is specifically as follows: Acquiring movement completion data corresponding to the recommended movement correction information; Comparing the exercise completion data with a preset first exercise completion threshold and a second exercise completion threshold respectively; The first movement completion threshold is less than the second movement completion threshold; If the exercise completion data is less than the first exercise completion threshold, an unsatisfactory rating is obtained; If the exercise completion data is greater than or equal to the first exercise completion threshold and less than the second exercise completion threshold, a compliance rating is obtained; If the exercise completion data is greater than or equal to the second exercise completion threshold, a perfect rating is obtained.

[0033] Among them, in order to evaluate the user's exercise completion, the exercise completion data corresponding to the recommended exercise correction information is obtained, and compared with the preset first exercise completion threshold and the second exercise completion threshold respectively. The first exercise completion threshold is less than the second exercise completion threshold. If the exercise completion data is less than the first exercise completion threshold, it means that the user has not met the basic exercise requirements and obtains an unsatisfactory rating. If the exercise completion data is greater than or equal to the first exercise completion threshold and less than the second exercise completion threshold, it means that the user has met the basic exercise requirements but has not fully reached the recommended amount of exercise, so a "satisfactory rating" is given. If the exercise completion data is greater than or equal to the second exercise completion threshold, it means that the user has fully or exceeded the recommended amount of exercise, so a "perfect rating" is given.

[0034] According to an embodiment of the present invention, it also includes: Obtain watch system operation information, extract screen switching smoothness data, battery life data and information collection accuracy data; Processing the screen switching smoothness data, the battery durability data, and the information collection accuracy data through a preset device evaluation model to obtain a watch performance index; comparing the watch performance index with a preset watch performance index threshold; If the watch performance index is greater than or equal to the watch performance index threshold, the smart watch performance meets the requirements; If the watch performance index is less than the watch performance index threshold, the smart watch performance does not meet the requirements; The calculation formula of the equipment evaluation model is: ; in, is the watch performance index, They are screen switching smoothness data, battery life data and information collection accuracy data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset watch performance database).

[0035] Among them, in order to improve the performance of smart watches, obtain watch system operation information, extract screen switching smoothness data, battery life data and information collection accuracy data, it is possible to simulate user operations, record screen switching parameters such as time and frame rate, and thus obtain screen switching smoothness data, it is possible to simulate daily usage scenarios, record the battery consumption of smart watches under different usage intensities, and thus obtain battery life data, it is possible to compare the data collected by the smart watch with standard data (such as heart rate, number of steps, etc.), and thus obtain information collection accuracy data, the screen switching smoothness data, battery life data and information collection accuracy data are processed through a preset device evaluation model to obtain a watch performance index, and then compared with a preset watch performance index threshold, if the watch performance index is greater than or equal to the watch performance index threshold, the smart watch performance meets the requirements, if the watch performance index is less than the watch performance index threshold, the smart watch performance does not meet the requirements.

[0036] According to an embodiment of the present invention, it also includes: Extracting remaining power data according to the watch system operation information; Comparing the remaining power data with a preset power threshold; If the remaining power data is less than or equal to the power threshold, a low power warning message is sent; If the remaining power data is greater than the power threshold, the remaining power is sufficient.

[0037] Among them, in order to avoid the situation where the watch automatically shuts down during exercise due to low battery, the remaining power data is extracted according to the watch system operation information, and the remaining power data is compared with the preset power threshold. If the remaining power data is less than or equal to the power threshold, a low power warning message is sent. If the remaining power data is greater than the power threshold, the remaining power is sufficient.

[0038] The present invention also discloses a sports data management system based on a smart watch, comprising a memory and a processor, wherein the memory comprises a sports data management method program based on a smart watch, and when the sports data management method program based on a smart watch is executed by the processor, the following steps are implemented: Acquire user setting information and data monitoring information, extract exercise pattern data according to the user setting information, and extract health status data according to the data monitoring information; Processing the motion law data through a preset motion prediction database to obtain recommended motion information; Processing the health status data through a preset health assessment model to obtain a health index and correcting it in combination with the user's physical examination information to obtain a health correction index; Comparing the health correction index with a preset health index threshold, determining whether the user's health status meets the requirements; Obtaining recommended exercise correction information according to the health correction index and the recommended exercise information; The motion completion data corresponding to the recommended motion correction information is acquired to obtain a completed motion rating.

[0039] Among them, the present application obtains user setting information and data monitoring information, extracts exercise pattern data according to the user setting information, including exercise type data, exercise volume data, exercise duration data, exercise intensity data and exercise interval data, extracts health status data according to the data monitoring information, including heart rate rhythm data, blood pressure range data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data, processes the exercise pattern data through a preset exercise prediction database to obtain recommended exercise information, processes the health status data through a preset health assessment model to obtain a health index, extracts serious disease characteristic data and congenital defect data according to the user's physical examination information, corrects the health index through a preset individual health model to obtain a health correction index, compares it with a preset health index threshold to determine whether the user's health status meets the requirements, obtains recommended exercise correction information according to the health correction index combined with the recommended exercise information, including corrected exercise type data, corrected exercise volume data, corrected exercise duration data, corrected exercise intensity data and corrected exercise interval data, obtains exercise completion data corresponding to the recommended exercise correction information, and obtains a completed exercise rating. This application provides users with scientific and accurate exercise recommendations and health assessments. By comprehensively considering multiple health factors, the accuracy of health assessments can be improved. It can promptly detect changes in users' health and provide users with timely health warnings. It can dynamically adjust recommended exercise correction information to ensure that exercise recommendations always match the user's health status, thereby achieving the purpose of personalized exercise based on individual health.

[0040] According to an embodiment of the present invention, the motion law data is processed through a preset motion prediction database to obtain recommended motion information, specifically: Extracting exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data according to the exercise law data; The exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data are processed through a preset exercise prediction database to obtain recommended exercise information.

[0041] Among them, in order to provide personalized running recommendation information, exercise type data, exercise volume data, exercise duration data, exercise intensity data and exercise interval data are extracted according to the exercise law data. The exercise type is usually judged based on the user's activity pattern, movement characteristics and the built-in sensor data of the smart watch (such as accelerometer, gyroscope, etc.). The exercise volume usually refers to the total amount of exercise completed by the user in a certain period of time, which can be measured by indicators such as step number, distance, and calorie consumption. Exercise duration refers to the time the user lasts for a certain exercise. Exercise intensity can be evaluated by indicators such as heart rate, speed, and power. Exercise interval refers to the rest time or low-intensity activity time between continuous exercises of the user, and then processed through the preset exercise prediction database to obtain recommended exercise information. The information in the database can help analyze the user's exercise habits, physical condition and exercise goals, thereby providing personalized exercise recommendations.

[0042] According to an embodiment of the present invention, the health status data is processed by a preset health assessment model to obtain a health index and corrected in combination with the user's physical examination information to obtain a health correction index, specifically: Extracting heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data according to the health status data; The health index is obtained by processing the heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data through a preset health assessment model; Obtain user physical examination information to extract critical illness characteristic data and congenital defect characteristic data; The health index is corrected according to the serious disease characteristic data and congenital defect data through a preset individual health model to obtain a health correction index.

[0043] Among them, in order to analyze the exercise suitable for the user, it is necessary to first understand the user's physical condition, and extract heart rate rhythm data, blood pressure range data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data based on the health status data. Heart rate rhythm data usually includes information such as heart beats per minute (BPM) and heart rate variability. Blood pressure range data refers to the values ​​of systolic and diastolic blood pressure, and classifies blood pressure into normal, high, high blood pressure and other different ranges according to the standard. Blood oxygen saturation data refers to the percentage of oxygen content in the blood. Blood sugar index data represents blood sugar concentration data. Sleep quality data includes sleep duration, Data such as deep sleep ratio, REM (rapid eye movement) sleep cycle, and body temperature data including daily body temperature changes, whether there is a fever, etc. are processed through a preset health assessment model to obtain a health index, and the user's physical examination information is obtained to extract serious disease characteristic data and congenital defect characteristic data. Serious disease characteristic data refers to characteristic data related to major diseases (such as cancer, cardiovascular disease, etc.), and congenital defect data refers to information on known congenital diseases or defects. This information can be obtained through a database or entered by the user himself, and then the health index is corrected through a preset individual health model to obtain a health correction index; The calculation formulas of the health assessment model and the individual health model are: ; ; in, is the health index, is the health correction index, They are heart rate data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data. They are serious disease characteristic data and congenital defect characteristic data, It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset sports health database).

[0044] According to an embodiment of the present invention, comparing the health correction index with a preset health index threshold to determine whether the user's health status meets the requirement is specifically as follows: Comparing the health correction index with a preset health correction index threshold to obtain a correction index deviation rate; comparing the correction index deviation rate with a preset correction index deviation rate threshold; If the correction index deviation rate is less than the correction index deviation rate threshold, the user's health status meets the requirements; If the correction index deviation rate is greater than or equal to the correction index deviation rate threshold, the user's health status does not meet the requirements.

[0045] Among them, in order to determine whether the user's health status meets the requirements, the health correction index is compared with the preset health correction index threshold to obtain the correction index deviation rate, and compared with the preset correction index deviation rate threshold. If the correction index deviation rate is less than the correction index deviation rate threshold, it means that the difference between the user's health status and the expected health status is within an acceptable range, and the user's health status meets the requirements. If the correction index deviation rate is greater than or equal to the correction index deviation rate threshold, it means that the difference between the user's health status and the expected health status is large, and there may be health risks or further health intervention is required. Therefore, the user's health status does not meet the requirements, and corresponding reminders are required when recommending exercise.

[0046] According to an embodiment of the present invention, obtaining the recommended exercise correction information according to the health correction index in combination with the recommended exercise information is specifically: According to the health correction index and the recommended exercise information, a query is performed through a preset exercise correction database to obtain recommended exercise correction information; The recommended exercise information includes recommended exercise type data, recommended exercise amount data, recommended exercise duration data, recommended exercise intensity data and recommended exercise interval data; The recommended motion correction information includes correction motion type data, correction motion amount data, correction motion duration data, correction motion intensity data and correction motion interval data.

[0047] Among them, in order to make exercise recommendations more accurately, the recommended exercise correction information is obtained by querying the preset exercise correction database according to the health correction index combined with the recommended exercise information. The exercise correction database is a database containing various exercise correction information. It provides personalized exercise correction suggestions based on the user's health correction index and recommended exercise information. The recommended exercise information includes recommended exercise type data, recommended exercise amount data, recommended exercise duration data, recommended exercise intensity data and recommended exercise interval data. The recommended exercise correction information includes corrected exercise type data, corrected exercise amount data, corrected exercise duration data, corrected exercise intensity data and corrected exercise interval data. The above method fully considers the user's health status to appropriately increase or decrease the amount of exercise, adjusts the exercise intensity to ensure that the user exercises within a safe range, and also adjusts the exercise duration and exercise interval considering the user's physical fitness.

[0048] According to an embodiment of the present invention, the acquiring of the motion completion data corresponding to the recommended motion correction information to obtain the completed motion rating is specifically as follows: Acquiring movement completion data corresponding to the recommended movement correction information; Comparing the exercise completion data with a preset first exercise completion threshold and a second exercise completion threshold respectively; The first movement completion threshold is less than the second movement completion threshold; If the exercise completion data is less than the first exercise completion threshold, an unsatisfactory rating is obtained; If the exercise completion data is greater than or equal to the first exercise completion threshold and less than the second exercise completion threshold, a compliance rating is obtained; If the exercise completion data is greater than or equal to the second exercise completion threshold, a perfect rating is obtained.

[0049] Among them, in order to evaluate the user's exercise completion, the exercise completion data corresponding to the recommended exercise correction information is obtained, and compared with the preset first exercise completion threshold and the second exercise completion threshold respectively. The first exercise completion threshold is less than the second exercise completion threshold. If the exercise completion data is less than the first exercise completion threshold, it means that the user has not met the basic exercise requirements and obtains an unsatisfactory rating. If the exercise completion data is greater than or equal to the first exercise completion threshold and less than the second exercise completion threshold, it means that the user has met the basic exercise requirements but has not fully reached the recommended amount of exercise, so a "satisfactory rating" is given. If the exercise completion data is greater than or equal to the second exercise completion threshold, it means that the user has fully or exceeded the recommended amount of exercise, so a "perfect rating" is given.

[0050] According to an embodiment of the present invention, it also includes: Obtain watch system operation information, extract screen switching smoothness data, battery life data and information collection accuracy data; Processing the screen switching smoothness data, the battery durability data, and the information collection accuracy data through a preset device evaluation model to obtain a watch performance index; comparing the watch performance index with a preset watch performance index threshold; If the watch performance index is greater than or equal to the watch performance index threshold, the smart watch performance meets the requirements; If the watch performance index is less than the watch performance index threshold, the smart watch performance does not meet the requirements; The calculation formula of the equipment evaluation model is: ; in, is the watch performance index, They are screen switching smoothness data, battery life data and information collection accuracy data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset watch performance database).

[0051] Among them, in order to improve the performance of smart watches, obtain watch system operation information, extract screen switching smoothness data, battery life data and information collection accuracy data, it is possible to simulate user operations, record screen switching parameters such as time and frame rate, and thus obtain screen switching smoothness data, it is possible to simulate daily usage scenarios, record the battery consumption of smart watches under different usage intensities, and thus obtain battery life data, it is possible to compare the data collected by the smart watch with standard data (such as heart rate, number of steps, etc.), and thus obtain information collection accuracy data, the screen switching smoothness data, battery life data and information collection accuracy data are processed through a preset device evaluation model to obtain a watch performance index, and then compared with a preset watch performance index threshold, if the watch performance index is greater than or equal to the watch performance index threshold, the smart watch performance meets the requirements, if the watch performance index is less than the watch performance index threshold, the smart watch performance does not meet the requirements.

[0052] According to an embodiment of the present invention, it also includes: Extracting remaining power data according to the watch system operation information; Comparing the remaining power data with a preset power threshold; If the remaining power data is less than or equal to the power threshold, a low power warning message is sent; If the remaining power data is greater than the power threshold, the remaining power is sufficient.

[0053] Among them, in order to avoid the situation where the watch automatically shuts down during exercise due to low battery, the remaining power data is extracted according to the watch system operation information, and the remaining power data is compared with the preset power threshold. If the remaining power data is less than or equal to the power threshold, a low power warning message is sent. If the remaining power data is greater than the power threshold, the remaining power is sufficient.

[0054] A third aspect of the present invention provides a computer-readable storage medium, which includes a sports data management method program based on a smart watch. When the sports data management method program based on a smart watch is executed by a processor, the steps of the sports data management method based on a smart watch as described in any one of the above items are implemented.

[0055] The present invention discloses a method, system and medium for managing sports data based on a smart watch. The method obtains user setting information and data monitoring information, extracts sports law data according to the user setting information, including sports type data, sports volume data, sports duration data, sports intensity data and sports interval data, extracts health status data according to the data monitoring information, including heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data, processes the sports law data through a preset sports prediction database to obtain recommended sports information, processes the health status data through a preset health assessment model to obtain a health index, extracts serious disease feature data and congenital defect data according to user physical examination information, corrects the health index through a preset individual health model to obtain a health correction index, compares the health index with a preset health index threshold, determines whether the user's health status meets the requirements, obtains recommended sports correction information according to the health correction index combined with the recommended sports information, including corrected sports type data, corrected sports volume data, corrected sports duration data, corrected sports intensity data and corrected sports interval data, obtains sports completion data corresponding to the recommended sports correction information, and obtains a completed sports rating. This application provides users with scientific and accurate exercise recommendations and health assessments. By comprehensively considering multiple health factors, the accuracy of health assessments can be improved. It can promptly detect changes in users' health and provide users with timely health warnings. It can dynamically adjust recommended exercise correction information to ensure that exercise recommendations always match the user's health status, thereby achieving the purpose of personalized exercise based on individual health.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0057] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0058] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0059] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0060] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A sports data management method based on a smart watch, characterized in that: The following steps are involved: Acquire user setting information and data monitoring information, extract exercise pattern data according to the user setting information, and extract health status data according to the data monitoring information; Processing the motion law data through a preset motion prediction database to obtain recommended motion information; Processing the health status data through a preset health assessment model to obtain a health index and correcting it in combination with the user's physical examination information to obtain a health correction index; Comparing the health correction index with a preset health index threshold, determining whether the user's health status meets the requirements; Obtaining recommended exercise correction information according to the health correction index combined with the recommended exercise information; The motion completion data corresponding to the recommended motion correction information is acquired to obtain a completed motion rating.

2. A method for managing sports data based on a smart watch according to claim 1, characterized in that: The motion law data is processed through a preset motion prediction database to obtain recommended motion information, specifically: Extracting exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data according to the exercise law data; The exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data are processed through a preset exercise prediction database to obtain recommended exercise information.

3. A method for managing sports data based on a smart watch according to claim 2, characterized in that: The health status data is processed by a preset health assessment model to obtain a health index and corrected in combination with the user's physical examination information to obtain a health correction index, specifically: Extracting heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data according to the health status data; The health index is obtained by processing the heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data through a preset health assessment model; Obtain user physical examination information to extract critical illness characteristic data and congenital defect characteristic data; The health index is corrected according to the serious disease characteristic data and congenital defect data through a preset individual health model to obtain a health correction index.

4. The method for managing sports data based on a smart watch according to claim 3, characterized in that: The health correction index is compared with a preset health index threshold to determine whether the user's health status meets the requirements, specifically: Comparing the health correction index with a preset health correction index threshold to obtain a correction index deviation rate; comparing the correction index deviation rate with a preset correction index deviation rate threshold; If the correction index deviation rate is less than the correction index deviation rate threshold, the user's health status meets the requirements; If the correction index deviation rate is greater than or equal to the correction index deviation rate threshold, the user's health status does not meet the requirements.

5. The method for managing sports data based on a smart watch according to claim 4, characterized in that: The obtaining of the recommended exercise correction information according to the health correction index combined with the recommended exercise information is specifically: According to the health correction index and the recommended exercise information, a query is performed through a preset exercise correction database to obtain recommended exercise correction information; The recommended exercise information includes recommended exercise type data, recommended exercise amount data, recommended exercise duration data, recommended exercise intensity data and recommended exercise interval data; The recommended motion correction information includes correction motion type data, correction motion amount data, correction motion duration data, correction motion intensity data and correction motion interval data.

6. The method for managing sports data based on a smart watch according to claim 5, characterized in that: The acquiring of the exercise completion data corresponding to the recommended exercise correction information to obtain the completed exercise rating is specifically as follows: Acquiring movement completion data corresponding to the recommended movement correction information; Comparing the exercise completion data with a preset first exercise completion threshold and a second exercise completion threshold respectively; The first movement completion threshold is less than the second movement completion threshold; If the exercise completion data is less than the first exercise completion threshold, an unsatisfactory rating is obtained; If the exercise completion data is greater than or equal to the first exercise completion threshold and less than the second exercise completion threshold, a compliance rating is obtained; If the exercise completion data is greater than or equal to the second exercise completion threshold, a perfect rating is obtained.

7. A sports data management system based on a smart watch, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a sports data management method program based on a smart watch, and when the sports data management method program based on a smart watch is executed by the processor, the following steps are implemented: Acquire user setting information and data monitoring information, extract exercise pattern data according to the user setting information, and extract health status data according to the data monitoring information; Processing the motion law data through a preset motion prediction database to obtain recommended motion information; Processing the health status data through a preset health assessment model to obtain a health index and correcting it in combination with the user's physical examination information to obtain a health correction index; Comparing the health correction index with a preset health index threshold, determining whether the user's health status meets the requirements; Obtaining recommended exercise correction information according to the health correction index combined with the recommended exercise information; The motion completion data corresponding to the recommended motion correction information is acquired to obtain a completed motion rating.

8. The sports data management system based on a smart watch according to claim 7, characterized in that: The motion law data is processed through a preset motion prediction database to obtain recommended motion information, specifically: Extracting exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data according to the exercise law data; The exercise type data, exercise amount data, exercise duration data, exercise intensity data and exercise interval data are processed through a preset exercise prediction database to obtain recommended exercise information.

9. A sports data management system based on a smart watch according to claim 8, characterized in that: The health status data is processed by a preset health assessment model to obtain a health index and corrected in combination with the user's physical examination information to obtain a health correction index, specifically: Extracting heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data according to the health status data; The health index is obtained by processing the heart rate rhythm data, blood pressure interval data, blood oxygen saturation data, blood sugar index data, sleep quality data and body temperature data through a preset health assessment model; Obtain user physical examination information to extract critical illness characteristic data and congenital defect characteristic data; The health index is corrected according to the serious disease characteristic data and congenital defect data through a preset individual health model to obtain a health correction index.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a sports data management method program based on a smart watch. When the sports data management method program based on a smart watch is executed by a processor, the steps of the sports data management method based on a smart watch as described in any one of claims 1 to 6 are implemented.

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