阅读说明：本技术 一种运动手环数据的处理方法和装置 (Method and device for processing sports bracelet data ) 是由 蒋旺奇 刘奎阳 于 2021-09-07 设计创作，主要内容包括：本发明实施例提供了一种运动手环数据的处理方法和装置,所述方法包括：步骤1,将从用户的运动手环获取的作息数据、运动数据按时间分组存储在区块链中；步骤2,对区块链中的所述作息数据、运动数据进行处理,生成用户的一周内的平均睡眠运动健康指数和当前时间段的运动健康指数；步骤3,根据所述当前时间段的运动健康指数和所述一周内的平均睡眠运动健康指数,求出当前健康指数偏差值；步骤4,根据所述当前健康指数偏差值,生成当前时间段的作息建议和运动建议；步骤5,给所述用户输出所述作息建议和运动建议。(The embodiment of the invention provides a method and a device for processing sports bracelet data, wherein the method comprises the following steps: step 1, storing work and rest data and motion data acquired from a motion bracelet of a user in a block chain in a time grouping manner; step 2, the work and rest data and the exercise data in the block chain are processed to generate an average sleep exercise health index of the user in a week and an exercise health index of the user in the current time period; step 3, solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week; step 4, generating work and rest suggestions and exercise suggestions in the current time period according to the current health index deviation value; and 5, outputting the work and rest suggestion and the exercise suggestion to the user.)

1. A processing method of sports bracelet data is characterized by comprising the following steps:

step 1, storing work and rest data and motion data acquired from a motion bracelet of a user in a block chain in a time grouping manner;

step 2, the work and rest data and the exercise data in the block chain are processed to generate an average sleep exercise health index of the user in a week and an exercise health index of the user in the current time period;

step 3, solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week;

step 4, solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week;

and 5, outputting the work and rest suggestion and the exercise suggestion to the user.

2. The method according to claim 1, wherein the step 2 of generating the average sleep-and-exercise health index over one week of the user is specifically:

acquiring work and rest data and motion data in a week from a block chain by using a formula 1, and calculating an average sleep and motion health index in the week;

wherein H_avgRepresents the mean exercise health coefficient over one week; f_avgRepresents the average heart rate health index over one week; s_avgRepresents the average sleep motor health index over one week; r_avgRepresents the average sleep health index over one week;

su_(i)represents the wake time on day i; sd_(i-1)Represents the time to sleep on day i-1; l_iRepresents the sleep time of day i; p is a radical of_i-1Representing the number of sleep interruptions on day i-1; ru is a Chinese character_(i)Represents the exercise start time on day i; rd_(i)Represents the exercise end time on day i; s_iRepresents exercise time on day i; f (j) heart rate for the j time period on day i; 1- | sd_(i-1)-22| represents a first correction value for the sleeping time on day i, which is positive when the sleeping time on the current day is between 21 and 23 and negative when not in this interval; 1- | su_(i)-7| represents a second correction value for the sleeping time of day i, which is positive when the wake-up time is between 6 and 8 and negative when not in this interval;indicating time of sleep on day iWhen the sleep interruption times are more than 5 times, the punishment generated by the third correction weight is more obvious; (1- | rd_(i)-7|) represents a first correction value for the exercise time on day i, which is positive when the exercise end time is between 7 and 9 and negative when not in this interval; (2- | sd_(i)-21|) represents a second correction value for the movement time on day i, the second correction weight being positive when the movement time is at 19-23 and negative when not in this interval;the third correction weight is the exercise time of the ith day, namely the more values which are greater than 180 heart rates are generated in the starting and stopping time period, the more obvious punishment is generated by the third correction weight;

e is the base of the natural logarithm.

3. The method of claim 1,

the work and rest data comprises: heart rate, sleep duration, sleep interruption times, time to fall asleep, time to wake up;

the motion data includes: the exercise pace, the exercise step number and the exercise time period.

4. The method of claim 1, wherein step 3 comprises:

step 31, solving a health index deviation value according to the exercise health index of the current time period and the average sleep health index in the week;

and step 32, comparing the health index deviation value with a preset threshold value to obtain a current health index deviation value, wherein the current health index deviation value represents the exercise health dynamic state of the user in the current time period.

5. The method of claim 4, wherein the step 32 comprises:

wherein G represents the current health index deviation value, H_avgRepresents the mean exercise health coefficient over one week;a heart rate health index representing a current time period; s_avg(i-1) represents a sleep health index of the day before the current time period; r_avg(i-1) represents the exercise health index of the day before the current time period.

6. The method according to claim 1, wherein step 4 is specifically:

giving a current sleep suggestion and a current exercise duration suggestion according to the current health index deviation value by using a formula 3 and combining the sleep information and the exercise information of the previous day;

wherein, T₁Indicating a recommended length of time of day sleep; t is₂Representing a suggested time of day's movement; t is₃Indicating a recommended time of day to sleep; t is₄Represents a suggested time of getting up for the next day; su_i-1Represents the actual time to sleep on day i-1; su_iRepresents the actual time of the day's waking up; rd_i-1-ru_i-1Representing the actual movement time of the day preceding the current time period; rd_i-ru_iRepresenting the actual exercise time of the day; max () represents taking the maximum value of the function in parentheses; min () represents taking the minimum of the function within brackets.

7. A processing apparatus of motion bracelet data, characterized by comprising:

the storage unit stores work and rest data and motion data acquired from a motion bracelet of a user in a block chain in a time grouping manner;

the computing unit is used for processing the work and rest data and the exercise data in the block chain to generate an average sleep exercise health index of a user in a week and an exercise health index of a current time period;

the second calculation unit is used for solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week;

the generating unit is used for generating work and rest suggestions and exercise suggestions in the current time period according to the current health index deviation value;

and the output unit is used for outputting the work and rest suggestion and the exercise suggestion to the user.

Technical Field

The invention relates to the field of health management, in particular to a method and a device for processing sports bracelet data.

Background

At present, health receives more and more attention, and how to make suggestions about health for users is also a topic concerned by consumers.

Disclosure of Invention

The embodiment of the invention provides a method and a device for processing sports bracelet data, which can send work and rest suggestions and sports suggestions to a user according to the sports bracelet data of the user.

A processing method of sports bracelet data comprises the following steps:

step 1, storing work and rest data and motion data acquired from a motion bracelet of a user in a block chain in a time grouping manner;

step 2, the work and rest data and the exercise data in the block chain are processed to generate an average sleep exercise health index of the user in a week and an exercise health index of the user in the current time period;

step 3, solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week;

step 4, generating work and rest suggestions and exercise suggestions in the current time period according to the current health index deviation value;

and 5, outputting the work and rest suggestion and the exercise suggestion to the user.

A processing device of sports bracelet data, comprising:

the storage unit stores work and rest data and motion data acquired from a motion bracelet of a user in a block chain in a time grouping manner;

the computing unit is used for processing the work and rest data and the exercise data in the block chain to generate an average sleep exercise health index of a user in a week and an exercise health index of a current time period;

the second calculation unit is used for solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week;

the generating unit is used for generating work and rest suggestions and exercise suggestions in the current time period according to the current health index deviation value;

and the output unit is used for outputting the work and rest suggestion and the exercise suggestion to the user.

According to the technical scheme provided by the embodiment of the invention, the work and rest suggestion and the exercise suggestion can be sent to the user according to the data of the exercise bracelet of the user, so that the user is assisted in managing sleep and health.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.

Fig. 1 is a schematic diagram of a processing method of sports bracelet data according to the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.

As shown in fig. 1, a method for processing data of a sports bracelet according to the present invention includes:

step 1, storing work and rest data and motion data acquired from a motion bracelet of a user in a block chain in a time grouping manner; the work and rest data may include: heart rate, sleep duration, sleep interruption times, time to fall asleep, time to wake up; the motion data may include: the exercise pace, the exercise step number and the exercise time period.

Step 2, the work and rest data and the exercise data in the block chain are processed to generate an average sleep exercise health index of the user in a week and an exercise health index of the user in the current time period;

the step 2 of generating the average sleep-exercise health index of the user in one week specifically includes:

acquiring work and rest data and motion data in a week from a block chain by using a formula 1, and calculating an average sleep and motion health index in the week;

wherein H_avgRepresents the mean exercise health coefficient over one week; f_avgRepresents the average heart rate health index over one week; s_avgRepresents the average sleep motor health index over one week; r_avgRepresents the average sleep health index over one week;

su_(i)represents the wake time on day i; sd_(i-1)Represents the time to sleep on day i-1; l_iRepresents the sleep time of day i; p is a radical of_i-1Representing the number of sleep interruptions on day i-1; ru (a)_i) Represents the exercise start time on day i; rd_(i)Represents the exercise end time on day i; s_iRepresents exercise time on day i; f (j) heart rate for the j time period on day i; 1- | sd_(i-1)-22| represents a first correction value for the sleeping time on day i, which is positive when the sleeping time on the current day is between 21 and 23 and negative when not in this interval; 1- | su_(i)-7| represents a second correction value for the sleeping time of day i, which is positive when the wake-up time is between 6 and 8 and negative when not in this interval;the third correction weight value represents the sleep time of the ith day, and when the sleep interruption times are more than 5 times, the punishment generated by the third correction weight value is more obvious; (1- | rd_(i)-7|) represents a first correction value for the exercise time on day i, which is positive when the exercise end time is between 7 and 9 and negative when not in this interval; (2- | sd_(i)-21|) represents a second correction value for the movement time on day i, the second correction weight being positive when the movement time is at 19-23 and negative when not in this interval;the third modified weight value is the movement time of the ith day, namely the start-stop time periodIn this case, the more values greater than 180 heart rates are generated, the more obvious the penalty is generated by the third correction weight.

Step 3, solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week;

step 4, generating work and rest suggestions and exercise suggestions in the current time period according to the current health index deviation value;

and 5, outputting the work and rest suggestion and the exercise suggestion to the user.

In the embodiment, work and rest suggestions and exercise suggestions can be sent to the user according to the data of the exercise bracelet of the user, and the user is assisted in managing health and sleep.

Wherein the step 3 comprises:

step 31, solving a health index deviation value according to the exercise health index of the current time period and the average sleep health index in the week;

and step 32, comparing the health index deviation value with a preset threshold value to obtain a current health index deviation value, wherein the current health index deviation value represents the exercise health dynamic state of the user in the current time period.

In the above embodiment, the work and rest state and the exercise state of the user in the current time period can be judged by combining the usual work and rest habits and exercise habits of the user, so as to prompt the user.

The step 32 comprises:

wherein G represents the current health index deviation value, H_avgRepresents the mean exercise health coefficient over one week;a heart rate health index representing a current time period; s_avg(i-1) represents a sleep health index of the day before the current time period; r_avg(i-1) represents the exercise health index of the day before the current time period.

The step 4 specifically comprises the following steps:

giving a current sleep suggestion and a current exercise duration suggestion according to the current health index deviation value by using a formula 3 and combining the sleep information and the exercise information of the previous day;

wherein, T₁Indicating a recommended length of time of day sleep; t is₂Representing a suggested time of day's movement; t is₃Indicating a recommended time of day to sleep; t is₄Represents a suggested time of getting up for the next day; su_i-1Represents the actual time to sleep on day i-1; su_iRepresents the actual time of the day's waking up; rd_i-1-ru_i-1Representing the actual movement time of the day preceding the current time period; rd_i-ru_iRepresenting the actual exercise time of the day; max () represents taking the maximum value of the function in parentheses; min () represents taking the minimum of the function within brackets.

The invention also provides a processing device of the sports bracelet data, which comprises:

the storage unit stores work and rest data and motion data acquired from a motion bracelet of a user in a block chain in a time grouping manner;

the computing unit is used for processing the work and rest data and the exercise data in the block chain to generate an average sleep exercise health index of a user in a week and an exercise health index of a current time period;

the second calculation unit is used for solving a current health index deviation value according to the exercise health index of the current time period and the average sleep exercise health index in the week;

the generating unit is used for generating work and rest suggestions and exercise suggestions in the current time period according to the current health index deviation value;

and the output unit is used for outputting the work and rest suggestion and the exercise suggestion to the user.

The following describes an application scenario of the present invention.

A sports bracelet data management method and system based on a block chain are characterized in that work and rest data and sports data acquired by a sports bracelet are stored in the block chain in a time grouping mode, information in the block chain is analyzed, the sports health state of a user is judged, a corresponding prompt is sent, and the corresponding prompt is stored in the block chain; the method comprises the following specific steps:

step A1: the average exercise health index in one week can be calculated according to the work and rest data and exercise data stored in the block chain and acquired from the bracelet in one week, including heart rate, sleep duration, sleep interruption times, sleep-in time, sleep-wake time, exercise duration, exercise pace, step number and exercise time period by using the formula (1).

Wherein H_avgExpressed as the mean exercise health coefficient over one week; f_avgExpressed as the average heart rate health index over one week; s_avgExpressed as the mean sleep health index over one week; su_(i)Wake time expressed as day i; sd_(i-1)Expressed as time to sleep on day i-1; l_iSleep time expressed as day i; p is a radical of_i-1Expressed as the number of sleep interruptions on day i-1; r_avgExpressed as the mean sleep health index over one week; ru is a Chinese character_(i)Expressed as exercise start time on day i; rd_(i)End of exercise time expressed as day i; s_iExpressed as exercise time on day i; f (j) heart rate for the j time period, denoted as day i; 1- | sd_(i-1)-22| represents a first correction value for the sleeping time of the ith day, which is positive when the sleeping time of the current day is between 21 and 23 and negative when not in this interval; 1- | su_(i)-7| represents a second correction value for the sleeping time of day i, which is positive when the wake-up time is between 6 and 8 and negative when not in this interval;a third correction weight value which is expressed as the sleep time of the ith day, wherein when the sleep interruption times are more than 5 times, the punishment generated by the third correction weight value is more obvious; (1- | rd_(i)-7|) represents a first correction value for the time of exercise on day i, which is positive when the time of exercise end is between 7 and 9 and negative when it is not in this interval; (2- | sd_(i)-21|) represents a second correction value for the movement time of the i-th day, the second correction weight being positive when the end movement time is between 19 and 23 and negative when not in this interval;the third correction weight is the exercise time of the ith day, namely the more values which are greater than 180 heart rates are generated in the starting and stopping time period, the more obvious punishment is generated by the third correction weight;

step A2: the data can be recorded into the block chain according to the work and rest data and the motion data of the current time period by using the formula (2), the motion health index of the current time period is calculated, the deviation value of the motion health index and the average sleep health index of a week is calculated, the deviation value is compared with a preset threshold value, the health information of the user at the moment is obtained, and the health information is recorded into the block chain

Wherein G greater than 0 indicates a high exercise health index and G less than 0 indicates a low exercise health index; h_avgExpressed as the mean exercise health coefficient over one week;a heart rate health index expressed as a current time period; s_avg(i-1) expressing the sleep health index as the day before the current time period; r_avg(i-1) expressed as the exercise health index of the day before the current time period;

step A3: the current sleep time length and exercise time length suggestion can be given by the judgment of the health index deviation value and the combination of the sleep information and the exercise information of the previous day by using the formula (3)

Wherein T is₁Indicating a suggested sleep duration for the current day; t is₂The suggested exercise duration is expressed as the current day; t is₃Indicated as sending suggested sleep moments; t is₄Expressed as the next day of bed-up time is sent; su_i-1Expressed as time to sleep on day i-1; su_iThe time to bed, expressed as day i; rd_i-1-ru_i-1Expressed as exercise time on day i-1; rd_i-ru_iExpressed as exercise time on day i; max () is expressed as taking the maximum value of the function in parentheses; min () is expressed as taking the minimum of the function within brackets.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.