CN118173223A - Sports training method for primary and secondary school students based on body measurement results - Google Patents

Abstract

The application provides a training method for a pupil exercise based on body measurement, which is used for determining the amount of exercise by acquiring body measurement and body type data from an exercise information database and analyzing the data by using a preset algorithm. The specific steps include obtaining current and historical body measurements, calculating a performance stability interval and a trend of change, and obtaining and analyzing body conformation data to generate a base training volume. The projected amount of motion is then adjusted in combination with the historical training data and the motion capability estimate. Actual training data is collected to analyze the effective amount of motion, compared to the planned amount to calculate the training completion. Finally, the estimated motion capability value is updated according to the training completion degree and the effective motion quantity. The process aims at providing individualized and scientific training quantity guidance for students in middle and primary schools, and achieves the effect of continuously improving the body measurement result by continuously setting reasonable quantity of exercise and carrying out effectiveness analysis and supervision on the quantity of exercise.

Description

Sports training method for primary and secondary school students based on body measurement results

Technical Field

The application relates to the field of exercise training, in particular to a training method for exercise of students in middle and primary schools based on body measurement results.

Background

Currently, the amount of physical training for primary and secondary school students is typically based on the personal experience of the teacher rather than accurate data. The training program is set by a coach or a sports teacher according to personal experience and physical performance of the students, and the method is simple and direct and is easy to implement. The training system has the advantages of being capable of being rapidly adapted to different training environments and student groups, and complex equipment or data analysis is not needed.

However, this experience-based approach has significant drawbacks. First, it lacks accuracy and individualization, and the inability to tailor the training program for each student may result in poor training results or athletic injuries. Second, this approach is difficult to quantify the progress and training effect of the student, and coaches and teachers cannot accurately assess the effectiveness of the training program. In addition, due to the lack of systematic data support, it is difficult to scientifically plan for the long-term development of students.

On the other hand, the experience-based method cannot adjust the corresponding exercise amount according to the body types of students, and is difficult to match the exercise amounts of students with different body types, and the students in middle and primary schools have large body type differences due to different development speeds, so that the proper exercise amount is difficult to set manually.

In particular, the exercise ability of students in different exercise performance stages is different, if the exercise ability cannot be well estimated, a reasonable exercise amount cannot be set, if the exercise amount is too much, the body of the students can be possibly injured, and if the exercise amount is too little, the exercise performance of the students cannot be continuously improved.

Disclosure of Invention

The application provides a training method for the exercise of a pupil based on the exercise score, which can set the matched exercise quantity for the corresponding exercise according to the exercise score, the body type condition and the exercise capacity condition of the pupil so as to improve the exercise score of the pupil through the matched exercise quantity.

The training method for the middle and primary school students based on the body measurement performance comprises the following steps:

A1, acquiring corresponding current body measurement results and historical body measurement results data from a preset motion information database according to preset user identity identification information and preset motion category information;

a2, calculating a score stabilizing interval and a score change trend value according to the current score value and the historical score data by a preset score estimation algorithm;

a3, acquiring corresponding current body type data and historical body type data from the motion information database according to the user identity identification information;

a4, calculating a body type index according to the current body type data and the historical body type data by using a preset body type quantitative algorithm;

a5, generating basic training exercise quantity according to the score stabilizing interval, the body type index and a preset body type exercise quantity comparison table and a preset basic exercise quantity algorithm;

a6, acquiring corresponding historical training motion quantity data and a preset motion capability estimated value from a motion information database according to the user identity identification information;

a7, calculating the planned training exercise quantity according to the basic training exercise quantity, the achievement change trend value and the exercise capacity estimated value by a preset exercise quantity adjusting algorithm;

A8, acquiring actual training motion data through a preset motion quantity acquisition method in a preset training period;

a9, calculating effective training motion quantity according to actual motion data, a score stability interval, a preset user age value and motion category information by a preset effective motion analysis algorithm;

A10, calculating the training exercise completion degree according to the effective training exercise amount and the planned training exercise amount by a preset training completion degree algorithm;

A11, acquiring corresponding historical training exercise completion degree data from an exercise information database according to the user identity identification information;

And A12, updating the motion capability estimation value according to the training motion completion degree, the effective training motion quantity, the historical training motion completion degree data and the motion capability estimation value by a preset motion capability updating algorithm.

By adopting the technical scheme, the training method for the middle and primary school students based on the body measurement results can set the matched training motion amount for the corresponding motion according to the body measurement results, the body type conditions and the motion capability conditions of the user, so that the body measurement results of the user are improved through the matched training motion amount, the motion capability conditions of the user are re-evaluated and updated according to the completion degree of the training motion amount, the motion capability of the user is reflected better, and the motion amount of the user can be set to be matched better.

Optionally, the body measurement score estimation algorithm includes the following steps:

b1, calculating an average body measurement score value according to a preset average score algorithm according to the current body measurement score value and the historical body measurement score data, wherein the average score algorithm is as follows: ，

Wherein, For average body score value,For current body measurement achievements,For the weight value corresponding to the current body measurement result,For the i-th measured body score value in the historical body score data,Weight value corresponding to the i-th measured score value in the historical score data, n being the number of data in the historical score data, whereinAnd (2) and；

And B2, calculating a body measurement score standard deviation according to the current body measurement score value and the historical body measurement score data by a preset weighted standard deviation algorithm, wherein the weighted standard deviation algorithm is as follows:

,

Wherein, Standard deviation of measured results;

Calculating a body measurement confidence interval according to an average body measurement score value, a body measurement score standard deviation and a preset confidence level coefficient by using a preset body measurement confidence interval algorithm, and defining the body measurement confidence interval as a score stability interval, wherein the body measurement confidence interval algorithm is as follows:

，

Wherein, For the body measurement score confidence interval,Is a confidence level coefficient;

B4, generating a score linear fitting function according to the current score value and the historical score data by a preset linear regression algorithm;

And B5, determining a corresponding function slope value according to the score linear fitting function and defining the function slope value as a score change trend value.

By adopting the technical scheme, the student sports training method based on the body measurement results can calculate the fluctuation interval of the body measurement results of the user and the variation trend of the results according to the current body measurement result value and the historical body measurement result data, and can reflect the interval and the variation condition of the body measurement results of the user more comprehensively.

Optionally, the body type quantification algorithm includes the steps of:

C1, acquiring a current height value and a current weight value from current body type data;

c2, calculating a current body mass index according to the current height value and the current weight value by a preset body mass algorithm;

c3, acquiring historical height data and corresponding historical weight data according to the historical body type data;

c4, generating historical body quality index data according to the historical height data and the corresponding historical weight data by using a body quality algorithm;

And C5, calculating a body mass index according to the calculated current body mass index and the historical body mass index data by a preset body mass average algorithm, wherein the body mass average algorithm is as follows:

，

Wherein, Is body type index,For the current body mass index,Is the/>, in the historical body mass index dataData,Is the/>, in the historical body mass index dataWeight value corresponding to each dataIs the number of data in the historical body mass index data.

By adopting the technical scheme, the student exercise training method based on the body measurement results can comprehensively calculate the corresponding body type index according to the current body type data and the historical body type data of the user, and is used for quantitatively reflecting the recent body type condition of the user.

Optionally, the basic motion amount algorithm includes the steps of:

D1, acquiring corresponding apparent fixed exercise amount from a body type exercise amount comparison table according to the body type index;

D2, obtaining a corresponding lower limit value according to the score stabilization interval and defining the lower limit value as a score stabilization lower limit value, and obtaining a corresponding upper limit value and defining the upper limit value as a score stabilization upper limit value;

And D3, determining a basic training exercise amount range according to a score stability lower limit value, a score stability upper limit value, a table exercise amount and a preset maximum score value by using a preset basic exercise amount range algorithm, wherein the basic exercise amount range algorithm is as follows:

,

,

Wherein, Upper limit value of basic training exercise quantity range,As a lower limit value of the range of basic training exercise amount,For maximum score value,For the achievement to stabilize the upper limit,For the score stability lower limit,The exercise amount is determined for the watch;

and D4, calculating basic training exercise quantity according to a basic training exercise quantity range, a body type index and a preset standard body type index interval by using a preset exercise quantity correction algorithm, wherein the exercise quantity correction algorithm is as follows:

,

Wherein, Training exercise amount for basis,Is the upper limit value of the standard body type index interval,The lower limit value of the standard body type index section.

By adopting the technical scheme, the student exercise training method based on the body measurement results can determine the basic exercise quantity suitable for the user according to the body measurement results and the body type conditions of the user, and provide a reference for the specific setting of the subsequent exercise quantity.

Optionally, the motion amount adjustment algorithm is:

,

Wherein, To plan the amount of exercise,For the motion capability estimation,For score change trend value,Is a preset adjustment coefficient.

By adopting the technical scheme, the training method for the middle and primary school students based on the body measurement results can calculate the corresponding planned movement amount according to the planned movement amount of the user, the estimated value of the movement ability and the variation trend value of the results so as to adapt to the training movement of the user.

Optionally, the effective motion analysis algorithm includes the steps of:

E1, acquiring exercise speed data, exercise displacement data and exercise heart rate data from actual exercise data;

e2, respectively extracting corresponding speed characteristic data and displacement characteristic data according to the movement speed data and the movement displacement data by a preset characteristic extraction algorithm;

E3, identifying and determining corresponding training period data through a pre-training period identification model corresponding to the motion category information according to the speed characteristic data and the displacement characteristic data;

E4, acquiring corresponding heart rate data from exercise heart rate data according to the training period data and defining the corresponding heart rate data as training heart rate data;

e5, calculating an effective training heart rate threshold value according to a preset effective training heart rate algorithm according to a preset user age value, a score stability lower limit value and a maximum score value of a score stability interval, wherein the effective training heart rate algorithm is as follows:

,

Wherein, To train heart rate threshold effectively,For the age value of the user,Is a preset adjusting coefficient;

e6, acquiring period data corresponding to heart rate data of not less than an effective training heart rate threshold value from the training heart rate data and defining the period data as effective training exercise period data;

And E7, obtaining effective training duration according to the effective training exercise period data statistics and defining the effective training exercise amount.

By adopting the technical scheme, the training method for the middle and primary school students based on the body measurement results can acquire corresponding training period data according to the actual motion data identification, calculate the corresponding effective training heart rate threshold according to the user age value, the score stabilizing interval and the maximum score value, and determine the effective training exercise amount according to the effective training heart rate threshold in the training period data so as to correctly calculate the actual effective training exercise amount during the user training.

Optionally, the training completion algorithm includes the following steps:

F1, obtaining total training time according to training time data statistics;

f2, calculating a difference value according to the total training duration and the effective training exercise amount to obtain an effective training duration and defining the effective training exercise amount as an effective training exercise amount;

and F3, calculating the training exercise completion degree according to the effective training exercise quantity, the low-efficiency training exercise quantity and the planned training exercise quantity by a preset exercise quantity comprehensive completion degree algorithm, wherein the exercise quantity comprehensive completion degree algorithm is as follows:

，

Wherein, To train the degree of completion of the exercise,For a preset adjustment coefficient,For the inefficient training of the amount of exercise,To effectively train exercise amount,To plan training exercise amount,Is a preset adjustment coefficient.

By adopting the technical scheme, the training method for the middle and primary school students based on the body measurement performance can calculate the exercise completion degree of the comprehensive user according to the training period data, the effective training exercise quantity and the planned training exercise quantity so as to better reflect the completion condition of the training exercise of the user.

Optionally, the motion capability updating algorithm includes the following steps:

G1, calculating an average historical training completion degree according to the average historical training completion degree data;

And G2, calculating a motion capability update value according to the training motion completion degree, the historical training average completion degree, the effective training motion quantity and the motion capability estimation value by a preset motion capability estimation algorithm, wherein the motion capability estimation algorithm is as follows:

，

Wherein, Update value for athletic ability,Average completion for historical training;

And G3, updating the motion capability estimated value according to the motion capability updated value.

By adopting the technical scheme, the training method for the middle and primary school students based on the body measurement performance can recalculate the estimated value of the exercise capacity of the calibration user according to the historical training exercise completion degree data, the training exercise completion degree and the effective training exercise quantity of the user so as to set the more matched training exercise quantity in the next training exercise.

Optionally, the training method for primary and secondary school students based on the body measurement score further comprises a score lifting potential estimation algorithm for estimating a lifting space of the body measurement score of the user, wherein the score lifting potential estimation algorithm comprises the following steps:

i1, defining a user age value as a screening age value, defining current body type data as screening body type data, and defining a motion capability estimated value as a current user motion capability value;

i2, acquiring user identity identification information with the user age value identical to the screening age value from the motion information database according to the screening age value, defining the user identity identification information as peer user information, and combining all peer user information to generate a peer user information group;

i3, generating a body type screening interval according to the screening body type data and a preset fluctuation threshold;

i4, acquiring the same-age user information of which the current body type data fall into the body type screening interval from the same-age user information group, defining the same-age user information as same-body type user information, and combining all the same-body type user information to generate a same-body type user information group;

i5, obtaining corresponding current body measurement results and motion ability estimated values according to all the body type user information in the body type user information group, and calculating motion result correlation coefficients by a preset correlation coefficient algorithm;

i6, calculating an average motion capability estimation value of the group according to the motion capability estimation values corresponding to all the homotypic user information;

i7, obtaining the maximum value from the motion capability estimated values corresponding to all the homotypic user information and defining the maximum motion capability as the group maximum motion capability;

i8, calculating a motion capability potential threshold value according to the group average motion capability estimation value and the group maximum motion capability calculation average value;

i9, calculating a predicted motion potential lifting value according to the motion potential threshold value and the current user motion capability value to obtain a difference value;

i10, calculating a group average body measurement score according to the average value of the current body measurement scores corresponding to all the same-body type user information;

i11, calculating a score ratio example value by calculating a quotient according to the group average score and the group average exercise capacity estimation value;

And i12, if the estimated movement potential lifting value is larger than 0, calculating the estimated movement potential lifting value according to the product of the estimated movement potential lifting value, the performance capability proportion value and the movement performance correlation coefficient.

By adopting the technical scheme, the training method for the primary and secondary school students based on the body measurement results can analyze the potential of the current user for improving the results according to the data information of other users of the same age and similar body types so as to estimate the space for improving the body measurement results of the users.

In summary, the present application includes at least one of the following beneficial technical effects:

1. the matched training motion quantity can be set for corresponding motions according to the body measurement result, the body type condition and the motion capability condition of the user, so that the body measurement result of the user is improved through the matched training motion quantity, and the motion capability condition of the user is re-evaluated and updated according to the completion degree of the training motion quantity, so that the motion capability of the user is reflected better, and the motion quantity of the user can be set to be matched better.

2. The fluctuation interval of the user's body measurement score and the change trend of the score can be calculated according to the current body measurement score value and the historical body measurement score data, and the interval and the change condition of the user's body measurement score can be comprehensively reflected.

3. The basic quantity of motion suitable for the user can be determined according to the body measurement result condition and the body type condition of the user, and a reference is provided for the concrete setting of the subsequent training quantity of motion.

Drawings

FIG. 1 is a diagram of a pupil exercise training method based on body measurement results.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

Embodiments of the application are described in further detail below with reference to the drawings.

Referring to fig. 1, the invention provides a training method for a middle and primary school student based on body measurement results, which can set matched training exercise amounts for corresponding exercise according to the body measurement results, body type conditions and exercise capacity conditions of the middle and primary school students, so as to improve the body measurement results of the middle and primary school students through the matched training exercise amounts.

The training method for the middle and primary school students based on the body measurement performance comprises the following steps:

A1, acquiring corresponding current body measurement results and historical body measurement results data from a preset motion information database according to preset user identity identification information and preset motion category information;

The user identity identification information is preset and is used for identifying specific users;

The exercise category information is preset information, and is used for determining a certain exercise, and common student body measurement projects such as 800, 1000 m middle-long running, standing long jump, rope skipping, front throwing solid ball, sit-up, pull-up, 50m running, 50m round trip running, swimming and the like can be preset correspondingly to different body measurement projects, so that different training methods can be set in advance, the exercise quantity can be set quantitatively, and the body measurement performance is improved through continuous exercise.

The exercise information database is a preset database and is used for storing various exercise related data of the user;

The current body measurement results are the latest body measurement results of the user or the simulated body measurement results, and the body measurement results can be obtained by comparing the actual measured values of time consumption, frequency and the like of the sports with corresponding score tables;

The historical body measurement result data is a data set of body measurement results obtained by past measurement.

A2, calculating a score stabilizing interval and a score change trend value according to the current score value and the historical score data by a preset score estimation algorithm;

The body measurement score estimation algorithm is a preset algorithm and is used for calculating a confidence interval of the body measurement score of the user and a change trend of the body measurement score;

The score stabilizing interval is a confidence interval of the body measurement score of the user and can reflect the fluctuation range of the body measurement score of the user;

The score change trend value is a value reflecting the score change trend of the user, and for example, linear regression may be performed according to the score of the past score and the corresponding time, and the slope of the obtained regression function may be used as the score change trend value, and if the score change trend value is greater than 0, it indicates that the score is in an upward trend, and if the score change trend value is less than 0, it indicates that the score is in a downward trend.

A3, acquiring corresponding current body type data and historical body type data from the motion information database according to the user identity identification information;

The precursor body type data is the current body type data of the user, the body type data can be obtained through the existing algorithms, for example, the calculation method of Body Mass Index (BMI) is calculated and obtained according to the height and the weight, and the body type condition of the user can be represented in a numerical mode;

the historical body type data is body type data obtained by past measurement and calculation of a user.

A4, calculating a body type index according to the current body type data and the historical body type data by using a preset body type quantitative algorithm;

The body type quantitative algorithm is a preset algorithm, and the body type index corresponding to the user is comprehensively calculated according to the current body type data and the historical body type data;

the body type index is a numerical value obtained by comprehensively calculating the precursor body type data and the history body type data of the user.

A5, generating basic training exercise quantity according to the score stabilizing interval, the body type index and a preset body type exercise quantity comparison table and a preset basic exercise quantity algorithm;

the body type exercise amount comparison table is a preset table, and the corresponding exercise amount can be determined on the table according to the body type index;

the basic exercise amount algorithm is a preset algorithm and is used for determining exercise amount serving as a reference of training exercise according to the score stabilizing interval and the body form index;

the base training quantity is a reference quantity for the training motion, which can be subsequently adjusted by various other factors of the user to generate a training quantity suitable for the specific situation of the user.

A6, acquiring corresponding historical training motion quantity data and a preset motion capability estimated value from a motion information database according to the user identity identification information;

the historical training exercise amount data is an exercise amount data set confirmed through collection and analysis during the past training exercise;

The exercise capacity estimated value is a preset value, and is used for quantitatively evaluating the exercise capacity of the user, such as the duration time or the number of single exercises of the user, for example, the duration time of single running training of the user, and can reflect the exercise capacity estimated value of running of the user; the estimated athletic ability may also be statistically calculated based on the performance of various athletic performances or other factors.

A7, calculating the planned training exercise quantity according to the basic training exercise quantity, the achievement change trend value and the exercise capacity estimated value by a preset exercise quantity adjusting algorithm;

the motion quantity adjusting algorithm is a preset algorithm and is used for adjusting and calculating the basic training motion quantity to generate a training motion quantity which is suitable for the condition of a user;

the planned training motion amount is a target motion amount that the user needs to complete for the next motion.

A8, acquiring actual training motion data through a preset motion quantity acquisition method in a preset training period;

The exercise amount acquisition method is a preset exercise data acquisition method, and more acquisition methods exist, for example, a sensor for acquiring data, a smart watch or a smart phone are arranged on a user, and certain kinds of exercise data such as speed, displacement and heart rate of the user can be generally obtained;

The actual training motion data are motion data actually generated by a user in training motion, and the existing acquisition equipment, particularly the intelligent watch, can better identify whether the user is in a motion state or not and acquire real-time motion data when the user is in the motion state.

A9, calculating effective training motion quantity according to actual motion data, a score stability interval, a preset user age value and motion category information by a preset effective motion analysis algorithm;

the age value of the user is the age of the user acquired in advance;

the effective motion analysis algorithm is a preset algorithm and is used for analyzing and calculating effective training motion quantity in actual motion data;

the effective training exercise amount is an effective training exercise amount determined by calculation.

A10, calculating the training exercise completion degree according to the effective training exercise amount and the planned training exercise amount by a preset training completion degree algorithm;

The training completion algorithm is a pre-selected set algorithm and is used for calculating the completion of training movement;

The training exercise completion degree is a numerical value obtained through calculation and is used for reflecting the training exercise completion condition.

A11, acquiring corresponding historical training exercise completion degree data from an exercise information database according to the user identity identification information;

The historical training exercise completion data is a data set of the completion of past training exercises.

A12, updating the motion capability estimation value according to the training motion completion degree, the effective training motion quantity, the historical training motion completion degree data and the motion capability estimation value by a preset motion capability updating algorithm;

The motion capability updating algorithm is a preset algorithm and is used for calculating and updating the motion capability estimated value of the user.

Through the steps, the training method for the middle and primary school students based on the sports score can set the matched training quantity of the sports according to the sports score, the body type condition and the sports ability condition of the user, so that the sports score of the user is improved through the matched training quantity of the sports, and the sports ability condition of the user is reevaluated and updated according to the completion degree of the training quantity of the sports, so that the sports ability of the user is reflected better, and the quantity of the sports of the user can be set to be matched better.

Further, the body measurement score estimation algorithm comprises the following steps:

b1, calculating an average body measurement score value according to a preset average score algorithm according to the current body measurement score value and the historical body measurement score data, wherein the average score algorithm is as follows: ，

Wherein, For average body score value,For current body measurement achievements,For the weight value corresponding to the current body measurement result,For the i-th measured body score value in the historical body score data,Weight value corresponding to the i-th measured score value in the historical score data, n being the number of data in the historical score data, whereinAnd (2) and。

By setting different weights for the current body measurement score value and the data in the historical body measurement score data, particularly setting higher weights for the recently acquired data in the historical body measurement score data and setting lower weights for the data acquired in a long term, the recent body measurement score condition can be reflected well; the average body measurement score value is the average body measurement score obtained through weighted calculation.

And B2, calculating a body measurement score standard deviation according to the current body measurement score value and the historical body measurement score data by a preset weighted standard deviation algorithm, wherein the weighted standard deviation algorithm is as follows:

,

Wherein, Standard deviation is measured for body.

The measured score standard deviation is the weighted standard deviation of the current measured score value and the historical measured score data.

Calculating a body measurement confidence interval according to an average body measurement score value, a body measurement score standard deviation and a preset confidence level coefficient by using a preset body measurement confidence interval algorithm, and defining the body measurement confidence interval as a score stability interval, wherein the body measurement confidence interval algorithm is as follows:

，

Wherein, For the body measurement score confidence interval,Is a confidence level coefficient.

The confidence level coefficient is a preset coefficient, and according to different confidence levels, the corresponding confidence level coefficient, such as 95% confidence level corresponding confidence level coefficient, can be setHas a value of 1.96; the performance stability interval is a fluctuation interval in which the user's physical performance may occur based on the set confidence level.

B4, generating a score linear fitting function according to the current score value and the historical score data by a preset linear regression algorithm;

the linear regression algorithm is a preset algorithm and is used for generating a corresponding regression function according to data, the existing more linear regression algorithms can realize the fitting of the linear regression function, such as a least square method, a gradient descent method, a normal equation method and the like, and the fitting function can be well generated;

The linear fitting function is a linear regression function generated by fitting the historical motion amount data.

B5, determining a corresponding function slope value according to the score linear fitting function and defining the function slope value as a score change trend value;

The score change trend value is the slope of the score linear fitting function.

Through the steps, the student sports training method based on the body measurement results can calculate the fluctuation interval of the body measurement results of the user and the change trend of the results according to the current body measurement result value and the historical body measurement result data, and can reflect the interval and the change condition of the body measurement results of the user more comprehensively.

Further, the body type quantification algorithm comprises the following steps:

C1, acquiring a current height value and a current weight value from current body type data;

the current height value is the current height of the user;

The current weight value is the current weight of the user.

C2, calculating a current body mass index according to the current height value and the current weight value by a preset body mass algorithm;

The body mass algorithm is a preset algorithm and is used for quantitatively calculating the body type condition of the user, for example, the existing body mass index BMI algorithm can calculate and judge the body type condition of the user through the height and the weight;

The current body mass index is a body mass index calculated from the current height and the current weight of the user.

C3, acquiring historical height data and corresponding historical weight data according to the historical body type data;

The historical height data is the height data of the user obtained by past measurement;

the historical weight data is weight data of the user obtained from past measurements.

C4, generating historical body quality index data according to the historical height data and the corresponding historical weight data by using a body quality algorithm;

the historical body mass index data is a data set of body mass indices generated from historical height data and corresponding historical weight data calculations.

And C5, calculating a body mass index according to the calculated current body mass index and the historical body mass index data by a preset body mass average algorithm, wherein the body mass average algorithm is as follows:

，

Wherein, Is body type index,For the current body mass index,Is the/>, in the historical body mass index dataData,Is the/>, in the historical body mass index dataWeight value corresponding to each dataIs the number of data in the historical body mass index data.

The body type index is a quantitative value obtained by comprehensive calculation according to the current body mass index and the historical body mass index data, and is used for comprehensively reflecting the body type condition of the user.

Through the steps, the student exercise training method based on the body measurement results can comprehensively calculate the corresponding body type index according to the current body type data and the historical body type data of the user, and is used for quantitatively reflecting the recent body type condition of the user.

Further, the basic motion amount algorithm includes the steps of:

D1, acquiring corresponding apparent fixed exercise amount from a body type exercise amount comparison table according to the body type index;

the specified exercise amount is an exercise amount corresponding to the body type index in the body type exercise amount comparison table.

D2, obtaining a corresponding lower limit value according to the score stabilization interval and defining the lower limit value as a score stabilization lower limit value, and obtaining a corresponding upper limit value and defining the upper limit value as a score stabilization upper limit value;

The score stability lower limit value is the upper limit value of the score stability interval;

The score stability upper limit is the lower limit of the score stability interval.

And D3, determining a basic training exercise amount range according to a score stability lower limit value, a score stability upper limit value, a table exercise amount and a preset maximum score value by using a preset basic exercise amount range algorithm, wherein the basic exercise amount range algorithm is as follows:

,

,

Wherein, Upper limit value of basic training exercise quantity range,As a lower limit value of the range of basic training exercise amount,For maximum score value,For the achievement to stabilize the upper limit,For the score stability lower limit,The amount of movement is determined for the watch.

The maximum score value is the maximum value that can be obtained by the body measurement score, for example, 100 minutes of body measurement score, and the maximum score value is 100;

the basic training exercise quantity range is a value range obtained by calculating according to the upper limit value and the lower limit value of the score stabilizing interval.

And D4, calculating basic training exercise quantity according to a basic training exercise quantity range, a body type index and a preset standard body type index interval by using a preset exercise quantity correction algorithm, wherein the exercise quantity correction algorithm is as follows:

,/>

Wherein, Training exercise amount for basis,Is the upper limit value of the standard body type index interval,The lower limit value of the standard body type index section.

The standard body type index interval is an index interval corresponding to a preset standard body type, and can be set according to experience or obtained by measuring the crowd;

the basic training motion quantity is a training motion quantity determined according to the body type condition and the performance condition of the user.

Through the steps, the pupil exercise training method based on the body measurement results can determine the basic exercise quantity suitable for the user according to the body measurement results and the body type conditions of the user, and provide a reference for the subsequent specific setting of the exercise quantity.

Further, the motion amount adjustment algorithm is:

,

Wherein, To plan the amount of exercise,For the motion capability estimation,For score change trend value,Is a preset adjustment coefficient.

Through the calculation formula, the training method for the primary and secondary school students based on the body measurement results can calculate the corresponding planned movement amount according to the planned movement amount, the movement capacity estimated value and the result change trend value of the user so as to adapt to the training movement of the user.

Further, the effective motion analysis algorithm comprises the steps of:

E1, acquiring exercise speed data, exercise displacement data and exercise heart rate data from actual exercise data;

The movement speed data are speed data acquired during the training movement of the user;

The movement displacement data are displacement data acquired during the training movement of the user;

the exercise heart rate data is heart rate data collected while the user trains the exercise.

E2, respectively extracting corresponding speed characteristic data and displacement characteristic data according to the movement speed data and the movement displacement data by a preset characteristic extraction algorithm;

the feature extraction algorithm is a preset algorithm, is used for extracting corresponding feature data from data, and the existing more feature extraction algorithms, such as time sequence decomposition, fourier transformation, wavelet transformation, dynamic Time Warping (DTW) and the like, can be used for feature extraction of data based on time sequence;

the speed characteristic data is characteristic data corresponding to the movement speed data;

The displacement characteristic data are characteristic data corresponding to the motion displacement data.

E3, identifying and determining corresponding training period data through a pre-training period identification model corresponding to the motion category information according to the speed characteristic data and the displacement characteristic data;

the training period identification model is a pre-trained model corresponding to the motion category information, can identify the motion period matched with the motion category information, and can be generated through the existing motion data set training model;

because of different motion types, the generated speed data and displacement data have different characteristics, the speed data and displacement data can be well identified through a trained model, and various available identification models such as a long short memory network (LSTM), a Convolutional Neural Network (CNN), a BERT and the like can be used for well processing data analysis and behavior identification based on time sequences;

The training period data is data of a period corresponding to the movement category information.

E4, acquiring corresponding heart rate data from exercise heart rate data according to the training period data and defining the corresponding heart rate data as training heart rate data;

the training heart rate data is heart rate data corresponding to training period data.

E5, calculating an effective training heart rate threshold value according to the current body type data, the score stability lower limit value and the maximum score value of the score stability interval by a preset effective training heart rate algorithm, wherein the effective training heart rate algorithm is as follows:

,

Wherein, To train heart rate threshold effectively,For the age value of the user,Is a preset adjustment coefficient.

The effective training heart rate threshold is a reference value that determines whether training is effective.

E6, acquiring period data corresponding to heart rate data of not less than an effective training heart rate threshold value from the training heart rate data and defining the period data as effective training exercise period data;

The effective training exercise period data is data of a period of time determined to be effective for training.

E7, obtaining effective training duration according to effective training exercise period data statistics and defining the effective training duration as effective training exercise quantity;

The effective training time length is the sum of time lengths of all time period data in the effective training exercise time period data.

Through the steps, the training method for the middle and primary school students based on the body measurement results can acquire corresponding training period data according to the actual motion data identification, calculate a corresponding effective training heart rate threshold according to the user age value, the score stabilizing interval and the maximum score value, and determine effective training exercise quantity according to the effective training heart rate threshold in the training period data so as to correctly calculate the actual effective training exercise quantity during user training.

Further, the training completion algorithm comprises the following steps:

F1, obtaining total training time according to training time data statistics;

The total training time length is the total training time length obtained by the statistics of the training time period data.

F2, calculating a difference value according to the total training duration and the effective training exercise amount to obtain an effective training duration and defining the effective training exercise amount as an effective training exercise amount;

the low-efficiency training time length is the training time length except the effective training exercise amount in the total training time length;

The amount of the inefficient training exercise is the time period of the inefficient training.

And F3, calculating the training exercise completion degree according to the effective training exercise quantity, the low-efficiency training exercise quantity and the planned training exercise quantity by a preset exercise quantity comprehensive completion degree algorithm, wherein the exercise quantity comprehensive completion degree algorithm is as follows:

，

Wherein, To train the degree of completion of the exercise,For a preset adjustment coefficient,For the inefficient training of the amount of exercise,To effectively train exercise amount,To plan training exercise amount,Is a preset adjustment coefficient.

The training exercise completion degree is a numerical value calculated by comprehensively considering the effective training exercise quantity, the low-efficiency training exercise quantity and the planned training exercise quantity of the user and is used for reflecting the completion degree of the planned training exercise quantity of the user.

Through the steps, the training method for the middle and primary school students based on the body measurement performance can calculate the exercise completion degree of the comprehensive user according to the training period data, the effective training exercise quantity and the planned training exercise quantity so as to better reflect the completion condition of the training exercise of the user.

Further, the athletic ability update algorithm includes the steps of:

G1, calculating an average historical training completion degree according to the average historical training completion degree data;

The historical training average completion is the average of the historical training athletic completion data.

And G2, calculating a motion capability update value according to the training motion completion degree, the historical training average completion degree, the effective training motion quantity and the motion capability estimation value by a preset motion capability estimation algorithm, wherein the motion capability estimation algorithm is as follows:

，

Wherein, Update value for athletic ability,Average completion for historical training.

The motion ability update value is a value for updating and replacing the motion ability estimation value.

And G3, updating the motion capability estimated value according to the motion capability updated value.

The motion capability estimate is replaced with a motion capability update value to update the motion capability estimate.

Through the steps, the training method for the middle and primary school students based on the body measurement performance can recalculate the estimated value of the exercise capacity of the calibration user according to the historical training exercise completion degree data, the training exercise completion degree and the effective training exercise quantity of the user, so that the more matched training exercise quantity can be set in the next training exercise.

Further, the primary and secondary school student exercise training method based on the body measurement results further comprises a score lifting potential estimation algorithm for estimating the lifting space of the body measurement results of the user, and the score lifting potential estimation algorithm comprises the following steps:

i1, defining a user age value as a screening age value, defining current body type data as screening body type data, and defining a motion capability estimated value as a current user motion capability value;

Screening the age value as the age value of the user needing to perform the physical measurement score improving space estimation at present;

screening the body type data to obtain body type data of a user needing to perform body measurement result improving space estimation at present;

The current user athletic ability value is an athletic ability estimated value of the user currently needing to perform the physical measurement performance lifting space estimation.

I2, acquiring user identity identification information with the user age value identical to the screening age value from the motion information database according to the screening age value, defining the user identity identification information as peer user information, and combining all peer user information to generate a peer user information group;

The same-age user information is user identification information with the same age as the current user;

The peer user information group is a collection of all peer user information.

I3, generating a body type screening interval according to the screening body type data and a preset fluctuation threshold;

the fluctuation threshold is a preset reference value for determining a numerical interval together with the screening body type data,

The body type screening section is a numerical section, and the upper limit value and the lower limit value of the body type screening section can be obtained by adding or subtracting the fluctuation threshold to or from the screening body type data, thereby determining the body type screening section.

I4, acquiring the same-age user information of which the current body type data fall into the body type screening interval from the same-age user information group, defining the same-age user information as same-body type user information, and combining all the same-body type user information to generate a same-body type user information group;

the homonym user information is user information of the homonym with the current body type data in the body type screening interval in the homonym user information group, namely the user information of the homonym with the body type similar to the body type of the current user;

The homotypic user information group is a collection of homotypic user information.

I5, obtaining corresponding current body measurement results and motion ability estimated values according to all the body type user information in the body type user information group, and calculating motion result correlation coefficients by a preset correlation coefficient algorithm;

The correlation coefficient algorithm is a preset algorithm and is used for calculating the correlation between the current body measurement result and the motion capability estimated value of the user information of each body type, and the existing correlation coefficient algorithm, such as a pearson correlation coefficient algorithm, a spearman correlation coefficient algorithm, a kendel correlation coefficient algorithm and the like, can be used for calculating the correlation coefficient;

The athletic performance correlation coefficient is a correlation coefficient between the physical performance and the athletic ability estimation.

I6, calculating an average motion capability estimation value of the group according to the motion capability estimation values corresponding to all the homotypic user information;

The group average motion capability estimation is the average of motion capability estimates corresponding to all homotypic user information.

I7, obtaining the maximum value from the motion capability estimated values corresponding to all the homotypic user information and defining the maximum motion capability as the group maximum motion capability;

The maximum motion capability of the group is the maximum value of motion capability estimated values corresponding to all the homotypic user information.

I8, calculating a motion capability potential threshold value according to the group average motion capability estimation value and the group maximum motion capability calculation average value;

the athletic performance potential threshold is an average of the group average athletic performance estimate and the group maximum athletic performance.

I9, calculating a predicted motion potential lifting value according to the motion potential threshold value and the current user motion capability value to obtain a difference value;

The estimated motion potential improvement value is the difference between the motion capability potential threshold value and the motion capability value of the current user and is used for estimating the motion capability improvement potential of the current user.

I10, calculating a group average body measurement score according to the average value of the current body measurement scores corresponding to all the same-body type user information;

the group average body measurement results are the average value of the current body measurement results corresponding to all the homotypic user information.

I11, calculating a score ratio example value by calculating a quotient according to the group average score and the group average exercise capacity estimation value;

The score-ability ratio value is a quotient of the group average score and the group average exercise ability estimation value, and represents a proportional relationship between the score and the exercise ability estimation value.

And i12, if the estimated movement potential lifting value is larger than 0, calculating the estimated movement potential lifting value according to the product of the estimated movement potential lifting value, the performance capability proportion value and the movement performance correlation coefficient.

The estimated score-improving potential value is that a user may have a score-improving space, and the physical measurement score of the user may be improved by properly increasing the quantity of motion.

Through the steps, the student sports training method based on the body measurement results can analyze the potential of the current user in improving the results according to the data information of other users with the same age and similar body types so as to estimate the space for improving the body measurement results of the users.

The foregoing description of the preferred embodiments of the application is not intended to limit the scope of the application in any way, including the abstract and drawings, in which case any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.

Claims (9)

1. The utility model provides a pupil exercise training method based on body measurement achievement which is characterized by comprising the following steps:

A1, acquiring corresponding current body measurement results and historical body measurement results data from a preset motion information database according to preset user identity identification information and preset motion category information;

a2, calculating a score stabilizing interval and a score change trend value according to the current score value and the historical score data by a preset score estimation algorithm;

a3, acquiring corresponding current body type data and historical body type data from the motion information database according to the user identity identification information;

a4, calculating a body type index according to the current body type data and the historical body type data by using a preset body type quantitative algorithm;

a5, generating basic training exercise quantity according to the score stabilizing interval, the body type index and a preset body type exercise quantity comparison table and a preset basic exercise quantity algorithm;

a6, acquiring corresponding historical training motion quantity data and a preset motion capability estimated value from a motion information database according to the user identity identification information;

a7, calculating the planned training exercise quantity according to the basic training exercise quantity, the achievement change trend value and the exercise capacity estimated value by a preset exercise quantity adjusting algorithm;

A8, acquiring actual training motion data through a preset motion quantity acquisition method in a preset training period;

a9, calculating effective training motion quantity according to actual motion data, a score stability interval, a preset user age value and motion category information by a preset effective motion analysis algorithm;

A10, calculating the training exercise completion degree according to the effective training exercise amount and the planned training exercise amount by a preset training completion degree algorithm;

A11, acquiring corresponding historical training exercise completion degree data from an exercise information database according to the user identity identification information;

And A12, updating the motion capability estimation value according to the training motion completion degree, the effective training motion quantity, the historical training motion completion degree data and the motion capability estimation value by a preset motion capability updating algorithm.

2. The body measurement score-based athletic training method of a pupil of claim 1, wherein the body measurement score estimation algorithm comprises the steps of:

b1, calculating an average body measurement score value according to a preset average score algorithm according to the current body measurement score value and the historical body measurement score data, wherein the average score algorithm is as follows: ，

Wherein, For average body score value,For current body measurement achievements,For the weight value corresponding to the current body measurement result,For the i-th measured body score value in the historical body score data,Weight value corresponding to the i-th measured score value in the historical score data, n being the number of data in the historical score data, whereinAnd (2) and；

And B2, calculating a body measurement score standard deviation according to the current body measurement score value and the historical body measurement score data by a preset weighted standard deviation algorithm, wherein the weighted standard deviation algorithm is as follows:

,

Wherein, Standard deviation of measured results;

Calculating a body measurement confidence interval according to an average body measurement score value, a body measurement score standard deviation and a preset confidence level coefficient by using a preset body measurement confidence interval algorithm, and defining the body measurement confidence interval as a score stability interval, wherein the body measurement confidence interval algorithm is as follows:

，

Wherein, For the body measurement score confidence interval,Is a confidence level coefficient;

B4, generating a score linear fitting function according to the current score value and the historical score data by a preset linear regression algorithm;

And B5, determining a corresponding function slope value according to the score linear fitting function and defining the function slope value as a score change trend value.

3. The body measurement performance-based athletic training method of a pupil of claim 2, wherein the body type quantification algorithm comprises the steps of:

C1, acquiring a current height value and a current weight value from current body type data;

c2, calculating a current body mass index according to the current height value and the current weight value by a preset body mass algorithm;

c3, acquiring historical height data and corresponding historical weight data according to the historical body type data;

c4, generating historical body quality index data according to the historical height data and the corresponding historical weight data by using a body quality algorithm;

And C5, calculating a body mass index according to the calculated current body mass index and the historical body mass index data by a preset body mass average algorithm, wherein the body mass average algorithm is as follows:

，

Wherein, Is body type index,For the current body mass index,Is the/>, in the historical body mass index dataThe data of the plurality of data,Is the/>, in the historical body mass index dataWeight value corresponding to each dataIs the number of data in the historical body mass index data.

4. A method of training a pupil exercise based on body measurements as claimed in claim 3, characterized in that the basic exercise amount algorithm comprises the steps of:

D1, acquiring corresponding apparent fixed exercise amount from a body type exercise amount comparison table according to the body type index;

D2, obtaining a corresponding lower limit value according to the score stabilization interval and defining the lower limit value as a score stabilization lower limit value, and obtaining a corresponding upper limit value and defining the upper limit value as a score stabilization upper limit value;

And D3, determining a basic training exercise amount range according to a score stability lower limit value, a score stability upper limit value, a table exercise amount and a preset maximum score value by using a preset basic exercise amount range algorithm, wherein the basic exercise amount range algorithm is as follows:

,

,

Wherein, Upper limit value of basic training exercise quantity range,For the lower limit of the basic training exercise quantity range,For maximum score value,For the achievement to stabilize the upper limit,For the score stability lower limit,The exercise amount is determined for the watch;

and D4, calculating basic training exercise quantity according to a basic training exercise quantity range, a body type index and a preset standard body type index interval by using a preset exercise quantity correction algorithm, wherein the exercise quantity correction algorithm is as follows:

,

Wherein, Training exercise amount for basis,Is the upper limit value of the standard body type index interval,The lower limit value of the standard body type index section.

5. The body measurement performance-based athletic training method of a pupil as defined in claim 4, wherein the motion amount adjustment algorithm is:

,

Wherein, To plan the amount of exercise,For the motion capability estimation,For score change trend value,Is a preset adjustment coefficient.

6. The body-score-based athletic training method of a pupil of claim 5, wherein the effective athletic analysis algorithm includes the steps of:

E1, acquiring exercise speed data, exercise displacement data and exercise heart rate data from actual exercise data;

e2, respectively extracting corresponding speed characteristic data and displacement characteristic data according to the movement speed data and the movement displacement data by a preset characteristic extraction algorithm;

E3, identifying and determining corresponding training period data through a pre-training period identification model corresponding to the motion category information according to the speed characteristic data and the displacement characteristic data;

E4, acquiring corresponding heart rate data from exercise heart rate data according to the training period data and defining the corresponding heart rate data as training heart rate data;

e5, calculating an effective training heart rate threshold value according to a preset effective training heart rate algorithm according to a preset user age value, a score stability lower limit value and a maximum score value of a score stability interval, wherein the effective training heart rate algorithm is as follows:

,

Wherein, To train heart rate threshold effectively,For the age value of the user,Is a preset adjusting coefficient;

e6, acquiring period data corresponding to heart rate data of not less than an effective training heart rate threshold value from the training heart rate data and defining the period data as effective training exercise period data;

And E7, obtaining effective training duration according to the effective training exercise period data statistics and defining the effective training exercise amount.

7. The body measurement performance based athletic training method of a pupil of claim 6, wherein the training completion algorithm comprises the steps of:

F1, obtaining total training time according to training time data statistics;

f2, calculating a difference value according to the total training duration and the effective training exercise amount to obtain an effective training duration and defining the effective training exercise amount as an effective training exercise amount;

and F3, calculating the training exercise completion degree according to the effective training exercise quantity, the low-efficiency training exercise quantity and the planned training exercise quantity by a preset exercise quantity comprehensive completion degree algorithm, wherein the exercise quantity comprehensive completion degree algorithm is as follows:

，

Wherein, To train the degree of completion of the exercise,For a preset adjustment coefficient,For the inefficient training of the amount of exercise,To effectively train exercise amount,To plan training exercise amount,Is a preset adjustment coefficient.

8. The athletic training method of a pupil based on body measurements of claim 7, wherein the athletic ability update algorithm comprises the steps of:

G1, calculating an average historical training completion degree according to the average historical training completion degree data;

And G2, calculating a motion capability update value according to the training motion completion degree, the historical training average completion degree, the effective training motion quantity and the motion capability estimation value by a preset motion capability estimation algorithm, wherein the motion capability estimation algorithm is as follows:

，

Wherein, Update value for athletic ability,Average completion for historical training;

And G3, updating the motion capability estimated value according to the motion capability updated value.

9. The body-score-based athletic training method of a pupil of claim 8, further comprising a score-lifting potential prediction algorithm comprising the steps of:

i1, defining a user age value as a screening age value, defining current body type data as screening body type data, and defining a motion capability estimated value as a current user motion capability value;

i2, acquiring user identity identification information with the user age value identical to the screening age value from the motion information database according to the screening age value, defining the user identity identification information as peer user information, and combining all peer user information to generate a peer user information group;

i3, generating a body type screening interval according to the screening body type data and a preset fluctuation threshold;

i4, acquiring the same-age user information of which the current body type data fall into the body type screening interval from the same-age user information group, defining the same-age user information as same-body type user information, and combining all the same-body type user information to generate a same-body type user information group;

i5, obtaining corresponding current body measurement results and motion ability estimated values according to all the body type user information in the body type user information group, and calculating motion result correlation coefficients by a preset correlation coefficient algorithm;

i6, calculating an average motion capability estimation value of the group according to the motion capability estimation values corresponding to all the homotypic user information;

i7, obtaining the maximum value from the motion capability estimated values corresponding to all the homotypic user information and defining the maximum motion capability as the group maximum motion capability;

i8, calculating a motion capability potential threshold value according to the group average motion capability estimation value and the group maximum motion capability calculation average value;

i9, calculating a predicted motion potential lifting value according to the motion potential threshold value and the current user motion capability value to obtain a difference value;

i10, calculating a group average body measurement score according to the average value of the current body measurement scores corresponding to all the same-body type user information;

i11, calculating a score ratio example value by calculating a quotient according to the group average score and the group average exercise capacity estimation value;

And i12, if the estimated movement potential lifting value is larger than 0, calculating the estimated movement potential lifting value according to the product of the estimated movement potential lifting value, the performance capability proportion value and the movement performance correlation coefficient.