CN111530036B - Running exercise score prediction method and electronic equipment - Google Patents

Running exercise score prediction method and electronic equipment Download PDF

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CN111530036B
CN111530036B CN202010405190.0A CN202010405190A CN111530036B CN 111530036 B CN111530036 B CN 111530036B CN 202010405190 A CN202010405190 A CN 202010405190A CN 111530036 B CN111530036 B CN 111530036B
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CN111530036A (en
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刘新
饶旋
汤彧
黄慕一
牛浩田
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Guangdong Coros Sports Technology Co Ltd
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Abstract

The invention discloses a running exercise score prediction method and electronic equipment, wherein the prediction method comprises the following steps: establishing a first model equation reflecting the relationship between the heart rate and the pace of the user; quantifying the magnitude of the physical force value; respectively establishing a plurality of second model equations corresponding to a plurality of different courses, wherein the second model equations reflect the relationship between the body force value and the pace of a certain course; selecting one course from a plurality of different courses as a standard course to obtain a reserve heart rate ratio reflecting the user in the standard course; obtaining a physical strength value reflecting the athletic performance of the user according to a calculation formula of the reserve heart rate ratio, a first model equation and a second model equation; substituting the physical strength value into a second model equation corresponding to the course to be predicted to obtain a predicted pace and further obtain predicted time; the prediction method can reflect the current physical ability level of the user and the result potential of each course, has relatively accurate prediction result, can support the prediction of the running results of various courses for the user, and is convenient to use.

Description

Running exercise score prediction method and electronic equipment
Technical Field
The invention relates to the technical field of running sport competition result prediction, in particular to a running sport result prediction method and electronic equipment.
Background
Running enthusiasts are increasing because running exercise is not restricted by factors such as fields, equipment and the like, and under the environment, marathon gradually becomes the most popular and most influential mass sports. With the dramatic increase in the number of events, a large number of amateur runners are involved in the training and competition of marathons. The irregular sports ability of the amateur runners, the training randomness and the game blindness not only increase the risk coefficient of the amateur runners in the game, but also are very unfavorable for the sustainable development of the sports. How to accurately predict the marathon performance of the user is important for reasonably arranging the training and participating course of marathon. At present, a method for predicting the marathon performance of a user generally predicts according to the usual training performance, the user needs to run the marathon in the whole course, amateur runners with poor exercise capacity are difficult to finish, the prediction difficulty is high, the universality is poor, the prediction efficiency is low, and some amateur or professional athletes only want to run half horse, 5KM, 10KM and other competition courses, so that the running performance of the user on any competition course is difficult to predict relatively accurately.
Disclosure of Invention
One of the objectives of the present invention is to provide a method for predicting running performance of a user, so as to relatively accurately predict the running performance of the user for different courses of the race according to the physical strength level of the user.
Another objective of the present invention is to provide an electronic device for predicting running performance, so as to relatively accurately predict the running performance of a user for different courses according to the physical strength level of the user.
In order to achieve the above object, the present invention discloses a running exercise performance prediction method, which includes:
acquiring real-time heart rate and real-time movement speed data in the recent N days of movement of a current user, and establishing a first model equation reflecting the relationship between the heart rate and the pace of the user;
self-defining a closed numerical value interval D1, and quantifying the physical force value by adopting the numerical value in the numerical value interval D1;
respectively establishing a plurality of second model equations corresponding to a plurality of different courses, wherein the second model equations reflect the relationship between the body force value and the pace of a certain course;
selecting one course from a plurality of different courses as a standard course, and acquiring a reserve heart rate ratio reflecting the appropriate exercise intensity of the user under the standard course;
obtaining the maximum heart rate and the resting heart rate of the user, and obtaining the exercise heart rate of the user relative to the standard course according to the calculation formula of the reserve heart rate ratio;
substituting the exercise heart rate into the first model equation to obtain a standard pace of the user relative to the standard course;
substituting the standard pace into a second model equation corresponding to the standard course to obtain a physical strength value reflecting the athletic performance of the user;
and substituting the physical strength value into a second model equation corresponding to the course to be predicted to obtain the predicted pace so as to obtain the predicted time for the user to run through the course to be predicted.
Compared with the prior art, the running exercise performance prediction method has the advantages that a first model equation and a second model equation are established, the first model equation reflects the latest exercise performance state of the user, and the second model equation reflects the relationship between the lower body strength value and the pace of the user in a certain race course; calculating the exercise pace of the user under the known standard course according to the reserve heart rate ratio suitable for the current user and the first model equation, and then calculating the physical strength value of the user according to the second model equation; after the physical strength value is obtained, substituting the physical strength value into a second model equation corresponding to a certain course to be predicted, so that the predicted pace of the user can be obtained, and the predicted time, namely the running score of the user, can be obtained; therefore, the prediction method calculates the physical strength value of the user according to the athletic performance data of the user in a recent period of time and the exercise strength possessed by the user, and then performs score prediction according to the physical strength value, so that the prediction result is relatively accurate, the user can predict running scores of various race courses, and the use is convenient.
Preferably, the method for acquiring the real-time heart rate and the real-time movement speed comprises the following steps:
s10: judging whether the single-time continuous movement time length T1 of the user is greater than the set time length T2, if so, entering S11; if not, returning;
s11: moving, extracting and judging whether the real-time heart rate and the real-time movement speed in each continuous unit time period meet the following three conditions according to a certain moving period and a time sequence,
the first condition is that: the real-time heart rate fluctuations are within the setpoint B1;
the second condition is that: the real-time movement speed fluctuation is within a set value V1;
a third condition: the reserve heart rate ratio is within a preset interval D2;
and if so, taking the real-time heart rate and the real-time movement speed in the unit time period as the acquisition data.
Preferably, the method for establishing the second model method includes:
the running matching speeds of athletes of different levels corresponding to a plurality of different courses are collected respectively, the different running matching speeds and the physical strength values of different sizes under different courses are matched into data pairs respectively according to the positive correlation between the physical strength values and the running matching speeds, the data pairs under different courses are processed, and a physical strength value-matching speed regression model is established to obtain the second model equation corresponding to each course.
Preferably, the method for obtaining the reserve heart rate ratio comprises the following steps:
acquiring physiological characteristic data and the maximum oxygen uptake data of a user;
making a reserve heart rate ratio look-up table corresponding to the standard course, wherein the reserve heart rate ratio look-up table records reserve heart rate ratios corresponding to users with different physiological characteristics and maximum oxygen intake relative to the standard course;
and inquiring the reserve heart rate ratio inquiry table according to the physiological characteristic data and the maximum oxygen uptake data of the user to obtain the reserve heart rate ratio of the user relative to the standard course.
Preferably, the method for obtaining the maximum oxygen uptake comprises the following steps:
acquiring the age, sex, weight, resting heart rate and maximum heart rate of a user;
collecting a real-time heart rate and a real-time movement speed of a user in the movement process;
selecting a characteristic time period with a preset time length from the current exercise time of the user, and calculating a characteristic average heart rate and a characteristic average speed in the characteristic time period by taking the real-time heart rate and the real-time exercise speed as basic data;
calculating the maximum oxygen uptake of the current user according to the following first formula
Figure BDA0002490223050000031
The first formula:
Figure BDA0002490223050000041
wherein A is a constant of 40-50, P1 is a constant of 7-8, S is a sex constant, male is 1, female is 0; p2 is a constant of 0.1-0.2, G is the user's weight; p3 is a constant of 4-5, V is a characteristic average velocity, P4 is a constant of 3-4, B is a constant of 1-2; c is a constant of 15 to 20, HRFeature(s)Characterised mean heart rate, HRAt restFor the resting heart rate, HR, of the user in a conscious and quiescent statemaxIs the maximum heart rate; a is the age of the user.
Preferably, the real-time heart rate and the real-time movement speed data of the current user in the last 7 days are collected, and the first model equation is established.
Preferably, when the course to be predicted is the distance of the whole marathon, if the predicted time is greater than the set time T3 and the running distance of the current user per month in the last M consecutive months is less than the set distance S1, the exercise performance of the current user is increased by the set time T4 based on the original predicted time.
The invention also discloses electronic equipment for predicting running performance, which comprises a base body, wherein the base body is provided with a heart rate detection module, a speed detection module, an input module, a processing module and an output module;
the heart rate detection module is used for detecting the real-time heart rate of the user;
the speed detection module is used for detecting the real-time movement speed of the user;
the input module is used for receiving external input data;
the processing module comprises a first model generation module, a physical strength value quantification module, a second model generation module, a reserve heart rate ratio acquisition module and a calculation module;
the first model generation module is used for establishing a first model equation reflecting the relationship between the heart rate and the pace of the user through the collected real-time heart rate and real-time movement speed data in the recent N days of movement of the current user;
the physical strength value quantification module is used for quantifying the physical strength value through a numerical value in a self-defined closed numerical value interval D1;
the second model generation module is used for respectively establishing a plurality of second model equations corresponding to a plurality of different courses, and the second model equations reflect the relationship between the lower body force value and the pace of a certain course;
the reserve heart rate ratio acquisition module is used for selecting one of a plurality of different competition courses as a standard competition course and acquiring a reserve heart rate ratio reflecting the appropriate exercise intensity of the user in the standard competition course;
the computing module comprises a first computing module, a second computing module, a third computing module and a fourth computing module;
the first calculation module is used for calculating the exercise heart rate of the user relative to the standard course according to the calculation formula of the reserve heart rate ratio;
the second calculation module is used for substituting the exercise heart rate into the first model equation to obtain the standard pace of the user relative to the standard course;
the third calculation module is used for substituting the standard pace into a second model equation corresponding to the standard course to obtain a physical strength value reflecting the athletic performance of the user;
the fourth calculation module is used for substituting the physical strength value into a second model equation corresponding to the course to be predicted, and calculating to obtain the predicted pace so as to obtain the predicted time for the user to run through the course to be predicted;
the output module is used for outputting the predicted time.
Preferably, the first model generation module comprises a data acquisition module for acquiring a real-time heart rate and a real-time movement speed, and the data acquisition module acquires data by the following method:
s10: judging whether the single-time continuous movement time length T1 of the user is greater than the set time length T2, if so, entering S11; if not, returning;
s11: moving, extracting and judging whether the real-time heart rate and the real-time movement speed in each continuous unit time period meet the following three conditions according to a certain moving period and a time sequence,
the first condition is that: the real-time heart rate fluctuations are within the setpoint B1;
the second condition is that: the real-time movement speed fluctuation is within a set value V1;
a third condition: the reserve heart rate ratio is within a preset interval D2;
and if so, taking the real-time heart rate and the real-time movement speed in the unit time period as the acquisition data.
Preferably, the second model generating module generates the second model equation by using the following method:
the running matching speeds of athletes of different levels corresponding to a plurality of different courses are collected respectively, the different running matching speeds and the physical strength values of different sizes under different courses are matched into data pairs respectively according to the positive correlation between the physical strength values and the running matching speeds, the data pairs under different courses are processed, and a physical strength value-matching speed regression model is established to obtain the second model equation corresponding to each course.
Preferably, the reserve heart rate ratio acquisition module acquires the reserve heart rate ratio by:
acquiring physiological characteristic data and the maximum oxygen uptake data of a user;
making a reserve heart rate ratio look-up table corresponding to the standard course, wherein the reserve heart rate ratio look-up table records reserve heart rate ratios corresponding to users with different physiological characteristics and maximum oxygen intake relative to the standard course;
and inquiring the reserve heart rate ratio inquiry table according to the physiological characteristic data and the maximum oxygen uptake data of the user to obtain the reserve heart rate ratio of the user relative to the standard course.
Preferably, the method for obtaining the maximum oxygen uptake comprises the following steps:
acquiring the age, sex, weight, resting heart rate and maximum heart rate of a user;
collecting a real-time heart rate and a real-time movement speed of a user in the movement process;
selecting a characteristic time period with a preset time length from the current exercise time of the user, and calculating a characteristic average heart rate and a characteristic average speed in the characteristic time period by taking the real-time heart rate and the real-time exercise speed as basic data;
calculating the maximum oxygen uptake of the current user according to the following first formula
Figure BDA0002490223050000062
The first formula:
Figure BDA0002490223050000061
wherein A is a constant of 40-50, P1 is a constant of 7-8, S is a sex constant, male is 1, female is 0; p2 is a constant of 0.1-0.2, G is the user's weight; p3 is a constant of 4-5, V is a characteristic average velocity, P4 is a constant of 3-4, B is a constant of 1-2; c is a constant of 15 to 20, HRFeature(s)Characterised mean heart rate, HRAt restFor the resting heart rate, HR, of the user in a conscious and quiescent statemaxIs the maximum heart rate; a is the age of the user.
Preferably, the processing module further comprises a score correction module; when the course to be predicted is the distance of the whole marathon, if the obtained predicted time is greater than the set time T3 and the running distance of the current user in each month in the last continuous M months is less than the set distance S1, the correction module is used for increasing the set time T4 to the exercise performance of the current user on the basis of the original predicted time so as to correct the predicted time.
The invention also discloses an electronic device for running exercise performance prediction, which comprises:
one or more processors;
a memory:
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for performing a running athletic performance prediction method as described above.
The present invention also discloses a computer readable storage medium comprising a computer program executable by a processor to perform a method of running athletic performance prediction as described above.
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Fig. 1 is a schematic flow chart illustrating an implementation of the running performance prediction method according to the embodiment of the present invention.
Fig. 2 is a schematic flow chart of a method for acquiring motion state data according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of a method for dynamically acquiring a reserve heart rate ratio according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Fig. 5 is a schematic diagram of a logical structure of a processing module in the electronic device.
FIG. 6 is a diagram illustrating fitting of a first model equation in an embodiment of the present invention.
Detailed Description
In order to explain technical contents, structural features, and objects and effects of the present invention in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.
As shown in fig. 1, the invention discloses a running exercise performance prediction method, which comprises the following steps (the following steps do not limit the sequence):
s1: acquiring motion state data of a current user in the motion process of the last N days, wherein the motion state data comprises a real-time heart rate and a real-time motion speed, and establishing a first model equation reflecting the relationship between the heart rate and the pace of the user; in the present embodiment, the exercise state data of the user for the last 7 days is preferred according to the mechanism of human muscle exercise recovery.
S2: defining a closed numerical range D1, and quantifying the physical strength value by using the numerical value in the numerical range D1. Since there is no unified quantification standard for the physical strength value, in order to perform statistical calculation on this reference variable, in this embodiment, a customized numerical range D1 is used to quantify the physical strength value, specifically, the numerical range D1 is [0,100], that is, the minimum physical strength value is defined as 0, and the maximum physical strength value is defined as 100.
S3: and respectively establishing a plurality of second model equations corresponding to a plurality of different courses, wherein the second model equations reflect the relationship between the body force value and the pace of a certain course. Specifically, if the course performances of the full horse (full-range marathon), the half horse (half-range marathon), the 5KM and the 10KM are to be predicted by the prediction method in the embodiment, four second model equations respectively corresponding to the courses of the full horse, the half horse, the 5KM and the 10KM need to be established. In addition, the establishment method of the second model equation can be obtained through theoretical data checking calculation, and can also be obtained through fitting statistical data of a past marathon game.
S4: selecting one course from a plurality of different courses as a standard course (hereinafter, half horse is taken as the standard course for distance description), and obtaining a reserve heart rate ratio rho reflecting the appropriate exercise intensity of a user under the half horse; the reserve heart rate ratio ρ reflects the exercise intensity of the user, and is calculated by the formula:
Figure BDA0002490223050000081
wherein HR isExercise of sportsIs the user's exercise heart rate, HRmaxIs the maximum heart rate, HR, of the userAt restThe resting heart rate of the user in a waking and resting state; maximum heart rate and rest heart rate accessible portable check out test set detect and obtain, under the condition that the user knows the maximum heart rate of oneself and rest heart rate numerical value, also can be by user manual setting, in addition, maximum heart rate still can be throughUser age calculation, HRmax208-0.7 a, a is the age of the user. Likewise, for the reserve heart rate ratio ρ, it may also be set manually, if the user knows his exercise intensity that is adaptable with respect to the horse half.
S5: and obtaining the maximum heart rate and the resting heart rate of the user, and obtaining the exercise heart rate of the user relative to the semihorse when the user exercises at the reserve heart rate ratio rho (also called exercise intensity or training intensity) according to a calculation formula of the reserve heart rate ratio rho after the reserve heart rate ratio rho is determined.
S6: and substituting the exercise heart rate into the first model equation to obtain the standard pace of the user relative to the half horse.
And S7, substituting the standard pace into a second model equation corresponding to the semihorse to obtain a physical strength value reflecting the athletic performance of the user, namely the physical strength state value which can be displayed by the user at present.
S8: and substituting the physical strength value into a second model equation corresponding to the course to be predicted to obtain the predicted pace so as to obtain the predicted time for the user to run through the course to be predicted. For example, the physical strength value calculated by the half horse is substituted into the second model equation corresponding to the full horse, so that the predicted pace of the user relative to the full horse can be obtained, and the predicted time can be further obtained.
In the step 1, when the motion state data is collected, some data irrelevant to the motion is filtered out to the maximum extent, so as to improve the prediction accuracy, as shown in fig. 2, the method for collecting the motion state data includes:
s10: judging whether the single-time continuous movement time length T1 of the user is greater than the set time length T2, if so, entering S11; if not, returning;
s11: moving, extracting and judging whether the real-time heart rate and the real-time movement speed in each continuous unit time period meet the following three conditions according to a certain moving period and a time sequence:
the first condition is that: the real-time heart rate fluctuations are within the setpoint B1;
the second condition is that: the real-time movement speed fluctuation is within a set value V1;
a third condition: the real-time heart rate ratio of the reserve heart rate calculated according to the real-time heart rate value is within a preset interval D2;
if yes, the real-time heart rate and the real-time movement speed in the unit time period are used as collected data, and if not, the data collection in the unit time period is abandoned.
Specifically, T2 is 15 minutes, B1 is 10bpm, V1 is 0.5m/s, and D2 is [ 50%, 90% ], when acquiring motion state data through an electronic device, it is first determined whether a user continuously moves for more than 15 minutes, if not, the data in the motion time period is not acquired, and if so, the real-time heart rate and the real-time motion speed in each continuous unit time period are extracted and determined to be analyzed in a time sequence with a certain movement period, where the unit time period is 2 minutes as an example, the extraction is to extract data in time periods of 1 st to 120 th seconds, 2 nd to 121 th seconds, and 3 rd to 122 th seconds … …, and the like, and for any 2-minute motion state data, only in the case that the fluctuation of the real-time heart rate is not more than 10bpm, the fluctuation of the real-time motion speed is not more than 0.5m/s, and only in the case that the real-time motion state data in any 2 minutes is not more than 10bpm, The 2 minute exercise status data is collected when the real-time reserve heart rate ratio calculated from the real-time heart rate value is between 50% and 90%, otherwise, the data is discarded.
After the motion state data meeting the requirements are collected, average operation is respectively carried out on the real-time heart rate and the real-time motion speed in each 2-minute time period through an arithmetic mean algorithm to obtain a unit average heart rate and a unit average motion speed, then a plurality of groups of the unit average heart rate and the unit average speed are drawn in a rectangular coordinate system, as shown in figure 6, a first model equation y is fitted through a fitting method1＝k1*x1+b1Wherein k is1And b1Is a constant number y1Representing the exercise heart rate, x, that the user may exhibit1Indicating that the user may exhibit running pace.
Further, the method for establishing the second model method comprises the following steps:
the running matching speeds of athletes of different levels corresponding to a plurality of different courses are collected respectively, the different running matching speeds and the physical strength values of different sizes under different courses are matched into data pairs respectively according to the positive correlation between the physical strength values and the running matching speeds, the data pairs under different courses are processed, and a physical strength value-matching speed regression model is established to obtain a second model equation corresponding to each course.
Specifically, in this embodiment, the full horse match speed, half horse match speed, 5KM match speed and 10KM match speed of different players in the past marathon game are collected, and the highest match speed 2:50 (the highest record of the current marathon) is corresponding to the highest physical strength value 100, and the following data are respectively corresponding to other match speeds and physical strength values in sequence, and the formed data are shown in table 1 below:
Figure BDA0002490223050000101
TABLE 1
According to the data in the table 1, each individual force value and the corresponding full horse pace are described in a rectangular coordinate system, and a second model equation matched with the full horse is obtained through a data fitting method:
y2＝k2*x2+b2wherein k is2And b2Is a constant number y2Representing a physical strength value, x, that a user may exhibit2Representing a full horse match rate that the user may exhibit;
similarly, each individual force value and the corresponding half horse matching speed are described in a rectangular coordinate system, and a second model equation matched with the half horse is obtained through a data fitting method:
y3＝k3*x3+b3wherein k is3And b3Is a constant number y3Representing a physical strength value, x, that a user may exhibit3Representing the half horse racing that the user may present, other races, and so on.
Further, in step 4, as shown in fig. 3, the method for dynamically acquiring the reserve heart rate ratio includes:
s40: acquiring physiological characteristic data of a user and the possessed maximum oxygen uptake data, wherein the physiological characteristic data comprises data of the age, the sex and the like of the user, and the maximum oxygen uptake can be directly generated according to the physiological characteristic data or can be manually set by the user;
s41: making a reserve heart rate ratio look-up table corresponding to the semihorse, wherein the reserve heart rate ratio look-up table records reserve heart rate ratios corresponding to users with different physiological characteristics and maximum oxygen intake relative to the semihorse, and the reserve heart rate ratios are shown in the following tables 2 and 3;
s42: and inquiring a reserve heart rate ratio inquiry table according to the physiological characteristic data and the maximum oxygen uptake data of the user to obtain the reserve heart rate ratio of the user relative to the horse.
Figure BDA0002490223050000111
TABLE 2
Figure BDA0002490223050000112
Figure BDA0002490223050000121
TABLE 3
Therefore, when the user is male, the reserve heart rate ratio of the user relative to the semihorse is obtained based on the age of the user and the maximum oxygen uptake data lookup table 2, and when the user is female, the reserve heart rate ratio of the user relative to the semihorse is obtained based on the age of the user and the maximum oxygen uptake data lookup table 3.
The maximum oxygen uptake is preferably obtained by:
acquiring the age, sex, weight, resting heart rate and maximum heart rate of a user;
collecting a real-time heart rate and a real-time movement speed of a user in the movement process;
selecting a characteristic time period with a preset time length from the current exercise time of the user, and calculating a characteristic average heart rate and a characteristic average speed in the characteristic time period by taking the real-time heart rate and the real-time exercise speed as basic data;
calculating the maximum oxygen uptake of the current user according to the following first formula
Figure BDA0002490223050000122
The first formula:
Figure BDA0002490223050000123
wherein A is a constant of 40-50, P1 is a constant of 7-8, S is a sex constant, male is 1, female is 0; p2 is a constant of 0.1-0.2, G is the user's weight; p3 is a constant of 4-5, V is a characteristic average velocity, P4 is a constant of 3-4, B is a constant of 1-2; c is a constant of 15 to 20, HRFeature(s)Characterised mean heart rate, HRAt restFor the resting heart rate, HR, of the user in a conscious and quiescent statemaxIs the maximum heart rate; a is the age of the user.
Preferably, in the above embodiment of maximum oxygen uptake acquisition, in order to make the calculated maximum oxygen uptake maximally approach the actual level, the real-time heart rate and the real-time exercise speed may be screened by:
s400: judging whether the time length T3 of the single continuous motion of the user is greater than the set time length T4, if so, entering S401; if not, returning; in this example, T4 was 20 minutes;
s401: and moving, extracting and judging whether all real-time heart rates in each continuous unit time period (1 minute) are in a set numerical value interval D3 of the maximum heart rate according to a certain moving period and a time sequence, if so, bringing the real-time heart rates and the real-time movement speed in the current unit time period into a data acquisition range, and if not, abandoning the acquisition of the real-time heart rates and the real-time movement in the current unit time period. In the present embodiment, the numerical range D3 is preferably: [ 70% HRmax,95％*HRmax]。
For the selection of the characteristic time period, preferably, when the continuous movement time period T3 of the user is greater than 30 minutes, the characteristic time period is a period of time within 10-30 minutes, and when the continuous movement time period T3 of the user is less than or equal to 30 minutes, the characteristic time period is a period of time within 10-T3.
In summary, the process of predicting the running performance by using the prediction method in the above embodiment is as follows: firstly, establishing a first model equation through motion state data of a current user in the last 7 days; then, four second model equations respectively corresponding to the whole horse, the half horse, the 5KM and the 10KM are established through table 1; then, according to the maximum oxygen uptake
Figure BDA0002490223050000131
The current maximum oxygen uptake of the user is calculated by the calculation formula, and then according to the age, the sex and the maximum oxygen uptake of the user, a table 2 (male) or a table 3 (female) is inquired to obtain the reserve heart rate ratio of the user; calculating the exercise heart rate of the user according to a calculation formula of the reserve heart rate ratio, and substituting the exercise heart rate into a first model equation y1Obtaining the half horse matching speed of the user relative to the half horse, and substituting the calculated half horse matching speed into a second model equation y3Obtaining the physical strength value of the user, and then substituting the calculated physical strength value into a second model equation y2And then dividing the distance of the whole horse by the speed of the whole horse to obtain the predicted time of the whole horse, namely the running score of the user relative to the whole horse. And similarly, substituting the calculated physical strength value into a second model equation corresponding to other courses to respectively obtain corresponding pace distribution and finally obtain corresponding running achievement. Therefore, the prediction method calculates the physical strength value of the user according to the athletic performance data of the user in a recent period of time and the exercise strength possessed by the user, then performs score prediction according to the physical strength value, reflects the current physical strength level of the user and the score potential of each course, has relatively accurate prediction result, can support the prediction of the running scores of various courses by the user, and is convenient to use.
Further, when the course to be predicted is a full horse, if the obtained predicted time is greater than the set time T1 and the running distance of the current user per month in the last M consecutive months is less than the set distance S1, the athletic performance of the current user is increased by the set time T2 based on the original predicted time. For example, when the predicted time of a certain user relative to the whole horse is more than 4.5h, if the total distance of running of the user in each month is less than 100KM for the last 3 consecutive months, the athletic performance of the user is increased by 40 minutes for the calculated predicted time, so as to improve the prediction accuracy.
The invention also discloses an electronic device for running performance prediction, which comprises a portable base body, wherein the base body is provided with a heart rate detection module, a speed detection module, an input module, a processing module and an output module, and the electronic device is shown in fig. 4 and 5. And the heart rate detection module comprises a heart rate sensor and is used for detecting the real-time heart rate of the user. The speed detection module is preferably a GPS positioning system and is used for detecting the real-time movement speed of the user. The input module is used for receiving external input data, such as the data of the age, the sex, the weight, the resting heart rate, the maximum heart rate and the like of the user can be input through the input module.
The processing module comprises a first model generation module, a physical strength value quantification module, a second model generation module, a reserve heart rate ratio acquisition module and a calculation module.
And the first model generation module is used for establishing a first model equation reflecting the relationship between the heart rate and the pace of the user according to the acquired real-time heart rate and real-time movement speed data in the current N-day movement process of the user.
And the physical strength value quantification module is used for quantifying the physical strength value through a numerical value in a self-defined closed numerical value interval D1.
And the second model generation module is used for respectively establishing a plurality of second model equations corresponding to a plurality of different courses, and the second model equations reflect the relationship between the body force value and the pace of a certain course.
And the reserve heart rate ratio acquisition module is used for selecting one of a plurality of different competition courses as a standard competition course and acquiring a reserve heart rate ratio reflecting the appropriate exercise intensity of the user under the standard competition course.
And the computing module comprises a first computing module, a second computing module, a third computing module and a fourth computing module.
And the first calculation module is used for calculating the exercise heart rate of the user relative to the standard course according to the calculation formula of the reserve heart rate ratio.
And the second calculation module is used for substituting the exercise heart rate into the first model equation to obtain the standard pace of the user relative to the standard course.
And the third calculation module is used for substituting the standard pace into a second model equation corresponding to the standard course to obtain a physical strength value reflecting the athletic performance of the user.
And the fourth calculation module is used for substituting the physical strength value into the second model equation corresponding to the course to be predicted, and calculating to obtain the predicted pace so as to obtain the predicted time for the user to run through the course to be predicted.
And the output module is used for outputting the predicted time.
Preferably, the processing module further comprises a performance correcting module, and when the course to be predicted is the distance of the whole marathon, if the obtained predicted time is greater than the set time T3 and the running distance of the current user in each month of last M consecutive months is less than the set distance S1, the correcting module is configured to increase the exercise performance of the current user by the set time T4 on the basis of the original predicted time so as to correct the predicted time.
For the working principle and the working process of the electronic device, the running performance prediction method is described in detail, and is not described herein again.
Yet another electronic device for running athletic performance prediction is disclosed that includes one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for performing a running athletic performance prediction method as described above.
The present invention also discloses a computer readable storage medium comprising a computer program executable by a processor to perform a running athletic performance prediction method as described above.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, therefore, the present invention is not limited by the appended claims.

Claims (15)

1. A running performance prediction method, comprising:
acquiring real-time heart rate and real-time movement speed data in the recent N days of movement of a current user, and establishing a first model equation reflecting the relationship between the heart rate and the pace of the user;
self-defining a closed numerical value interval D1, and quantifying the physical force value by adopting the numerical value in the numerical value interval D1;
respectively establishing a plurality of second model equations corresponding to a plurality of different courses, wherein the second model equations reflect the relationship between the body force value and the pace of a certain course;
selecting one course from a plurality of different courses as a standard course, and acquiring a reserve heart rate ratio reflecting the appropriate exercise intensity of the user under the standard course;
obtaining the maximum heart rate and the resting heart rate of the user, and obtaining the exercise heart rate of the user relative to the standard course according to the calculation formula of the reserve heart rate ratio;
substituting the exercise heart rate into the first model equation to obtain a standard pace of the user relative to the standard course;
substituting the standard pace into a second model equation corresponding to the standard course to obtain a physical strength value reflecting the athletic performance of the user;
and substituting the physical strength value into a second model equation corresponding to the course to be predicted to obtain the predicted pace so as to obtain the predicted time for the user to run through the course to be predicted.
2. The running athletic performance prediction method of claim 1, wherein the real-time heart rate and the real-time exercise speed are collected by a method comprising:
s10: judging whether the single-time continuous movement time length T1 of the user is greater than the set time length T2, if so, entering S11; if not, returning;
s11: moving, extracting and judging whether the real-time heart rate and the real-time movement speed in each continuous unit time period meet the following three conditions according to a certain moving period and a time sequence,
the first condition is that: the real-time heart rate fluctuations are within the setpoint B1;
the second condition is that: the real-time movement speed fluctuation is within a set value V1;
a third condition: the reserve heart rate ratio is within a preset interval D2;
and if so, taking the real-time heart rate and the real-time movement speed in the unit time period as the acquisition data.
3. The running exercise performance prediction method of claim 1, wherein the second model equation is established by a method comprising:
the running matching speeds of athletes of different levels corresponding to a plurality of different courses are collected respectively, the different running matching speeds and the physical strength values of different sizes under different courses are matched into data pairs respectively according to the positive correlation between the physical strength values and the running matching speeds, the data pairs under different courses are processed, and a physical strength value-matching speed regression model is established to obtain the second model equation corresponding to each course.
4. The running athletic performance prediction method of claim 1, wherein the method for obtaining the reserve heart rate ratio comprises:
acquiring physiological characteristic data and the maximum oxygen uptake data of a user;
making a reserve heart rate ratio look-up table corresponding to the standard course, wherein the reserve heart rate ratio look-up table records reserve heart rate ratios corresponding to users with different physiological characteristics and maximum oxygen intake relative to the standard course;
and inquiring the reserve heart rate ratio inquiry table according to the physiological characteristic data and the maximum oxygen uptake data of the user to obtain the reserve heart rate ratio of the user relative to the standard course.
5. The running athletic performance prediction method of claim 4, wherein the maximum oxygen uptake acquisition method comprises:
acquiring the age, sex, weight, resting heart rate and maximum heart rate of a user;
collecting a real-time heart rate and a real-time movement speed of a user in the movement process;
selecting a characteristic time period with a preset time length from the current exercise time of the user, and calculating a characteristic average heart rate and a characteristic average speed in the characteristic time period by taking the real-time heart rate and the real-time exercise speed as basic data;
calculating the maximum oxygen uptake of the current user according to the following first formula
Figure FDA0002622303700000031
The first formula:
Figure FDA0002622303700000032
wherein A is a constant of 40-50, P1 is a constant of 7-8, S is a sex constant, male is 1, female is 0; p2 is a constant of 0.1-0.2, G is the user's weight; p3 is a constant of 4-5, V is a characteristic average velocity, P4 is a constant of 3-4, B is a constant of 1-2; c is a constant of 15 to 20, HRFeature(s)Characterised mean heart rate, HRAt restFor the resting heart rate, HR, of the user in a conscious and quiescent statemaxIs the maximum heart rate; a is the age of the user.
6. The method of predicting running performance of claim 1, wherein the real-time heart rate and the real-time exercise speed data of the current user in the last 7 days are collected to establish the first model equation.
7. The running athletic performance prediction method of claim 1, wherein when the course to be predicted is a full marathon distance, if the obtained predicted time is greater than the set time T3 and the running distance per month of the last M consecutive months of the current user is less than the set distance S1, the athletic performance of the current user is increased by the set time T4 based on the original predicted time.
8. The electronic equipment for predicting running sports scores is characterized by comprising a base body, wherein a heart rate detection module, a speed detection module, an input module, a processing module and an output module are arranged on the base body;
the heart rate detection module is used for detecting the real-time heart rate of the user;
the speed detection module is used for detecting the real-time movement speed of the user;
the input module is used for receiving external input data;
the processing module comprises a first model generation module, a physical strength value quantification module, a second model generation module, a reserve heart rate ratio acquisition module and a calculation module;
the first model generation module is used for establishing a first model equation reflecting the relationship between the heart rate and the pace of the user through the collected real-time heart rate and real-time movement speed data in the recent N days of movement of the current user;
the physical strength value quantification module is used for quantifying the physical strength value through a numerical value in a self-defined closed numerical value interval D1;
the second model generation module is used for respectively establishing a plurality of second model equations corresponding to a plurality of different courses, and the second model equations reflect the relationship between the lower body force value and the pace of a certain course;
the reserve heart rate ratio acquisition module is used for selecting one of a plurality of different competition courses as a standard competition course and acquiring a reserve heart rate ratio reflecting the appropriate exercise intensity of the user in the standard competition course;
the computing module comprises a first computing module, a second computing module, a third computing module and a fourth computing module;
the first calculation module is used for calculating the exercise heart rate of the user relative to the standard course according to the calculation formula of the reserve heart rate ratio;
the second calculation module is used for substituting the exercise heart rate into the first model equation to obtain the standard pace of the user relative to the standard course;
the third calculation module is used for substituting the standard pace into a second model equation corresponding to the standard course to obtain a physical strength value reflecting the athletic performance of the user;
the fourth calculation module is used for substituting the physical strength value into a second model equation corresponding to the course to be predicted, and calculating to obtain the predicted pace so as to obtain the predicted time for the user to run through the course to be predicted;
the output module is used for outputting the predicted time.
9. The electronic device for running athletic performance prediction of claim 8, wherein the first model generation module includes a data collection module for collecting real-time heart rate and real-time athletic speed, the data collection module collecting data by:
s10: judging whether the single-time continuous movement time length T1 of the user is greater than the set time length T2, if so, entering S11; if not, returning;
s11: moving, extracting and judging whether the real-time heart rate and the real-time movement speed in each continuous unit time period meet the following three conditions according to a certain moving period and a time sequence,
the first condition is that: the real-time heart rate fluctuations are within the setpoint B1;
the second condition is that: the real-time movement speed fluctuation is within a set value V1;
a third condition: the reserve heart rate ratio is within a preset interval D2;
and if so, taking the real-time heart rate and the real-time movement speed in the unit time period as the acquisition data.
10. The electronic device for running athletic performance prediction of claim 8, wherein the second model generation module generates the second model equation using the following method:
the running matching speeds of athletes of different levels corresponding to a plurality of different courses are collected respectively, the different running matching speeds and the physical strength values of different sizes under different courses are matched into data pairs respectively according to the positive correlation between the physical strength values and the running matching speeds, the data pairs under different courses are processed, and a physical strength value-matching speed regression model is established to obtain the second model equation corresponding to each course.
11. The electronic device for running athletic performance prediction of claim 8, wherein the reserve heart rate ratio acquisition module acquires the reserve heart rate ratio by:
acquiring physiological characteristic data and the maximum oxygen uptake data of a user;
making a reserve heart rate ratio look-up table corresponding to the standard course, wherein the reserve heart rate ratio look-up table records reserve heart rate ratios corresponding to users with different physiological characteristics and maximum oxygen intake relative to the standard course;
and inquiring the reserve heart rate ratio inquiry table according to the physiological characteristic data and the maximum oxygen uptake data of the user to obtain the reserve heart rate ratio of the user relative to the standard course.
12. The electronic device for running athletic performance prediction of claim 11, wherein the method of obtaining the maximum oxygen uptake comprises:
acquiring the age, sex, weight, resting heart rate and maximum heart rate of a user;
collecting a real-time heart rate and a real-time movement speed of a user in the movement process;
selecting a characteristic time period with a preset time length from the current exercise time of the user, and calculating a characteristic average heart rate and a characteristic average speed in the characteristic time period by taking the real-time heart rate and the real-time exercise speed as basic data;
calculating the maximum oxygen uptake of the current user according to the following first formula
Figure FDA0002622303700000061
The first formula:
Figure FDA0002622303700000062
wherein A is a constant of 40-50, P1 is a constant of 7-8, S is a sex constant, male is 1, female is 0; p2 is a constant of 0.1-0.2, G is the user's weight; p3 is a constant of 4-5, V is a characteristic average velocity, P4 is a constant of 3-4, B is a constant of 1-2; c is a constant of 15 to 20, HRFeature(s)Characterised mean heart rate, HRAt restFor the resting heart rate, HR, of the user in a conscious and quiescent statemaxIs the maximum heart rate; a is the age of the user.
13. The electronic device for running athletic performance prediction of claim 8, wherein the processing module further comprises a performance revision module; when the course to be predicted is the distance of the whole marathon, if the obtained predicted time is greater than the set time T3 and the running distance of the current user in each month in the last continuous M months is less than the set distance S1, the correction module is used for increasing the set time T4 to the exercise performance of the current user on the basis of the original predicted time so as to correct the predicted time.
14. An electronic device for running athletic performance prediction, comprising:
one or more processors;
a memory:
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the running athletic performance prediction method of any of claims 1-7.
15. A computer-readable storage medium comprising a computer program executable by a processor to perform a method of running performance prediction as claimed in any one of claims 1 to 7.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106166063A (en) * 2015-05-21 2016-11-30 博晶医电股份有限公司 Muscle power monitoring method and device
CN107595273A (en) * 2017-09-12 2018-01-19 广东远峰电子科技股份有限公司 A kind of heart rate evaluation method and device
WO2018065937A1 (en) * 2016-10-06 2018-04-12 Myotest Sa A method and apparatus for predicting a race time
CN110084434A (en) * 2019-05-06 2019-08-02 上海海济斯健康管理咨询有限公司 Marathon result prediction method, apparatus, terminal and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106166063A (en) * 2015-05-21 2016-11-30 博晶医电股份有限公司 Muscle power monitoring method and device
WO2018065937A1 (en) * 2016-10-06 2018-04-12 Myotest Sa A method and apparatus for predicting a race time
CN107595273A (en) * 2017-09-12 2018-01-19 广东远峰电子科技股份有限公司 A kind of heart rate evaluation method and device
CN110084434A (en) * 2019-05-06 2019-08-02 上海海济斯健康管理咨询有限公司 Marathon result prediction method, apparatus, terminal and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
最新2019跑步等级标准，最全马拉松配速表！你在哪一级？;超级俱乐部;《搜狐》;20190829;全文 *

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