Evaluation method, device, processor and electronic device for training

Abstract

The application discloses a kind of evaluation method, device, processor and electronic equipment of training.The method comprises: in the process that user exercises, obtain user characteristic parameter and training parameter in current time period;According to training parameter and user characteristic parameter, calculate first total energy consumption value;First aerobic energy consumption value and first anaerobic energy consumption value are determined using first total energy consumption value and energy consumption ratio;Second aerobic energy consumption value and second anaerobic energy consumption value in previous time period and third aerobic energy consumption value and third anaerobic energy consumption value of next time period are determined, obtain N aerobic energy consumption value and N anaerobic energy consumption value;Summation is carried out to N aerobic energy consumption value and N anaerobic energy consumption value, and training effect value is calculated using aerobic energy consumption total value and anaerobic energy consumption total value.By the application, the problem that it is difficult to determine exercise effect in the related art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, and in particular, to a training evaluation method and device, a processor and an electronic device. BACKGROUND

[0002] With more and more users paying attention to the exercise effect during exercise, the user uses a device to record exercise data, determines the exercise effect of each exercise through the recorded exercise data, and then understands the body condition and adjusts the exercise in time according to the exercise effect. The exercise effect can include the influence of aerobic exercise and anaerobic exercise on the body condition, and the maximum factor affecting the exercise effect can include the power of exercise and the duration of exercise.

[0003] In related technologies, when the data collected by the device is used to determine the exercise effect, only the influence of intermittent high-intensity exercise on anaerobic capacity can be determined, and the influence of aerobic exercise and mixed oxygen exercise on the training effect is not considered.

[0004] At present, there is no effective solution to the problem that the exercise effect is difficult to determine in related technologies. SUMMARY

[0005] The main purpose of the present application is to provide a training evaluation method, device, processor and electronic device to solve the problem that the exercise effect is difficult to determine in related technologies.

[0006] In order to achieve the above purpose, according to one aspect of the present application, a training evaluation method is provided. The method comprises: obtaining user feature parameters of a user and training parameters of the user in a current time period during the user performs a target exercise; calculating a first total energy consumption value in the current time period according to the training parameters and the user feature parameters; determining a first aerobic energy consumption value and a first anaerobic energy consumption value of the current time period by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to the ratio of energy consumption of aerobic exercise and anaerobic exercise; determining a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period, and determining a third aerobic energy consumption value and a third anaerobic energy consumption value in a next time period according to the user feature parameters, the training parameters of the next time period and the energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values of the exercise process are obtained, wherein N is a positive integer; summing the N aerobic energy consumption values and the N anaerobic energy consumption values respectively to obtain an aerobic energy consumption total value and an anaerobic energy consumption total value, and calculating a training effect value by using the aerobic energy consumption total value and the anaerobic energy consumption total value.

[0007] Optionally, the energy consumption ratio associated with the current time period is determined by: determining a plurality of motion state intervals based on the training parameters in a previous time period of the current time period, and determining an interval energy consumption ratio based on data of each motion state interval, and combining all the interval energy consumption ratios to obtain the energy consumption ratio associated with the current time period, wherein different motion state intervals are associated with different motion powers; the energy consumption ratio associated with the next time period is determined by: determining a plurality of motion state intervals based on the training parameters in the current time period, and determining an interval energy consumption ratio based on data of each motion state interval, and combining all the interval energy consumption ratios to obtain the energy consumption ratio associated with the next time period, wherein different motion state intervals are associated with different motion powers.

[0008] Optionally, the user characteristic parameters include height, weight and age of the user, and the first total energy consumption value in the current time period is calculated based on the training parameters and the user characteristic parameters, including: substituting the height, weight and age into a basal metabolism formula to calculate a basal metabolism parameter of the user; calculating a metabolic equivalent parameter based on the user characteristic parameters and the training parameters; and calculating a product of the basal metabolism parameter, the metabolic equivalent parameter and a time length of the current time period to obtain the first total energy consumption value.

[0009] Optionally, the training parameters at least include: motion power, oxygen consumption, motion speed, heart rate data and respiration rate data of the user, and the metabolic equivalent parameter is calculated by at least one of the following: determining a motion type of the target motion, and determining the metabolic equivalent parameter from a metabolic equivalent parameter table based on the motion type, wherein the metabolic equivalent table is composed of a plurality of motion types and metabolic equivalent parameters of each motion type; determining oxygen uptake of the user, and calculating a product of the oxygen uptake and an oxygen uptake weight value to obtain the metabolic equivalent parameter, wherein the oxygen uptake is calculated by at least one of the following: calculating a weighted sum result of the heart rate data, the respiration rate data and the motion speed and a corresponding first preset weight value to obtain the oxygen uptake; calculating a weighted sum result of the motion power, the oxygen consumption and a corresponding second preset weight value to obtain the oxygen uptake; and calculating a product of the motion speed and a slope of a motion scene used by the user and a corresponding third preset weight value to obtain the oxygen uptake.

[0010] Optionally, the training parameters at least include: exercise power, oxygen consumption, exercise speed, heart rate data and respiratory rate data of the user, and the multiple exercise state intervals are determined by the training parameters in a previous time period of the current time period, comprising: obtaining first heart rate data when the exercise speed is less than a first threshold, the respiratory rate data continuously increases, and the heart rate data increases by more than a first preset amplitude in the previous time period; obtaining second heart rate data when the exercise speed is greater than a second threshold, the respiratory rate data changes by less than a second preset amplitude, and the heart rate data increases by less than a third preset amplitude in the previous time period; determining multiple sets of curve coefficients in different exercise states according to the respiratory rate data, the heart rate data and the exercise speed in the previous time period, determining an exercise curve by using each set of curve coefficients to obtain multiple exercise curves, wherein the exercise curve represents the trend of the ratio of anaerobic energy consumption value to total energy consumption value with respect to the heart rate data; and dividing the multiple exercise curves according to the first heart rate data and the second heart rate data to obtain the multiple exercise state intervals in the previous time period.

[0011] Optionally, in the case where the exercise state includes three kinds, the multiple exercise curves include a first exercise curve, a second exercise curve and a third exercise curve, and the multiple exercise curves are divided according to the first heart rate data and the second heart rate data to obtain the multiple exercise state intervals in the previous time period, comprising: intercepting a curve segment between the starting heart rate data and the first heart rate data in the first exercise curve to obtain a first curve segment, and determining a first exercise state interval according to the first curve segment; intercepting a curve segment between the first heart rate data and the second heart rate data in the second exercise curve to obtain a second curve segment, and determining a second exercise state interval according to the second curve segment; and intercepting a curve segment between the second heart rate data and the terminal heart rate data in the third exercise curve to obtain a third curve segment, and determining a third exercise state interval according to the third curve segment.

[0012] Optionally, the first aerobic energy consumption value and the first anaerobic energy consumption value of the current time period are determined by using the first total energy consumption value and the data of the multiple exercise state intervals, comprising: for each exercise state interval, calculating a heart rate parameter by using the heart rate data of the exercise state interval, calculating the product of the heart rate parameter and each set of curve coefficients to obtain the anaerobic energy supply ratio of the exercise state interval; calculating the product of the anaerobic energy supply ratio of the exercise state interval and the total energy consumption value to obtain the anaerobic energy consumption value of the exercise state interval; calculating the difference between the total energy consumption value and the anaerobic energy consumption value to obtain the aerobic energy consumption value of the exercise state interval; combining the anaerobic energy consumption values of all exercise state intervals into the first anaerobic energy consumption value, and combining the aerobic energy consumption values of all exercise state intervals into the first aerobic energy consumption value.

[0013] Optionally, the calculating the training effect value by using the total aerobic energy consumption value and the total anaerobic energy consumption value comprises: judging whether there is an oxygen uptake threshold in the training parameters of the N time periods in which the user performs the target exercise; in the case that there is no oxygen uptake threshold in the training parameters, determining the maximum heart rate data, the maximum respiratory rate data and the maximum exercise speed in the N time periods; calculating the product of the maximum heart rate data, the maximum respiratory rate data and the maximum exercise speed to obtain the oxygen uptake threshold; in the case that there is the oxygen uptake threshold in the training parameters, determining a consumption threshold according to the oxygen uptake threshold, calculating the ratio of the aerobic energy consumption value to the total duration of the N time periods to obtain an aerobic energy consumption rate, and calculating the ratio of the anaerobic energy consumption value to the total duration of the N time periods to obtain an anaerobic energy consumption rate; calculating the ratio of the aerobic energy consumption value to the consumption threshold to obtain a first consumption ratio, calculating the product of the first consumption ratio and a first consumption coefficient to obtain a first product, and calculating the product of the aerobic energy consumption rate and a second consumption coefficient to obtain a second product, calculating the sum of the first product and the second product to obtain an aerobic training effect value; calculating the ratio of the anaerobic energy consumption value to the consumption threshold to obtain a second consumption ratio, calculating the product of the second consumption ratio and a third consumption coefficient to obtain a third product, calculating the product of the aerobic energy consumption rate and a fourth consumption coefficient to obtain a fourth product, and calculating the sum of the third product and the fourth product to obtain an anaerobic training effect value; and the training effect value is composed of the aerobic training effect value and the anaerobic training effect value.

[0014] To achieve the above object, according to another aspect of the present application, an evaluation device for training is provided. The device comprises: an acquisition unit configured to acquire user characteristic parameters of a user and training parameters of the user in a current time period during which the user performs a target exercise; a calculation unit configured to calculate a first total energy consumption value in the current time period according to the training parameters and the user characteristic parameters; a first determination unit configured to determine a first aerobic energy consumption value and a first anaerobic energy consumption value of the current time period by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to the ratio of energy consumption of aerobic exercise and anaerobic exercise; a second determination unit configured to determine a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period, and determine a third aerobic energy consumption value and a third anaerobic energy consumption value in a next time period according to the user characteristic parameters, training parameters of the next time period and an energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values of the exercise process are obtained, wherein N is a positive integer; and a summation unit configured to sum the N aerobic energy consumption values and the N anaerobic energy consumption values respectively to obtain a total aerobic energy consumption value and a total anaerobic energy consumption value, and calculate a training effect value by using the total aerobic energy consumption value and the total anaerobic energy consumption value.

[0015] According to another aspect of the embodiments of the present application, a processor is also provided, which is configured to run a program, wherein the program is configured to control a device in which a nonvolatile storage medium is located to perform the evaluation method for training when the program is running.

[0016] According to another aspect of the embodiments of the present application, an electronic device is also provided, which comprises one or more processors and a memory, and the memory stores computer readable instructions, and the processor is configured to run the computer readable instructions, wherein the computer readable instructions are configured to perform the evaluation method for training when the computer readable instructions are running.

[0017] By the present application, the following steps are adopted: in the process of target movement of a user, a user feature parameter of the user is acquired, and a training parameter of the user in a current time period is acquired; a first total energy consumption value in the current time period is calculated according to the training parameter and the user feature parameter; a first aerobic energy consumption value and a first anaerobic energy consumption value of the current time period are determined by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to the ratio of energy consumption of aerobic movement and anaerobic movement; a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period are determined, and a third aerobic energy consumption value and a third anaerobic energy consumption value of a next time period are determined according to the user feature parameter, a training parameter of the next time period and an energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values of the movement process are obtained, wherein N is a positive integer; the N aerobic energy consumption values and the N anaerobic energy consumption values are summed respectively to obtain a total aerobic energy consumption value and a total anaerobic energy consumption value, and a training effect value is calculated by using the total aerobic energy consumption value and the total anaerobic energy consumption value, thereby solving the problem that the movement effect is difficult to determine in the related art, and by acquiring the training parameter of the user and the user feature parameter, the aerobic energy consumption value and the anaerobic energy consumption value are calculated by using the training parameter and the user feature parameter, and the training effect value is calculated based on the aerobic energy consumption value and the anaerobic energy consumption value, thereby accurately determining the training effect and guiding the movement effect based on the training effect. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and are used to interpret the embodiments of the present application and their descriptions, and do not constitute improper limitations to the present application. In the drawings:

[0019] Figure 1 is a flowchart of the evaluation method for training provided according to the embodiments of the present application;

[0020] Figure 2 is a schematic diagram of a movement state interval according to the embodiments of the present application;

[0021] Figure 3 is a schematic diagram of an optional evaluation method of training provided according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of an evaluation device of training provided according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict. The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present disclosure are all information and data authorized by the user or authorized by all parties.

[0028] The present application will be described below in combination with the preferred implementation steps, Figure 1 is a flowchart of an evaluation method of training provided according to an embodiment of the present application, as Figure 1 shown, the method comprises the following steps:

[0029] Step S101, in the process of the user performing the target exercise, the user feature parameter of the user is acquired, and the training parameter of the user in the current time period is acquired.

[0030] Specifically, the target exercise refers to different kinds of exercises performed by the user, for example, the target exercise can be a fat-reducing and slimming exercise, a long-distance running exercise, etc.

[0031] It should be noted that in order to determine the training effect of the user in this target exercise, the user's multiple data need to be acquired first, the consumption data of aerobic exercise and the consumption data of anaerobic exercise in this exercise are calculated by using the acquired data, and then the training effect is determined. The multiple data of the user can include the user feature parameter and the training parameter of the user in the current time period.

[0032] Among them, the user feature parameter refers to the personal data of the user, for example, the user feature parameter can include user gender, age, height, weight and other parameters; the current time period refers to a period of time in the complete exercise time of the user, and the training parameter refers to the exercise data collected according to the wearable device worn by the user or the exercise equipment used, which can include speed data, heart rate data, respiratory rate data and other data when the user exercises in the current time period.

[0033] For example, when the user exercises using a spinning bike, the user's riding speed recorded by the bike, the user's heart rate data and respiratory rate data collected by the user's sports bracelet, etc. can be displayed on the display screen of the bracelet.

[0034] Step S102, the first total energy consumption value in the current time period is calculated according to the training parameter and the user feature parameter.

[0035] Specifically, the first total energy consumption value refers to the sum of the energy consumption value of aerobic exercise and the energy consumption value of anaerobic exercise of the user in the current time period of the target exercise process. The first total energy consumption value is calculated by using the training parameter and the user feature parameter, which can lay a foundation for subsequent calculation of the training effect value.

[0036] Step S103, the first aerobic energy consumption value and the first anaerobic energy consumption value in the current time period are determined by using the first total energy consumption value and the energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to the ratio of energy consumption of aerobic exercise and anaerobic exercise.

[0037] Specifically, the energy consumption ratio associated with the current time period is used to describe the proportion of energy consumption of aerobic exercise and anaerobic exercise in the current time period. In order to improve the timeliness of determining the training effect, the energy consumption ratio associated with the current time period can be determined by historical training data. In order to obtain the training effect of the target exercise performed by the user in the current time period, it is necessary to determine the first aerobic energy consumption value and the first anaerobic energy consumption value. After determining the energy consumption ratio of the current time period, the first aerobic energy consumption value and the first anaerobic energy consumption value are calculated by using the ratio and the first total energy consumption value.

[0038] In step S104, the second aerobic energy consumption value and the second anaerobic energy consumption value in the previous time period are determined, and the third aerobic energy consumption value and the third anaerobic energy consumption value in the next time period are determined according to the user characteristic parameters, the training parameters in the next time period, and the energy consumption ratio associated with the next time period. Until N aerobic energy consumption values and N anaerobic energy consumption values of the exercise process are obtained, wherein N is a positive integer.

[0039] Specifically, in order to obtain the training effect of the target exercise, it is necessary to determine the total aerobic energy consumption value and the total anaerobic energy consumption value consumed in the time of completing the exercise. For example, the total time of performing the exercise can be divided into N time periods, and the total aerobic energy consumption value and the total anaerobic energy consumption value are calculated from the aerobic energy consumption value and the anaerobic energy consumption value of each time period.

[0040] The current time period can be the second time period in the time consumed by the user to perform the target exercise. The energy consumption ratio associated with the current time period can be determined in combination with the historical training data of the previous time period, so as to obtain the aerobic energy consumption value and the anaerobic energy consumption value of the current time period, that is, after obtaining the first aerobic energy consumption value and the first anaerobic energy consumption value. The determination method of aerobic energy consumption value and anaerobic energy consumption value of multiple time periods after the current time period is the same as that of the current time period, so as to obtain N-1 aerobic energy consumption values and N-1 anaerobic energy consumption values.

[0041] It should be noted that when the current time period is the second time period in the entire exercise process, the previous time period is the first time period in the time consumed by the user to perform the target exercise. In the case that the energy consumption ratio associated with the time period cannot be obtained in time, the aerobic energy consumption value and the anaerobic energy consumption value of the time period can be estimated, for example, the aerobic energy consumption value and the anaerobic energy consumption value of the first time period can be determined by the training time of the first time period in the target exercise of the user in the past. The aerobic energy consumption value and the anaerobic energy consumption value of the first time period plus N-1 aerobic energy consumption values and N-1 anaerobic energy consumption values obtain N aerobic energy consumption values and N anaerobic energy consumption values.

[0042] Step S105, summing up the N aerobic energy consumption values and the N anaerobic energy consumption values respectively to obtain an aerobic energy consumption total value and an anaerobic energy consumption total value, and calculating a training effect value by using the aerobic energy consumption total value and the anaerobic energy consumption total value.

[0043] Specifically, after obtaining the N aerobic energy consumption values and the N anaerobic energy consumption values in the time consumed for completing the target exercise, summing up all the aerobic energy consumption values and all the anaerobic energy consumption values to obtain an aerobic energy consumption total value and an anaerobic energy consumption total value, multiplying and adding the aerobic energy consumption total value and the anaerobic energy consumption total value with a preset parameter respectively to obtain the training effect value of the target exercise.

[0044] It should be noted that after calculating the training effect value of each exercise, the intensity level or the training frequency of the user during the exercise can be adjusted according to the training effect value.

[0045] For example, after a user performs a fat-burning running exercise, the calculated training effect value is 0 to 1, which indicates that the training level of the user is a low intensity level, and the difficulty or intensity can be appropriately increased. If the training effect value is 1 to 2, it indicates that the training level of the user is a lower intensity level, and the user can perform the target exercise of this intensity multiple times. If the training effect value is 2 to 3, it indicates that the training level of the user is a medium intensity level, and the user can maintain the exercise intensity unchanged. If the training effect value is 3 to 4, it indicates that the training level of the user is a higher intensity level, and the training intensity can be appropriately reduced. If the training effect value is 4 to 5, it indicates that the training level of the user is a high intensity level, and the intensity of the target exercise is too large or the difficulty is too high, and the difficulty and intensity need to be adjusted.

[0046] Further, after obtaining the training effect value, the training effect value can be analyzed, and more accurate exercise guidance can be provided to the user in combination with the type of the current exercise. For example, the user's current fitness requirement is to reduce fat and slim down, and the training effect value of the user's historical exercise is 1, and the user can be recommended to participate in some exercise courses with greater aerobic effect.

[0047] The evaluation method for training provided in the embodiments of the present application comprises the following steps: obtaining user feature parameters of a user and training parameters of the user in a current time period during the user performs a target exercise; calculating a first total energy consumption value in the current time period according to the training parameters and the user feature parameters; determining a first aerobic energy consumption value and a first anaerobic energy consumption value of the current time period by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to the ratio of energy consumption of aerobic exercise and anaerobic exercise; determining a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period, and determining a third aerobic energy consumption value and a third anaerobic energy consumption value in a next time period according to the user feature parameters, the training parameters in the next time period and the energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values in the exercise process are obtained, wherein N is a positive integer; summing the N aerobic energy consumption values and the N anaerobic energy consumption values respectively to obtain a total aerobic energy consumption value and a total anaerobic energy consumption value, and calculating a training effect value by using the total aerobic energy consumption value and the total anaerobic energy consumption value, thereby solving the problem that the exercise effect is difficult to determine in the related art, and achieving accurate determination of the training effect and guiding the exercise effect based on the training effect by obtaining the training parameters and the user feature parameters of the user, calculating the aerobic energy consumption value and the anaerobic energy consumption value by using the training parameters and the user feature parameters, and calculating the training effect value based on the aerobic energy consumption value and the anaerobic energy consumption value.

[0048] The first total energy consumption value is calculated by using the training parameters and the user feature parameters. Optionally, in the evaluation method for training provided in the embodiments of the present application, the user feature parameters comprise the height, weight and age of the user, and the first total energy consumption value in the current time period is calculated according to the training parameters and the user feature parameters, which comprises: substituting the height, weight and age into a basal metabolism formula to calculate a basal metabolism parameter of the user; calculating a metabolic equivalent parameter by using the user feature parameters and the training parameters; and calculating the product of the basal metabolism parameter, the metabolic equivalent parameter and the time length of the current time period to obtain the first total energy consumption value.

[0049] Specifically, the basal metabolism formula refers to a formula for calculating basal metabolic rate, wherein the basal metabolic rate (BMR) refers to the energy consumption required by the human body to maintain normal physiological activities in a completely quiet, empty stomach, and unaffected by environmental temperature and emotional factors, i.e. in a resting state, and the calculation of BMR is usually based on factors such as gender, age, height and weight of the individual. The basal metabolism formula can comprise the Harris-Benedict formula and the MSJE (Mifflin-StJeor Equations) formula.

[0050] Among them, the Harris-Benedict formula is suitable for obese people and the elderly, and the formula is:

[0051] Male: BMR = 88.362 + (13.397 x weight kg) + (4.799 x height cm) - (5.677 x age);

[0052] Female: BMR = 447.593 + (9.247 x weight kg) + (3.098 x height cm) - (4.330 x age);

[0053] The MSJE formula is:

[0054] Male: BMR = (10 x weight kg) + (6.25 x height cm) - (5 x age) + 5;

[0055] Female: BMR = (10 x weight kg) + (6.25 x height cm) - (5 x age) - 161;

[0056] It should be noted that the basal metabolic parameters in this embodiment are obtained by synthesizing the above two calculation results, that is, when the result of formula one is recorded as BMR1 and the result of formula two is recorded as BMR2, the final output result is:

[0057] BMR = f(BMR1, BMR2, BMI, age);

[0058] Among them, BMI (Body Mass Index) is a body mass index, which is an index for measuring the relationship between body weight and height, and is used to assess whether a person's weight is low, normal, overweight or obese.

[0059] Further, in order to calculate the first total energy consumption value, the metabolic equivalent parameter needs to be obtained, wherein the metabolic equivalent parameter (METs: Metabolic Equivalent of Task) is an index for measuring activity intensity, which is based on resting metabolism and is used to measure the energy consumed by the human body when performing different activities. After calculating the metabolic equivalent parameter according to the user characteristic parameters and the training parameters, the parameter is multiplied by the basal metabolic parameter and the time of the current time period when the user exercises to obtain the first total energy consumption value.

[0060] The metabolic equivalent parameter can be calculated in various ways. In the evaluation method of the training provided in the embodiments of the present application, the training parameters can include, but are not limited to, the user's exercise power, oxygen consumption, exercise speed, heart rate data, and respiratory rate data. The metabolic equivalent parameter can be calculated in at least one of the following ways: determining the exercise type of the target exercise, determining the metabolic equivalent parameter from the metabolic equivalent parameter table according to the exercise type, wherein the metabolic equivalent table is composed of various exercise types and the metabolic equivalent parameters of each exercise type; determining the oxygen uptake of the user, and calculating the product of the oxygen uptake and the oxygen uptake weight value to obtain the metabolic equivalent parameter, wherein the oxygen uptake can be calculated in at least one of the following ways: calculating the weighted sum of the heart rate data, the respiratory rate data, and the exercise speed and the corresponding first preset weight value to obtain the oxygen uptake; calculating the weighted sum of the exercise power, the oxygen consumption, and the corresponding second preset weight value to obtain the oxygen uptake; and calculating the product of the exercise speed and the slope of the exercise scene used by the user and the corresponding third preset weight value to obtain the oxygen uptake.

[0061] In an embodiment, the metabolic equivalent parameter can be determined by a metabolic equivalent parameter table. For example, the METs of slow walking can be 2.0, the METs of medium-speed walking can be 3.3, the METs of fast walking can be 3.9, the METs of slow cycling can be 4.0, the METs of medium-speed cycling can be 6.8, the METs of fast cycling can be 8.0, and so on.

[0062] In another embodiment, the metabolic equivalent parameter can also be obtained according to the product of the oxygen uptake and the correlation coefficient, wherein the oxygen uptake refers to the amount of oxygen taken in by the human body per unit time, and is related to the exercise intensity and oxygen consumption capacity of the human body. Through this data, the endurance level and health status of the human body can be evaluated.

[0063] The oxygen uptake can be determined in various ways. For example, the oxygen uptake can be calculated by the Astrand-Larsson formula, that is:

[0064] VO2=(0.2×workrate)+(3.5×restingVO2); wherein VO2 represents the oxygen uptake, workrate represents the exercise power, and restingVO2 represents the oxygen uptake per minute at rest. The exercise power can be obtained by using the exercise equipment, for example, the data set by the bicycle can be directly determined when cycling, or the estimated value can be obtained by the exercise bracelet. The oxygen consumption can be obtained by calculating the respiratory rate statistically obtained by the exercise bracelet.

[0065] For another example, the oxygen uptake can also be calculated by the improved version of the Astrand-Larsson formula:

[0066] VO2=(0.1*speed)+(1.8*speed*grade)+3.5; wherein, speed represents the speed of movement, and grade represents the slope. The speed of movement and the slope can also be obtained by the data collected by the sports bracelet or by the sports equipment.

[0067] For another example, the oxygen uptake can also be calculated by the training parameters:

[0068] VO2=(a*HR+b*speed)*Respiratoryrate*c+d; wherein, HR represents the heart rate data, Respiratoryrate represents the respiratory rate data, both of which can be collected by the sports bracelet, a, b, c, and d are preset coefficients. It should be noted that, since the user characteristic parameters of each user are different, the preset coefficients can be adjusted according to the user characteristic parameters before determining the training effect value of each user. For example, when a user is a male and 25 years old, the coefficient a can be 1.25, and the coefficient b can be 1.

[0069] It should be noted that, after obtaining the training parameters, in order to more accurately obtain the first total energy consumption value, the metabolic equivalent parameter can be calculated by using the calculation formula, and in the case where the training parameters cannot be obtained, the metabolic equivalent parameter can be determined by using the metabolic equivalent parameter table. The metabolic equivalent parameter is determined by multiple ways in this embodiment, which can more accurately obtain the metabolic equivalent parameter, and lays a foundation for more accurately obtaining the training effect.

[0070] The movement state interval of each time period can be determined first, and then the energy consumption ratio can be determined according to each movement state interval. Alternatively, in the training evaluation method provided in this embodiment, the energy consumption ratio associated with the current time period is determined by the following method: the training parameters in the previous time period of the current time period are used to determine a plurality of movement state intervals, and an interval energy consumption ratio is determined according to the data of each movement state interval, and all interval energy consumption ratios are combined to obtain the energy consumption ratio associated with the current time period, wherein different movement state intervals are associated with different movement powers; the energy consumption ratio associated with the next time period is determined by the following method: the training parameters in the current time period are used to determine a plurality of movement state intervals, and an interval energy consumption ratio is determined according to the data of each movement state interval, and all interval energy consumption ratios are combined to obtain the energy consumption ratio associated with the next time period, wherein different movement state intervals are associated with different movement powers.

[0071] Specifically, the heart rate data of each motion state interval is calculated by using a preset algorithm to obtain a heart rate parameter x, and then the heart rate parameter x is substituted into a parabolic equation y=ax2+bx+c corresponding to the motion state interval to obtain the anaerobic consumption ratio y of the motion state interval, and then the anaerobic consumption ratios of multiple motion state intervals in a time period are combined to obtain the energy consumption ratio in the time period.

[0072] It should be noted that if the energy consumption ratio of the current time period needs to be calculated, the energy consumption ratio can be determined according to the heart rate parameter of the previous time period of the current time period, and the ratio is taken as the energy consumption ratio of the current time period; if the energy consumption ratio of the next time period of the current time period needs to be calculated, the energy consumption ratio can be determined according to the heart rate data of the current time period, and then the ratio is determined as the energy consumption ratio of the next time period. By determining the energy consumption ratio of each time period, the energy consumption value of each time period can be obtained more accurately.

[0073] In order to obtain the energy consumption ratio of all time periods of the target motion consumption, it is necessary to first determine the motion state interval of each time period. Optionally, in the training evaluation method provided in the present application, the training parameters at least include: the motion power, oxygen consumption, motion speed, heart rate data and respiratory rate data of the user, and the multiple motion state intervals are determined according to the training parameters in the previous time period of the current time period, including: obtaining the first heart rate data when the motion speed in the previous time period is less than a first threshold value, the respiratory rate data continuously increases, and the heart rate data increases by more than a first preset amplitude; obtaining the second heart rate data when the motion speed in the previous time period is greater than a second threshold value, the respiratory rate data changes by less than a second preset amplitude, and the heart rate data increases by less than a third preset amplitude; determining multiple sets of curve coefficients in different motion states according to the respiratory rate data, the heart rate data and the motion speed in the previous time period, determining a motion curve by using each set of curve coefficients to obtain multiple motion curves, wherein the motion curve represents the trend of the ratio of anaerobic energy consumption value to total energy consumption value with respect to heart rate data; dividing the multiple motion curves according to the first heart rate data and the second heart rate data to obtain multiple motion state intervals in the previous time period.

[0074] Specifically, the motion state interval is obtained by dividing the motion state of each time period by multiple "division points". The "division points" can be selected from the obtained heart rate data. For example, when the motion state interval of the current time period needs to be determined, the "division points" can be determined by the training parameters in the previous time period of the current time period.

[0075] It should be noted that the motion state interval refers to an interval obtained according to different motion states, and after the first heart rate data and the second heart rate data are selected from the previous time period, the heart rate data is determined as a "demarcation point" for dividing the motion state. Further, the curve coefficients in different motion state intervals are determined by using the plurality of respiration rate data, heart rate data and motion speed in the previous time period, the curve coefficients are substituted into the parabolic equation with the heart rate data as the independent variable and the ratio of the anaerobic energy consumption value to the total energy consumption value as the dependent variable, a plurality of motion curves are obtained, and then the plurality of motion curves are intercepted according to the first heart rate data and the second heart rate data, and a plurality of motion state intervals are obtained. The embodiment determines the motion state interval by using the training parameters of the previous time period, and uses the motion state interval of the previous time period as the motion state interval of the current time period, thereby laying a foundation for calculating the energy consumption value of the current time period.

[0076] Optionally, in the evaluation method of the training provided in the embodiment of the present application, in the case where the motion state includes three kinds, the plurality of motion curves include a first motion curve, a second motion curve and a third motion curve, and the plurality of motion curves are divided according to the first heart rate data and the second heart rate data to obtain a plurality of motion state intervals in the previous time period, including: intercepting a curve segment between the starting heart rate data and the first heart rate data in the first motion curve to obtain a first curve segment, and determining a first motion state interval according to the first curve segment; intercepting a curve segment between the first heart rate data and the second heart rate data in the second motion curve to obtain a second curve segment, and determining a second motion state interval according to the second curve segment; and intercepting a curve segment between the second heart rate data and the terminal heart rate data in the third motion curve to obtain a third curve segment, and determining a third motion state interval according to the third curve segment.

[0077] Specifically, the motion state can include three motion states of low intensity, medium-high intensity and high intensity, and therefore, the motion curve can be a low-intensity motion curve, i.e., a first motion curve, a medium-high-intensity motion curve, i.e., a second motion curve, and a high-intensity motion curve, i.e., a third motion curve.

[0078] Figure 2 is a schematic diagram of the motion state interval according to the embodiment of the present application, as shown in Figure 2 The heart rate data, respiration rate data and motion speed in the previous time period are first obtained, and the heart rate data of the starting state in which the motion speed is less than the first threshold value, the respiration rate data continuously increases and the heart rate data rapidly increases is determined, and the data is taken as the first heart rate data, i.e., the demarcation point A of the first motion state interval.

[0079] Further, the starting state of the heart rate data in the previous time period is obtained, in which the movement speed is greater than the second threshold value, the respiratory rate data slowly increases, and the heart rate data slowly increases. The data is taken as the second heart rate data, that is, the dividing point B of the second movement state interval.

[0080] Still further, three groups of curve coefficients in three movement states are determined according to the respiratory rate data, the heart rate data and the movement speed in the previous time period, wherein each group of curve coefficients includes a, b and c. Further, the curve coefficients are substituted into the parabola equation y=ax2+bx+c, and three parabola equations X, Y and Z are obtained.

[0081] The curve segment between the point corresponding to the starting heart rate data and the point A in the parabola X is intercepted to obtain a first curve segment. The curve segment between the point A and the point B in the parabola Y is intercepted to obtain a second curve segment. The curve segment between the point B and the point corresponding to the ending heart rate data in the parabola Z is intercepted to obtain a third curve segment. The three curve segments are combined, and the interval in which each curve segment is located is taken as a movement state interval, so as to obtain three movement state intervals.

[0082] The first aerobic energy consumption value and the first anaerobic energy consumption value are determined by the anaerobic energy supply ratio and the data of the plurality of movement state intervals. Optionally, in the training evaluation method provided in the embodiments of the present application, the first aerobic energy consumption value and the first anaerobic energy consumption value in the current time period are determined by the total energy consumption value and the data of the plurality of movement state intervals, which includes: for each movement state interval, calculating a heart rate parameter by using the heart rate data of the movement state interval, calculating the product of the heart rate parameter and each group of curve coefficients to obtain the anaerobic energy supply ratio of the movement state interval; calculating the product of the anaerobic energy supply ratio of the movement state interval and the total energy consumption value to obtain the anaerobic energy consumption value of the movement state interval; calculating the difference between the total energy consumption value and the anaerobic energy consumption value to obtain the aerobic energy consumption value of the movement state interval; combining the anaerobic energy consumption values of all movement state intervals into the first anaerobic energy consumption value, and combining the aerobic energy consumption values of all movement state intervals into the first aerobic energy consumption value.

[0083] Specifically, the anaerobic energy supply ratio of each movement state interval refers to the proportion of the energy consumption of anaerobic movement in the total energy consumption in each movement state interval. For example, to calculate the first aerobic energy consumption value and the first anaerobic energy consumption value in the current time period, the energy consumption ratio of each movement state interval can be determined according to the heart rate data in the previous time period, and the product of the energy consumption ratio of each movement state interval and the total energy consumption value can be calculated to obtain the anaerobic energy consumption value of the movement state interval.

[0084] Further, the anaerobic energy consumption value of the motion state interval can be obtained by subtracting the total energy consumption value from the anaerobic energy consumption value of the motion state interval. Further, the first anaerobic energy consumption value and the first aerobic energy consumption value of the current time period can be obtained by summing the anaerobic energy consumption values and the aerobic energy consumption values of all motion state intervals in the current time period. The first aerobic energy consumption value and the first anaerobic energy consumption value can be determined by using the first total energy consumption value and the data of the plurality of motion state intervals, which can lay a foundation for calculating the energy consumption values of time periods other than the current time period.

[0085] The training effect value is calculated according to the total aerobic energy consumption value and the total anaerobic energy consumption value. In the evaluation method of training provided in the embodiments of the present application, the training effect value is calculated according to the total aerobic energy consumption value and the total anaerobic energy consumption value, which includes: determining whether there is an oxygen uptake threshold in the training parameters of the N time periods in which the user performs the target motion; in the case that there is no oxygen uptake threshold in the training parameters, determining the maximum heart rate data, the maximum respiratory rate data and the maximum motion speed in the N time periods; calculating the product of the maximum heart rate data, the maximum respiratory rate data and the maximum motion speed to obtain the oxygen uptake threshold; in the case that there is an oxygen uptake threshold in the training parameters, determining a consumption threshold according to the oxygen uptake threshold, calculating the ratio of the total aerobic energy consumption value to the total duration of the N time periods to obtain an aerobic energy consumption rate, and calculating the ratio of the total anaerobic energy consumption value to the total duration of the N time periods to obtain an anaerobic energy consumption rate; calculating the ratio of the total aerobic energy consumption value to the consumption threshold to obtain a first consumption ratio, calculating the product of the first consumption ratio and a first consumption coefficient to obtain a first product, calculating the product of the aerobic energy consumption rate and a second consumption coefficient to obtain a second product, and calculating the sum of the first product and the second product to obtain an aerobic training effect value; calculating the ratio of the total anaerobic energy consumption value to the consumption threshold to obtain a second consumption ratio, calculating the product of the second consumption ratio and a third consumption coefficient to obtain a third product, calculating the product of the aerobic energy consumption rate and a fourth consumption coefficient to obtain a fourth product, and calculating the sum of the third product and the fourth product to obtain an anaerobic training effect value; and the training effect value is composed of the aerobic training effect value and the anaerobic training effect value.

[0086] After obtaining the total aerobic energy consumption value and the total anaerobic energy consumption value of each time period, the training effect value can be determined according to the total aerobic energy consumption value and the total anaerobic energy consumption value. Specifically, first, it is determined whether the collection device collects the maximum oxygen uptake, i.e., the oxygen uptake threshold, in the N time periods in which the user performs the target motion.

[0087] When the collecting device does not collect the maximum oxygen uptake, the maximum value of the collected heart rate data, the maximum value of the movement speed and the maximum value of the respiratory rate data are substituted into VO2=(a*HR+b*speed)*Respiratory rate*c+d to obtain an estimated value of VO2max, and the VO2max is taken as the maximum oxygen uptake.

[0088] Further, the aerobic energy consumption rate and the anaerobic energy consumption rate are calculated by calculating the ratio of the aerobic energy consumption value to the total duration of the N time periods of the target movement, and calculating the ratio of the anaerobic energy consumption value to the total duration of the N time periods of the target movement, to obtain the aerobic energy consumption rate and the anaerobic energy consumption rate.

[0089] Still further, the metabolic equivalent parameter is obtained by using METs=VO2 / 3.5, the consumption threshold is obtained by using Consumption threshold=BMR*METs*total duration of N time periods, and the training effect value is calculated by using the consumption threshold, the total aerobic energy consumption value and the total anaerobic energy consumption value, that is, the training effect value is calculated by the following method:

[0090] The aerobic training effect value=a1*(aerobic energy consumption rate / consumption threshold)+aerobic energy consumption rate / b1;

[0091] The anaerobic training effect value=a2*(anaerobic energy consumption rate / consumption threshold)+anaerobic energy consumption rate / b2;

[0092] Wherein, the coefficients a1, a2, b1 and b2 are preset parameters, that is, the first consumption coefficient, the third consumption coefficient, the second consumption coefficient and the fourth consumption coefficient. After obtaining the aerobic training effect value and the anaerobic training effect value, the aerobic movement and the anaerobic movement involved in the target movement can be optimized and adjusted according to the two training effect values respectively.

[0093] The embodiment calculates the training effect value by using the total aerobic energy consumption value and the total anaerobic energy consumption value, so that the training effect can be obtained more accurately, and the intensity and type of the target movement can be adjusted more accurately.

[0094] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0095] The embodiment of the present application also provides a training evaluation method, Figure 3 is a schematic diagram of an optional training evaluation method provided by the embodiment of the present application, which comprises:

[0096] In the process of the user performing the target exercise, parameters of individual characteristics of the user and training parameters of the user in a current time period are acquired, a basal metabolic rate is calculated by using the individual characteristic parameters, metabolic equivalents are calculated by using the speed, heart rate and respiratory rate in the training parameters, and the energy consumption is calculated according to the basal metabolic rate, the metabolic equivalents and the exercise duration.

[0097] Further, it is judged whether there is an oxygen uptake threshold (i.e., maximum oxygen uptake) in the training parameters acquired when the user performs the target exercise, when the training parameters do not include the oxygen uptake threshold, the maximum oxygen uptake threshold is calculated by using the maximum heart rate data, the maximum respiratory rate data and the maximum exercise speed in the time period when the user performs the target exercise, and then the energy consumption threshold is calculated according to the oxygen uptake threshold; in the case that the training parameters include the oxygen uptake threshold, the energy consumption threshold is calculated according to the oxygen uptake threshold, wherein the energy consumption threshold is also the maximum consumption.

[0098] Further, the lactate threshold of each time period in the time when the user performs the target exercise is calculated according to the individual characteristics, the speed, the heart rate and the respiratory rate, i.e., the demarcation heart rate data in each time period, and then the aerobic and anaerobic consumption proportions, i.e., the aerobic energy supply proportion and the anaerobic energy supply proportion, are calculated according to the lactate threshold of each time period, the product of the aerobic energy supply proportion and the anaerobic energy supply proportion and the energy consumption is calculated respectively, and the energy consumption values of the aerobic exercise and the anaerobic exercise in the target exercise are obtained.

[0099] Further, the consumption rates of the aerobic exercise and the anaerobic exercise are obtained according to the ratio of the energy consumption values of the aerobic exercise and the anaerobic exercise to the target exercise consumption time, the aerobic training effect and the anaerobic training effect are calculated by using the consumption rates of the aerobic exercise and the anaerobic exercise, the energy consumption values of the aerobic exercise and the anaerobic exercise and the energy consumption threshold, and the target exercise is optimized and adjusted based on the aerobic training effect and the anaerobic training effect.

[0100] The embodiment acquires the training parameters of the user and the user characteristic parameters, calculates the aerobic energy consumption value and the anaerobic energy consumption value by using the training parameters and the user characteristic parameters, calculates the aerobic training effect value and the anaerobic training effect value based on the aerobic energy consumption value and the anaerobic energy consumption value, and thus achieves the effect of accurately determining the aerobic training effect and the anaerobic training effect and timely optimizing and adjusting the exercise based on the aerobic training effect and the anaerobic training effect.

[0101] The training evaluation device provided in the embodiment of the present application can be used to execute the training evaluation method provided in the embodiment of the present application.

[0102] Figure 4 is a schematic diagram of an evaluation device for training provided according to an embodiment of the present application, as shown, the device comprises: an acquisition unit 40, a calculation unit 41, a first determination unit 42, a second determination unit 43, a summation unit 44. Figure 4

[0103] The acquisition unit 40 is configured to acquire user feature parameters of a user and training parameters of the user in a current time period during which the user performs a target exercise.

[0104] The calculation unit 41 is configured to calculate a first total energy consumption value in the current time period according to the training parameters and the user feature parameters.

[0105] The first determination unit 42 is configured to determine a first aerobic energy consumption value and a first anaerobic energy consumption value of the current time period by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to a ratio of energy consumption of aerobic exercise and anaerobic exercise.

[0106] The second determination unit 43 is configured to determine a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period, and determine a third aerobic energy consumption value and a third anaerobic energy consumption value of a next time period according to the user feature parameters, training parameters of the next time period and an energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values of the exercise process are obtained, wherein N is a positive integer.

[0107] The summation unit 44 is configured to sum the N aerobic energy consumption values and the N anaerobic energy consumption values respectively to obtain a total aerobic energy consumption value and a total anaerobic energy consumption value, and calculate a training effect value by using the total aerobic energy consumption value and the total anaerobic energy consumption value.

[0108] Optionally, in the evaluation device for training provided in the embodiments of the present application, the first determination unit 42 comprises: the energy consumption ratio associated with the current time period is determined by the following manner: a first determination module is configured to determine a plurality of exercise state intervals from the training parameters in a previous time period of the current time period, and determine an interval energy consumption ratio from data of each exercise state interval, and combine all interval energy consumption ratios as the energy consumption ratio associated with the current time period, wherein different exercise state intervals are associated with different exercise powers; the energy consumption ratio associated with the next time period is determined by the following manner: a second determination module is configured to determine a plurality of exercise state intervals from the training parameters in the current time period, and determine an interval energy consumption ratio from data of each exercise state interval, and combine all interval energy consumption ratios as the energy consumption ratio associated with the next time period, wherein different exercise state intervals are associated with different exercise powers. ​

[0109] Optionally, in the evaluation device for training provided by the embodiment of the present application, the user feature parameters include height, weight and age of the user, and the calculation unit 41 includes: a first calculation module configured to substitute the height, weight and age into a basal metabolism formula to calculate a basal metabolism parameter of the user; a second calculation module configured to calculate a metabolic equivalent parameter by using the user feature parameters and the training parameters; and a third calculation module configured to calculate a product of the basal metabolism parameter, the metabolic equivalent parameter and a time length of a current time period to obtain a first total energy consumption value.

[0110] Optionally, in the evaluation device for training provided by the embodiment of the present application, the training parameters at least include motion power, oxygen consumption, motion speed, heart rate data and respiratory rate data of the user, and the metabolic equivalent parameter is calculated by at least one of the following manners: a third determination module configured to determine a motion type of the target motion, and determine the metabolic equivalent parameter from a metabolic equivalent parameter table according to the motion type, wherein the metabolic equivalent table is composed of multiple motion types and metabolic equivalent parameters of each motion type; and a fourth determination module configured to determine oxygen uptake of the user, and calculate a product of the oxygen uptake and an oxygen uptake weight value to obtain the metabolic equivalent parameter, wherein the oxygen uptake is calculated by at least one of the following manners: a fourth calculation module configured to calculate a weighted sum result of the heart rate data, the respiratory rate data and the motion speed and a corresponding first preset weight value to obtain the oxygen uptake; a fifth calculation module configured to calculate a weighted sum result of the motion power, the oxygen consumption and a corresponding second preset weight value to obtain the oxygen uptake; and a sixth calculation module configured to calculate a product of the motion speed and a slope of a motion scene used by the user and a corresponding third preset weight value to obtain the oxygen uptake.

[0111] Optionally, in the evaluation device for training provided by the embodiment of the present application, the training parameters at least include motion power, oxygen consumption, motion speed, heart rate data and respiratory rate data of the user, and the first determination unit 42 includes: a first acquisition module configured to acquire first heart rate data when the motion speed is less than a first threshold value, the respiratory rate data continuously increases, and the heart rate data increases by a first preset amplitude in a previous time period; a second acquisition module configured to acquire second heart rate data when the motion speed is greater than a second threshold value, a change amplitude of the respiratory rate data is less than a second preset amplitude, and the heart rate data increases by a third preset amplitude in the previous time period; a fifth determination module configured to determine multiple sets of curve coefficients in different motion states according to the respiratory rate data, the heart rate data and the motion speed in the previous time period, and determine a motion curve by using each set of curve coefficients to obtain multiple motion curves, wherein the motion curve represents a change trend of a ratio of anaerobic energy consumption value to total energy consumption value with respect to the heart rate data; and a division module configured to divide the multiple motion curves according to the first heart rate data and the second heart rate data to obtain multiple motion state intervals in the previous time period.

[0112] Optionally, in the evaluation device for training provided by the embodiment of the present application, in the case that the motion state includes three kinds, the plurality of motion curves include a first motion curve, a second motion curve and a third motion curve, the first determining unit 42 includes: a first intercepting module, configured to intercept a curve segment between the starting heart rate data and the first heart rate data in the first motion curve to obtain a first curve segment, and determine a first motion state interval according to the first curve segment; a second intercepting module, configured to intercept a curve segment between the first heart rate data and the second heart rate data in the second motion curve to obtain a second curve segment, and determine a second motion state interval according to the second curve segment; and a third intercepting module, configured to intercept a curve segment between the second heart rate data and the ending heart rate data in the third motion curve to obtain a third curve segment, and determine a third motion state interval according to the third curve segment.

[0113] Optionally, in the evaluation device for training provided by the embodiment of the present application, the first determining unit 42 includes: a seventh calculating module, configured to calculate a heart rate parameter by using the heart rate data of each motion state interval, calculate a product of the heart rate parameter and each set of curve coefficients to obtain an anaerobic energy supply ratio of the motion state interval; an eighth calculating module, configured to calculate a product of the anaerobic energy supply ratio of the motion state interval and the total energy consumption value to obtain an anaerobic energy consumption value of the motion state interval; a ninth calculating module, configured to calculate a difference between the total energy consumption value and the anaerobic energy consumption value to obtain an aerobic energy consumption value of the motion state interval; and a combination module, configured to combine the anaerobic energy consumption values of all the motion state intervals into a first anaerobic energy consumption value, and combine the aerobic energy consumption values of all the motion state intervals into a first aerobic energy consumption value.

[0114] Optionally, in the evaluation device for training provided by the embodiment of the present application, the summation unit 44 comprises: a judging module, configured to judge whether there is an oxygen uptake threshold in the training parameters in the N time periods in which the user performs the target exercise; a sixth determining module, configured to determine the maximum heart rate data, the maximum respiratory rate data and the maximum exercise speed in the N time periods in case that there is no oxygen uptake threshold in the training parameters; a tenth calculating module, configured to calculate the product of the maximum heart rate data, the maximum respiratory rate data and the maximum exercise speed to obtain the oxygen uptake threshold; a seventh determining module, configured to determine the consumption threshold according to the oxygen uptake threshold in case that there is the oxygen uptake threshold in the training parameters, calculate the ratio of the aerobic energy consumption value to the total duration of the N time periods to obtain the aerobic energy consumption rate, and calculate the ratio of the anaerobic energy consumption value to the total duration of the N time periods to obtain the anaerobic energy consumption rate; an eleventh calculating module, configured to calculate the ratio of the aerobic energy consumption value to the consumption threshold to obtain a first consumption ratio, calculate the product of the first consumption ratio and a first consumption coefficient to obtain a first product, and calculate the product of the aerobic energy consumption rate and a second consumption coefficient to obtain a second product, and calculate the sum of the first product and the second product to obtain an aerobic training effect value; a twelfth calculating module, configured to calculate the ratio of the anaerobic energy consumption value to the consumption threshold to obtain a second consumption ratio, calculate the product of the second consumption ratio and a third consumption coefficient to obtain a third product, calculate the product of the aerobic energy consumption rate and a fourth consumption coefficient to obtain a fourth product, and calculate the sum of the third product and the fourth product to obtain an anaerobic training effect value; and a constituting module, configured to constitute the training effect value by the aerobic training effect value and the anaerobic training effect value.

[0115] The evaluation device for training provided in the embodiments of the present application comprises an obtaining unit 40, which is configured to obtain user feature parameters of a user and training parameters of the user in a current time period during the user performs a target exercise; a calculation unit 41, which is configured to calculate a first total energy consumption value in the current time period according to the training parameters and the user feature parameters; a first determination unit 42, which is configured to determine a first aerobic energy consumption value and a first anaerobic energy consumption value of the current time period by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to a ratio of energy consumption of aerobic exercise and anaerobic exercise; a second determination unit 43, which is configured to determine a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period, and determine a third aerobic energy consumption value and a third anaerobic energy consumption value in a next time period according to the user feature parameters, the training parameters in the next time period and an energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values in the exercise process are obtained, wherein N is a positive integer; and a summation unit 44, which is configured to sum the N aerobic energy consumption values and the N anaerobic energy consumption values respectively to obtain a total aerobic energy consumption value and a total anaerobic energy consumption value, and calculate a training effect value by using the total aerobic energy consumption value and the total anaerobic energy consumption value, thereby solving the problem that it is difficult to determine the exercise effect in the related art. By obtaining the training parameters of the user and the user feature parameters, the aerobic energy consumption value and the anaerobic energy consumption value are calculated by using the training parameters and the user feature parameters, and the training effect value is calculated based on the aerobic energy consumption value and the anaerobic energy consumption value, so that the training effect can be accurately determined, and the exercise effect can be guided based on the training effect.

[0116] The evaluation device for training comprises a processor and a memory, and the above-mentioned obtaining unit 40, calculation unit 41, first determination unit 42, second determination unit 43 and summation unit 44 are all stored in the memory as program units, and the corresponding functions are realized by the processor executing the above-mentioned program units stored in the memory.

[0117] The processor comprises a core, and the core retrieves the corresponding program units from the memory. One or more than one core can be set, and the problem that it is difficult to determine the exercise effect in the related art can be solved by adjusting the core parameters.

[0118] The memory can comprise a non-permanent memory in a computer readable medium, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM), and the memory comprises at least one memory chip.

[0119] The embodiments of the present application provide a computer readable storage medium, which stores a program, and the program is executed by a processor to realize the evaluation method for training.

[0120] The embodiment of the present application provides a processor, which is used for running a program, wherein the program runs to execute an evaluation method of training.

[0121] Figure 5 is a schematic diagram of an electronic device provided by the embodiment of the present application, as Figure 5 shown, the embodiment of the present application provides an electronic device 50, which comprises a processor, a memory, and a program stored in the memory and capable of running on the processor, and the processor is used for running computer readable instructions, wherein the computer readable instructions run to execute an evaluation method of training. The device in the present application can be a server, a PC, a PAD, a mobile phone, and the like.

[0122] The present application further provides a computer program product, which is suitable for executing an evaluation method of training when executed on a data processing device.

[0123] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0124] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices generate a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The functions specified in one flow or multiple flows and / or blocks

[0125] These computer program instructions can also be stored in a computer readable storage medium capable of guiding the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The functions specified in one flow or multiple flows and / or blocks

[0126] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0127] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0128] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, or other memory technologies, CD-ROM, digital versatile disc (DVD), or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information for access by a computing device. In no case does the medium include a transitory signal.

[0129] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. According to the definition herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0130] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article or apparatus that includes the element.

[0131] ​​Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code thereon for use by or in connection with an instruction execution system. For the purposes of this description, a computer-usable or computer readable storage medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0132] The foregoing is merely illustrative of the embodiments of this application, and is not intended to limit the application. Numerous variations and modifications can be possible to the embodiments without departing from the spirit and scope of the application. Any equivalent modifications or variations, made within the spirit and scope of the application, should be considered within the scope of the application.

Claims

1. A method of evaluating a training, characterized by, The application relates to a method for calculating training effect value. During the process of target movement of a user, user characteristic parameters of the user are acquired, and training parameters of the user in a current time period are acquired; a first total energy consumption value in the current time period is calculated according to the training parameters and the user characteristic parameters; a first aerobic energy consumption value and a first anaerobic energy consumption value in the current time period are determined by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to the ratio of energy consumption of aerobic exercise and anaerobic exercise; a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period are determined, and a third aerobic energy consumption value and a third anaerobic energy consumption value in a next time period are determined according to the user characteristic parameters, training parameters in the next time period and an energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values in the process of movement are obtained, wherein N is a positive integer; a total aerobic energy consumption value and a total anaerobic energy consumption value are obtained by summing the N aerobic energy consumption values and the N anaerobic energy consumption values respectively, and a training effect value is calculated by using the total aerobic energy consumption value and the total anaerobic energy consumption value; The energy consumption ratio associated with the current time period is determined by the following method: a plurality of movement state intervals are determined from the training parameters in the previous time period of the current time period, an interval energy consumption ratio is determined according to the data of each movement state interval, and all interval energy consumption ratios are combined to obtain the energy consumption ratio associated with the current time period, wherein different movement state intervals are associated with different movement powers; The energy consumption ratio associated with the next time period is determined by the following method: a plurality of movement state intervals are determined from the training parameters in the current time period, an interval energy consumption ratio is determined according to the data of each movement state interval, and all interval energy consumption ratios are combined to obtain the energy consumption ratio associated with the next time period, wherein different movement state intervals are associated with different movement powers.

2. The method of claim 1, wherein, The user characteristic parameters include the height, weight and age of the user, and the first total energy consumption value in the current time period is calculated according to the training parameters and the user characteristic parameters, including: The height, weight and age are substituted into a basal metabolism formula to calculate a basal metabolism parameter of the user; a metabolic equivalent parameter is calculated by using the user characteristic parameters and the training parameters; The product of the basal metabolism parameter, the metabolic equivalent parameter and the time length of the current time period is calculated to obtain the first total energy consumption value.

3. The method of claim 2, wherein, The training parameters at least include the movement power, oxygen consumption, movement speed, heart rate data and respiratory rate data of the user, and the metabolic equivalent parameter is calculated by at least one of the following methods: determining a movement type of the target movement, and determining the metabolic equivalent parameter from a metabolic equivalent parameter table according to the movement type, wherein the metabolic equivalent parameter table is composed of a plurality of movement types and metabolic equivalent parameters of each movement type; determining an oxygen uptake of the user, and calculating a product of the oxygen uptake and an oxygen uptake weight value to obtain the metabolic equivalent parameter, wherein the oxygen uptake is calculated by at least one of the following manners: calculating a weighted sum result of the heart rate data, the respiratory rate data, and corresponding first preset weight values to obtain the oxygen uptake; calculating a weighted sum result of the movement power, the oxygen consumption, and corresponding second preset weight values to obtain the oxygen uptake; calculating a product of the movement speed and a slope of a movement scene used by the user and corresponding third preset weight values to obtain the oxygen uptake.

4. The method of claim 1, wherein, The training parameters at least include the movement power, the oxygen consumption, the movement speed, the heart rate data, and the respiratory rate data of the user, and a plurality of movement state intervals are determined by the training parameters in a previous time period of the current time period, including: obtaining first heart rate data when the movement speed is less than a first threshold value, the respiratory rate data continuously increases, and the heart rate data increases by a first preset amplitude in the previous time period; obtaining second heart rate data when the movement speed is greater than a second threshold value, the respiratory rate data changes by a second preset amplitude, and the heart rate data increases by a third preset amplitude in the previous time period; determining a plurality of groups of curve coefficients in different movement states according to the respiratory rate data, the heart rate data, and the movement speed in the previous time period, and determining a plurality of movement curves by using each group of curve coefficients to obtain the plurality of movement curves, wherein the movement curves represent a trend of a ratio of anaerobic energy consumption to total energy consumption changing with the heart rate data; dividing the plurality of movement curves according to the first heart rate data and the second heart rate data to obtain a plurality of movement state intervals in the previous time period.

5. The method of claim 4, wherein, In a case where the movement state includes three types, the plurality of movement curves include a first movement curve, a second movement curve, and a third movement curve, and dividing the plurality of movement curves according to the first heart rate data and the second heart rate data to obtain a plurality of movement state intervals in the previous time period includes: obtaining a first curve segment by intercepting a curve segment between the starting heart rate data and the first heart rate data in the first movement curve, and determining a first movement state interval according to the first curve segment; obtaining a second curve segment by intercepting a curve segment between the first heart rate data and the second heart rate data in the second movement curve, and determining a second movement state interval according to the second curve segment; obtaining a third curve segment by intercepting a curve segment between the second heart rate data and the ending heart rate data in the third movement curve, and determining a third movement state interval according to the third curve segment.

6. The method of claim 4, wherein, Determining the first aerobic energy consumption value and the first anaerobic energy consumption value of the current time period using the first total energy consumption value and data of a plurality of motion state intervals comprises: For each motion state interval, calculating a heart rate parameter using heart rate data of the motion state interval, calculating a product of the heart rate parameter and each set of curve coefficients to obtain an anaerobic energy supply ratio of the motion state interval; Calculating a product of the anaerobic energy supply ratio of the motion state interval and the total energy consumption value to obtain an anaerobic energy consumption value of the motion state interval; Calculating a difference between the total energy consumption value and the anaerobic energy consumption value to obtain an aerobic energy consumption value of the motion state interval; Combining the anaerobic energy consumption values of all motion state intervals into the first anaerobic energy consumption value, and combining the aerobic energy consumption values of all motion state intervals into the first aerobic energy consumption value.

7. The method of claim 1, wherein, Calculating a training effect value using the aerobic energy consumption total value and the anaerobic energy consumption total value comprises: Determining whether there is an oxygen uptake threshold in training parameters of N time periods in which the user performs the target motion; In the case where there is no oxygen uptake threshold in the training parameters, determining maximum heart rate data, maximum respiratory rate data and maximum motion speed in the N time periods; Calculating a product of the maximum heart rate data, the maximum respiratory rate data and the maximum motion speed to obtain the oxygen uptake threshold; In the case where there is the oxygen uptake threshold in the training parameters, determining a consumption threshold according to the oxygen uptake threshold, calculating a ratio of the aerobic energy consumption value to a total duration of the N time periods to obtain an aerobic energy consumption rate, and calculating a ratio of the anaerobic energy consumption value to the total duration of the N time periods to obtain an anaerobic energy consumption rate; Calculating a ratio of the aerobic energy consumption value to the consumption threshold to obtain a first consumption ratio, calculating a product of the first consumption ratio and a first consumption coefficient to obtain a first product, and calculating a product of the aerobic energy consumption rate and a second consumption coefficient to obtain a second product, calculating a sum of the first product and the second product to obtain an aerobic training effect value; Calculating a ratio of the anaerobic energy consumption value to the consumption threshold to obtain a second consumption ratio, calculating a product of the second consumption ratio and a third consumption coefficient to obtain a third product, and calculating a product of the aerobic energy consumption rate and a fourth consumption coefficient to obtain a fourth product, calculating a sum of the third product and the fourth product to obtain an anaerobic training effect value; The training effect value is composed of the aerobic training effect value and the anaerobic training effect value.

8. An evaluation device for training, characterized in that Comprises: An acquisition unit is configured to acquire user feature parameters of a user and training parameters of the user in a current time period during the user performing a target motion; A calculation unit is configured to calculate a first total energy consumption value in the current time period according to the training parameters and the user feature parameters; The first determining unit is configured to determine a first aerobic energy consumption value and a first anaerobic energy consumption value of the current time period by using the first total energy consumption value and an energy consumption ratio associated with the current time period, wherein the energy consumption ratio refers to a ratio of energy consumption of aerobic exercise and anaerobic exercise. The second determining unit is configured to determine a second aerobic energy consumption value and a second anaerobic energy consumption value in a previous time period, and determine a third aerobic energy consumption value and a third anaerobic energy consumption value of a next time period according to the user characteristic parameter, a training parameter of the next time period and an energy consumption ratio associated with the next time period, until N aerobic energy consumption values and N anaerobic energy consumption values of a movement process are obtained, wherein N is a positive integer. The summing unit is configured to sum the N aerobic energy consumption values and the N anaerobic energy consumption values respectively to obtain a total aerobic energy consumption value and a total anaerobic energy consumption value, and calculate a training effect value by using the total aerobic energy consumption value and the total anaerobic energy consumption value. The first determining unit comprises: the energy consumption ratio associated with the current time period is determined by the following manner: a first determining module is configured to determine a plurality of movement state intervals from a training parameter in a previous time period of the current time period, and determine an interval energy consumption ratio from data of each movement state interval, and combine all interval energy consumption ratios as the energy consumption ratio associated with the current time period, wherein different movement state intervals are associated with different movement powers. The first determining unit further comprises: the energy consumption ratio associated with the next time period is determined by the following manner: a second determining module is configured to determine a plurality of movement state intervals from a training parameter in the current time period, and determine an interval energy consumption ratio from data of each movement state interval, and combine all interval energy consumption ratios as the energy consumption ratio associated with the next time period, wherein different movement state intervals are associated with different movement powers.

9. A processor, comprising: The processor is configured to run a program, wherein the program performs the training evaluation method of any one of claims 1 to 7 when the program is run.

10. An electronic device, comprising: The processor is configured to run a program, wherein the program performs the training evaluation method of any one of claims 1 to 7 when the program is run.

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