Maximum heart rate estimation method, estimation system and estimation interaction system

Abstract

The invention discloses a maximum heart rate estimation method, a maximum heart rate estimation system and an estimation interaction system. The estimation method comprises the following steps: screening a power cache sequence and a heart rate cache sequence from historical riding data sequences of sports users; fitting a function relationship between the maximum heart rate and the maximum power by adopting the screened power cache sequence and the heart rate cache sequence; on the basis of a preset model, the corresponding maximum aerobic power is estimated by using the weight, the height and the FTP of the exercise user; on the basis of the function relation and the maximum aerobic power, the corresponding heart rate is obtained to serve as the estimated maximum heart rate; wherein the power threshold FTP is updated based on the physical fitness change of the exercise user; the preset model describes the relationship among the maximum aerobic power, the weight, the height and the power threshold value of the exercise user. According to the method, the maximum heart rate of the exercise user can be reliably and accurately obtained without extreme exercise.

Description

Technical Field

[0001] The present invention relates to the field of sports health technology, and in particular to a maximum heart rate estimation method, an estimation system and an estimation interaction system. Background Art

[0002] Maximum heart rate is an important sports health indicator. Generally speaking, the most direct measurement method is to perform extreme exercise of maximum intensity and collect the heart rate at the extreme state through a heart rate collection device (such as a heart rate belt), but this method is not suitable for everyone (for example, the user has heart health problems). Secondly, there is also a method of estimating the maximum heart rate by age. When the age is less than 40 years old, the maximum heart rate is equal to 220 minus the age. When the age is greater than or equal to 40 years old, the maximum heart rate is equal to 206.9-0.67 and the age. The maximum heart rate obtained in this way has certain limitations. When testing groups with large differences in maximum heart rates but similar ages, the solution performance is poor.

[0003] Therefore, there is an urgent need for a method that can obtain the maximum heart rate without extreme exercise, while ensuring that a relatively accurate maximum heart rate is obtained and avoiding excessive exercise intensity. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a maximum heart rate estimation method, which estimates the maximum heart rate based on the historical riding data sequence of the sports user.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0006] The present application relates to a maximum heart rate estimation method, comprising:

[0007] Acquire power data and heart rate data of the sports user from a historical cycling data sequence of the sports user;

[0008] Based on the acquired power data and heart rate data, a power cache sequence and a heart rate cache sequence are screened out;

[0009] The screened power buffer sequence and heart rate buffer sequence are used to fit the functional relationship between the maximum heart rate and the maximum power;

[0010] Based on a preset model, the corresponding maximum aerobic power Map is estimated using the weight, height and power threshold FTP of the exercise user;

[0011] Based on the functional relationship and the maximum aerobic power Map, obtaining a corresponding heart rate as the estimated maximum heart rate;

[0012] Wherein, the power threshold FTP is updated based on the physical fitness change of the sports user;

[0013] The preset model describes the relationship between the maximum aerobic power of the exercise user and his / her weight, height and power threshold.

[0014] In some embodiments of the present application, based on the acquired power data and heart rate data, a power cache sequence and a heart rate cache sequence are screened out, specifically:

[0015] Based on the acquired power data and heart rate data, filter and extract power data whose magnitude reaches the product of the first preset value and the power threshold FTP, and synchronously record the heart rate data at the corresponding moment;

[0016] A sliding window method is adopted to slide and sample the average power and average heart rate in each window. After sliding the entire data sequence length, the average power under each window forms the power cache sequence, and the average heart rate under each window forms the heart rate cache sequence.

[0017] In some embodiments of the present application, the first preset value is 60%.

[0018] In some embodiments of the present application, the functional relationship is a linear relationship between maximum heart rate and maximum power;

[0019] The screened power buffer sequence and heart rate buffer sequence are used to calculate fitting parameters grad and bias for fitting the linear relationship based on the least square method, so that the maximum heart rate = grad * maximum power + bias.

[0020] In some embodiments of the present application, the fitting parameters grad and bias are calculated using the following formula:

[0021]

[0022] Among them, power_batch represents the power cache sequence, heatat_batch represents the heart rate cache sequence, and n represents the length of the sliding window. represents the average value of the power buffer sequence, Represents the average value of the heart rate buffer sequence.

[0023] In some embodiments of the present application, the updated FTP of the sports user is applied in the next round of preset models.

[0024] In some embodiments of the present application, the power threshold FTP is updated based on the physical fitness change of the sports user, specifically:

[0025] The power threshold FTP is updated according to the estimated real-time oxygen uptake vo2_power using the following formula;

[0026]

[0027] Among them, power represents the power recorded by the athlete during the cycling process, m represents the weight of the athlete, hr_max and hr_rest represent the maximum heart rate and resting heart rate of the athlete, k1, k2 and k3 are all experimental coefficients, k is the empirical coefficient, and hr_power is the average heart rate corresponding to the window of maximum average power obtained by sliding window.

[0028] The maximum heart rate estimation method involved in some embodiments provided in this application has the following advantages and beneficial effects:

[0029] (1) Estimate the maximum heart rate based on the user's historical cycling data series, without the need for extreme exercise, and avoid the heart rate health risks caused by high-intensity exercise;

[0030] (2) Maximum heart rate estimation only cares about the historical riding data sequence and has nothing to do with age. Therefore, it avoids the problem of inaccurate heart rate calculation caused by estimating heart rate based on age (for example, when people of similar age have large differences in heart rate), and improves the reliability and accuracy of heart rate calculation;

[0031] (3) Based on the positive correlation between power and heart rate, the maximum heart rate is calculated using the maximum aerobic power. The maximum aerobic power is correlated with the user's power threshold. Therefore, the power threshold is updated according to the changes in the user's physical fitness data during multiple rides, thereby more reliably estimating the maximum heart rate.

[0032] Some embodiments of the present application also relate to a maximum heart rate estimation system, including:

[0033] A data acquisition module, which acquires power data and heart rate data of the sports user from the historical riding data sequence of the sports user;

[0034] A data screening module, which screens out a power cache sequence and a heart rate cache sequence based on the acquired power data and heart rate data;

[0035] A function fitting module, which uses the screened power buffer sequence and heart rate buffer sequence to fit the functional relationship between the maximum heart rate and the maximum power;

[0036] A power estimation module, which estimates the corresponding maximum aerobic power Map based on a preset model and using the weight, height and power threshold FTP of the exercise user;

[0037] a maximum heart rate reverse calculation module, which obtains the corresponding heart rate as the estimated maximum heart rate based on the functional relationship and the maximum aerobic power Map;

[0038] Wherein, the power threshold FTP is updated based on the physical fitness change of the sports user;

[0039] The preset model describes the relationship between the maximum aerobic power of the exercise user and his / her weight, height and power threshold.

[0040] In some embodiments of the present application, the data screening module includes:

[0041] A data extraction unit, which, based on the acquired power data and heart rate data, screens and extracts power data whose magnitude reaches the product of a first preset value and a power threshold FTP, and synchronously records the heart rate data at the corresponding moment;

[0042] The data sampling unit adopts a sliding window method to slide and sample the average power and average heart rate in each window. After sliding the entire data sequence length, the average power under each window forms the power cache sequence, and the average heart rate under each window forms the heart rate cache sequence.

[0043] In some embodiments of the present application, the maximum heart rate estimation system further includes:

[0044] An updating module is used to update the power threshold FTP based on the physical fitness change of the sports user, specifically:

[0045] The power threshold FTP is updated according to the estimated real-time oxygen uptake vo2_power using the following formula;

[0046]

[0047] Among them, power represents the power recorded by the athlete during the cycling process, m represents the weight of the athlete, hr_max and hr_rest represent the maximum heart rate and resting heart rate of the athlete, k1, k2 and k3 are all experimental coefficients, k is the empirical coefficient, and hr_power is the average heart rate corresponding to the window of maximum average power obtained by sliding window.

[0048] Some embodiments of the present application also relate to a maximum heart rate estimation interactive system, including:

[0049] Heart rate collection equipment, which is used to collect heart rate data of sports users;

[0050] Power collection equipment, which is used to collect power data of sports users;

[0051] The terminal receives the heart rate data and the power data, and is used to execute the maximum heart rate estimation method as described above, and display the estimated maximum heart rate at the front end of the terminal.

[0052] After reading the specific embodiments of the present invention in conjunction with the accompanying drawings, other features and advantages of the present invention will become more clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 is a flow chart of an embodiment of a maximum heart rate estimation method proposed in the present application;

[0055] Figure 2 It is the power data and heart rate data after filtering;

[0056] Figure 3 is a flow chart of obtaining maximum aerobic power in an embodiment of the maximum heart rate estimation method proposed in this application;

[0057] Figure 4 is a comparison diagram between the maximum heart rate estimated by the maximum heart rate estimation method proposed in the present application and the actual maximum heart rate tested;

[0058] Figure 5 It is a hardware block diagram of an embodiment of the maximum heart rate estimation interactive system proposed in this application.

[0059] Reference numerals:

[0060] 100. Heart rate acquisition device; 200. Power acquisition device; 300. Terminal. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0062] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0063] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0064] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0065] In order to solve the limitation problem that extreme exercise is required to obtain the maximum heart rate and the age estimation model estimates the maximum heart rate, which causes the solution difference, some embodiments of the present application involve a maximum heart rate estimation method, which is based on the riding data of the sports user, does not require extreme exercise and age is not involved in the estimation, so as to achieve reliable and accurate estimation of the maximum heart rate, and facilitate the measurement of the health status of the sports user.

[0066] Some embodiments of the present application relate to a maximum heart rate estimation method.

[0067] The maximum heart rate estimation method is implemented based on a maximum heart rate estimation system. As follows, the maximum heart rate estimation method will be described in conjunction with the maximum heart rate estimation system.

[0068] Figure 1 A flowchart showing the maximum heart rate estimation method is shown, therefore, refer to Figure 1 ,The maximum heart rate estimation method is described in detail as follows.

[0069] S1: Acquire power data and heart rate data of the sports user from a historical cycling data sequence of the sports user.

[0070] The historical cycling data series of the sports user includes heart rate data, power data, speed data, etc. Therefore, when the heart rate data and power data are needed, the historical cycling data series can be filtered.

[0071] This data screening process can be implemented by a data acquisition module (not shown) in the maximum heart rate estimation system.

[0072] In some embodiments of the present application, the cycling system may include multiple hardware structures. For example, a heart rate acquisition device 100 (such as a heart rate belt) may be used to collect heart rate data generated by an athlete during exercise. For example, a power acquisition device 200 (such as a power meter) may be used to collect power data generated by an athlete during exercise.

[0073] S2: Filter out a power cache sequence and a heart rate cache sequence based on the acquired power data and heart rate data.

[0074] This data screening process can be implemented by a data screening module (not shown) in the maximum heart rate estimation system.

[0075] During cycling, the heart rate will increase with the increase of exercise intensity. When the output power exceeds the power threshold FTP (Functional Threshold Power), the heart rate will further increase until the power value reaches Map (Maximal aerobic power). Then the heart rate will converge to the maximum level. The heart rate in this state is close to the maximum heart rate.

[0076] Since the positive correlation between power and heart rate is relatively strong during the period from moderate-intensity exercise to heart rate convergence, if you want to estimate the maximum heart rate, the power corresponding to the heart rate needs to be screened to avoid the problem of poor accuracy in maximum heart rate estimation caused by the introduction of power data at low power.

[0077] Among them, FTP refers to the average power output of the user when riding as hard as possible for one hour; Map refers to the power value corresponding to the maximum heart rate during exercise.

[0078] In some embodiments of the present application, the filtered power is limited to be greater than the product of the first preset value and FTP to filter out lower power, which helps to accurately achieve the fitting of the following functional relationship.

[0079] As described above, the heart rate data and power data in the historical riding data sequence can form a fit file.

[0080] The specific screening method is as follows.

[0081] (1) Further screening is performed on the fit file to extract power data whose size reaches the product of the first preset value and the power threshold FTP, and the heart rate data at the corresponding time is synchronously sampled.

[0082] The data extraction part may be implemented by a data extraction unit (not shown) in the estimation system.

[0083] The first preset value may be preset as needed. As mentioned above, in order to improve the accuracy of the maximum heart rate estimation, the first preset value may be set between 50% and 70%.

[0084] In some embodiments of the present application, the first preset value may be selected to be 60%.

[0085] That is, the power data having a power value greater than 60%*FTP and the heart rate data of the filtered power data are filtered.

[0086] See also Figure 2 , which shows the filtered power data and heart rate data, from Figure 2 As can be seen from the graph, when maximum aerobic power is reached, the heart rate converges to the maximum heart rate.

[0087] In some embodiments of the present application, since FTP is required, the user is required to provide FTP in advance. If it cannot be provided, the FTP needs to be calculated using the FTP calculation method involved in Patent Publication No. CN117688275A.

[0088] The FTP calculation method involved in CN117688275A is not described in detail here and is incorporated into this article by reference.

[0089] (2) A sliding window method is used to slide and sample the average power and average heart rate in each window. After the entire fit file is slid, the average power in each window forms the power cache sequence power_batch, and the average heart rate in each window forms the heart rate cache sequence heatat_batch.

[0090] The calculation method is shown in the following formula (1).

[0091]

[0092] In some embodiments of the present application, a 30 s sliding window and a 1 s step are used for average sampling.

[0093] As in (1) above, i represents the i-th movement of the sliding window, and k is a counting point, representing 1 to 30 in the sliding window.

[0094] power represents power data, heatat represents heart rate data, power_batch(i) represents the average power of the i-th sliding window, and heatat_batch(i) represents the average heart rate of the i-th sliding window.

[0095] The data sampling part may be implemented by a data sampling unit (not shown) in the estimation system.

[0096] S3: Using the screened power buffer sequence and heart rate buffer sequence, fit the functional relationship between the maximum heart rate and the maximum power.

[0097] The relationship fitting part can be implemented by a function fitting module (not shown) in the estimation system.

[0098] See also Figure 2 The filtered power data and heart rate data are shown according to Figure 2 From the heart rate data and power data shown in , it can be seen that heart rate and power have a strong positive correlation.

[0099] That is, when the output power is higher, there will also be a correspondingly higher heart rate.

[0100] Therefore, when there are enough data in the power buffer sequence and the heart rate buffer sequence in S2, the buffered heart rate data and power data are used to perform function fitting, and this functional relationship refers to the linear relationship between the maximum power and the maximum heart rate.

[0101] In some embodiments of the present application, the power data power_batch(i) in the power cache sequence power_batch and the heart rate data heatat_batch(i) in the heart rate cache sequence heatat_batch as described above are used to calculate the fitting parameters grad and bias used to fit the functional relationship based on the least squares method, so that the maximum heart rate = grad*maximum power+bias.

[0102] The fitting parameters grad and bias are obtained using the following formula (2).

[0103]

[0104] Where n represents the length of the sliding window, represents the average value of the power buffer sequence, Represents the average value of the heart rate buffer sequence.

[0105] S4: Based on the preset model, the corresponding maximum aerobic power Map is estimated using the weight, height and power threshold FTP of the exercise user.

[0106] The power estimation part may be implemented by a power estimation module (not shown) in the estimation system.

[0107] As mentioned above, after the power reaches the maximum aerobic power Map, the heart rate will converge to a maximum level close to the maximum heart rate. Therefore, if the fitting function relationship between the heart rate and power is known, the Map can be estimated more accurately, and then the maximum heart rate can be reversely calculated using the function relationship.

[0108] Therefore, a reliable and accurate estimation of the Map helps to reliably and accurately estimate the maximum heart rate.

[0109] Since the Map is related to the FTP and physiological information (including height h and weight m) of the exercise user, a preset model between the Map of the exercise user and his weight, height and power threshold is established in advance.

[0110] The preset model is obtained by fitting the Map output by multiple users through extreme sports, FTP, height and weight, and is an empirical model, as shown in the following formula (3).

[0111]

[0112] Among them, q1, q2 and q3 are fitting coefficients, h is height, and m is weight.

[0113] For example, q1 may be 0.7326, q2 may be 44.13, and q3 may be 22.035.

[0114] See also Figure 3 , which shows a flowchart for estimating the maximum aerobic power Map.

[0115] First, the height h and weight m of the exercise user are obtained from their physiological information.

[0116] Physiological information may include, in addition to height and weight, age, resting heart rate, etc., for example, but in this application, only height and weight are used.

[0117] Secondly, it is determined whether there is an FTP input in advance. If not, the FTP is calculated using the method described in Patent Publication No. CN117688275A as described above.

[0118] After that, after obtaining the height h, weight m and FTP, the Map can be obtained according to formula (3).

[0119] S5: Based on the functional relationship and the maximum aerobic power Map, the corresponding heart rate is obtained as the estimated maximum heart rate.

[0120] The heart rate acquisition part can be implemented by a maximum heart rate back-calculation module (not shown) in the estimation system.

[0121] As described above, the heart rate corresponding to the maximum aerobic power is considered to be the maximum heart rate. Therefore, based on the functional relationship obtained in S3 and the Map obtained in S4, the maximum heart rate hr_max is obtained.

[0122] hr_max=grad*Map+bias.

[0123] After a sports user rides multiple times, the physical fitness data changes, and the user's FTP also changes. Therefore, when the maximum heart rate hr_max is calculated based on the new FTP, a calculation error will be caused.

[0124] Therefore, the present application updates the power threshold FTP based on the physical fitness changes of the sports user to achieve reliable estimation of the maximum heart rate hr_max.

[0125] In some embodiments of the present application, it is necessary to obtain the speed power of the corresponding riding, update the FTP by estimating the oxygen uptake vo2_power, and the updated FTP is used to filter the data in S2 of the next wheel.

[0126] The data updating part may be implemented by an updating module (not shown) in the estimation system.

[0127] In some embodiments of the present application, the following formula (4) is used to update FTP.

[0128]

[0129] Among them, power represents the power recorded by the athlete during the cycling process, m represents the weight of the athlete, hr_max and hr_rest represent the maximum heart rate and resting heart rate of the athlete, k1, k2 and k3 are all experimental coefficients, k is the heart rate to oxygen uptake coefficient, this value will change with the user's athletic ability, hr_power is the average heart rate corresponding to the window of maximum average power obtained by sliding window.

[0130] In some embodiments of the present application, a 30s sliding window and a 1s step sliding are used to obtain hr_power.

[0131] After calculating the average power of each window, the maximum average power is obtained, and the average heart rate at the time corresponding to the maximum average power is recorded as hr_power.

[0132] In some embodiments of the present application, for example, k1 may be 12.24, k2 may be 350, and k3 may be 15.024.

[0133] When testing data of different sports users, the experimental coefficient k will also be different.

[0134] By adopting the maximum heart rate estimation method as described above, the sports user can avoid extreme sports and estimate the maximum heart rate by ensuring normal riding, which is used to assist in measuring the health status of the sports user.

[0135] See also Figure 4, which provides a comparison chart between the maximum heart rate estimated by the maximum heart rate estimation method involved in the embodiment of the present application (ie, the estimated value) and the actual measured maximum heart rate (ie, the true value).

[0136] The heart rate measured during extreme exercise is the maximum heart rate, which can be obtained by the user wearing a heart rate collection device (such as a heart rate belt). Figure 4 Shown by the dashed line.

[0137] The maximum heart rate obtained by the maximum heart rate estimation method of this application is shown in Figure 4 Shown by the solid line.

[0138] pass Figure 4 It can be seen that the maximum heart rate estimated by the maximum heart rate estimation method according to the embodiment of the present application is basically similar to the actual maximum heart rate of the exercise user and has a high accuracy rate.

[0139] In some embodiments of the present application, see Figure 5 , and also relates to an interactive system for estimating a maximum heart rate.

[0140] The maximum heart rate estimation interactive system includes a heart rate collection device 100 , a power collection device 200 and a terminal 300 .

[0141] The heart rate collection device 100 (eg, a heart rate belt) is used to collect the heart rate data of a sports user and send it to the terminal 300 .

[0142] The power collection device 200 (eg, a power meter) is used to collect power data of a sports user and send the data to the terminal 300 .

[0143] The terminal 300 may be a cycling computer or a smart device (eg, a mobile phone or a pad) that is connected to the heart rate collection device 100 and the power collection device 200 for communication.

[0144] The maximum heart rate estimation method as described above is integrated in the terminal 300 for performing maximum heart rate estimation.

[0145] The estimated maximum heart rate can be displayed on the front end of the terminal 300 for easy viewing by the exercising user.

[0146] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for a person skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions claimed to be protected by the present invention.

Claims

1. A maximum heart rate estimation method, characterized in that: include: Acquire power data and heart rate data of the sports user from a historical cycling data sequence of the sports user; Based on the acquired power data and heart rate data, a power cache sequence and a heart rate cache sequence are screened out; The screened power buffer sequence and heart rate buffer sequence are used to fit the functional relationship between the maximum heart rate and the maximum power; Based on a preset model, the corresponding maximum aerobic power Map is estimated using the weight, height and power threshold FTP of the exercise user; Based on the functional relationship and the maximum aerobic power Map, obtaining a corresponding heart rate as the estimated maximum heart rate; Wherein, the power threshold FTP is updated based on the physical fitness change of the sports user; The preset model describes the relationship between the maximum aerobic power of the exercise user and his / her weight, height and power threshold.

2. The maximum heart rate estimation method according to claim 1, characterized in that: Based on the acquired power data and heart rate data, a power cache sequence and a heart rate cache sequence are screened out, specifically: Based on the acquired power data and heart rate data, filter and extract power data whose magnitude reaches the product of the first preset value and the power threshold FTP, and synchronously record the heart rate data at the corresponding moment; A sliding window method is adopted to slide and sample the average power and average heart rate in each window. After sliding the entire data sequence length, the average power under each window forms the power cache sequence, and the average heart rate under each window forms the heart rate cache sequence.

3. The maximum heart rate estimation method according to claim 1, characterized in that: The functional relationship is a linear relationship between maximum heart rate and maximum power; The screened power buffer sequence and heart rate buffer sequence are used to calculate fitting parameters grad and bias for fitting the linear relationship based on the least square method, so that the maximum heart rate = grad * maximum power + bias.

4. The maximum heart rate estimation method according to claim 3, characterized in that: The fitting parameters grad and bias are calculated using the following formula: Among them, power_batch represents the power cache sequence, heatat_batch represents the heart rate cache sequence, and n represents the length of the sliding window. represents the average value of the power buffer sequence, Represents the average value of the heart rate buffer sequence.

5. The maximum heart rate estimation method according to claim 1, characterized in that: The updated FTP of the sports user is applied in the next round of preset models.

6. The maximum heart rate estimation method according to claim 1, characterized in that: The power threshold FTP is updated based on the physical fitness change of the sports user, specifically: The power threshold FTP is updated according to the estimated real-time oxygen uptake vo2_power using the following formula; Among them, power represents the power recorded by the athlete during the cycling process, m represents the weight of the athlete, hr_max and hr_rest represent the maximum heart rate and resting heart rate of the athlete, k1, k2 and k3 are all experimental coefficients, k is the empirical coefficient, and hr_power is the average heart rate corresponding to the window of maximum average power obtained by sliding window.

7. A maximum heart rate estimation system, characterized in that: include: A data acquisition module, which acquires power data and heart rate data of the sports user from the historical riding data sequence of the sports user; A data screening module, which screens out a power cache sequence and a heart rate cache sequence based on the acquired power data and heart rate data; A function fitting module, which uses the screened power buffer sequence and heart rate buffer sequence to fit the functional relationship between the maximum heart rate and the maximum power; A power estimation module, which estimates the corresponding maximum aerobic power Map based on a preset model and using the weight, height and power threshold FTP of the exercise user; a maximum heart rate reverse calculation module, which obtains the corresponding heart rate as the estimated maximum heart rate based on the functional relationship and the maximum aerobic power Map; Wherein, the power threshold FTP is updated based on the physical fitness change of the sports user; The preset model describes the relationship between the maximum aerobic power of the exercise user and his / her weight, height and power threshold.

8. The maximum heart rate estimation system according to claim 7, characterized in that: The data screening module comprises: A data extraction unit, which, based on the acquired power data and heart rate data, screens and extracts power data whose magnitude reaches the product of a first preset value and a power threshold FTP, and synchronously records the heart rate data at the corresponding moment; The data sampling unit adopts a sliding window method to slide and sample the average power and average heart rate in each window. After sliding the entire data sequence length, the average power under each window forms the power cache sequence, and the average heart rate under each window forms the heart rate cache sequence.

9. The maximum heart rate estimation system according to claim 7, characterized in that: The maximum heart rate estimation system further comprises: An updating module is used to update the power threshold FTP based on the physical fitness change of the sports user, specifically: The power threshold FTP is updated according to the estimated real-time oxygen uptake vo2_power using the following formula; Among them, power represents the power recorded by the athlete during the cycling process, m represents the weight of the athlete, hr_max and hr_rest represent the maximum heart rate and resting heart rate of the athlete, k1, k2 and k3 are all experimental coefficients, k is the empirical coefficient, and hr_power is the average heart rate corresponding to the window of maximum average power obtained by sliding window.

10. A maximum heart rate estimation interactive system, characterized in that: include: Heart rate collection equipment, which is used to collect heart rate data of sports users; Power collection equipment, which is used to collect power data of sports users; A terminal, which receives the heart rate data and the power data, and is used to execute the maximum heart rate estimation method according to any one of claims 1 to 6, and display the estimated maximum heart rate at the front end of the terminal.

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