Riding power estimation method, estimation system and estimation interaction system

Abstract

The invention discloses a riding power estimation method, an estimation system and an estimation interaction system. The estimation method comprises the following steps: acquiring the maximum heart rate of a sports user; based on the maximum heart rate, the average heart rate and the resting heart rate, the real-time oxygen uptake of the exercise user is obtained; based on the real-time oxygen uptake, the weight of the exercise user and the weight of the bicycle, the riding power is reversely estimated; based on the heart rate gradients of the moving user in the different moving states, offset correction in the different moving states is calculated; and correcting the estimated riding power by using offset correction in different motion states so as to obtain the riding power of the moving user in different motion states. According to the method, power monitoring equipment does not need to be adopted, and the riding power in different exercise states is estimated based on the physiological information of the exercise user, the weight of the bicycle and the heart rate data.

Description

Technical Field

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

[0002] Power is an important output parameter of cycling and an important reference indicator for measuring cycling ability. Generally, power detection requires external power detection equipment (such as a power meter) to obtain power. However, a large number of users who do not wear power meters also have a demand for power as an indicator.

[0003] Therefore, there is an urgent need for a method to obtain cycling power without wearing a power meter.

[0004] The above information disclosed in the background technology is only used to increase the understanding of the background technology of the present application, and therefore, it may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a cycling power estimation method, which estimates the cycling power under different exercise states based on the physiological information of the exercise user himself, the weight of the bicycle and the heart rate data of the exercise user.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: The present application relates to a cycling power estimation method, comprising: Get the maximum heart rate hr_max of the exercise user; Based on the maximum heart rate hr_max, average heart rate and resting heart rate hr_rest, the real-time oxygen uptake of the exercise user is obtained vo2 ; Based on the real-time oxygen uptake vo2 , the weight of the sports user m_body and the weight of the bicycle m_bake, and reverse estimation of the riding power p_dummy; Calculating bias corrections in different exercise states based on the heart rate gradients of the exercise user in different exercise states; The bias correction under different motion states is used to correct the estimated cycling power to obtain the cycling power of the sports user under different motion states; Among them, different exercise states include an exercise state in which the heart rate changes slowly, an exercise state in which the heart rate increases rapidly, and an exercise state in which the heart rate decreases slowly.

[0007] In some embodiments of the present application, the maximum heart rate hr_max of the exercising user is obtained, specifically: ; Use the age of the exercise user to calculate the maximum heart rate hr_max1; Use the maximum heart rate hr_max1 to initialize the maximum heart rate hr_max; Use a sliding window to obtain the real-time heart rate of the latest exercise user who enters the window; The real-time heart rate is compared with the maximum heart rate hr_max. If the real-time heart rate is greater than or equal to the maximum heart rate hr_max, the maximum heart rate hr_max is updated using the real-time heart rate. If the real-time heart rate is less than the maximum heart rate hr_max, the maximum heart rate hr_max is not updated.

[0008] In some embodiments of the present application, the cycling power estimation method further includes: identifying different exercise states based on the heart rate gradient of the exercise user, specifically: Use sliding window to obtain the heart rate gradient in the window in real time grad , as the heart rate gradient of the sports user; When the heart rate gradient is greater than the preset gradient value d0, it indicates that the user is in a state of rapid heart rate increase. Otherwise, it is determined whether the heart rate gradient is less than 0. If so, it indicates that the user is in a state of slow heart rate decrease. Otherwise, it indicates that the user is in a state of steady heart rate change. The preset gradient value d0 is greater than 0.5 and less than or equal to 1.

[0009] In some embodiments of the present application, the window heart rate gradient grad , specifically: ; in, n represents the length of the sliding window, i Represents the count points within the sliding window; hr(i) Represented as the first i heart rate value.

[0010] In some embodiments of the present application, based on the heart rate gradient of the sports user in different sports states, the bias correction in different sports states is calculated. bias , specifically: ; Among them, k0, b0, c0, k1, and b1 are experimental coefficients.

[0011] In some embodiments of the present application, the real-time oxygen uptake of the exercise user is obtained based on the maximum heart rate hr_max, the average heart rate and the resting heart rate hr_rest. vo2 , specifically: Using a sliding window method, the mean heart rate in the window is obtained in real time as the average heart rate hr_mean of the exercise user; Using the formula , get real-time oxygen uptake vo2 ; Among them, α is the experimental coefficient that characterizes the conversion of oxygen uptake to heart rate.

[0012] In some embodiments of the present application, based on the real-time oxygen uptake vo2 , the weight of the sports user m_body and the weight of the bicycle m_bake, reverse estimation of the cycling power p_dummy: p_dummy =vo2*( m_body+m_bake)-a / b; Among them, a and b are the bias correction empirical value and coefficient correction empirical value respectively.

[0013] In some embodiments of the present application, the bias correction under different motion states bias For the estimated cycling power p_dummy Make corrections, specifically: p_dummy= p_dummy+bias.

[0014] The cycling power estimation method provided in some embodiments of the present application has the following advantages and beneficial effects: (1) Cycling power estimation is performed based on the user’s heart rate data, weight, and bicycle weight. This eliminates the need for a power meter and satisfies the user’s power requirements without the need for a power meter. (2) The hardware equipment required for this power estimation method is low-cost and can provide more abundant riding feedback data for entry-level users; (3) Considering the relationship between heart rate and power, the changing trend of the heart rate of athletes in different exercise states is used to correct the cycling power, which broadens the applicable scenarios of the power estimation method and improves the accuracy of power estimation of athletes in different exercise states.

[0015] The present application relates to a cycling power estimation system, comprising: The heart rate acquisition module is used to obtain the maximum heart rate of the exercise user. hr_max ; The oxygen uptake acquisition module is used to obtain the maximum heart rate hr_max , average heart rate, and resting heart rate hr_rest , obtain the real-time oxygen uptake of the sports user vo2 ; A power estimation module is used to estimate the power based on the real-time oxygen uptake vo2 , the weight of the exercise user m_bodyand bicycle weight m_bake , reverse estimate cycling power p_dummy ; A bias correction calculation module, which is used to calculate the bias correction under different exercise states based on the heart rate gradient of the exercise user under different exercise states; A power correction module, which uses the bias correction under different motion states to correct the estimated cycling power to obtain the cycling power of the sports user under different motion states; Among them, different exercise states include an exercise state in which the heart rate changes slowly, an exercise state in which the heart rate increases rapidly, and an exercise state in which the heart rate decreases slowly.

[0016] The present application also relates to a cycling power estimation interactive system, comprising: Heart rate collection equipment, which is used to collect heart rate data of sports users; The terminal receives the heart rate data, the physiological information of the sports user and the weight of the bicycle, executes the cycling power estimation method as described above, and displays the cycling power on the front end of the terminal.

[0017] 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

[0018] 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.

[0019] Figure 1 is a flow chart of an embodiment of a cycling power estimation method proposed in the present application; Figure 2 is a flow chart of obtaining bias correction under different motion states in an embodiment of the cycling power estimation method proposed in the present application; Figure 3 is a comparison chart between the cycling power estimated by the cycling power estimation method proposed in the present application and the actual cycling power tested; Figure 4 is a curve diagram of the absolute error cumulative function of the cycling power estimated by the cycling power estimation method proposed in the present application; Figure 5 is a curve diagram of a relative error accumulation function of cycling power estimated by the cycling power estimation method proposed in the present application; Figure 6 It is a hardware block diagram of an embodiment of the cycling power estimation interactive system proposed in this application.

[0020] Reference numerals: 100. Heart rate acquisition device; 200. Terminal. DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] In order to solve the problem that users need to wear power monitoring equipment to obtain cycling power during cycling, some embodiments of the present application involve a cycling power estimation method, which is based on the heart rate data of the exercise user, the weight of the bicycle and the weight of the exercise user. It does not require the wearing of power monitoring equipment, and can achieve reliable and accurate estimation of cycling power. It is convenient to measure the health status of the exercise user, and also provides entry-level users with rich cycling feedback data.

[0026] Some embodiments of the present application relate to a cycling power estimation method.

[0027] The cycling power estimation method is implemented based on a cycling power estimation system. As follows, the cycling power estimation method will be described in conjunction with the cycling power estimation system.

[0028] Figure 1 A flowchart showing a cycling power estimation method is shown. Figure 1 ,The cycling power estimation method is described in detail as follows.

[0029] S1: Obtain the maximum heart rate hr_max of the exercise user.

[0030] The heart rate data may be acquired through a heart rate acquisition module (not shown) of the cycling power estimation system.

[0031] Before riding, it is necessary to obtain the user's physiological information and the weight of the bicycle. m_bake .

[0032] The physiological information includes: height h、 weight m_body , age, resting heart rate hr_rest , maximum heart rate hr_max, etc. 。

[0033] In some embodiments of the present application, the maximum heart rate hr_max The age of the exercise user can be calculated using the following formula (1): hr_max=hr_max1 .

[0034] (1).

[0035] Maximum heart rate as above hr_max1 The calculation of depends on age. Therefore, people of similar age may have large differences in heart rate. In this case, the maximum heart rate obtained by formula (1) is hr_max It is not accurate enough.

[0036] In some embodiments of the present application, the accuracy of obtaining the maximum heart rate is improved by obtaining it in real time.

[0037] A heart rate acquisition device 100 (eg, a heart rate belt) is used to acquire the heart rate of a user during cycling in real time.

[0038] Using the above formula (1) to obtain hr_max1 Initialize maximum heart rate hr_max , that is, hr_max=hr_ max1.

[0039] A sliding window method is used to obtain the real-time heart rate of the latest exercise user entering the window.

[0040] Compare the real-time heart rate with the maximum heart rate hr_max. If the real-time heart rate is greater than or equal to the maximum heart rate hr_max, use the real-time heart rate to update the maximum heart rate hr_max, that is, assign the real-time heart rate to hr_max. If the real-time heart rate is less than the maximum heart rate hr_max, do not update the maximum heart rate hr_max, that is, keep hr_max.

[0041] The entry length of the sliding window is 11 and the step length is 1. Of course, sliding windows of other sizes and other step lengths may also be selected.

[0042] The smaller the entry length used by the sliding window is, the smaller the calculation delay will be. For example, when the entry length of the sliding window is 11 and the step length is 1, the calculation delay is less than 10s.

[0043] S2: Based on the maximum heart rate hr_max, average heart rate and resting heart rate hr_rest, obtain the real-time oxygen uptake of the exercise user vo2 .

[0044] The real-time oxygen uptake acquisition process can be implemented by an oxygen uptake acquisition module (not shown) in the cycling power estimation system.

[0045] During exercise, the body will provide corresponding energy according to the intensity of the exercise. Heart rate is a metabolic marker. The level of heart rate can reflect the amount of oxygen uptake to a certain extent. That is, heart rate is related to the level of respiratory oxygen uptake.

[0046] In some embodiments of the present application, in order to improve the accuracy of oxygen uptake, the average heart rate in the sliding window is used. hr_mean , maximum heart rate and hr_rest, calculate oxygen uptake vo2 , see formula (2).

[0047] (2)

[0048] Among them, α is the experimental coefficient that characterizes the conversion of oxygen uptake to heart rate, which can be obtained by fitting according to the user's physiological information. For example, it can be set to 15.3, oxygen uptake vo2 It can reflect the speed at which the user consumes energy in the current exercise state.

[0049] As described above, a sliding window is used to obtain the average heart rate. Each time the window is slid, a calculation is performed. The sliding window can be selected to have a length of 11 and a step size of 1.

[0050] The average heart rate within the sliding window can be calculated using formula (3).

[0051] (3) Where n represents the length of the sliding window (i.e. 11), i Represents the count points within the sliding window; hr(i) Represented as the first i Heart rate value, hr_mean Represents the average heart rate within the sliding window.

[0052] Therefore, every time the sliding window slides, the average heart rate is calculated, and a maximum heart rate is also obtained. The average heart rate, maximum heart rate and resting heart rate are then used to calculate the real-time oxygen uptake. vo2.

[0053] S3: Based on real-time oxygen uptake vo2 , the user’s weight m_body and the bicycle’s weight m_bake, and reversely estimate the cycling power p_dummy.

[0054] The cycling power estimation process may be implemented by a power estimation module (not shown) in the cycling power estimation system.

[0055] Real-time oxygen uptake through heart rate vo2 After estimation, based on real-time oxygen uptake vo2, Weight of the sports user m_ body and bicycle weight m_bake Using formula (4), cycling power p_dummy Perform reverse estimation 。

[0056] p_dummy =vo2*( m_body+m_bake)-a / b (4) Among them, a and b are the bias correction empirical value and coefficient correction empirical value respectively, for example, a It can be set to 350. b Can be set to 9.5 。

[0057] S4: Calculate the bias correction under different exercise states based on the heart rate gradient of the exercise user under different exercise states.

[0058] In some embodiments of the present application, different motion states include a motion state in which the heart rate changes slowly (recorded as motion state I), a motion state in which the heart rate increases rapidly (recorded as motion state II), and a motion state in which the heart rate decreases slowly (recorded as motion state III).

[0059] The calculation process of the bias correction may be implemented by a bias correction calculation module (not shown) in the cycling power estimation system.

[0060] In order to improve the estimation accuracy of cycling power, in some embodiments of the present application, the cycling power obtained by using formula (4) is corrected according to the heart rate gradient.

[0061] When performing a short burst of sprint exercise, the heart rate will rise rapidly and the heart rate gradient will become larger. When the cycling exercise is suspended, the heart rate will drop rapidly and the heart rate gradient will become smaller. When the user is exercising steadily, the heart rate remains relatively stable and the heart rate gradient is within a small fluctuation range. Therefore, the cycling power is corrected according to the heart rate gradient.

[0062] According to the heart rate gradient, the user's exercise state is judged, and then the corresponding bias correction is obtained according to the corresponding heart rate gradient under the corresponding exercise state. bias .

[0063] First, the exercise status of the user is determined based on the heart rate gradient.

[0064] (1) Using a sliding window, use formula (5) to obtain the heart rate gradient in the window in real time grad , as the heart rate gradient of sports users.

[0065] (5) in, n represents the length of the sliding window, i Represents the count points within the sliding window; hr(i) Represented as the first i heart rate value.

[0066] Each time the window is slid, the heart rate gradient is calculated grad.

[0067] (2) Determine the heart rate gradient grad The size of the preset gradient value d0 is greater than 0.5 and less than or equal to 1.

[0068] In the heart rate gradient grad When the heart rate gradient is greater than the preset value d0, it indicates that the user is in a state of rapid heart rate increase (i.e., exercise state II). Otherwise, the heart rate gradient is determined. grad Is it less than 0? If so, it indicates that the user is in a state of slowly decreasing heart rate (i.e., exercise state III); otherwise, it indicates that the user is in a state of steadily changing heart rate (i.e., exercise state I).

[0069] Then, see Figure 2 , using formula (6), according to the corresponding heart rate gradient under the corresponding exercise state, obtain the corresponding bias correction bias .

[0070] (6) Among them, k0, b0, c0, k1, and b1 are experimental coefficients, d0 can be 1; k1 will be assigned a larger value relative to k0, for example, the range of k1 can be 5~10.

[0071] k0, b0, c0, k1, and b1 are all universal empirical values ​​obtained by fitting the heart rate and power data of multiple user samples.

[0072] In some embodiments of the present application, cycling power in different motion states is compensated.

[0073] S5: Using bias correction under different motion states bias , which is used to correct the estimated cycling power to obtain the cycling power of the sports user in different sports states.

[0074] This power correction process may be implemented by a power correction module (not shown) in the cycling power estimation system.

[0075] p_dummy = p_dummy + bias.

[0076] In the three sports states of the sports user mentioned above, see Figure 2 , different film corrections are given, and then different bias corrections are used to correct the cycling power.

[0077] In the exercise state with a gentle change in heart rate (i.e., exercise state I), bias is zero, p_dummy It is calculated using formula (4).

[0078] In the exercise state with a rapid increase in heart rate (i.e., exercise state II), bias for grad(k 0 *grad+b 0 +c 0 ) ,use grad(k 0 *grad+b 0 +c 0 ) Correction calculated using formula (4) p_dummy ,Right now, p_dummy=p_dummy+grad(k 0 * grad+b 0 +c 0 ) .

[0079] In the exercise state where the heart rate slowly decreases (i.e., exercise state III), bias k1* grad+b 1 , using k1* grad+b 1 Correction calculated using formula (4) p_dummy ,Right now, p_dummy=p_dummy+ k1* grad+b 1 .

[0080] See also Figure 3 , which provides a comparison chart between the cycling power estimated by the cycling power estimation method involved in the embodiment of the present application (ie, the estimated value) and the actually measured cycling power (ie, the true value).

[0081] Cycling power can be obtained by wearing power monitoring equipment (such as a power meter) for sports users. Figure 3 Shown by the dashed line.

[0082] The cycling power obtained by the cycling power estimation method of this application is shown in Figure 3 Shown by the solid line.

[0083] pass Figure 3 It can be seen that the trend of the cycling power estimated by the cycling power estimation method as described above is substantially the same as the trend of the actual cycling power of the sports user, and the estimated cycling power is substantially similar to the actual cycling power.

[0084] See also Figure 4 , which gives a curve of the absolute error accumulation function based on the average absolute error between the estimated cycling power (i.e., the estimated value) and the actually measured cycling power (i.e., the true value) relative to the confidence level; see Figure 5 , which gives a curve of the relative error accumulation function of the average relative error between the estimated power and the true power relative to the confidence.

[0085] Among them, the horizontal axis of the absolute error accumulation function represents 0~maximum average absolute error, and the vertical axis represents confidence. The horizontal axis of the relative error accumulation function represents 0~maximum average relative error, and the vertical axis represents confidence.

[0086] See also Figure 4 , it can be seen that the confidence level of the mean absolute error less than 30W reaches more than 80%; see Figure 5 , it can be seen that the confidence level of the average relative error less than 0.3W reaches about 90%.

[0087] Therefore, the cycling power estimation method has a high accuracy in estimating the cycling power, and does not require wearing power monitoring equipment, thereby reducing cost investment.

[0088] In some embodiments of the present application, see Figure 6, and also relates to a cycling power estimation interactive system.

[0089] The cycling power estimation interactive system includes a heart rate collection device 100 and a terminal 200 .

[0090] The heart rate collection device 100 is used to collect the heart rate data of the exercising user and send it to the terminal 200 .

[0091] The terminal 200 can be a code meter or a smart device (eg, a mobile phone, a smart watch, a pad) that is connected to the heart rate acquisition device 100 for communication.

[0092] The cycling power estimation method as described above is integrated in the terminal 200 for performing cycling power estimation.

[0093] The estimated cycling power can be displayed on the front end of the terminal 200 for easy viewing by sports users.

[0094] 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 cycling power estimation method, characterized in that: include: Get the maximum heart rate hr_max of the exercise user; Based on the maximum heart rate hr_max, average heart rate and resting heart rate hr_rest, the real-time oxygen uptake of the exercise user is obtained vo2 ; Based on the real-time oxygen uptake vo2 , the weight of the sports user m_body and the weight of the bicycle m_bake, and reverse estimation of the riding power p_dummy; Calculating bias corrections in different exercise states based on the heart rate gradients of the exercise user in different exercise states; The bias correction under different motion states is used to correct the estimated cycling power to obtain the cycling power of the sports user under different motion states; Among them, different exercise states include an exercise state in which the heart rate changes slowly, an exercise state in which the heart rate increases rapidly, and an exercise state in which the heart rate decreases slowly.

2. The cycling power estimation method according to claim 1, characterized in that: Get the maximum heart rate hr_max of the exercise user, specifically: Use the age of the exercise user to calculate the maximum heart rate hr_max1; ； Use the maximum heart rate hr_max1 to initialize the maximum heart rate hr_max; Use a sliding window to obtain the real-time heart rate of the latest exercise user who enters the window; The real-time heart rate is compared with the maximum heart rate hr_max. If the real-time heart rate is greater than or equal to the maximum heart rate hr_max, the maximum heart rate hr_max is updated using the real-time heart rate. If the real-time heart rate is less than the maximum heart rate hr_max, the maximum heart rate hr_max is not updated.

3. The cycling power estimation method according to claim 1, characterized in that: The cycling power estimation method further includes: identifying different exercise states based on the heart rate gradient of the exercise user, specifically: Use sliding window to obtain the heart rate gradient in the window in real time grad , as the heart rate gradient of the sports user; When the heart rate gradient is greater than the preset gradient value d0, it indicates that the user is in a state of rapid heart rate increase. Otherwise, it is determined whether the heart rate gradient is less than 0. If so, it indicates that the user is in a state of slow heart rate decrease. Otherwise, it indicates that the user is in a state of steady heart rate change. The preset gradient value d0 is greater than 0.5 and less than or equal to 1.

4. The cycling power estimation method according to claim 3, characterized in that: The window heart rate gradient grad , specifically: ； in, n represents the length of the sliding window, i Represents the count points within the sliding window; hr(i) Represented as the first i heart rate value.

5. The cycling power estimation method according to claim 3, characterized in that: Based on the heart rate gradient of the sports user in different sports states, the bias correction in different sports states is calculated bias , specifically: ； Among them, k0, b0, c0, k1, and b1 are experimental coefficients.

6. The cycling power estimation method according to claim 1, characterized in that: Based on the maximum heart rate hr_max, average heart rate and resting heart rate hr_rest, the real-time oxygen uptake of the exercise user is obtained vo2 , specifically: Using a sliding window method, the mean heart rate in the window is obtained in real time as the average heart rate hr_mean of the exercise user; Using the formula , get real-time oxygen uptake vo2 ; in, α It is the experimental coefficient that characterizes the conversion of oxygen uptake to heart rate.

7. The cycling power estimation method according to claim 6, characterized in that: Based on the real-time oxygen uptake vo2 , the weight of the exercise user m_body and bicycle weight m_bake , reverse estimate cycling power p_dummy: p_dummy =vo2*( m_body+m_bake)-a / b ； Among them, a and b They are bias correction experience value and coefficient correction experience value respectively.

8. The cycling power estimation method according to claim 1, characterized in that: Using bias correction in different motion states bias, For the estimated cycling power p_dummy Make corrections, specifically: p_dummy= p_dummy+bias.

9. A cycling power estimation system, characterized in that: include: The heart rate acquisition module is used to obtain the maximum heart rate of the exercise user. hr_max ; The oxygen uptake acquisition module is used to obtain the maximum heart rate hr_max , average heart rate, and resting heart rate hr_rest , obtain the real-time oxygen uptake of the sports user vo2 ; A power estimation module is used to estimate the power based on the real-time oxygen uptake vo2 , the weight of the exercise user m_body and bicycle weight m_bake , reverse estimate cycling power p_dummy ; A bias correction calculation module, which is used to calculate the bias correction under different exercise states based on the heart rate gradient of the exercise user under different exercise states; A power correction module, which uses the bias correction under different motion states to correct the estimated cycling power to obtain the cycling power of the sports user under different motion states; Among them, different exercise states include an exercise state in which the heart rate changes slowly, an exercise state in which the heart rate increases rapidly, and an exercise state in which the heart rate decreases slowly.

10. A cycling power estimation interactive system, characterized in that: include: Heart rate collection equipment, which is used to collect heart rate data of sports users; A terminal receives the heart rate data, physiological information of the sports user and the weight of the bicycle, executes the cycling power estimation method according to any one of claims 1 to 8, and displays the cycling power at the front end of the terminal.

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