Physical exertion analysis, devices, electronic equipment and storage media

Abstract

This application provides a physical fitness analysis device, electronic device, and storage medium. First, during the exercise preparation phase, optimal physical fitness reserves are adjusted based on the user's current physiological indicators and normal physiological baseline to obtain initial exercise physical fitness. Then, during the exercise phase, baseline energy consumption is adjusted based on physiological fatigue factors and environmental factors to obtain a adjusted baseline energy consumption value. Finally, the baseline recovery constant is adjusted based on heart rate variability (HRV) state factors and rest depth factors to obtain a real-time recovery value. Finally, based on the initial exercise physical fitness, the adjusted baseline energy consumption value, and the real-time recovery value, the current remaining physical fitness is determined. This approach fully considers the differences in physical fitness reserves during the exercise preparation phase, the combined impact of physiological fatigue and environmental load on physical fitness consumption during exercise, and the regulatory role of autonomic nervous system balance and rest quality on physical fitness recovery. It avoids methods that rely solely on a single dimension of physiological signals to estimate physical fitness consumption, thus achieving accurate analysis of physical fitness consumption.

Description

Technical Field

[0001] This application relates to the field of smart wearable technology, and in particular to energy consumption analysis, devices, electronic devices, and storage media. Background Technology

[0002] With the increasing popularity of smart wearable devices (such as smartwatches and fitness trackers), users are increasingly using these devices to monitor their physical exertion during exercise in real time.

[0003] Currently, smart wearable devices typically only collect users' heart rate data as a basis for estimating physical exertion. However, relying solely on a single physiological signal is insufficient to accurately reflect a user's true physical exertion under different exercise conditions, leading to misjudgments of remaining physical energy. Summary of the Invention

[0004] In view of this, embodiments of this application provide physical energy consumption analysis, apparatus, electronic devices and storage media to accurately analyze physical energy consumption and obtain accurate remaining physical energy.

[0005] This application provides a method for analyzing physical exertion, the method comprising: During the exercise preparation phase, the user's current physiological indicators and the user's normal physiological baseline are used to adjust the user's optimal physical reserve under theoretical peak conditions in order to obtain the user's initial physical fitness for exercise. During the exercise phase, based on the physiological fatigue factor used to quantify the user's physiological fatigue level and metabolic efficiency decline at the current monitoring time, and the environmental factor used to quantify the additional resistance and energy consumption of the external environment on the user's exercise at the current monitoring time, the user's basic power consumption is corrected to obtain the user's basic power consumption correction value at the current monitoring time; and based on the heart rate variability (HRV) state factor used to quantify the user's autonomic nervous system balance state and physiological recovery potential at the current monitoring time, and the rest depth factor used to quantify the user's physiological repair efficiency and sleep quality during rest, the user's basic recovery constant is corrected to obtain the user's real-time recovery value at the current monitoring time. Based on the user's initial physical fitness, the user's basic power consumption correction value at the current monitoring time, and the real-time recovery value, the user's remaining physical fitness at the current monitoring time is determined.

[0006] As one embodiment, the step of adjusting the user's optimal physical reserve under theoretical peak conditions based on the user's current physiological indicators and the user's normal physiological baseline to obtain the user's initial exercise physical fitness includes: Substituting the current physiological indicators and the normal physiological baseline into the set correction function, the physical fitness coefficient is obtained; The optimal physical reserve is adjusted based on the physical state coefficient to obtain the initial physical fitness for exercise.

[0007] As one embodiment, the current physiological indicator includes HRV value; the physiological norm baseline includes HRV baseline value; the HRV baseline value is obtained by fitting and calculating based on the user's HRV values ​​over a specified historical time period; The adjustment of the optimal physical reserve based on the physical condition coefficient to obtain the initial physical fitness for exercise includes: Calculate the product of the physical condition coefficient and the optimal physical reserve, and determine the product as the initial physical fitness for exercise.

[0008] As an example, the physiological fatigue factor is calculated based on the decrease in the user's heart rate drift rate and heart rate variability (HRV) at the current monitoring time during exercise. The environmental factors are calculated based on at least one of the slope data, altitude data, and temperature data in the current sports environment.

[0009] As an example, the heart rate drift rate is determined through the following steps: The first deviation between the user's current heart rate (HR) at the current monitoring time and the user's predicted ideal heart rate is obtained; the predicted ideal heart rate is determined based on the user's personal information, the preset exercise parameters of the exercise stage, and the current exercise environment. The heart rate drift rate is determined based on the first deviation; wherein the heart rate drift rate is positively correlated with the deviation. The magnitude of the HRV decrease is determined by the following steps: Obtain the second deviation between the user's current HRV value at the current monitoring time and the HRV value at the start of movement; The decrease in HRV is determined based on the second deviation; wherein the decrease in HRV is positively correlated with the second deviation.

[0010] As an example, the HRV status factor is determined based on the third deviation between the user's current HRV value and the HRV baseline value at the current monitoring time; the HRV status factor is positively correlated with the third deviation.

[0011] The rest depth factor is calculated based on the user's current heart rate at the current monitoring time, the user's resting heart rate when in deep sleep, and a heart rate threshold; the heart rate threshold refers to the critical point of heart rate between restoring physical strength and consuming physical strength.

[0012] As one embodiment, determining the user's remaining physical fitness at the current monitoring moment based on the user's initial physical fitness, the user's baseline power consumption correction value at the current monitoring moment, and the real-time recovery value includes: Calculate the difference between the base power consumption correction value and the real-time recovery value at the current monitoring time to obtain the net energy change value at the current monitoring time; From the start of exercise to the current monitoring time, the net change in physical fitness is integrated to obtain the cumulative change in physical fitness from the start of exercise to the current monitoring time; Subtracting the cumulative change in physical fitness from the initial physical fitness yields the remaining physical fitness at the current monitoring time.

[0013] As one embodiment, the method further includes: Determine the target fitness range where the remaining fitness level is located; the target fitness range is one of a set of preset fitness ranges that corresponds to the remaining fitness level; different fitness ranges correspond to different exercise suggestions; Output exercise suggestions corresponding to the target physical fitness range.

[0014] This application embodiment also provides a physical exertion analysis device, the device comprising: The first determining module is used to, during the exercise preparation phase, correct the user's optimal physical reserve under theoretical peak conditions based on the user's current physiological indicators and the user's normal physiological baseline, so as to obtain the user's initial physical fitness for exercise. The first correction module is used to correct the user's basic power consumption during the exercise phase based on the physiological fatigue factor used to quantify the user's physiological fatigue level and metabolic efficiency decline at the current monitoring time, and the environmental factor used to quantify the additional resistance and energy consumption of the external environment on the user's exercise at the current monitoring time, so as to obtain the user's basic power consumption correction value at the current monitoring time. The second correction module is used to correct the user's baseline recovery constant during the exercise phase based on the heart rate variability (HRV) state factor, which is used to quantify the user's autonomic nervous system balance state and physiological recovery potential at the current monitoring time, and the rest depth factor, which is used to quantify the user's physiological repair efficiency and sleep quality during rest, so as to obtain the user's real-time recovery value at the current monitoring time. The second determining module is used to determine the user's remaining physical fitness at the current monitoring time based on the user's initial physical fitness, the user's basic power consumption correction value at the current monitoring time, and the real-time recovery value.

[0015] As one embodiment, the step of adjusting the user's optimal physical reserve under theoretical peak conditions based on the user's current physiological indicators and the user's normal physiological baseline to obtain the user's initial exercise physical fitness includes: Substituting the current physiological indicators and the normal physiological baseline into the set correction function, the physical fitness coefficient is obtained; The optimal physical reserve is adjusted based on the physical state coefficient to obtain the initial physical fitness for exercise.

[0016] As one embodiment, the current physiological indicator includes HRV value; the physiological norm baseline includes HRV baseline value; the HRV baseline value is obtained by fitting and calculating based on the user's HRV values ​​over a specified historical time period; The adjustment of the optimal physical reserve based on the physical condition coefficient to obtain the initial physical fitness for exercise includes: Calculate the product of the physical condition coefficient and the optimal physical reserve, and determine the product as the initial physical fitness for exercise.

[0017] As an example, the physiological fatigue factor is calculated based on the decrease in the user's heart rate drift rate and heart rate variability (HRV) at the current monitoring time during exercise. The environmental factors are calculated based on at least one of the slope data, altitude data, and temperature data in the current sports environment.

[0018] As an example, the heart rate drift rate is determined through the following steps: The first deviation between the user's current heart rate (HR) at the current monitoring time and the user's predicted ideal heart rate is obtained; the predicted ideal heart rate is determined based on the user's personal information, the preset exercise parameters of the exercise stage, and the current exercise environment. The heart rate drift rate is determined based on the first deviation; wherein the heart rate drift rate is positively correlated with the deviation. The magnitude of the HRV decrease is determined by the following steps: Obtain the second deviation between the user's current HRV value at the current monitoring time and the HRV value at the start of movement; The decrease in HRV is determined based on the second deviation; wherein the decrease in HRV is positively correlated with the second deviation.

[0019] As an example, the HRV status factor is determined based on the third deviation between the user's current HRV value and the HRV baseline value at the current monitoring time; the HRV status factor is positively correlated with the third deviation.

[0020] The rest depth factor is calculated based on the user's current heart rate at the current monitoring time, the user's resting heart rate when in deep sleep, and a heart rate threshold; the heart rate threshold refers to the critical point of heart rate between restoring physical strength and consuming physical strength.

[0021] As one embodiment, determining the user's remaining physical fitness at the current monitoring moment based on the user's initial physical fitness, the user's baseline power consumption correction value at the current monitoring moment, and the real-time recovery value includes: Calculate the difference between the base power consumption correction value and the real-time recovery value at the current monitoring time to obtain the net energy change value at the current monitoring time; From the start of exercise to the current monitoring time, the net change in physical fitness is integrated to obtain the cumulative change in physical fitness from the start of exercise to the current monitoring time; Subtracting the cumulative change in physical fitness from the initial physical fitness yields the remaining physical fitness at the current monitoring time.

[0022] As one embodiment, the device further includes: The output module is used to determine the target physical fitness range in which the remaining physical fitness is located; the target physical fitness range is one of a set of preset physical fitness ranges that corresponds to the remaining physical fitness; different physical fitness ranges correspond to different subsequent exercise suggestions; Output exercise suggestions corresponding to the target physical fitness range.

[0023] This application also provides an electronic device, including: a processor and a machine-readable storage medium for storing machine-executable instructions, wherein the machine-executable instructions, when run by the machine-readable storage medium, cause the processor to perform the steps of the above method.

[0024] This application also provides a machine-readable storage medium storing machine-executable instructions that, when executed, enable the implementation of the steps described above.

[0025] As can be seen from the above technical solution, in this embodiment, the user's initial physical fitness is obtained by first correcting the user's optimal physical fitness reserve under theoretical peak conditions based on the user's current physiological indicators and physiological norms during the exercise preparation phase. Then, during the exercise phase, the user's basic power consumption is corrected based on physiological fatigue factors and environmental factors at the current monitoring time to obtain a basic power consumption correction value. Furthermore, the user's basic recovery constant is corrected based on the user's heart rate variability (HRV) state factor and rest depth factor at the current monitoring time to obtain a real-time recovery value. Finally, based on the user's initial physical fitness, the user's basic power consumption correction value at the current monitoring time, and the real-time recovery value, the user's remaining physical fitness at the current monitoring time is determined. This method of determining remaining physical fitness fully considers the difference in physical fitness reserves between the user's theoretical peak state and actual state, as well as the comprehensive impact of physiological fatigue, decreased metabolic efficiency, and external environment on physical fitness consumption during exercise, and the impact of the user's autonomic nervous system balance and physiological repair efficiency during rest on physical fitness recovery. It completely avoids the existing method of estimating physical fitness consumption by relying solely on a single-dimensional physiological signal, and achieves accurate analysis of physical fitness consumption. Attached Figure Description

[0026] Figure 1 A flowchart illustrating the method provided in the embodiments of this application; Figure 2 A schematic diagram of the process for correcting initial physical fitness provided in an embodiment of this application; Figure 3 This is a schematic diagram of the process for determining the base power consumption correction value provided in an embodiment of this application; Figure 4 This is a flowchart illustrating the process of determining real-time recovery values ​​provided in an embodiment of this application. Figure 5 This is a schematic diagram of the device provided in the embodiments of this application; Figure 6 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] To make the methods provided in this application easier to understand, the methods provided in this application will be described in detail below with reference to the accompanying drawings and embodiments.

[0028] The method provided in the embodiments of this application is described in detail below: See Figure 1 , Figure 1 This is a flowchart illustrating the method provided in an embodiment of this application. Optionally, the subject executing this method may be a smart wearable device such as a sports watch.

[0029] like Figure 1 As shown, the method may include the following steps: S101, during the exercise preparation phase, the user's current physiological indicators and the user's normal physiological baseline are used to correct the user's optimal physical reserve under theoretical peak conditions in order to obtain the user's initial physical fitness for exercise.

[0030] In this embodiment, the exercise preparation phase refers to the preparation time before the user begins exercise. It is the period after the user puts on the smart wearable device and activates the exercise preparation function, but before the formal exercise begins, such as 3-5 minutes before the formal exercise starts. During this period, the user is in a resting state, and the physiological signals collected by the device are less affected by exercise, so they can more accurately reflect the user's physiological state on that day.

[0031] Current physiological indicators are used to characterize the user's real-time physiological state on that day, including but not limited to heart rate variability (HRV) values. The physiological baseline is used to characterize the user's baseline physiological level under normal conditions, including but not limited to the HRV baseline value, which can be determined based on statistical values ​​of the user's HRV values ​​at rest over a specified historical time period. Optionally, as an example, the specified historical time period can be the past 7 to 14 days, and the HRV baseline value is the average of multiple historical HRV values ​​of the user at rest during this time period.

[0032] The optimal physical reserve at theoretical peak states refers to the maximum amount of physical reserve a user can maintain when their physical condition reaches its theoretical best. This optimal physical reserve can be predetermined based on the user's personal information and historical exercise parameters. Personal information includes, but is not limited to, age, gender, weight, and height, while historical exercise parameters include historical exercise time and type of exercise. The specific method for determining the optimal physical reserve can be implemented by those skilled in the art using appropriate metabolic assessment methods according to actual needs, and this application does not impose specific limitations on this.

[0033] The specific implementation of step S101 will be illustrated with examples later, and will not be elaborated here.

[0034] S102, during the exercise phase, based on the physiological fatigue factor used to quantify the user's physiological fatigue level and metabolic efficiency decline at the current monitoring time, and the environmental factor used to quantify the additional resistance and energy consumption of the external environment on the user's exercise at the current monitoring time, the user's basic power consumption is corrected to obtain the user's basic power consumption correction value at the current monitoring time; and based on the heart rate variability (HRV) state factor used to quantify the user's autonomic nervous system balance state and physiological recovery potential at the current monitoring time, and the rest depth factor used to quantify the user's physiological repair efficiency and sleep quality during rest, the user's basic recovery constant is corrected to obtain the user's real-time recovery value at the current monitoring time.

[0035] In this embodiment, the motion phase refers to the entire process from the start to the end of the user's exercise. The current monitoring time refers to any monitoring point in the motion phase, such as the sampling time every second or every 5 seconds.

[0036] In this embodiment, the basic power consumption refers to the basic physical energy consumption rate of a user under ideal conditions (no fatigue accumulation, no additional environmental load), which can be determined based on the user's personal information through a basal metabolic rate assessment method.

[0037] The physiological fatigue factor quantifies the decrease in metabolic efficiency caused by heart rate drift and HRV decline during exercise. Its value is related to the deviation of the user's current heart rate from their predicted ideal heart rate at the current monitoring moment, and the decrease in current HRV compared to the HRV at the start of exercise. The environmental factor quantifies the additional impact of the external environment on physical exertion; its value is related to the current exercise environment. The specific methods for determining the physiological fatigue factor and environmental factor will be explained later and will not be elaborated here.

[0038] The basal recovery constant (HRV) refers to the user's baseline physical recovery rate under ideal conditions (no fatigue, sufficient rest). It can be predetermined based on the user's personal information or personalized based on the user's historical exercise data. The HRV state factor quantifies the user's autonomic nervous system balance state; its value is related to the deviation of the current HRV from the HRV baseline. The rest depth factor quantifies the user's physiological repair efficiency during rest; its value is related to the deviation of the current heart rate from the resting heart rate and the deviation of the current heart rate from the heart rate threshold. The specific methods for determining the HRV state factor and rest depth factor will be explained later and will not be elaborated here.

[0039] S103, based on the user's initial physical fitness, the user's basic power consumption correction value and real-time recovery value at the current monitoring time, determine the user's remaining physical fitness at the current monitoring time.

[0040] In this embodiment, the remaining physical energy at any monitoring moment is obtained by subtracting the cumulative net change in physical energy from the start of exercise to that monitoring moment after the two-way cancellation of physical energy consumption and recovery, based on the initial exercise physical energy. Specifically, the difference between the base power consumption correction value and the real-time recovery value at the current monitoring moment is calculated to obtain the net physical energy change value at the current monitoring moment. If the base power consumption correction value is greater than the real-time recovery value, the net physical energy change value is positive, indicating that the physical energy is in a net consumption state at that moment; if the real-time recovery value is greater than the base power consumption correction value, the net physical energy change value is negative, indicating that the physical energy is in a net recovery state at that moment. From the start of exercise to the current monitoring moment, the net physical energy change value is integrated (accumulated moment by moment in the discrete case) to obtain the cumulative physical energy change from the start of exercise to the current monitoring moment. Subtracting this cumulative physical energy change from the initial exercise physical energy yields the remaining physical energy at the current monitoring moment. In this way, physical energy consumption and physical energy recovery are mutually canceled in the same formula, and the calculation of remaining physical energy takes into account both the effects of consumption and recovery.

[0041] The specific implementation of step S103 will be illustrated with examples later, and will not be elaborated here.

[0042] This concludes the process. Figure 1 The process is shown below.

[0043] pass Figure 1 As shown in the flowchart, in this embodiment, the user's initial physical fitness is obtained by first correcting the user's optimal physical fitness reserve under theoretical peak conditions based on the user's current physiological indicators and physiological norms during the exercise preparation phase. Then, during the exercise phase, the user's basic power consumption is corrected based on physiological fatigue factors and environmental factors at the current monitoring time to obtain a basic power consumption correction value. Furthermore, the user's basic recovery constant is corrected based on the user's heart rate variability (HRV) state factor and rest depth factor at the current monitoring time to obtain a real-time recovery value. Finally, based on the user's initial physical fitness, the user's basic power consumption correction value at the current monitoring time, and the real-time recovery value, the user's remaining physical fitness at the current monitoring time is determined. This method of determining remaining physical fitness fully considers the difference in physical fitness reserves between the user's theoretical peak state and actual state, as well as the comprehensive impact of physiological fatigue, decreased metabolic efficiency, and external environment on physical fitness consumption during exercise, and the impact of the user's autonomic nervous system balance and physiological repair efficiency during rest on physical fitness recovery. It completely avoids the existing method of estimating physical fitness consumption by relying solely on a single-dimensional physiological signal, and achieves accurate analysis of physical fitness consumption.

[0044] The specific implementation method of step S101 above is described in detail below: See Figure 2 , Figure 2This is a schematic diagram illustrating the process of correcting initial physical fitness as provided in an embodiment of this application. Figure 2 As shown, the process may include the following steps: S201, substitute the current physiological indicators and normal physiological baseline into the set correction function to obtain the physical condition coefficient.

[0045] In this embodiment, the current physiological indicators include, but are not limited to, HRV values, denoted as HRV. now Physiological baselines include, but are not limited to, HRV baseline values, denoted as HRV. base .

[0046] Substitute the current HRV value and the HRV baseline value into the set correction function Φ to obtain the physical condition coefficient.

[0047] Optionally, as an example, the input to the correction function Φ is the ratio r of the current HRV value to the HRV baseline value, that is: Specifically, the correction function Φ takes the ratio r of the current HRV value to the HRV baseline value as input and the performance status coefficient Φ(r) as output, that is: Formula (1) The physical condition coefficient Φ(r) is used to characterize the degree of deviation of a user's physical reserves on a given day from their theoretical peak condition.

[0048] When a user's daily HRV is at a normal level, the performance status coefficient approaches 1, indicating that the user's physical reserves are close to their theoretical peak. When a user's daily HRV is significantly lower than normal, the performance status coefficient is less than 1, indicating that the user's autonomic nervous system is out of balance due to factors such as insufficient sleep, overtraining, or mental stress, resulting in a decrease in physical reserves and requiring a reduction from the theoretical peak. When a user's daily HRV is significantly higher than normal, the performance status coefficient is slightly greater than 1, indicating that the user's body is in a state of extreme excitement or supercompensation, and physical reserves are slightly increased.

[0049] Specifically, the value of the correction function Φ(r) varies with the ratio r. Optionally, as an example: When r > 1.2 (recovery overload zone), Φ(r) takes the value of 1.05-1.10, indicating that the user's body is in a state of extreme excitement and the initial physical strength is slightly increased, but an upper limit is set to avoid over-evaluation.

[0050] When 0.9≤r≤1.2 (physiological equilibrium zone), Φ(r) takes the value of 1.0, indicating that the user's body is in a normal state and no significant correction is made.

[0051] When 0.7≤r<0.9 (mild fatigue zone), Φ(r) decreases linearly with decreasing r in the interval of 0.8-0.9, indicating that the user is in a state of mild fatigue and the initial physical strength has been moderately reduced.

[0052] When r < 0.7 (high-risk / deep fatigue zone), Φ(r) decreases rapidly as r decreases in the range of 0.5-0.7, indicating that the user's physical performance has declined significantly due to autonomic nervous system dysfunction.

[0053] It should be noted that the specific values ​​of the boundary values ​​of each interval and Φ(r) mentioned above are only illustrative examples. Those skilled in the art can adjust the above parameters according to the actual application scenario and user group characteristics. This application does not make any specific limitations in this regard.

[0054] S202 adjusts the optimal physical reserves based on the physical condition coefficient to obtain the initial physical fitness for exercise.

[0055] In this embodiment, the optimal physical reserve is denoted as E. max It is pre-determined based on the user's personal information (such as weight, height, etc.) and historical exercise parameters (such as exercise goals, exercise type, exercise duration, maximum oxygen uptake during exercise, maximum heart rate during exercise, etc.). The fitness status coefficient Φ(r) is then compared with the optimal fitness reserve E. max Multiply by each other to obtain the initial physical energy S. initial .

[0056] Initial physical fitness S initial It can be expressed by the following formula Formula (2) in, Initial physical fitness for exercise; HRV value for users during the exercise preparation phase, used to characterize the user's current physiological indicators; The user's HRV baseline value is used to characterize the physiological norm baseline.

[0057] This concludes the process. Figure 2 The process is shown below. In this embodiment, by substituting the ratio of the current HRV to the HRV baseline value into the correction function, the user's theoretical optimal physical reserve can be dynamically adjusted, making the initial physical fitness of the exercise more consistent with the user's actual physiological state on that day, and avoiding the deviation in the initial physical fitness assessment caused by ignoring the differences in daytime conditions.

[0058] The specific implementation method of step S101 above has been described in detail above.

[0059] The following section elaborates on the specific implementation method of correcting the user's basic power consumption based on physiological fatigue factors and environmental factors in step S102 above, so as to obtain the user's basic power consumption correction value at the current monitoring time.

[0060] See Figure 3 , Figure 3 This is a schematic diagram illustrating the process of determining the base power consumption correction value according to an embodiment of this application. In this embodiment, the base power consumption can be the base power consumption rate, and the base power consumption correction value can be the base power consumption correction rate.

[0061] like Figure 3 As shown, the process may include the following steps: S301 determines the user's base power consumption at the current monitoring moment.

[0062] In this embodiment, the base power consumption rate Pbase can be calculated using the Mifflin-StJeor formula based on the user's personal information (gender, age, height, weight). The specific calculation method of Pbase can be implemented by those skilled in the art using appropriate metabolic assessment methods according to actual needs, and this application does not impose specific limitations on it.

[0063] S302, determine the user's physiological fatigue factors at the current monitoring time.

[0064] In this embodiment, physiological fatigue factor This factor is used to quantify the level of physiological fatigue and decline in metabolic efficiency experienced by users during exercise. It is calculated based on the user's heart rate drift rate and the magnitude of the decrease in heart rate velocity (HRV) at the current monitoring moment during exercise.

[0065] Heart rate drift rate is determined by the following steps: obtaining the user's current heart rate (HR) at the current monitoring time. now And the user's predicted ideal heart rate HR exp The first deviation between the predicted ideal heart rate and the target heart rate is determined based on the user's personal information (such as age, gender, weight, etc.), preset exercise parameters for the exercise phase, and the current exercise environment. The heart rate drift rate is determined based on this first deviation, and the heart rate drift rate is positively correlated with this first deviation.

[0066] The magnitude of the HRV decrease is determined by the following steps: obtaining the user's current HRV value at the current monitoring time. now HRV value at the start of movement start The second deviation between the two values. The magnitude of the HRV decrease is determined based on this second deviation, and the magnitude of the HRV decrease is positively correlated with this second deviation. Optionally, HRV... startIt can be the HRV value at the first monitoring moment after the start of exercise, or it can be the HRV during the preparation phase. now .

[0067] For example, physiological fatigue factors It can be calculated using the following formula: Formula (3) in, It is a physiological fatigue factor; The user's current heart rate at the current monitoring moment; Predicting the user's ideal heart rate; The current HRV value for the user at the current monitoring time; The HRV value when the user starts exercising; α and β are preset weighting coefficients used to control the influence of the heart rate drift term and the HRV decrease term on the physiological fatigue factor, respectively, and are empirically set values.

[0068] When a user's heart rate increases relative to the predicted ideal heart rate under the current exercise load, and their heart rate (HRV) decreases relative to the start of exercise, A shift upwards from 1.0 indicates that the rate of energy expenditure has increased by a corresponding percentage.

[0069] S303, determine the environmental factors for the user at the current monitoring time.

[0070] In this embodiment, environmental factors This environmental factor is used to quantify the additional resistance and energy consumption of the external environment on a user's movement. It is calculated based on at least one of the following: slope data, altitude data, and temperature data from the current movement environment.

[0071] Environmental factors can be calculated using the following formula: Formula (4) in, This is the slope correction factor; This is the altitude correction factor; This is the temperature correction factor.

[0072] Slope correction factor Determined based on slope data in the current motion environment. Optionally, as an example, the slope correction factor can be calculated using the following formula: Formula (5) in, θThe slope angle; G This is the gravity compensation constant.

[0073] Altitude correction factor The altitude correction factor is determined based on the altitude data of the current motion environment. The altitude correction factor can be calculated using the following formula: Formula (6) The temperature correction factor Ftemp is determined based on temperature data from the current motion environment. Alternatively, as an example, the temperature correction factor can be calculated using the following formula: Formula (7) S304 determines the base power correction rate based on the base power rate, physiological fatigue factor, and environmental factors.

[0074] In this embodiment, the base power consumption correction value is denoted as Padjusted, which is determined by comparing the base power consumption rate Pbase with the physiological fatigue factor. The product of the environmental factor Fenv and the product of the environmental factor Fenv can be calculated using the following formula: Formula (8) in, Based on the power consumption correction rate; Based on base power consumption; It is a physiological fatigue factor; Environmental factors.

[0075] This concludes the process. Figure 3 The process is shown below.

[0076] In this way, when the user is fatigued, the basic power consumption correction rate is adjusted upward accordingly. When there is an additional load in the external environment, the basic power consumption correction rate is also adjusted upward accordingly, so as to more accurately reflect the user's actual physical exertion rate in the current exercise state.

[0077] The above provides a detailed explanation of the specific implementation method for correcting the user's basic power consumption based on physiological fatigue factors and environmental factors in step S102 to obtain the user's basic power consumption correction value at the current monitoring time.

[0078] The following section elaborates on the specific implementation method of correcting the user's basic recovery constant based on the HRV state factor and rest depth factor in step S102 above to obtain the user's real-time recovery value at the current monitoring time.

[0079] See Figure 4 , Figure 4This is a schematic diagram of the process for determining the real-time recovery value provided in an embodiment of this application.

[0080] In this embodiment, the basic recovery constant can be denoted as the basic recovery rate. The real-time recovery value is the real-time response rate. .

[0081] like Figure 4 As shown, the process may include the following steps: S401, determine the user's HRV status factor at the current monitoring time.

[0082] In this embodiment, HRV state factor This is used to quantify the user's autonomic nervous system balance and physiological recovery potential. The HRV state factor is determined based on the third deviation between the user's current HRV value (HRVnow) and the HRV baseline value (HRVbase) at the current monitoring time, and the HRV state factor is positively correlated with this third deviation.

[0083] The HRV state factor can be calculated using the following formula: Formula (9) in, The current HRV value at the current monitoring time; The HRV baseline value is determined based on the statistical value of the user's HRV value in a resting state during a specified historical period.

[0084] when > hour, A score >1 indicates that the user's autonomic nervous system is in a relatively balanced state and has enhanced recovery ability.

[0085] when < hour, A value less than 1 indicates that the user's autonomic nervous system is in a state of imbalance and its recovery ability is hindered.

[0086] S402, determine the user's rest depth factor at the current monitoring time.

[0087] In this embodiment, the rest depth factor This factor is used to quantify a user's physiological repair efficiency and sleep quality during rest. The rest depth factor is based on the user's current heart rate at the current monitoring moment. Resting heart rate of the user during deep sleep and heart rate threshold The calculation yields the following: The heart rate threshold refers to the critical point between the heart rate for restoring physical strength and the heart rate for expending physical strength, used to define whether the user's current heart rate state belongs to the recovery zone or the expenditure zone.

[0088] Rest depth factor It can be determined using the following formula: Formula (10) Formula (11) in, The user's current heart rate at the current monitoring moment; The user's resting heart rate when they are in deep sleep; The user's heart rate threshold; This is the user's maximum heart rate.

[0089] The closer the current heart rate is to the resting heart rate, the larger the rest depth factor, indicating that the user's physiological repair efficiency is higher and physical recovery is faster. When the current heart rate is higher than the heart rate threshold, the rest depth factor is negative, indicating that the user's current heart rate is insufficient to support effective physical recovery.

[0090] S403 determines the real-time recovery rate based on the baseline recovery rate, HRV status factor, and rest depth factor.

[0091] In this embodiment, the baseline recovery constant Cbase represents the user's recovery ability benchmark under ideal conditions (no fatigue, no stress, and sufficient rest). It can be predetermined based on the user's personal information (such as age, gender, aerobic capacity, etc.) or personalized based on the user's historical exercise data.

[0092] The real-time recovery value Rrecovery can be calculated using the following formula: Formula (12) in, Baseline recovery rate; HRV state factor; This is the rest depth factor.

[0093] When the HRV status factor is greater than 1 and the rest depth factor is large, it indicates that the user is currently in a good recovery state, and the real-time recovery value is high. Conversely, if the HRV status factor is less than 1 or the rest depth factor is small, the real-time recovery value will decrease accordingly.

[0094] The above provides a detailed explanation of the specific implementation method of correcting the user's baseline recovery rate based on the HRV state factor and rest depth factor in step S102 to obtain the user's real-time recovery rate at the current monitoring time.

[0095] The specific implementation of step S103 above will be described in detail below.

[0096] In this embodiment, the remaining physical fitness St at any monitoring time is obtained by subtracting the cumulative net change in physical fitness Sinitial from the start time t0 of exercise to the monitoring time t after the two-way offsetting of physical fitness consumption and recovery.

[0097] First, calculate the baseline power consumption correction rate at the current monitoring time t. With real-time recovery rate The difference between the values ​​of ΔS and Δt is used to obtain the net change in physical energy at the current monitoring time, which can be calculated using the following formula: Formula (13) in, To monitor the real-time recovery rate at time t; () represents the baseline power consumption correction rate at monitoring time t.

[0098] like > A positive net change in physical fitness (which can be the net rate of change in physical fitness) indicates that physical fitness is in a state of net depletion at that moment. > A negative net change in physical fitness indicates that physical fitness is in a state of net recovery at that moment.

[0099] Then, from the start of exercise t0 to the current monitoring time t, the net rate of change in physical fitness is integrated (accumulated time-by-time in the discrete case) to obtain the cumulative change in physical fitness from the start of exercise to the current monitoring time, which can be calculated using the following formula: Formula (14) in, To accumulate changes in physical fitness; For monitoring time The base power consumption correction rate; For monitoring time Real-time recovery rate; ti represents the i-th monitoring time. Δt is the monitoring sampling interval. n represents the number of sampling points from the start of the movement to the current monitoring time.

[0100] Finally, subtracting the cumulative change in physical fitness ΔStotal from the initial physical fitness Sinitial yields the remaining physical fitness St at the current monitoring time. The remaining physical fitness at the current monitoring time t is expressed by the following formula: Formula (15) In this way, physical exertion and physical recovery can be offset in the same formula, and the calculation of remaining physical energy takes into account both the effects of exertion and recovery.

[0101] In this embodiment, the net physical fitness change rate at the current monitoring moment is determined based on the base power consumption correction rate and the real-time recovery rate. This ensures that physical fitness consumption and recovery are mutually offset within this net physical fitness change rate. The remaining physical fitness at the current monitoring moment is then iteratively deducted from the initial physical fitness based on this net physical fitness change rate. Thus, by dynamically adjusting the net physical fitness change rate based on real-time physiological signals, the calculation of remaining physical fitness simultaneously considers the dual impacts of physical fitness consumption and recovery, achieving accurate tracking of the user's real-time physical fitness, effectively improving the accuracy of physical fitness consumption estimation, and ensuring the reliability of remaining physical fitness prediction.

[0102] The specific implementation method of step S103 above has been described in detail above.

[0103] Optionally, as an example, after determining the remaining physical fitness at the current monitoring time, a target physical fitness interval is determined where the remaining physical fitness lies. Multiple physical fitness intervals are preset, with different exercise suggestions corresponding to different intervals. After calculating the remaining physical fitness St at the current monitoring time, St is compared with the preset multiple physical fitness intervals to determine the target physical fitness interval where St lies.

[0104] Optionally, as an example, the preset fitness levels and their corresponding exercise recommendations are shown in the table below: Table 1 - Mapping of Fitness Zones and Exercise Recommendations After determining the target fitness range where the current remaining fitness level is located, the system obtains exercise suggestions for that range and outputs these suggestions to the user in text and / or voice form through a smart wearable device to help the user adjust their exercise strategy and improve exercise safety and performance.

[0105] It should be noted that the boundary values ​​of each physical fitness zone and the corresponding exercise suggestions are merely illustrative examples. Those skilled in the art can adjust the above zone divisions and suggestions according to actual application scenarios and user group characteristics. This application does not impose any specific limitations on this.

[0106] To illustrate the methods provided in the embodiments of this application in more detail, the following description is provided in more detail by way of specific embodiments.

[0107] In this embodiment, the user wears a smartwatch equipped with a heart rate sensor, accelerometer, GPS module, barometer, and temperature sensor. The user activates the activity monitoring function to prepare for an outdoor running exercise.

[0108] The process includes the following steps: 1. During the exercise preparation phase, the smartwatch acquires the user's resting HRV value (assumed to be 65ms) within 5 minutes prior to the current moment as the current physiological indicator, and reads the average of the user's historical HRV data over the past 14 days (assumed to be 70ms) as the HRV baseline value. The user's optimal physical reserve (Emax) under theoretical peak conditions is preset to 100 units.

[0109] The smartwatch calculates the ratio of the current HRV value (65ms) to the HRV baseline value (70ms), resulting in a performance status coefficient of 65 / 70 = 0.93. Multiplying this performance status coefficient by the optimal performance reserve (Emax) yields an initial fitness level of 100 × 0.93 = 93 units (e.g., kcal).

[0110] 2. The user begins running. At a certain monitoring point during the exercise (e.g., 10 minutes after the start of the exercise), the smartwatch collects the current HR, HRV, slope, altitude, and temperature.

[0111] Assume the real-time data at the current moment is as follows: current heart rate HR = 155 bpm, predicted ideal heart rate = 150 bpm (based on (220 - age) × intensity percentage), current HRV = 50 ms, HRV at the start of exercise = 60 ms, slope = 5°, altitude = 2000 m, temperature = 32℃.

[0112] 3. Smartwatches can identify physiological fatigue factors.

[0113] Calculate heart rate drift rate: First deviation = (155-150) / 150 = 0.033, heart rate drift rate = 1 + 0.033 = 1.033.

[0114] Calculate the HRV decrease: Second deviation = (60-50) / 60 = 0.167, HRV decrease = 1 + 0.167 = 1.167.

[0115] Determining physiological fatigue factors based on heart rate drift rate and the magnitude of HRV decrease. Optionally, =1 + α × heart rate drift rate + β × HRV decrease (where α and β are preset weighting coefficients, assuming α = 0.3 and β = 0.7), then =1+0.3×0.033+0.7×0.167=1.127.

[0116] 4. Smartwatches determine environmental factors.

[0117] Slope correction factor: Based on a slope of 5°, look up the preset mapping table or calculate using a function, assuming... =1.08.

[0118] Altitude correction factor: Based on an altitude of 2000m, assuming =1.12.

[0119] Temperature correction factor: The temperature of 32℃ is higher than the first threshold (30℃), assuming =1.05.

[0120] Environmental factors =1.08×1.12×1.05=1.27.

[0121] 5. The smartwatch calculates the basic power consumption correction value.

[0122] Based on the user's personal information (male, 30 years old, weight 70kg, height 175cm), the base power consumption rate Pbase is calculated using the Mifflin-St Jeor formula, assuming Pbase = 3.0 units / minute.

[0123] Base power consumption correction rate =3.0×1.127×1.27=4.29 units / minute.

[0124] 6. The smartwatch determines the HRV status factor and rest depth factor, and calculates the real-time recovery value.

[0125] Assuming the HRV baseline value is 70ms and the current HRV is 50ms, the third deviation is (70-50) / 70=0.286, and the HRV state factor is 1-0.286=0.714.

[0126] Assuming a current heart rate (HR) of 155 bpm, a resting heart rate of 60 bpm, and a heart rate threshold of 130 bpm (estimated based on age), the rest depth factor is determined based on the deviation between the current heart rate and the resting heart rate, as well as the deviation between the current heart rate and the heart rate threshold. =0.65.

[0127] Baseline recovery rate Assume it is 1.2 units / minute.

[0128] Real-time recovery value =1.2×0.714×0.65=0.56 units / minute.

[0129] 7. The smartwatch determines the remaining physical energy at the current moment.

[0130] Calculate the change in net physical energy. - =4.29-0.56=3.73 units / minute.

[0131] Assuming that the net change in physical fitness is accumulated minute by minute from the start of exercise (minute 0) to the current moment (minute 10), the cumulative change in physical fitness is ∑( - )×Δt. To simplify the explanation, assume that the average net change in physical fitness in the first 10 minutes is 3.5 units / minute, then the cumulative change in physical fitness = 3.5×10 = 35 units.

[0132] Current remaining physical fitness = initial physical fitness (93 units) - cumulative change in physical fitness (35 units) = 58 units.

[0133] The smartwatch outputs the remaining energy value of 58 units at the current monitoring time and displays it to the user on the screen.

[0134] As can be seen from the above specific embodiments, the method provided in this application can dynamically adjust the initial physical fitness according to the user's physiological state on the day during the exercise preparation stage, perceive the impact of physiological fatigue and environmental changes on power consumption in real time during exercise, and dynamically correct the recovery rate, thereby achieving accurate tracking and prediction of the user's real-time physical fitness.

[0135] The methods provided in the embodiments of this application have been described above. The apparatus provided in the embodiments of this application is described below: See Figure 5 , Figure 5 This is a schematic diagram of the device provided in an embodiment of this application. Figure 5 As shown, the device includes: a first determining module 501, a first correcting module 502, a second correcting module 503, and a second determining module 504.

[0136] The first determining module 501 is used to, during the exercise preparation phase, correct the user's optimal physical reserve under theoretical peak conditions based on the user's current physiological indicators and the user's normal physiological baseline, so as to obtain the user's initial physical fitness for exercise. The first correction module 502 is used to correct the user's basic power consumption during the exercise phase based on the physiological fatigue factor used to quantify the user's physiological fatigue level and metabolic efficiency decline at the current monitoring time, and the environmental factor used to quantify the additional resistance and energy consumption of the external environment on the user's exercise at the current monitoring time, so as to obtain the user's basic power consumption correction value at the current monitoring time. The second correction module 503 is used to correct the user's baseline recovery constant during the exercise phase based on the heart rate variability (HRV) state factor used to quantify the user's autonomic nervous system balance state and physiological recovery potential at the current monitoring time, and the rest depth factor used to quantify the user's physiological repair efficiency and sleep quality during rest, so as to obtain the user's real-time recovery value at the current monitoring time. The second determining module 504 is used to determine the user's remaining physical fitness at the current monitoring time based on the user's initial physical fitness, the user's basic power consumption correction value at the current monitoring time, and the real-time recovery value.

[0137] As an example, based on the user's current physiological indicators and the user's normal physiological baseline, the user's optimal physical reserve under theoretical peak conditions is adjusted to obtain the user's initial exercise physical fitness, including: Substituting the current physiological indicators and normal physiological baseline into the set correction function, the physical condition coefficient is obtained; The optimal physical reserve is adjusted based on the physical condition coefficient to obtain the initial physical fitness for exercise.

[0138] As an example, the current physiological indicators include HRV values; the physiological baseline includes HRV baseline values; the HRV baseline values ​​are calculated based on the user's HRV values ​​over a specified historical time period. Adjusting for optimal physical reserves based on the fitness level coefficient yields the initial physical fitness for exercise, including: Calculate the product of the physical condition coefficient and the optimal physical reserve, and determine the initial physical fitness for exercise.

[0139] As an example, the physiological fatigue factor is calculated based on the decrease in heart rate drift rate and heart rate variability (HRV) at the current monitoring moment during exercise. Environmental factors are calculated based on at least one of the following: slope data, altitude data, and temperature data in the current sports environment.

[0140] As an example, the heart rate drift rate is determined through the following steps: Obtain the first deviation between the user's current heart rate (HR) at the current monitoring time and the user's predicted ideal heart rate; the predicted ideal heart rate is determined based on the user's personal information, preset exercise parameters for the exercise phase, and the current exercise environment; The heart rate drift rate is determined based on the first deviation; where the heart rate drift rate is positively correlated with the deviation. The magnitude of the HRV decrease is determined by the following steps: Obtain the second deviation between the user's current HRV value at the current monitoring moment and the HRV value at the start of exercise; The magnitude of the HRV decrease is determined based on the second deviation; where the magnitude of the HRV decrease is positively correlated with the second deviation.

[0141] As an example, the HRV status factor is determined based on the third deviation between the user's current HRV value and the HRV baseline value at the current monitoring time; the HRV status factor is positively correlated with the third deviation.

[0142] The rest depth factor is calculated based on the user's current heart rate at the current monitoring time, the user's resting heart rate when in deep sleep, and the heart rate threshold; the heart rate threshold refers to the critical point of heart rate between physical recovery and physical exertion.

[0143] As one example, based on the user's initial physical fitness, the user's baseline power consumption correction value at the current monitoring time, and the real-time recovery value, the user's remaining physical fitness at the current monitoring time is determined, including: Calculate the difference between the base power consumption correction value and the real-time recovery value at the current monitoring moment to obtain the net energy change value at the current monitoring moment; From the start of exercise to the current monitoring time, the net change in physical fitness is integrated to obtain the cumulative change in physical fitness from the start of exercise to the current monitoring time; Subtracting the cumulative change in physical fitness from the initial physical fitness yields the remaining physical fitness at the current monitoring moment.

[0144] As one embodiment, the apparatus further includes: The output module is used to determine the target fitness range where the remaining fitness level is located; the target fitness range is one of a set of preset fitness ranges that corresponds to the remaining fitness level; different fitness ranges correspond to different subsequent exercise suggestions; Output exercise suggestions corresponding to the target fitness level range.

[0145] This concludes the process. Figure 5 Structural description of the device shown.

[0146] See Figure 6 , Figure 6 This is a structural diagram of an electronic device provided in an embodiment of this application. Figure 6 As shown, the hardware structure may include: a processor and a machine-readable storage medium, the machine-readable storage medium storing machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the method disclosed in the above example of this application.

[0147] Based on the same application concept as the above method, this application embodiment also provides a machine-readable storage medium storing a plurality of computer instructions, which, when executed by a processor, can implement the method disclosed in the above examples of this application.

[0148] For example, the aforementioned machine-readable storage medium can be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, etc. For instance, machine-readable storage media can be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), solid-state drives, any type of storage disk (such as optical discs, DVDs, etc.), or similar storage media, or combinations thereof.

[0149] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0150] This application can be implemented by executing several computer program code flows using an electronic device. The electronic device loads machine-executable instructions into a machine-readable storage medium and then uses its processor to read these machine-executable instructions into memory for execution. A hardware structure diagram of the electronic device, besides... Figure 5 In addition to the processor, memory, network interface, and non-volatile memory shown, electronic devices may also include other hardware depending on their actual functions, which will not be elaborated further.

Claims

1. A method for analyzing physical exertion, characterized in that, The method includes: During the exercise preparation phase, the user's current physiological indicators and the user's normal physiological baseline are used to adjust the user's optimal physical reserve under theoretical peak conditions in order to obtain the user's initial physical fitness for exercise. During the exercise phase, based on the physiological fatigue factor used to quantify the user's physiological fatigue level and metabolic efficiency decline at the current monitoring time, and the environmental factor used to quantify the additional resistance and energy consumption of the external environment on the user's exercise at the current monitoring time, the user's basic power consumption is corrected to obtain the user's basic power consumption correction value at the current monitoring time; and based on the heart rate variability (HRV) state factor used to quantify the user's autonomic nervous system balance state and physiological recovery potential at the current monitoring time, and the rest depth factor used to quantify the user's physiological repair efficiency and sleep quality during rest, the user's basic recovery constant is corrected to obtain the user's real-time recovery value at the current monitoring time. Based on the user's initial physical fitness, the user's basic power consumption correction value at the current monitoring time, and the real-time recovery value, the user's remaining physical fitness at the current monitoring time is determined.

2. The method according to claim 1, characterized in that, The process of adjusting the user's optimal physical reserve under theoretical peak conditions based on the user's current physiological indicators and the user's normal physiological baseline to obtain the user's initial physical fitness includes: Substituting the current physiological indicators and the normal physiological baseline into the set correction function, the physical fitness coefficient is obtained; The optimal physical reserve is adjusted based on the physical state coefficient to obtain the initial physical fitness for exercise.

3. The method according to claim 2, characterized in that, The current physiological indicators include HRV values; the physiological baseline includes HRV baseline values; the HRV baseline values ​​are calculated based on the user's HRV values ​​over a specified historical time period. The adjustment of the optimal physical reserve based on the physical condition coefficient to obtain the initial physical fitness for exercise includes: Calculate the product of the physical condition coefficient and the optimal physical reserve, and determine the product as the initial physical fitness for exercise.

4. The method according to claim 1, characterized in that, The physiological fatigue factor is calculated based on the decrease in the user's heart rate drift rate and heart rate variability (HRV) at the current monitoring time during exercise. The environmental factors are calculated based on at least one of the slope data, altitude data, and temperature data in the current sports environment.

5. The method according to claim 4, characterized in that, The heart rate drift rate is determined through the following steps: The first deviation between the user's current heart rate (HR) at the current monitoring time and the user's predicted ideal heart rate is obtained; the predicted ideal heart rate is determined based on the user's personal information, the preset exercise parameters of the exercise stage, and the current exercise environment. The heart rate drift rate is determined based on the first deviation; wherein the heart rate drift rate is positively correlated with the deviation. The magnitude of the HRV decrease is determined by the following steps: Obtain the second deviation between the user's current HRV value at the current monitoring time and the HRV value at the start of movement; The decrease in HRV is determined based on the second deviation; wherein the decrease in HRV is positively correlated with the second deviation.

6. The method according to claim 1, characterized in that, The HRV status factor is determined based on the third deviation between the user's current HRV value and the HRV baseline value at the current monitoring time; the HRV status factor is positively correlated with the third deviation. The rest depth factor is calculated based on the user's current heart rate at the current monitoring time, the user's resting heart rate when in deep sleep, and a heart rate threshold. The heart rate threshold refers to the critical point at which the heart rate is at which physical energy is being restored and when physical energy is being depleted.

7. The method according to claim 1, characterized in that, The determination of the user's remaining physical fitness at the current monitoring time, based on the user's initial physical fitness, the user's baseline power consumption correction value at the current monitoring time, and the real-time recovery value, includes: Calculate the difference between the base power consumption correction value and the real-time recovery value at the current monitoring time to obtain the net energy change value at the current monitoring time; From the start of exercise to the current monitoring time, the net change in physical fitness is integrated to obtain the cumulative change in physical fitness from the start of exercise to the current monitoring time; Subtracting the cumulative change in physical fitness from the initial physical fitness yields the remaining physical fitness at the current monitoring time.

8. The method according to claim 1, characterized in that, The method further includes: Determine the target fitness range where the remaining fitness level is located; the target fitness range is one of a set of preset fitness ranges that corresponds to the remaining fitness level; different fitness ranges correspond to different exercise suggestions; Output exercise suggestions corresponding to the target physical fitness range.

9. A physical exertion analysis device, characterized in that, The device includes: The first determining module is used to, during the exercise preparation phase, correct the user's optimal physical reserve under theoretical peak conditions based on the user's current physiological indicators and the user's normal physiological baseline, so as to obtain the user's initial physical fitness for exercise. The first correction module is used to correct the user's basic power consumption during the exercise phase based on the physiological fatigue factor used to quantify the user's physiological fatigue level and metabolic efficiency decline at the current monitoring time, and the environmental factor used to quantify the additional resistance and energy consumption of the external environment on the user's exercise at the current monitoring time, so as to obtain the user's basic power consumption correction value at the current monitoring time. The second correction module is used to correct the user's baseline recovery constant during the exercise phase based on the heart rate variability (HRV) state factor, which is used to quantify the user's autonomic nervous system balance state and physiological recovery potential at the current monitoring time, and the rest depth factor, which is used to quantify the user's physiological repair efficiency and sleep quality during rest, so as to obtain the user's real-time recovery value at the current monitoring time. The second determining module is used to determine the user's remaining physical fitness at the current monitoring time based on the user's initial physical fitness, the user's basic power consumption correction value at the current monitoring time, and the real-time recovery value.

10. The apparatus according to claim 9, characterized in that, The process of adjusting the user's optimal physical reserve under theoretical peak conditions based on the user's current physiological indicators and the user's normal physiological baseline to obtain the user's initial physical fitness includes: Substituting the current physiological indicators and the normal physiological baseline into the set correction function, the physical fitness coefficient is obtained; The initial physical fitness is obtained by adjusting the optimal physical fitness reserve based on the physical fitness state coefficient. And / or, The current physiological indicators include HRV values; the physiological baseline includes HRV baseline values; the HRV baseline values ​​are calculated based on the user's HRV values ​​over a specified historical time period. The adjustment of the optimal physical reserve based on the physical condition coefficient to obtain the initial physical fitness for exercise includes: Calculate the product of the physical condition coefficient and the optimal physical reserve, and determine the product as the initial physical fitness for exercise; And / or, The physiological fatigue factor is calculated based on the decrease in the user's heart rate drift rate and heart rate variability (HRV) at the current monitoring time during exercise. The environmental factors are calculated based on at least one of the following: slope data, altitude data, and temperature data in the current sports environment. And / or, The heart rate drift rate is determined through the following steps: The first deviation between the user's current heart rate (HR) at the current monitoring time and the user's predicted ideal heart rate is obtained; the predicted ideal heart rate is determined based on the user's personal information, the preset exercise parameters of the exercise stage, and the current exercise environment. The heart rate drift rate is determined based on the first deviation; wherein the heart rate drift rate is positively correlated with the deviation. The magnitude of the HRV decrease is determined by the following steps: Obtain the second deviation between the user's current HRV value at the current monitoring time and the HRV value at the start of movement; The decrease in HRV is determined based on the second deviation; wherein the decrease in HRV is positively correlated with the second deviation. And / or, The HRV status factor is determined based on the third deviation between the user's current HRV value and the HRV baseline value at the current monitoring time; the HRV status factor is positively correlated with the third deviation. The rest depth factor is calculated based on the user's current heart rate at the current monitoring time, the user's resting heart rate when in deep sleep, and a heart rate threshold; the heart rate threshold refers to the critical heart rate point between physical recovery and physical exertion. And / or, The determination of the user's remaining physical fitness at the current monitoring time, based on the user's initial physical fitness, the user's baseline power consumption correction value at the current monitoring time, and the real-time recovery value, includes: Calculate the difference between the base power consumption correction value and the real-time recovery value at the current monitoring time to obtain the net energy change value at the current monitoring time; From the start of exercise to the current monitoring time, the net change in physical fitness is integrated to obtain the cumulative change in physical fitness from the start of exercise to the current monitoring time; Subtracting the cumulative change in physical fitness from the initial physical fitness yields the remaining physical fitness at the current monitoring time. And / or, the device further includes: The output module is used to determine the target physical fitness range in which the remaining physical fitness is located; the target physical fitness range is one of a set of preset physical fitness ranges that corresponds to the remaining physical fitness; different physical fitness ranges correspond to different subsequent exercise suggestions; Output exercise suggestions corresponding to the target physical fitness range.

11. An electronic device, characterized in that, The electronic device includes: Processor; and A machine-readable storage medium storing machine-executable instructions that, when executed by the processor, cause the processor to perform the steps of the method as described in any one of claims 1 to 8.

12. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores machine-executable instructions that, when executed by a processor, cause the processor to perform the steps of the method as described in any one of claims 1 to 8.

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