Method and device for determining motion recovery duration, equipment, medium and product

Abstract

The invention discloses a motion recovery duration determination method and device, equipment, a medium and a product, and belongs to the technical field of computers. The exercise recovery duration determination method comprises the steps that user basic information of a user, exercise physiology information of the user in a first time period and physical and mental state information of the user in a second time period are obtained, and the first time period is the exercise time period of the user; according to the basic information of the user, the exercise physiology information and a target association relationship, the initial exercise recovery duration of the user is determined, and the target association relationship is used for representing the association relationship among the basic information of the user, the exercise physiology information and the exercise recovery duration; and according to the physical and mental state information, adjusting the initial motion recovery duration, and determining the adjusted initial motion recovery duration as the target motion recovery duration.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and specifically relates to a method, device, equipment, medium and product for determining the duration of exercise recovery. Background Art

[0002] Exercise recovery time refers to the time it takes for the body to be ready for the next exercise with the same load after the end of one exercise. With the improvement of living standards, people pay more and more attention to their own health status, and exercise has gradually become part of people's lives. Formulating an exercise plan based on exercise recovery time can effectively prevent users from over-exercising and avoid physical injuries. Therefore, it is of great significance to reasonably determine the exercise recovery time after the user finishes exercise.

[0003] Currently, the exercise recovery time is mainly determined based on the user's exercise data during exercise, that is, only the user's current exercise data is considered, and the factors considered are relatively simple, which leads to poor accuracy of the determined exercise recovery time. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, equipment, medium and product for determining the duration of exercise recovery, which can solve the technical problem of poor accuracy in determining the duration of exercise recovery in related technologies.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a motion recovery duration, comprising:

[0006] Obtaining basic information of the user, exercise physiological information of the user in a first time period, and physical and mental state information of the user in a second time period, wherein the first time period is a time period during which the user exercises;

[0007] Determine the initial exercise recovery time of the user according to the user's basic information, exercise physiological information and target association relationship, where the target association relationship is used to characterize the association relationship between the user's basic information, exercise physiological information and exercise recovery time;

[0008] The initial exercise recovery time is adjusted according to the physical and mental state information, and the adjusted initial exercise recovery time is determined as the target exercise recovery time.

[0009] In a second aspect, an embodiment of the present application provides a device for determining a motion recovery duration, comprising:

[0010] An acquisition module, used to acquire basic information of the user, exercise physiological information of the user in a first time period, and physical and mental state information of the user in a second time period, wherein the first time period is a time period during which the user exercises;

[0011] A determination module, used to determine the initial exercise recovery time of the user according to the basic information of the user, the exercise physiological information and the target association relationship, wherein the target association relationship is used to characterize the association relationship among the basic information of the user, the exercise physiological information and the exercise recovery time;

[0012] The adjustment module is used to adjust the initial exercise recovery time according to the physical and mental state information, and determine the adjusted initial exercise recovery time as the target exercise recovery time.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0015] In a fifth aspect, an embodiment of the present application provides a chip, the chip including a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the steps of the method described in the first aspect.

[0016] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the method described in the first aspect.

[0017] In an embodiment of the present application, the user's basic information, the user's exercise physiological information in a first time period, and the user's physical and mental state information in a second time period are obtained, and the first time period is the time period of the user's exercise; according to the user's basic information, exercise physiological information, and target association relationship, the user's initial exercise recovery duration is determined, and the target association relationship is used to characterize the association relationship between the user's basic information, exercise physiological information, and exercise recovery duration; the initial exercise recovery duration is adjusted according to the physical and mental state information, and the adjusted initial exercise recovery duration is determined as the target exercise recovery duration. That is, the embodiment of the present application first determines the initial exercise recovery duration according to the user's basic information and the exercise physiological information of the current exercise, and then dynamically adjusts the initial exercise recovery duration according to the user's physical and mental state information to obtain the final exercise recovery duration, that is, not only the user's exercise physiological information of the current exercise is considered, but also the user's basic information and the user's physical and mental state information are considered, and the factors considered are more comprehensive, that is, the embodiment of the present application can comprehensively determine the exercise recovery duration according to the user's basic information, exercise data, and physical and mental state information, and the accuracy is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method for determining the duration of exercise recovery provided in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of the structure of a neural network model provided in an embodiment of the present application;

[0020] Figure 3 A flowchart of another method for determining the duration of exercise recovery provided in an embodiment of the present application;

[0021] Figure 4 A flowchart of another method for determining the duration of exercise recovery provided in an embodiment of the present application;

[0022] Figure 5 A flowchart of another method for determining the duration of exercise recovery provided in an embodiment of the present application;

[0023] Figure 6 A schematic diagram of the structure of a device for determining the duration of exercise recovery provided in an embodiment of the present application;

[0024] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0025] Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are usually a class, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0028] As mentioned above, currently the user's exercise recovery time is mainly determined based on the user's current exercise data, and the factors considered are relatively simple, which leads to low accuracy of the determined exercise recovery time and easily causes physical injuries to the user.

[0029] To this end, the embodiments of the present application provide a method, device, equipment, medium and product for determining the duration of motion recovery, which can solve the technical problem of poor accuracy in determining the duration of motion recovery in related technologies.

[0030] In conjunction with the accompanying drawings, the following detailed description is given of the method, device, equipment, medium and product for determining the exercise recovery time provided by the embodiments of the present application through specific embodiments and their application scenarios.

[0031] The method for determining the exercise recovery time provided in the embodiment of the present application can be applied to electronic devices that can collect the user's exercise data during the user's exercise, for example, including but not limited to wearable smart devices, sports health monitoring devices, and other electronic devices that establish communication connections with wearable smart devices or sports health monitoring devices, such as mobile phones, tablet computers, and laptop computers. Among them, wearable smart devices can include smart watches, smart bracelets, etc.

[0032] Figure 1 A flow chart of a method for determining the duration of exercise recovery provided in an embodiment of the present application, such as Figure 1 As shown, the method for determining the exercise recovery time may include the following steps:

[0033] S110, obtaining basic information of the user, exercise physiological information of the user in a first time period, and physical and mental state information of the user in a second time period.

[0034] The first time period is a time period when the user is exercising.

[0035] S120: Determine the initial exercise recovery time of the user according to the basic information of the user, the exercise physiological information and the target association relationship.

[0036] Among them, the target association relationship is used to characterize the association between user basic information, exercise physiological information and exercise recovery time.

[0037] S130: adjusting the initial exercise recovery time according to the physical and mental state information, and determining the adjusted initial exercise recovery time as the target exercise recovery time.

[0038] The embodiment of the present application first determines the initial exercise recovery time based on the user's basic information and the exercise physiological information of the current exercise, and then dynamically adjusts the initial exercise recovery time based on the user's physical and mental state information to obtain the final exercise recovery time. That is, not only the user's exercise physiological information of the current exercise is taken into account, but also the user's basic information and the user's physical and mental state information. The factors considered are more comprehensive, that is, the embodiment of the present application can comprehensively determine the exercise recovery time based on the user's basic information, exercise data, and physical and mental state information, with higher accuracy.

[0039] The above steps are described in detail below:

[0040] In S110, the basic user information may be the basic identity information of the user, which may affect the user's recovery time after exercise, that is, different identity information may correspond to different recovery times after the same degree of exercise. The basic identity information may include but is not limited to the user's gender, age, weight, height, etc. The basic user information may be pre-stored by the user in the electronic device, or may be obtained in other ways.

[0041] The first time period is the time period of the user's exercise. For example, the user can record the start time and the end time of the exercise respectively at the beginning and the end of the exercise, and the user's exercise duration can be obtained based on the difference between the end time and the start time.

[0042] Exemplarily, when a user starts exercising, the user may send an exercise start instruction to the electronic device, and the electronic device records the start time of the exercise in response to the received exercise start instruction. Similarly, after the user finishes exercising, the user may send an exercise end instruction to the electronic device, and the electronic device records the end time of the exercise in response to the received exercise end instruction. The embodiments of the present application do not limit the sending method of the exercise start instruction and the exercise end instruction. For example, the exercise start instruction or the exercise end instruction may be sent to the electronic device by voice, touch, gesture, etc.

[0043] Sports physiological information can be information about the user's body response during exercise, including but not limited to energy consumption information, oxygen uptake information, etc. Sports physiological information can be directly measured by sensors in wearable devices, or indirectly determined based on the user's exercise heart rate, exercise speed, exercise intensity and other data.

[0044] Exemplarily, the second time period may be a time period after the first time period. For example, if the first time period ends at 5:00 p.m., the second time period may be a time period starting at 5:00 p.m. The length of the second time period may be set according to actual needs, for example, it may be set to 12 hours or 24 hours, etc. Taking 24 hours as an example, the second time period may be from 5:00 p.m. when the exercise ends to 5:00 p.m. the next day.

[0045] Exemplarily, the second time period may be a time period before the first time period, for example, the first time period is from 3:00 pm to 5:00 pm today, and the second time period may be a time period of the previous day, such as a time period of the previous night or the previous day.

[0046] Exemplarily, the second time period may also be a time period including the first time period.

[0047] Physical and mental state information may be information that can reflect the physical and mental state of the user, for example, it may include physiological state information such as sleep information, and psychological state information such as stress information, emotional information, etc. Physical and mental state information may be obtained through sensors, user's response to information pushed by electronic devices, etc.

[0048] That is, the embodiment of the present application takes into account information of multiple dimensions, that is, not only the information of the user's exercise at that time, but also the user's basic information and physical and mental state information, and determines the recovery time of the user after exercise based on information of multiple dimensions, and the result is more objective and accurate.

[0049] In S120, the initial exercise recovery time is the recovery time determined based on the user's basic information and the exercise physiological information of the current exercise. The initial exercise recovery time can be further adjusted based on the user's physical and mental state information. This can improve the accuracy of the exercise recovery time and reduce sports injuries caused by insufficient rest, poor mental state, etc.

[0050] The target association relationship is used to characterize the association relationship between user basic information, exercise physiological information and exercise recovery time. The embodiment of the present application does not limit the specific form of the target association relationship. For example, the target association relationship can be a model relationship, a function relationship, a table relationship, etc.

[0051] Taking the target association relationship as a model relationship as an example, the model takes the user's basic information and exercise physiological information as input and the exercise recovery time as output. Therefore, the user's basic information and exercise physiological information can be input into the corresponding model, and the corresponding model outputs the user's exercise recovery time as the initial exercise recovery time. The model here can be a deep learning model.

[0052] Taking the target association relationship as a functional relationship as an example, the function takes the user's basic information and exercise physiological information as independent variables and the exercise recovery time as the dependent variable. Therefore, by substituting the above-obtained user basic information and exercise physiological information into the corresponding function, the user's exercise recovery time can be obtained as the initial exercise recovery time. The function here can be a linear function or a nonlinear function.

[0053] Taking the target association relationship as a table relationship as an example, the table can store the exercise recovery time corresponding to different user basic information and exercise physiological information. Therefore, based on the user basic information and exercise physiological information obtained above, the exercise recovery time of the user can be obtained by looking up the table as the initial exercise recovery time.

[0054] In S130, the target motion recovery time is the adjusted initial motion recovery time. The target motion recovery time may be greater than the initial motion recovery time, or less than the initial motion recovery time, or equal to the initial motion recovery time.

[0055] After obtaining the initial exercise recovery time, the embodiment of the present application can further dynamically adjust the initial exercise recovery time according to the user's physical and mental state information, thereby ensuring the accuracy of the exercise recovery time and reducing sports injuries.

[0056] The embodiments of the present application do not limit the specific adjustment method. For example, the user's physical and mental state information and initial exercise recovery duration can be input into a duration adjustment model, and the duration adjustment model outputs a target exercise recovery.

[0057] For another example, the user's physical and mental state information can be matched with reference physical and mental state information by feature matching, and the corresponding duration adjustment amount can be obtained according to the matched reference physical and mental state information, and then the target exercise recovery duration can be determined according to the cumulative sum of the initial exercise recovery duration and the duration adjustment amount. Each reference physical and mental state information corresponds to a duration adjustment amount.

[0058] For example, the user's physical and mental state can be scored based on the physical and mental state information, and the initial exercise recovery time can be appropriately adjusted based on the scoring results. For example, if the score is high, the initial exercise recovery time can be appropriately reduced. If the score is low, the initial exercise recovery time can be appropriately increased.

[0059] The embodiment of the present application comprehensively considers the user's basic information, physical and mental state, and current exercise situation, so that the determined exercise recovery time is more objective and accurate, thereby reducing sports injuries caused by poor physical and mental state.

[0060] In some embodiments, the wearable device may include an optical sensor, a three-axis acceleration sensor, and a global positioning system (GPS) sensor, etc. The optical sensor may collect the user's photoplethysmography (PPG) signal, the three-axis acceleration sensor may collect the user's acceleration signals on the x-axis, y-axis, and z-axis, and the GPS sensor may collect the user's location signal.

[0061] Based on this, in some embodiments, the user's exercise physiological information in the first time period can be obtained in the following manner:

[0062] Acquire the user's exercise data in a first time period, where the exercise data includes at least one of exercise heart rate, exercise speed, exercise acceleration, and heart rate interval;

[0063] determining a mean and a standard deviation of the motion data during a first time period;

[0064] Determine energy consumption information and oxygen uptake information of the user in a first time period according to the exercise data and the mean and standard deviation corresponding to the exercise data;

[0065] The energy expenditure information and the oxygen uptake information are determined as the exercise physiological information of the user in the first time period.

[0066] Exercise heart rate, exercise speed, exercise acceleration and heart rate interval can be obtained based on the signals collected by the sensors in the wearable device. Specifically, the user needs to wear the wearable smart device in the correct wearing method before exercising to ensure that valid data is collected.

[0067] In order to ensure that the signal is interfered with by noise as little as possible, the PPG signal, acceleration signal and position signal can be preprocessed, for example, the collected PPG signal, GPS signal and acceleration signal can be filtered by a filtering algorithm. For example, a 0.5-5Hz bandpass filter can be used to filter the PPG signal, and a 1-5Hz bandpass filter can be used to filter the acceleration signal.

[0068] Exercise heart rate and heart rate interval can be obtained based on PPG signals, exercise speed can be obtained based on GPS signals and exercise duration, and exercise acceleration can be obtained based on acceleration signals. Exercise acceleration can reflect the user's exercise intensity.

[0069] The embodiment of the present application can obtain the user's exercise data during exercise through the sensors provided by the wearable device, without the need for professional operation, and has good universality.

[0070] In actual application, when the first time period is relatively long, the first time period may be divided to obtain a plurality of time windows. When the first time period is relatively short, the first time period may be directly used as a time window.

[0071] For the kth time window, the mean and variance of the heart rate interval in the time window can be calculated. The specific calculation formula is as follows:

[0072]

[0073] Among them, L1 is the size of the kth time window, y k-rri-mean is the mean of the heart rate intervals in the time window, y k-rri-std is the variance of the heart rate interval in the time window, RR i is the i-th heart rate interval in the time window.

[0074] For the kth time window, the mean and variance of the motion acceleration in the time window can be calculated. The specific calculation formula is as follows:

[0075]

[0076] Among them, y k-accx-std ,y k-accy-std and k-accz-std are the standard deviations of the accelerations of the x, y and z axes in the kth sliding window, are the average values ​​of the acceleration of the x, y and z axes in the kth sliding window.

[0077] Based on the motion data and the corresponding mean and standard deviation of the motion data, the user's energy consumption information and oxygen uptake information in the corresponding time window can be determined. When the first time period includes multiple time windows, the energy consumption information of each time window is accumulated to obtain the user's energy consumption information in the first time period. Similarly, the oxygen uptake information of each time window is accumulated to obtain the user's oxygen uptake information in the first time period.

[0078] Exemplarily, the energy consumption information and oxygen uptake information of the user in each time window may be determined through a neural network model.

[0079] Exemplarily, for each time window, the user's exercise heart rate, exercise speed, exercise acceleration, heart rate interval mean, heart rate interval standard deviation, exercise acceleration mean and exercise acceleration standard deviation within the time window can be input into the neural network model, and the neural network model outputs the user's energy consumption information and oxygen uptake information within the time window.

[0080] The structure of the neural network model can be set according to actual needs. In some embodiments, Figure 2 As shown, the neural network model 200 may include an input layer 201, a first network layer 202, a second network layer 203, and an output layer 204. Among them, the first network layer 202 may include a convolution layer 2021, an activation layer 2022, a pooling layer 2023, a local normalization layer 2024, and a dropout layer 2025. The second network layer 203 may include a convolution layer 2031, an activation layer 2032, a pooling layer 2033, a local normalization layer 2034, a dropout layer 2035, and a fully connected layer 2036.

[0081] The functions of each layer can be found in the relevant technology and will not be described here. In order to prevent the model from overfitting, the activation function can use the Relu function, and the mean square error loss function and RMSprop optimization algorithm can be used to update the model parameters during model training.

[0082] In actual application, the user's exercise heart rate, exercise speed, exercise acceleration, heart rate interval mean, heart rate interval standard deviation, exercise acceleration mean and exercise acceleration standard deviation within a certain time window can be input Figure 2 The input layer 201 shown is then processed by the first network layer 202 and the second network layer 203, and the output layer 204 outputs the energy consumption information and oxygen uptake information of the user in the time window.

[0083] The embodiment of the present application uses a neural network model, combined with PPG signals, acceleration signals and GPS signals collected by sensors on the wearable device, to obtain energy consumption information and oxygen uptake information, providing a basis for subsequent determination of exercise recovery time without relying on professional operations or the use of specific equipment, thereby improving the universality and practicality of the solution.

[0084] Taking sports physiology information including energy consumption information and oxygen uptake information as an example, Figure 3 A flowchart of another method for determining the duration of exercise recovery provided in an embodiment of the present application, Figure 3 and Figure 1 The difference is that Figure 1 The S120 in can be refined into Figure 3 S310-S330 in.

[0085] S310: Determine a first exercise recovery duration of the user according to the user basic information, the energy consumption information and the first association relationship.

[0086] The first association relationship is used to characterize the association relationship among the user's basic information, energy consumption information, and exercise recovery time.

[0087] The energy consumption information here can be represented by calories, and the more calories the user consumes, the more energy is consumed. The form of the first association relationship can refer to the form of the target association relationship in the above embodiment, that is, the form of the first association relationship can be similar to the form of the target association relationship. Of course, the first association relationship can also be in a different form from the target association relationship, as long as it can represent the relationship between the user's basic information, energy consumption information and exercise recovery time.

[0088] According to the basic information of the user and the energy consumption information, in combination with the first association relationship, the first exercise recovery time corresponding to the basic information of the user and the energy consumption information can be obtained.

[0089] S320: Determine a second exercise recovery time of the user according to the user basic information, the oxygen uptake information and the second association relationship.

[0090] The second association relationship is used to characterize the association relationship among the user's basic information, oxygen uptake information, and exercise recovery time.

[0091] The form of the second association relationship may be similar to that of the first association relationship. In an embodiment of the present application, when the basic information of the user remains unchanged, the exercise recovery time required for the user may be determined based on the energy consumption information and the oxygen uptake information, respectively, to provide a basis for subsequently determining the initial exercise recovery time.

[0092] It should be noted that the embodiments of the present application do not limit the execution order of the above-mentioned S310 and S320. For example, in actual application, S310 can be executed first, and then S320; S320 can be executed first, and then S310; S310 and S320 can also be executed at the same time.

[0093] S330: Determine an initial exercise recovery duration according to the first exercise recovery duration and the second exercise recovery duration.

[0094] In some embodiments, the larger one of the first motion recovery duration and the second motion recovery duration can be determined as the initial motion recovery duration. For example, if the first motion recovery duration is greater than the second motion recovery duration, the first motion recovery duration can be determined as the initial motion recovery duration.

[0095] In some embodiments, the first exercise recovery time and the second exercise recovery time may be weighted and summed, and the summed result may be determined as the initial exercise recovery time. The weights of the first exercise recovery time and the second exercise recovery time may be set according to the influence of the energy consumption information and the oxygen uptake information on the exercise recovery time, that is, the greater the influence on the exercise recovery time, the greater the corresponding weight.

[0096] Of course, the initial motion recovery duration may also be determined in combination with other strategies, which is not limited in the embodiments of the present application.

[0097] The embodiment of the present application can determine the corresponding exercise recovery time according to the energy information consumed by the user during exercise and the oxygen uptake information, and then determine the initial exercise recovery time according to the two exercise recovery times. Because the energy information consumed by the user during exercise and the oxygen uptake information can be easily and accurately obtained, the initial exercise recovery time of the user obtained in this way is also more accurate.

[0098] Take the user's basic information including height, weight, age and gender as an example. Figure 4 A flowchart of another method for determining the duration of exercise recovery provided in an embodiment of the present application, Figure 4 and Figure 1 The difference is that Figure 1 The S120 in can be refined into Figure 4 S410 and S420 in.

[0099] S410: Determine a height coefficient, a weight coefficient, an age coefficient, and a sports physiology coefficient according to the gender of the user.

[0100] S420, performing weighted summation of the height, weight, age and exercise physiology information according to the height coefficient, weight coefficient, age coefficient and exercise physiology coefficient respectively to obtain the initial exercise recovery time of the user.

[0101] Taking the target association relationship as a linear function relationship as an example, the height coefficient, weight coefficient, age coefficient and exercise physiology coefficient here are coefficients associated with the user's height, weight, age and exercise physiology information in the target association relationship.

[0102] For example, the target association relationship can be expressed as follows:

[0103] y=a*x1+b*x2+c*x3+e*x4

[0104] Among them, y represents the initial exercise recovery time, a, b, c, e represent the height coefficient, weight coefficient, age coefficient and exercise physiology coefficient respectively, and x1, x2, x3, x4 represent the user's height, weight, age and exercise physiology information respectively.

[0105] The sizes of a, b, c, and e can be obtained by linear fitting of sample data, and the sample data may include sample height, sample weight, sample age, and sample exercise physiology information of multiple sample users and corresponding sample exercise recovery time. The specific fitting process is not limited in the embodiments of the present application. Due to different genders, when height, weight, age, and exercise physiology information are the same, the required exercise recovery time is also different. Therefore, in order to more accurately determine the exercise recovery time of users of different genders, when performing relationship fitting, it can be fitted separately according to gender.

[0106] In actual application, the corresponding height coefficient, weight coefficient, age coefficient and sports physiology coefficient can be obtained according to the user's gender.

[0107] According to the above formula, the above coefficients are weighted and summed with the corresponding parameters respectively to obtain the initial exercise recovery time of the user.

[0108] In some embodiments, when the exercise physiological information includes energy consumption information and oxygen uptake information, the following formulas can be obtained for the energy consumption information and the oxygen uptake information respectively:

[0109] y1=a1*x1+b1*x2+c1*x3+e1*x41

[0110] y2=a2*x1+b2*x2+c2*x3+e2*x42

[0111] Among them, y1 represents the first exercise recovery time, y2 represents the second exercise recovery time, x41 represents energy consumption information, x42 represents oxygen uptake information, a1, b1, c1 and e1 are the correlation coefficients of the first exercise recovery time calculated based on the energy consumption information, and a2, b2, c2 and e2 are the correlation coefficients of the second exercise recovery time calculated based on the oxygen uptake information.

[0112] In the above formula y1, for example, for males, a11=-4.2, b1=2.8, c1=3.5, e1=10; for females, a1=-3.8, b1=2, c1=4, e1=12. In the above formula y2, for males, a2=-15, b2=8, c2=10, e2=1.9; for females, a2=-13.5, b2=5, c2=11, e2=2.

[0113] When calculating the exercise recovery time required by the user, the embodiment of the present application integrates the user's gender, height and other basic information, and considers the impact of the user's basic information on the exercise recovery time, so that the result is more accurate.

[0114] Figure 5 A flowchart of another method for determining the duration of exercise recovery provided in an embodiment of the present application, Figure 5 and Figure 1 The difference is that Figure 1 The S130 in can be refined into Figure 5 S510-S530 in.

[0115] S510: Score the user's physical and mental state according to the physical and mental state information to obtain a physical and mental state scoring result.

[0116] The scoring result here is used to represent the physical and mental state of the user. For example, the higher the score, the better the physical and mental state of the user is, and the more conducive it is to physical recovery.

[0117] The physical and mental state information here may include one or more, for example, one or more of sleep information, stress information, and emotional information. When there are multiple physical and mental state information, illustratively, the physical and mental state of the user may be scored for each physical and mental state information to obtain a scoring result. For example, the physical and mental state information includes sleep information and stress information, then the sleep state of the user may be scored for the sleep information to obtain a sleep scoring result, and the stress state of the user may be scored for the stress information to obtain a stress scoring result.

[0118] When there are multiple pieces of physical and mental state information, illustratively, the physical and mental state of the user can also be comprehensively scored for the multiple pieces of physical and mental state information to obtain a comprehensive scoring result. For example, if the physical and mental state information includes sleep information and stress information, the physical and mental state of the user can be comprehensively scored based on the sleep information and stress information to obtain a comprehensive scoring result.

[0119] The embodiments of the present application do not limit the specific scoring process. For example, the physical and mental state information can be input into a scoring model to obtain a scoring result. The scoring model can be a deep learning model.

[0120] For example, the similarity between the physical and mental state information and the reference physical and mental state can be determined, and then the reference physical and mental state corresponding to the maximum similarity can be determined. Based on the maximum similarity, the scoring result corresponding to the reference physical and mental state can be appropriately adjusted to obtain the scoring result corresponding to the user's physical and mental state.

[0121] S520: Determine a duration adjustment amount of the initial exercise recovery duration according to the scoring result and the third association relationship.

[0122] The third association relationship is used to characterize the association relationship between the scoring result and the duration adjustment amount. For example, the third association relationship may be a model relationship, a function relationship, a table relationship, or the like.

[0123] According to the scoring result, combined with the third association, the duration adjustment amount of the initial motion recovery duration can be determined. The duration adjustment amount can be 0, a positive value, or a negative value, wherein when the duration adjustment amount is 0, it means that the target motion recovery duration is the same as the initial motion recovery duration, when the duration adjustment amount is a positive value, it means that the target motion recovery duration is greater than the initial motion recovery duration, and when the duration adjustment amount is a negative value, it means that the target motion recovery duration is less than the initial motion recovery duration.

[0124] Taking the physical and mental state scoring results including the sleep state scoring results, the stress state scoring results and the emotional state scoring results, and the third association relationship being a functional relationship as an example, illustratively, the above S420 may include the following steps:

[0125] Get sleep coefficient, stress coefficient and mood coefficient;

[0126] The sleep state scoring results, the stress state scoring results, and the emotional state scoring results are weighted and summed according to the sleep coefficient, the stress coefficient, and the emotional coefficient, respectively, to obtain the duration adjustment of the initial exercise recovery time.

[0127] The sleep coefficient, stress coefficient and emotion coefficient are respectively the coefficients corresponding to the sleep state scoring result, stress state scoring result and emotion state scoring result in the third association relationship. The coefficients can be obtained by function fitting, and the specific fitting process is not limited in the embodiment of the present application.

[0128] Exemplarily, the third association relationship can be expressed as follows:

[0129]

[0130] Wherein, Δy represents the duration adjustment amount, a3, b3 and c3 are respectively the sleep coefficient, stress coefficient and emotion coefficient, and the corresponding coefficients of users of different genders can be the same, for example, the sleep coefficients of males and females can be the same. x5, x6 and x7 represent the sleep state scoring results, stress state scoring results and emotion state scoring results, respectively. According to the above formula, the duration adjustment amount can be obtained. In some embodiments, a3=4, b3=3, c3=1.5, t=75.

[0131] S530: Determine a target exercise recovery duration according to the cumulative sum of the initial exercise recovery duration and the duration adjustment amount.

[0132] Exemplarily, the target motion recovery duration can be directly determined by the sum of the initial motion recovery duration and the duration adjustment amount, for example y0=y3+Δy, where y0 represents the target motion recovery duration, y3 represents the initial motion recovery duration, and exemplarily, y3=max(y1,y2).

[0133] Exemplarily, a certain value may be fluctuated up and down based on the cumulative sum to obtain a range of the target motion recovery time.

[0134] Based on the initial exercise recovery time, the embodiment of the present application can further dynamically adjust the initial exercise recovery time according to the user's rest state, psychological state, etc., that is, it fully considers the influence of factors such as the user's rest, sleep, stress, and emotions on the exercise recovery time, effectively improves the accuracy of the exercise recovery time, and reduces sports injuries caused by poor physical and mental state.

[0135] The following is an explanation of the method for determining the exercise recovery duration provided in an embodiment of the present application through a specific example.

[0136] Take a male as an example, with a height of 170cm, a weight of 60kg, and an age of 30 years old. The calories consumed during exercise are 300kcal, and the oxygen intake is 2500ml. Substituting the above data into the above formula y1 and formula y2, we can get:

[0137] y1=-4.2*170+2.8*60+3.5*30+10*300=2559min

[0138] y2=-15*170+8*60+10*30+1.9*2500=2980min

[0139] y3=max(y1,y2)=2980min

[0140] If the user's sleep score is 70 points, stress score is 90 points, and emotion score is 60 points, then substituting Δy and y0 into the above formula, we can get:

[0142] 88min, y0=2980+88=3068min.

[0143] If the user's sleep score is 90, stress score is 70, and emotion score is 90, then substituting Δy and y0 into the above formula, we can get:

[0145] -105min, y0=2980-105=2875min.

[0146] The embodiments of the present application can collect the user's motion data without the user noticing, and determine the user's motion recovery time in combination with the user's basic information. It can also integrate information such as sleep, stress, and emotions to dynamically adjust the motion recovery time, thereby effectively improving the accuracy of the motion recovery time, reducing sports injuries caused by lack of rest, and increasing the universality and practicality of the solution.

[0147] It should be noted that the method for determining the motion recovery duration provided in the embodiment of the present application can be executed by a device for determining the motion recovery duration, or a processing module in the device for determining the motion recovery duration for executing the method for determining the motion recovery duration. In the embodiment of the present application, the method for determining the motion recovery duration is executed by a device for determining the motion recovery duration as an example to illustrate the device for determining the motion recovery duration provided in the embodiment of the present application.

[0148] Figure 6 A schematic diagram of the structure of a device for determining the duration of exercise recovery provided in an embodiment of the present application.

[0149] like Figure 6 As shown, the motion recovery duration determination device 600 may include:

[0150] An acquisition module 601 is used to acquire basic information of the user, exercise physiological information of the user in a first time period, and physical and mental state information of the user in a second time period, wherein the first time period is a time period during which the user exercises;

[0151] A determination module 602 is used to determine the initial exercise recovery time of the user according to the basic information of the user, the exercise physiological information and the target association relationship, where the target association relationship is used to characterize the association relationship between the basic information of the user, the exercise physiological information and the exercise recovery time;

[0152] The adjustment module 603 is used to adjust the initial exercise recovery time according to the physical and mental state information, and determine the adjusted initial exercise recovery time as the target exercise recovery time.

[0153] The embodiment of the present application first determines the initial exercise recovery time based on the user's basic information and the exercise physiological information of the current exercise, and then dynamically adjusts the initial exercise recovery time based on the user's physical and mental state information to obtain the final exercise recovery time. That is, not only the user's exercise physiological information of the current exercise is taken into account, but also the user's basic information and the user's physical and mental state information. The factors considered are more comprehensive, that is, the embodiment of the present application can comprehensively determine the exercise recovery time based on the user's basic information, exercise data, and physical and mental state information, with higher accuracy.

[0154] In some possible implementations of the embodiments of the present application, the apparatus 600 for determining the exercise recovery duration may be applied to a wearable device, wherein the wearable device includes a sensor;

[0155] The acquisition module 601 is specifically used for:

[0156] Acquiring, through a sensor, motion data of the user in a first time period, the motion data including at least one of a motion heart rate, a motion speed, a motion acceleration, and a heart rate interval;

[0157] The determination module 602 is also used to determine the mean and standard deviation of the motion data in the first time period; and determine the energy consumption information and oxygen uptake information of the user in the first time period based on the motion data and the mean and standard deviation corresponding to the motion data.

[0158] In some possible implementations of the embodiments of the present application, the exercise physiology information includes energy consumption information and oxygen uptake information;

[0159] The determination module 602 is specifically configured to:

[0160] Determine a first exercise recovery time of the user according to the user's basic information, the energy consumption information and the first association relationship, where the first association relationship is used to characterize the association relationship between the user's basic information, the energy consumption information and the exercise recovery time;

[0161] Determine a second exercise recovery time of the user according to the user's basic information, the oxygen uptake information, and the second association relationship, where the second association relationship is used to characterize the association relationship between the user's basic information, the oxygen uptake information, and the exercise recovery time;

[0162] An initial motion recovery duration is determined according to the first motion recovery duration and the second motion recovery duration.

[0163] In some possible implementations of the embodiments of the present application, the basic information of the user includes height, weight, age and gender;

[0164] The determination module 602 is specifically configured to:

[0165] According to the user's gender, determine the height coefficient, weight coefficient, age coefficient and sports physiology coefficient;

[0166] The height, weight, age and exercise physiology information are weighted and summed according to the height coefficient, weight coefficient, age coefficient and exercise physiology coefficient respectively to obtain the user's initial exercise recovery time.

[0167] In some possible implementations of the embodiments of the present application, the apparatus 600 for determining the exercise recovery duration may further include:

[0168] A scoring module is used to score the user's physical and mental state according to the physical and mental state information to obtain a physical and mental state scoring result;

[0169] Determination module 602 is also used to determine the duration adjustment amount of the initial motion recovery time according to the scoring result and the third association relationship, and the third association relationship is used to characterize the association relationship between the scoring result and the duration adjustment amount; determine the target motion recovery time according to the cumulative sum of the initial motion recovery time and the duration adjustment amount.

[0170] In some possible implementations of the embodiments of the present application, the scoring results of the physical and mental state include a sleeping state scoring result, a stress state scoring result, and an emotional state scoring result;

[0171] The acquisition module 601 is also used to acquire the sleep coefficient, the stress coefficient and the emotion coefficient;

[0172] The determination module 602 is specifically configured to:

[0173] The sleep state scoring results, the stress state scoring results, and the emotional state scoring results are weighted and summed according to the sleep coefficient, the stress coefficient, and the emotional coefficient, respectively, to obtain the duration adjustment of the initial exercise recovery time.

[0174] The embodiments of the present application can collect the user's motion data without the user noticing, and determine the user's exercise recovery time in combination with the user's basic information. It can also integrate information such as sleep, stress, and emotions after the exercise to dynamically adjust the exercise recovery time, thereby effectively improving the accuracy of the exercise recovery time, reducing sports injuries caused by lack of rest, and increasing the universality and practicality of the solution.

[0175] The determination device of the motion recovery time in the embodiment of the present application can be a device, or a component in an electronic device, such as an integrated circuit or a chip. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmentedreality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a user digital assistant (personaldigital assistant, PDA), etc., and can also be a server, a network attached storage (Network AttachedStorage, NAS), a user computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., and the embodiment of the present application is not specifically limited.

[0176] The electronic device in the embodiment of the present application may be a terminal with an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0177] The apparatus for determining the exercise recovery time provided in the embodiment of the present application can achieve Figures 1 to 4 The various processes in the embodiment of the method for determining the exercise recovery time are similar to each other and can achieve the same technical effect. To avoid repetition, they will not be described here.

[0178] like Figure 7 As shown, an embodiment of the present application also provides an electronic device 700, including a processor 701 and a memory 702, wherein the memory 702 stores programs or instructions that can be executed on the processor 701, and when the program or instructions are executed by the processor 701, the various steps of the above-mentioned method for determining the motion recovery time are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be repeated here.

[0179] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile terminals and non-mobile terminals.

[0180] Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0181] The electronic device 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, and a processor 810 and other components.

[0182] Those skilled in the art will appreciate that the electronic device 800 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 810 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 8 The structure of the electronic device 800 shown in the figure does not constitute a limitation on the electronic device 800. The electronic device 800 may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0183] The processor 810 is configured to:

[0184] Obtaining basic information of the user, exercise physiological information of the user in a first time period, and physical and mental state information of the user in a second time period, wherein the first time period is a time period during which the user exercises;

[0185] Determine the initial exercise recovery time of the user according to the user's basic information, exercise physiological information and target association relationship, where the target association relationship is used to characterize the association relationship between the user's basic information, exercise physiological information and exercise recovery time;

[0186] The initial exercise recovery time is adjusted according to the physical and mental state information, and the adjusted initial exercise recovery time is determined as the target exercise recovery time.

[0187] The embodiment of the present application first determines the initial exercise recovery time based on the user's basic information and the exercise physiological information of the current exercise, and then dynamically adjusts the initial exercise recovery time based on the user's physical and mental state information to obtain the final exercise recovery time. That is, not only the user's exercise physiological information of the current exercise is taken into account, but also the user's basic information and the user's physical and mental state information. The factors considered are more comprehensive, that is, the embodiment of the present application can comprehensively determine the exercise recovery time based on the user's basic information, exercise data, and physical and mental state information, with higher accuracy.

[0188] In some possible implementations of the embodiments of the present application, the sensor 805 is used to obtain the user's motion data in a first time period, where the motion data includes at least one of a motion heart rate, a motion speed, a motion acceleration, and a heart rate interval;

[0189] The processor 810 is specifically configured to:

[0190] determining a mean and a standard deviation of the motion data during a first time period;

[0191] Energy consumption information and oxygen uptake information of the user in a first time period are determined based on the exercise data and a mean and a standard deviation corresponding to the exercise data.

[0192] In some possible implementations of the embodiments of the present application, the exercise physiology information includes energy consumption information and oxygen uptake information;

[0193] The processor 810 is specifically configured to:

[0194] Determine a first exercise recovery time of the user according to the user's basic information, the energy consumption information and the first association relationship, where the first association relationship is used to characterize the association relationship between the user's basic information, the energy consumption information and the exercise recovery time;

[0195] Determine a second exercise recovery time of the user according to the user's basic information, the oxygen uptake information, and the second association relationship, where the second association relationship is used to characterize the association relationship between the user's basic information, the oxygen uptake information, and the exercise recovery time;

[0196] An initial motion recovery duration is determined according to the first motion recovery duration and the second motion recovery duration.

[0197] In some possible implementations of the embodiments of the present application, the basic information of the user includes height, weight, age and gender;

[0198] The processor 810 is specifically configured to:

[0199] According to the user's gender, determine the height coefficient, weight coefficient, age coefficient and sports physiology coefficient;

[0200] The height, weight, age and exercise physiology information are weighted and summed according to the height coefficient, weight coefficient, age coefficient and exercise physiology coefficient respectively to obtain the user's initial exercise recovery time.

[0201] In some possible implementations of the embodiments of the present application, the processor 810 is specifically configured to:

[0202] Score the user's physical and mental state according to the physical and mental state information to obtain a physical and mental state score result;

[0203] Determine a duration adjustment amount of the initial exercise recovery duration according to the scoring result and the third association relationship, wherein the third association relationship is used to characterize the association relationship between the scoring result and the duration adjustment amount;

[0204] The target exercise recovery time is determined based on the cumulative sum of the initial exercise recovery time and the time adjustment amount.

[0205] In some possible implementations of the embodiments of the present application, the scoring results of the physical and mental state include a sleeping state scoring result, a stress state scoring result, and an emotional state scoring result;

[0206] The processor 810 is specifically configured to:

[0207] Get sleep coefficient, stress coefficient and mood coefficient;

[0208] According to the sleep coefficient, stress coefficient and emotion coefficient, the sleep state scoring results, stress state scoring results and emotion state scoring results are weighted and summed to obtain the duration adjustment of the initial exercise recovery time.

[0209] The embodiments of the present application can collect the user's motion data without the user noticing, and determine the user's exercise recovery time in combination with the user's basic information. It can also integrate information such as sleep, stress, and emotions after the exercise to dynamically adjust the exercise recovery time, thereby effectively improving the accuracy of the exercise recovery time, reducing sports injuries caused by lack of rest, and increasing the universality and practicality of the solution.

[0210] It should be understood that in the embodiment of the present application, the input unit 804 may include a graphics processor (Graphics Processing Unit, GPU) 8041 and a microphone 8042, and the graphics processor 8041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0211] The memory 809 can be used to store software programs and various data. The memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 809 may include a volatile memory or a non-volatile memory, or the memory 809 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0212] The processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 810.

[0213] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned method for determining the motion recovery time is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0214] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0215] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned method for determining the motion recovery time, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0216] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0217] An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned method for determining the motion recovery duration, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0218] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0219] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0220] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for determining the duration of exercise recovery, characterized in that: include: Acquire basic information of a user, exercise physiological information of the user in a first time period, and physical and mental state information of the user in a second time period, wherein the first time period is a time period during which the user exercises; Determine the initial exercise recovery time of the user according to the basic information of the user, the exercise physiological information and the target association relationship, wherein the target association relationship is used to characterize the association relationship among the basic information of the user, the exercise physiological information and the exercise recovery time; The initial exercise recovery time is adjusted according to the physical and mental state information, and the adjusted initial exercise recovery time is determined as the target exercise recovery time.

2. The method according to claim 1, characterized in that The method is applied to a wearable device, wherein the wearable device includes a sensor; Acquiring the exercise physiological information of the user in a first time period, including: Acquiring, by means of the sensor, motion data of the user in the first time period, the motion data comprising at least one of a motion heart rate, a motion speed, a motion acceleration, and a heart rate interval; Determining a mean and a standard deviation of the motion data within the first time period; Determining energy consumption information and oxygen uptake information of the user in a first time period according to the exercise data and the mean and standard deviation corresponding to the exercise data; The energy consumption information and the oxygen uptake information are determined as exercise physiological information of the user in a first time period.

3. The method according to claim 1, characterized in that The sports physiology information includes energy consumption information and oxygen uptake information; The determining, according to the basic information of the user, the exercise physiological information and the target association relationship, the initial exercise recovery time of the user includes: Determine a first exercise recovery time of the user according to the basic information of the user, the energy consumption information and a first association relationship, wherein the first association relationship is used to characterize an association relationship between the basic information of the user, the energy consumption information and the exercise recovery time; determining a second exercise recovery time of the user according to the basic information of the user, the oxygen uptake information, and a second association relationship, wherein the second association relationship is used to characterize an association relationship between the basic information of the user, the oxygen uptake information, and the exercise recovery time; The initial motion recovery duration is determined according to the first motion recovery duration and the second motion recovery duration.

4. The method according to any one of claims 1 to 3, characterized in that: The user's basic information includes height, weight, age and gender; The determining, according to the basic information of the user, the exercise physiological information and the target association relationship, the initial exercise recovery time of the user includes: Determining a height coefficient, a weight coefficient, an age coefficient, and a sports physiology coefficient according to the gender of the user; The height, weight, age and exercise physiology information are weighted and summed according to the height coefficient, weight coefficient, age coefficient and exercise physiology coefficient respectively to obtain the initial exercise recovery time of the user.

5. The method according to any one of claims 1 to 3, characterized in that: The adjusting the initial exercise recovery time according to the physical and mental state information, and determining the adjusted initial exercise recovery time as the target exercise recovery time, includes: Scoring the user's physical and mental state according to the physical and mental state information to obtain a scoring result of the physical and mental state; Determining a duration adjustment amount of the initial exercise recovery duration according to the scoring result and a third association relationship, wherein the third association relationship is used to characterize an association relationship between the scoring result and the duration adjustment amount; The target motion recovery duration is determined according to the cumulative sum of the initial motion recovery duration and the duration adjustment amount.

6. The method according to claim 5, characterized in that The physical and mental state scoring results include sleep state scoring results, stress state scoring results and emotional state scoring results; The step of determining the duration adjustment amount of the initial exercise recovery duration according to the scoring result and the third association relationship includes: Get sleep coefficient, stress coefficient and mood coefficient; The sleep state scoring result, the stress state scoring result and the emotional state scoring result are weighted and summed according to the sleep coefficient, the stress coefficient and the emotional coefficient respectively to obtain a duration adjustment amount of the initial exercise recovery duration.

7. A device for determining the duration of exercise recovery, characterized in that: include: an acquisition module, used to acquire basic information of a user, exercise physiological information of the user in a first time period, and physical and mental state information of the user in a second time period, wherein the first time period is a time period during which the user exercises; A determination module, used to determine the initial exercise recovery time of the user according to the basic information of the user, the exercise physiological information and the target association relationship, wherein the target association relationship is used to characterize the association relationship among the basic information of the user, the exercise physiological information and the exercise recovery time; The adjustment module is used to adjust the initial exercise recovery time according to the physical and mental state information, and determine the adjusted initial exercise recovery time as the target exercise recovery time.

8. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An electronically readable storage medium, characterized in that The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the method according to any one of claims 1 to 6.

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