Training parameter determination method and device, electronic equipment and storage medium

Abstract

The invention provides a training parameter determination method and apparatus, an electronic device and a storage medium, the method is applied to the technical field of exercise training, and the method comprises the steps of obtaining historical exercise data of a target user; if the historical motion data is larger than or equal to a preset threshold value, historical motion parameters of the target user are obtained; determining a target training load of the target user based on the historical motion parameters; if the historical motion data is smaller than a preset threshold value, obtaining user attribute information of the target user; determining a target training load of the target user based on the user attribute information; and based on the target training load, determining recommendation training parameters of the target user and outputting the recommendation training parameters. According to the method, the recommendation training parameter suitable for the user can be determined in combination with the actual demand of the user, the accuracy of the recommendation training parameter is improved, the training effect is improved, and the user injury risk is reduced.

Description

Technical Field

[0001] The present application relates to the field of sports training technology, and more specifically, to a method for determining training parameters, a determination device, an electronic device and a storage medium in the field of sports training technology. Background Art

[0002] With the continuous development and progress of society, people's living standards are gradually improving, and their economic ability and consumption awareness are also gradually improving. More and more people pay more attention to their physical health and follow the trend of sports and fitness.

[0003] With the upgrading of intelligent devices, most users will wear wearable devices such as smart watches and smart bracelets to detect exercise indicators and body indicators during exercise. In order to further improve the practicality of wearable devices during exercise, some wearable devices are also equipped with training plan recommendation functions, but the existing training plan recommendation functions are all fixed courses or fixed plans, which cannot match the actual needs of users.

[0004] Therefore, how to improve the accuracy of recommended training parameters to improve training effects and reduce the risk of user injuries is an urgent problem to be solved. Summary of the invention

[0005] The present application provides a method for determining training parameters, a determination device, an electronic device and a storage medium. The method can determine recommended training parameters suitable for the user based on the actual needs of the user, improve the accuracy of the recommended training parameters, thereby improving the training effect and reducing the risk of user injury.

[0006] In a first aspect, a method for determining a training parameter is provided, the method comprising:

[0007] Obtain historical exercise data of target users;

[0008] If the historical exercise data is greater than or equal to a preset threshold, obtaining the historical exercise parameters of the target user; and determining the target training load of the target user based on the historical exercise parameters;

[0009] If the historical exercise data is less than a preset threshold, obtaining user attribute information of the target user; and determining a target training load of the target user based on the user attribute information;

[0010] Based on the target training load, the recommended training parameters for the target user are determined and output.

[0011] In the above technical solution, the historical motion data of the target user is obtained; the method for determining the target training load is determined by judging the size relationship between the historical motion data and the preset threshold; when the historical motion data is greater than or equal to the preset threshold, the target training load is determined according to the historical motion parameters of the target user; when the historical motion data is less than the preset threshold, the target training load is determined according to the user attribute information of the target user; after determining the target training load, the corresponding recommended training parameters are further determined and recommended to the target user. The historical motion data reflects the amount of exercise of the user in the historical period. According to the different amounts of exercise of the target user, different training load calculation methods are determined to meet the actual needs of different users, improve the accuracy of the target training load, further ensure the accuracy of the recommended training parameters, realize the personalized customization of the training parameters, meet the differentiated needs of different users, optimize the training effect, and reduce the risk of injury to the user during training.

[0012] In conjunction with the first aspect, in some possible implementations, the historical exercise parameter includes a first training parameter, a second training parameter, and a third training parameter; the first training parameter is used to represent the total training load of the target user in a historical preset period; the second training parameter is used to represent the single training load of the target user in the historical training process; the third training parameter is used to represent the physical state of the target user in the historical training process;

[0013] Determine the target training load for the target user based on historical exercise parameters, including:

[0014] Determining a current physical fitness parameter of the target user based on the first training parameter and the second training parameter;

[0015] Based on the current physical fitness parameter and the third training parameter, a target training load is determined.

[0016] In combination with the first aspect and the above implementations, in some possible implementations, determining the target training load based on the current physical fitness parameter and the third training parameter includes:

[0017] Determine a first training load based on current physical fitness parameters;

[0018] A target training load is determined based on the first training load and the third training parameter.

[0019] In the above technical scheme, the historical motion parameters include a first training parameter, a second training parameter and a third training parameter to represent different indicator parameters of the target user in the historical training process; when the historical motion data of the target user is greater than or equal to a preset threshold, firstly, the current physical fitness parameter is determined according to the first training parameter and the second training parameter; and the first training load is determined based on the current physical fitness parameter; finally, the target training load is determined in combination with the first training load and the third training parameter; the target training load is determined based on multiple historical data to ensure that the physical condition of the target user can withstand the target training load, so as to avoid insufficient training or excessive training due to inaccurate target training load, which will affect the body of the target user.

[0020] In combination with the first aspect and the above implementations, in some possible implementations, the user attribute information includes the age, gender, height and weight of the target user;

[0021] Based on the user attribute information, determine the target training load of the target user, including:

[0022] Determine the body mass index of the target user based on the target user's height and weight;

[0023] Determine the target training load based on the target user's age, gender and body mass index.

[0024] In combination with the first aspect and the above implementations, in some possible implementations, determining the target training load based on the age, gender, and body mass index of the target user includes:

[0025] Standardizing the age and body mass index of the target user to obtain standard age parameters and standard index parameters;

[0026] Determine target parameters based on target user's gender, standard age parameter and standard index parameter;

[0027] Based on the target parameters, determine the target training load.

[0028] In combination with the first aspect and the above implementations, in some possible implementations, determining the target parameter based on the gender, standard age parameter, and standard index parameter of the target user includes:

[0029] Determining a first parameter based on a standard age parameter and a standard index parameter;

[0030] Determining a second parameter based on the gender, standard age parameter, and standard index parameter of the target user;

[0031] The minimum value of the first parameter and the second parameter is determined as the target parameter.

[0032] In combination with the first aspect and the above implementations, in some possible implementations, determining the first parameter based on the standard age parameter and the standard index parameter includes:

[0033] The minimum value of the standard age parameter and the standard index parameter is determined as the first parameter.

[0034] In combination with the first aspect and the above implementations, in some possible implementations, determining the second parameter based on the gender, standard age parameter, and standard index parameter of the target user includes:

[0035] Determine the target ratio based on standard age parameters and standard index parameters;

[0036] Based on the target ratio and the gender of the target user, a second parameter is determined.

[0037] In the above technical solution, when the historical exercise data of the target user is less than a preset threshold, only a small amount of information of the target user is obtained, including height, weight, age and gender, so as to reduce the difficulty of information collection and avoid the user's concerns about information collection, which may lead to inaccurate data and other problems; first, the body mass index is determined according to the height and weight of the target user; and the age and body mass index of the target user are standardized to facilitate the analysis and processing of the age and body mass index; then, the target training load matching the attribute parameters of the target user is gradually calculated in combination with the standard age parameter and the standard index parameter of the target user; the minimum value and harmonic mean method are used to process the obtained user attribute parameters, and multiple attribute parameters can be considered, and the minimum value is taken as a reference to ensure that the determined target training load does not exceed the safe range that the target user's body can bear, thereby reducing the risk of injury and excessive fatigue; it can also avoid the transitional influence of larger values ​​on the average result, more accurately reflect the overall physical condition of the target user, and thus improve the accuracy of the target training load.

[0038] In combination with the first aspect and the above implementation manner, in some possible implementation manners, the method further includes:

[0039] Get the current date and the planned start date;

[0040] If the current date is before the planned start date, or the current date is the same as the planned start date, the target training load is determined based on historical exercise parameters or user attribute information;

[0041] If the current date is after the planned start date, output the recommended training parameters.

[0042] In the above technical scheme, the current date and the planned start date of the target setting are compared; when the current date is before the planned start date, or the current date is consistent with the planned start date, the target training load is continuously updated according to the historical motion parameters or user attribute information, so as to realize dynamic adjustment of the target training load, so as to ensure that the determined target training load is consistent with the dynamically changing physical state and motion condition of the target user, and further improve the accuracy of the target training load; when the current date is after the planned start date, the recommended training parameters can be directly output, so that the target user can train according to the recommended training parameters, so as to ensure the training effect of the target user, and also reduce the risk of injury of the target user during the training process.

[0043] In combination with the first aspect and the above implementations, in some possible implementations, based on the target training load, determining and outputting recommended training parameters for the target user includes:

[0044] Obtain the historical training items and training load ratio of the target user;

[0045] Based on the historical training items, training load ratio and target training load, determine the project training load corresponding to each historical training item;

[0046] Based on the project training load, the recommended training parameters for the target user are determined and output.

[0047] In the above technical solution, the target training load is divided according to the training items and training load ratio that the target user has trained in the historical training process; the project training load corresponding to each historical training item is determined; and the recommended training parameters of the target user are determined and output according to the project training load. It can ensure that the recommended training parameters are in line with the exercise habits of the target user and improve the accuracy and personalization of the recommended training parameters.

[0048] In combination with the first aspect and the above implementation manner, in some possible implementation manners, the step of obtaining the second training parameter includes:

[0049] Obtain the historical training duration, standard training power, and functional threshold power of the target user;

[0050] Determine intensity factors based on standard training power and functional threshold power;

[0051] Based on the historical training duration and intensity factor, a second training parameter is determined.

[0052] In the above technical solution, the historical training duration can reflect the training duration of the target user in the historical training process; the standard training power reflects the training intensity; and the functional threshold power can represent the power that the target user can stably output within one hour. Combining multiple factors, the second training parameter is determined to improve the accuracy of the second training parameter, so that the second training parameter can more comprehensively and accurately evaluate the training process of the target user, thereby ensuring the accuracy of determining the target training load and recommended training parameters of the target user.

[0053] In combination with the first aspect and the above implementation manner, in some possible implementation manners, the step of obtaining the third training parameter includes:

[0054] Obtaining an indicator parameter of at least one physical indicator of the target user after exercise training, and a target weight value corresponding to each physical indicator;

[0055] Based on the indicator parameter and the target weight value, a third training parameter is determined.

[0056] In the above technical scheme, the third training parameter is determined by combining the indicator parameters of at least one physical indicator of the target user and the weights corresponding to different physical indicators; taking into account the different physical conditions of each user and the different weights of each physical indicator for physical condition assessment, the indicator parameters and weight values ​​are combined to achieve personalized evaluation of the user, improve the accuracy of the third training parameter and its targeting to the target user; and then, when determining the target training load and recommended training parameters based on the third training parameters, the accuracy of the target training load and recommended training parameters can be guaranteed.

[0057] In a second aspect, a device for determining a training parameter is provided, the device comprising:

[0058] A first acquisition module is used to acquire historical sports data of a target user;

[0059] A first determination module is used to obtain historical motion parameters of the target user if the historical motion data is greater than or equal to a preset threshold; and determine a target training load of the target user based on the historical motion parameters;

[0060] A second determination module is used to obtain user attribute information of the target user if the historical exercise data is less than a preset threshold; and determine a target training load of the target user based on the user attribute information;

[0061] The first output module is used to determine and output the recommended training parameters for the target user based on the target training load.

[0062] In combination with the second aspect, in some possible implementations, the historical motion parameters include a first training parameter, a second training parameter and a third training parameter; the first training parameter is used to represent the total training load of the target user within a historical preset time period; the second training parameter is used to represent the single training load of the target user during the historical training process; the third training parameter is used to represent the physical state of the target user during the historical training process; the first determination module is also used to determine the current physical fitness parameters of the target user based on the first training parameter and the second training parameter; and determine the target training load based on the current physical fitness parameters and the third training parameter.

[0063] In combination with the second aspect and the above-mentioned implementations, in some possible implementations, the first determination module is further used to determine the first training load based on the current physical fitness parameters; and to determine the target training load based on the first training load and the third training parameters.

[0064] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the user attribute information includes the target user's age, gender, height and weight; the second determination module is also used to determine the target user's body mass index based on the target user's height and weight; based on the target user's age, gender and body mass index, determine the target training load.

[0065] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the second determination module is also used to standardize the age and body mass index of the target user to obtain standard age parameters and standard index parameters; determine the target parameters based on the target user's gender, standard age parameters and standard index parameters; and determine the target training load based on the target parameters.

[0066] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the second determination module is also used to determine the first parameter based on the standard age parameter and the standard index parameter; determine the second parameter based on the gender, standard age parameter and standard index parameter of the target user; and determine the minimum value between the first parameter and the second parameter as the target parameter.

[0067] In combination with the second aspect and the above implementations, in some possible implementations, the second determination module is further used to determine the minimum value of the standard age parameter and the standard index parameter as the first parameter.

[0068] In combination with the second aspect and the above-mentioned implementations, in some possible implementations, the second determination module is further used to determine the target ratio based on the standard age parameter and the standard index parameter; and to determine the second parameter based on the target ratio and the gender of the target user.

[0069] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the determining device further includes:

[0070] The second acquisition module is used to obtain the current date and the planned start date;

[0071] A third determination module is used to determine the target training load based on historical exercise parameters or user attribute information if the current date is before the planned start date or the current date is consistent with the planned start date;

[0072] The second output module is used to output recommended training parameters if the current date is after the planned start date.

[0073] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the first output module is also used to obtain the historical training projects and training load ratio of the target user; based on the historical training projects, training load ratio and target training load, determine the project training load corresponding to each historical training project; based on the project training load, determine and output the recommended training parameters of the target user.

[0074] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the determining device further includes:

[0075] The third acquisition module is used to obtain the historical training duration, standard training power and functional threshold power of the target user;

[0076] The fourth determination module is used to determine the intensity factor based on the standard training power and the functional threshold power; and to determine the second training parameter based on the historical training duration and the intensity factor.

[0077] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the determining device further includes:

[0078] A fourth acquisition module is used to acquire an index parameter of at least one physical index of the target user after exercise training, and a target weight value corresponding to each physical index;

[0079] The fifth determination module is used to determine the third training parameter based on the indicator parameter and the target weight value.

[0080] In a third aspect, an electronic device is provided, comprising a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the electronic device executes the method in the first aspect or any possible implementation of the first aspect.

[0081] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0082] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation manner of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a scenario diagram of a method for determining training parameters provided in an embodiment of the present application;

[0084] Figure 2 is a schematic flow chart of a method for determining training parameters provided in an embodiment of the present application;

[0085] Figure 3 is a schematic flow chart of another method for determining training parameters provided in an embodiment of the present application

[0086] Figure 4 It is a structural schematic diagram of a device for determining training parameters provided in an embodiment of the present application;

[0087] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0088] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0089] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0090] Before introducing the solutions of the embodiments of the present application, the professional terms that may be involved in the embodiments of the present application are first explained.

[0091] Chronic Training Load (CTL): An indicator used to quantify the long-term training load of athletes during training. By calculating the exponential moving average of daily training load over a period of time, CTL can help athletes and coaches better evaluate training effects and ensure the scientificity and rationality of training plans. By monitoring CTL, overtraining can be avoided and athletes can gradually improve their physical fitness during training.

[0092] Training Stress Score (TSS): A metric used to quantify training load, calculated based on the intensity and duration of training. TSS helps assess the physiological and psychological stress of training and provides a reference for subsequent training plans.

[0093] Training Readiness Score (TRS): An indicator that assesses the athlete's current physiological and psychological state to determine how ready they are to train. This score usually takes into account multiple factors, including sleep quality, recovery status, fatigue, psychological state, and recent training load. By analyzing these factors, athletes and coaches can better understand when to train hard, when to rest or reduce the intensity of training to optimize training effects and reduce the risk of injury.

[0094] Functional Threshold Power (FTP): A measure of the maximum power output an athlete can maintain in a steady state.

[0095] Intensity Factor (IF): An indicator used to quantify training intensity. It is mainly used to describe the ratio of training intensity to FTP and to indicate the intensity of training.

[0096] The following is an introduction to the application scenarios of the method for determining training parameters provided in the embodiments of the present application.

[0097] Nowadays, more and more people are paying attention to sports to maintain good health, or preparing for sports events by completing training tasks. During the user's training process, the user can monitor the sports data (such as training items, exercise duration, etc.) and physical condition (such as heart rate, breathing rate, etc.) through smart terminal devices; with the continuous updating and progress of smart devices, in order to improve the convenience of smart devices, wearable and convenient devices such as smart watches, smart bracelets and smart glasses are gradually introduced. Wearable and convenient devices can also monitor the user's sports data and physiological data during the user's training process. In order to provide users with a better sports experience, related equipment can also recommend training courses and training plans to users.

[0098] However, most of the recommendation schemes in the prior art adopt fixed templates; or, before making recommendations, a large amount of user information is collected through questionnaires or the like; during the information collection process, due to the existence of information involving user privacy and the cumbersome process of filling in personal information, it may be impossible to collect accurate and complete user information, which in turn may lead to low accuracy of the recommended content and inability to adapt to the actual needs of users, which may cause users to be injured during training or have insufficient training.

[0099] Personalized recommendation systems rely on a large amount of high-quality data, but in practice there may be problems with data collection difficulties, insufficient data volume, and low data quality, all of which will affect the accuracy and effectiveness of recommendations. The recommendation method that sets specific rules needs to rely on expert experience and preset rules, which has certain subjectivity and limitations and is difficult to adapt to individual differences and dynamically changing needs of users. In the process of data collection and processing, there is also a lack of effective privacy protection and security measures, which can easily cause users to worry about data privacy and affect their enthusiasm for use.

[0100] Based on the above problems, an embodiment of the present application provides a method for determining training parameters, a determination device, an electronic device and a storage medium. The method combines the user's historical exercise duration to determine different training load calculation methods, determines the target training load and recommended training parameters suitable for the user, improves the accuracy of the training load and recommended training parameters, thereby improving the training effect and reducing the risk of user injury.

[0101] The following will introduce the hardware device on which the method for determining a training parameter provided in an embodiment of the present application relies.

[0102] Figure 1 It is a scenario diagram of a method for determining training parameters provided in an embodiment of the present application.

[0103] Exemplarily, the method for determining the training parameters provided in the embodiments of the present application can be applied to the following: Figure 1 The electronic device 100 or cloud platform 101 shown in the figure may include a smart terminal device 102 and a wearable convenience device 103, etc.

[0104] It should be noted that the smart terminal device 102 can be a smart phone, a computer, etc., and the wearable convenience device 103 can be a smart watch, a smart bracelet, smart glasses, etc.; the embodiments of the present application only take smart phones and smart watches as examples, and do not limit the specific electronic devices.

[0105] In one implementation, when the determination method is applied to the cloud platform 101, the cloud platform 101 can send the obtained training parameters to the smart terminal device 102 and / or the wearable convenience device 103, so that the user can view the output results through the smart terminal device 102 and / or the wearable convenience device 103.

[0106] In one implementation, when the determination method is applied to the smart terminal device 102, after the smart terminal device 102 obtains the training parameters, the training parameters can be output through an application in the smart terminal device 102; or, the training parameters are sent to a wearable convenience device 103 associated with the smart terminal device 102, and the training parameters are output through the wearable convenience device 103.

[0107] In one implementation, when the determination method is applied to the wearable convenience device 103 , after the wearable convenience device 103 obtains the training parameters, the training parameters may be output through an application in the wearable convenience device 103 .

[0108] The following will be Figure 2 and Figure 3 The detailed process of a method for determining training parameters provided in an embodiment of the present application is introduced.

[0109] Figure 2 FIG. 1 is a schematic flow chart of a method for determining a training parameter provided in an embodiment of the present application. It should be understood that the method can be applied to Figure 1 An electronic device 100 and / or a cloud platform 101 are shown.

[0110] For example, Figure 2 As shown, the method 200 includes:

[0111] S201, obtaining historical exercise data of a target user.

[0112] Among them, historical exercise data includes historical exercise duration and historical exercise intensity; historical exercise duration can indicate the number of exercise days or total exercise duration of the target user in the historical preset period; historical exercise intensity can indicate the exercise intensity of the target user's exercise training in the historical preset period; exercise intensity can be expressed using intensity levels or intensity indexes.

[0113] For example, the historical preset period may be 60 days before the current date, and the number of days or total duration of exercise during which the target user has exercise behavior within the historical 60 days may be further determined. For example, the historical exercise duration of the target user is 20 days, or the historical exercise duration of the target user is 50 hours; or the historical exercise intensity of the target user is 30%, or low intensity.

[0114] Optionally, the method for acquiring historical sports data may include: manual input by a user, or acquiring historical data of a related application or sports equipment.

[0115] Exemplarily, upon receiving a training parameter recommendation request from a target user, the target user is prompted to input historical motion data; or, the current device obtains motion data recorded in an associated application or sports device to determine the target user's historical motion data.

[0116] Optionally, a training parameter recommendation control is provided in the electronic device, and the user can trigger the method for determining the training parameters provided in the embodiment of the present application through the control, so that the determined training parameters are displayed through the electronic device.

[0117] Optionally, the process of calculating the historical exercise duration of the target user can be expressed using the following formula 1.

[0118]

[0119] In formula 1, D is used to represent the number of days in the historical preset period during the statistical process; count_past_D_days is used to represent the historical movement duration; past_D_days is used to represent the historical preset period; d i Used to indicate date; current_date is used to indicate the current date.

[0120] Exemplarily, the number of days in the historical preset time period is preset to 60, and the above formula is used to count the historical exercise duration within 60 days before the current date.

[0121] S202: If the historical motion data is greater than or equal to a preset threshold, obtain historical motion parameters of the target user; and determine a target training load of the target user based on the historical motion parameters.

[0122] The historical motion parameters are used to indicate various indicator parameters of the target user during the historical training process.

[0123] The historical motion data may include historical training duration or historical training intensity; when the historical motion data is historical training duration, the preset threshold indicates a duration threshold; when the historical motion data maintains historical training intensity, the preset threshold indicates an intensity threshold.

[0124] For example, taking the historical exercise data as the historical exercise duration, the duration threshold is set to 15 days. If the historical exercise duration of the target user within the historical 60 days is 20 days, which is greater than the duration threshold of 15 days, then the historical exercise parameters of the exercise process within the historical exercise duration can be obtained. The historical exercise parameters can indicate the training intensity value of the target user's training; after obtaining a large number of training intensity values, the target training load of the target user is determined according to the training intensity values.

[0125] For example, taking the historical exercise data as historical exercise intensity, the intensity threshold is set to 50%. If the historical exercise intensity of the target user within the historical 60 days is 70%, which is greater than the intensity threshold of 50%, the historical exercise parameters of the exercise process within the historical exercise duration can be obtained to determine the target training load of the target user based on the historical exercise parameters.

[0126] In one implementation, the historical motion parameters of the target user may include a first training parameter, a second training parameter and a third training parameter; the first training parameter is used to represent the total training load of the target user within a historical preset time period; the second training parameter is used to represent the single training load of the target user during the historical training process; the third training parameter is used to represent the physical state of the target user during the historical training process.

[0127] In the embodiment of the present application, the first training parameter specifically refers to the historical CTL value; the second training parameter specifically refers to the historical TSS value; and the third training parameter specifically refers to the historical TRS value.

[0128] For example, the current CTL value can be displayed in real time when the user logs into the relevant application program. Therefore, the current CTL value can be directly obtained based on the user's personal account.

[0129] In one implementation, the step of obtaining the second training parameter includes:

[0130] Obtain the historical training duration, standard training power, and functional threshold power of the target user;

[0131] Determine intensity factors based on standard training power and functional threshold power;

[0132] Based on the historical training duration and intensity factor, a second training parameter is determined.

[0133] Among them, the value of the second training parameter can reflect the load of a training session. Generally speaking, the higher the value of the second training parameter, the greater the training load. Normalized Power (NP) is a smoothing process of power changes in actual training. It takes into account the power fluctuations during the entire training process and can more accurately reflect the intensity of training than average power. The calculation of NP usually requires the use of specialized power meter software or equipment.

[0134] For example, in a training session, the target user's power output may be high or low, and if you simply look at the average power, you may underestimate the intensity of the training. NP will take the power fluctuation into account, making the calculated training intensity closer to the actual situation.

[0135] The second training parameter is used to indicate the load of the target user in a single training process. Therefore, the training power and functional threshold power of the target user in each historical training process are obtained to determine the intensity factor IF, and then the second training parameter is determined based on the length of each training session and the corresponding IF value.

[0136] Generally speaking, an IF below 0.7 indicates low-intensity training, 0.7-0.85 indicates moderate-intensity training, and above 0.85 indicates high-intensity training.

[0137] Specifically, the calculation process of the second training parameter can be expressed using the following formula 2 and formula 3:

[0138]

[0139] Among them, NP is the standard training power of the target user; FTP is the functional threshold power of the target user; IF is the intensity factor; TSS is the second training parameter; t is the historical training duration of the target user in a single training session; T is the FTP test time with a duration of 1 hour, usually 60 minutes.

[0140] For example, assuming that the target user rides for 60 minutes during a training session, the NP of the target user is 250 watts, and the FTP is 200 watts; it can be calculated that the IF of the target user for this training session is 1.25; further, according to Formula 3, it can be calculated that the TSS for this session is approximately 278.

[0141] In the embodiment of the present application, the historical training duration can reflect the training duration of the target user in the historical training process; the standard training power reflects the training intensity; and the functional threshold power can represent the power that the target user can stably output within one hour. Combining multiple factors, the second training parameter is determined to improve the accuracy of the second training parameter, so that the second training parameter can more comprehensively and accurately evaluate the training process of the target user, thereby ensuring the accuracy of determining the target training load and recommended training parameters of the target user.

[0142] In one implementation, the step of obtaining the third training parameter includes:

[0143] Obtaining an indicator parameter of at least one physical indicator of the target user after exercise training, and a target weight value corresponding to each physical indicator;

[0144] Based on the indicator parameter and the target weight value, a third training parameter is determined.

[0145] The third training parameter is the training readiness score (TRS), which is used to reflect the body's physiological and psychological reactions to the training load. It is a comprehensive assessment of the impact of training on the body through multiple indicators. Physical indicators may include heart rate variability (HRV), sleep quality, fatigue, recovery heart rate, lactate level, emotional state, and strength performance.

[0146] Among them, HRV can reflect changes in the autonomic nervous system; sleep quality is used to indicate the target user's sleep pattern and quality after training; fatigue indicates the target user's subjective sense of fatigue; recovery heart rate indicates the target user's heart rate recovery after training; lactate level indicates the lactate concentration in the target user's blood; emotional state indicates the target user's emotional changes after training; strength performance can be jumping height, maximum strength, etc.

[0147] For example, the indicator parameters corresponding to each physical indicator can be obtained through an electronic device, or the indicator parameter scores of the target user can be collected through questionnaires or self-assessment, etc. The third training parameter is calculated based on the indicator parameters and the target weight values ​​corresponding to the physical indicators.

[0148] Different physical indicators may have different importance when calculating TRS. Take the physical indicators including HRV, sleep quality and subjective feelings (including fatigue level, stress level, emotional state, etc.) as an example; generally speaking, HRV may occupy a larger target weight value, such as 50%; the target weight value corresponding to sleep quality can be 30%; the target weight value corresponding to subjective feelings can be 20%.

[0149] Since different body indicators have different value ranges, the indicator parameters can be standardized to facilitate uniform comparison during the calculation process; for example, the indicator parameters can be converted into scores between 0 and 100.

[0150] For example, assuming that the HRV obtained after standardization is 80, the sleep quality is 70, and the subjective feeling is 60, the TRS calculated according to the above target weight value is 73, that is, the third training parameter of the target user is 73.

[0151] When TRS is high, for example, TRS is greater than 80, it means that the body is in good condition and ready for high-intensity training; when TRS is between 50 and 80, it means that the body is in general condition and can perform moderate training; when TRS is less than 50, it means that the body may be tired or not fully recovered, and it is necessary to reduce the training intensity or increase the rest time.

[0152] In the embodiment of the present application, the third training parameter is determined in combination with the indicator parameters of at least one physical indicator of the target user and the weights corresponding to different physical indicators; taking into account the different physical conditions of each user and the different weights of each physical indicator for physical condition assessment, the indicator parameters and weight values ​​are combined to achieve personalized evaluation of the user, thereby improving the accuracy of the third training parameter and its targeting to the target user; and then, when determining the target training load and recommended training parameters based on the third training parameters, the accuracy of the target training load and recommended training parameters can be guaranteed.

[0153] In one implementation, the process of determining the target training load of the target user according to the historical exercise parameters may include:

[0154] Determining a current physical fitness parameter of the target user based on the first training parameter and the second training parameter;

[0155] Based on the current physical fitness parameter and the third training parameter, a target training load is determined.

[0156] After obtaining the first training parameter and the second training parameter of the target user in the historical training process, the current physical fitness parameter of the target user is calculated according to the first training parameter and the second training parameter; and the target training load is calculated by combining the current physical fitness parameter and the third training parameter.

[0157] Specifically, the calculation process of the current physical fitness parameter can be expressed using the following formula 4:

[0158]

[0159] Wherein, CTL(t) is the physical fitness parameter of the target user on the current day; CTL(t-1) is the first training parameter of the target user on the previous day; TSS is the second training parameter of the target user; τ CTL is a constant used to control the weight decay speed of the second training parameter when calculating CTL.

[0160] τ CTL The setting of needs to be combined with the characteristics of the training project and the actual situation of the user. CTL It will make the weight decay faster, which means that the recent training load has a greater impact on CTL; a larger τ CTL The weight decay will be slower, and the long-term training load will account for a relatively large proportion of CTL. In practical applications, the appropriate τ can be determined through experiments and observations. CTL An initial value can be chosen and then adjusted based on the athlete’s training response and performance. If the athlete shows signs of overtraining, τ can be reduced. CTL ; If the athlete's training effect is not obvious, you can increase τ CTL .

[0161] Therefore, the current training load of the target user can be calculated according to the above formula 4; and then the target training load can be determined according to the current training load and the third training parameter.

[0162] In one implementation, based on the current physical fitness parameter and the third training parameter, the process of determining the target training load may include:

[0163] Determine a first training load based on current physical fitness parameters;

[0164] A target training load is determined based on the first training load and the third training parameter.

[0165] Specifically, the calculation process of the first training load can be expressed by the following formula 5:

[0166] NextWeekTSS=CTL(t)×7+Ramprate×7; (Formula 5)

[0167] Among them, NextWeekTss is the first training load; CTL(t) is the current physical parameter; Ramprate is the growth rate of the training load within a certain period of time. For example, if the training load increases from 100 to 120 within a week, then the Ramprate of this week is (120-100) / 100×100%÷7, which is about 2.86% / day.

[0168] After the first training load is calculated according to the above formula 5, the target training load is calculated using the following formulas 6 to 9.

[0169]

[0170] TSS1=0.3×NextWeekTSS; (Formula 7)

[0171]

[0172] AdjNextWeekTSS=max(TSS1,min(TSS2,NextWeekTSS)); (Formula 9)

[0173] Among them, TRS i is the third training parameter of the i-th day in the past seven days; AugTRS7D is the average of the third training parameters in the past seven days; NextWeekTSS is the first training load in the next cycle; AdjNextWweekTSS is the target training load in the next cycle; e is an exponential constant, which is approximately 2.71828.

[0174] In the process of determining the target training load, multiple candidate values ​​of the training load are determined, and the maximum value is selected from the multiple candidate values ​​as the target training load.

[0175] In an embodiment of the present application, the historical motion parameters include a first training parameter, a second training parameter and a third training parameter to represent different indicator parameters of the target user in the historical training process; when the historical motion data of the target user is greater than or equal to a preset threshold, first determine the current physical fitness parameter based on the first training parameter and the second training parameter; and determine the first training load based on the current physical fitness parameter; finally, determine the target training load by combining the first training load and the third training parameter; determine the target training load based on multiple historical data to ensure that the target user's physical condition can withstand the target training load, so as to avoid insufficient training or excessive training due to inaccurate target training load, which will affect the target user's body.

[0176] S203: If the historical exercise data is less than a preset threshold, obtain user attribute information of the target user; and determine a target training load of the target user based on the user attribute information.

[0177] When the target user has exercised less frequently or for a shorter duration in the recent period, or has exercised at a lower intensity, the less historical exercise data cannot fully reflect the target user's exercise status; or, if the target user is a novice in exercise, or a new user of an exercise program, and the target user's historical exercise data cannot be obtained, the target training load can be set for the target user in combination with the target user's basic information.

[0178] Exemplarily, when the historical exercise data is historical exercise duration, the duration threshold is set to 15 days. If it is obtained that the target user's exercise duration within the historical 60 days is less than 15 days, for example, 5 days, then the user attribute information of the target user can be further obtained; and based on the user attribute information, the target training load suitable for the target user is determined.

[0179] Exemplarily, when the historical exercise data is historical exercise intensity, the intensity threshold is set to 50%. If it is obtained that the target user's exercise intensity in the historical 60 days is less than 50% of the intensity threshold, for example, 20%, the user attribute information of the target user can be further obtained; and based on the user attribute information, the target training load suitable for the target user is determined.

[0180] In one implementation, the user attribute information includes the age, gender, height, and weight of the target user; based on the user attribute information, the process of determining the target training load of the target user may include:

[0181] Determine the body mass index of the target user based on the height and weight of the target user;

[0182] Determine the target training load based on the target user's age, gender and body mass index.

[0183] Exemplarily, the user attribute information of the target user can reflect the basic physical condition of the target user, and according to the actual physical condition of the target user, a training load that matches the current physical condition of the target user can be determined.

[0184] Specifically, the calculation process of the body mass index (BMI) can be expressed by the following formula:

[0185]

[0186] Among them, the BMI value can roughly reflect a person's body fat content.

[0187] The target training load of the target user can then be calculated based on the age, gender and body mass index of the target user.

[0188] Optionally, when a user registers to use a sports program for the first time, an information collection window is output to record the user's attribute information; when the user has a training parameter recommendation requirement, the user attribute information recorded when the user first registered is obtained.

[0189] Optionally, the user's attribute information may change over time. In order to improve the accuracy of the user's attribute information, when it is detected that the user has a need for training parameter recommendation, an information collection window can be output to collect the user's latest attribute information and update the stored historical attribute information.

[0190] In one implementation, based on the age, gender, and body mass index of the target user, the process of determining the target training load may include:

[0191] Standardizing the age and body mass index of the target user to obtain standard age parameters and standard index parameters;

[0192] Determine target parameters based on target user's gender, standard age parameter and standard index parameter;

[0193] Based on the target parameters, determine the target training load.

[0194] Since age and BMI are in different dimensions and have different value ranges, in order to facilitate data analysis and evaluation of age and BMI, the age and BMI of the target user can be standardized (also called normalization) respectively, and the age and BMI can be mapped to a specific interval to analyze the age and BMI between different users or groups.

[0195] Specifically, the process of standardizing age and BMI can refer to the following formula 11 and formula 12:

[0196]

[0197] Among them, Age norm is the standard age parameter; Age max The maximum value of the preset age range; Age min is the minimum value of the preset age range; Age is the age of the target user; BMI norm is a standard index parameter; BMI max is the maximum value of the preset BMI range; BMI min is the minimum value of the preset BMI range; BMI is the BMI of the target user.

[0198] For example, the preset age range can be [18, 90]; the preset BMI range can be [18.5, 40]; if the target user is 24 years old and the BMI is 22, the standard age parameter can be calculated to be (90-24) / (90-18), which is approximately 0.92, and the standard index parameter can be calculated to be (40-22) / (40-18.5), which is approximately 0.84.

[0199] After the standard age parameter and the standard index parameter are calculated, the target parameter is determined according to the gender, standard age parameter and standard index parameter of the target user; and the target training load of the target user is determined according to the target parameter.

[0200] In one implementation, the target parameter is determined based on the gender, standard age parameter, and standard index parameter of the target user, including:

[0201] Determining a first parameter based on a standard age parameter and a standard index parameter;

[0202] Determining a second parameter based on the gender, standard age parameter, and standard index parameter of the target user;

[0203] The minimum value of the first parameter and the second parameter is determined as the target parameter.

[0204] First, the first parameter is determined according to the standard age parameter and the standard index parameter. Different user genders will also affect the setting of the training load. Then, the second parameter is determined by combining the gender, the standard age parameter and the standard index parameter. The minimum value of the first parameter and the second parameter is determined as the target parameter to determine the target training load, so as to avoid the target training load being too high and the target user being unable to adapt, resulting in overtraining.

[0205] In one implementation, the minimum value of the standard age parameter and the standard index parameter is determined as the first parameter Score 1. This avoids the existence of large values ​​in the standard age parameter and the standard index parameter, which causes the determination of the target parameter to change with the large value, resulting in a large deviation.

[0206] In one implementation, based on the gender, standard age parameter, and standard index parameter of the target user, the process of determining the second parameter includes:

[0207] Determine the target ratio based on standard age parameters and standard index parameters;

[0208] Based on the target ratio and the gender of the target user, a second parameter is determined.

[0209] Specifically, the calculation process of the second parameter can be expressed using the following formula 13 and formula 14.

[0210]

[0211] Score2=(1-ω sex )×Score harmonic +ω sex ×Sex; (Formula 14)

[0212] Among them, Score harmonicrepresents the harmonic mean between the standard age parameter and the standard index parameter, that is, the target ratio; Score2 represents the second parameter; ω sex Indicates the gender weight value; Sex indicates the gender of the target user. In the embodiment of the present application, male is represented by 1 and female is represented by 0.

[0213] For example, the age and BMI of the female target user are standardized, and the standard age parameter is 0.92, the standard index parameter is 0.84, and ω sex The value is 0.2; the target ratio Score can be calculated harmonic It is about 0.9; further combined with the user's gender, the second parameter can be calculated to be about 0.72; the first parameter obtained according to the standard age parameter and the standard index parameter is 0.84, and then the minimum value of 0.72 between the first parameter and the second parameter is taken as the target parameter.

[0214] After the target parameters are calculated, the target training load of the target user can be calculated according to the following formula:

[0215] WeekTSS=A+B×Score; (Formula 15)

[0216] Among them, WeekTSS represents the target training load; A represents the minimum value of the training load; B is determined based on the training load of a large number of users; and Score is the target parameter.

[0217] Exemplarily, the minimum value A of the training load can be 50, and B can be 450; after determining that the target parameter is 0.72, the target training load can be calculated as 500.72 according to Formula 15.

[0218] In an embodiment of the present application, when the historical exercise data of the target user is less than a preset threshold, only a small amount of information of the target user is obtained, including height, weight, age and gender, to reduce the difficulty of information collection and avoid the user's concerns about information collection, which may lead to inaccurate data and other problems; first, the body mass index is determined based on the height and weight of the target user; and the age and body mass index of the target user are standardized to facilitate analysis and processing of the age and body mass index; then, the target training load matching the attribute parameters of the target user is gradually calculated in combination with the standard age parameter and standard index parameter of the target user; the minimum value and harmonic mean method are used to process the acquired user attribute parameters, and multiple attribute parameters can be considered, and the minimum value is taken as a reference to ensure that the determined target training load does not exceed the safe range that the target user's body can bear, thereby reducing the risk of injury and excessive fatigue; it can also avoid the transitional influence of larger values ​​on the average result, more accurately reflect the overall physical condition of the target user, and thus improve the accuracy of the target training load.

[0219] S204: Based on the target training load, determine and output recommended training parameters for the target user.

[0220] Among them, the recommended training parameters may include training items, exercise duration, exercise distance, strength intensity, etc. that are suitable for the user.

[0221] After determining the target training load that matches the current training situation of the target user, corresponding recommended training parameters can be determined for the user's reference based on the value of the target training load.

[0222] Exemplarily, the training items may include jogging, sprinting, cycling, etc.; when the target training load is determined to be 500, the training items corresponding to the target user may be determined to be jogging for 4 hours, sprinting for 3 hours, and cycling for 5 hours.

[0223] When the historical data of the target user records the training items that have been conducted in the historical period, the corresponding training parameters can be recommended for the target user according to the training items. In one implementation, the process of determining and outputting the recommended training parameters for the target user based on the target training load may include:

[0224] Obtain the historical training items and training load ratio of the target user;

[0225] Based on the historical training items, training load ratio and target training load, determine the project training load corresponding to each historical training item;

[0226] Based on the project training load, the recommended training parameters for the target user are determined and output.

[0227] The historical training items and training load ratio can reflect the target user's exercise habits during historical exercise. Combined with the target user's historical training items and training load ratio, the training parameters of the load exercise habits can be matched for the target user.

[0228] For example, the target user has trained in jogging and cycling during historical exercise, and the exercise volume ratio of the jogging project to the cycling project is approximately 3:2. When the target training load is calculated to be 500, it can be determined that the project training load corresponding to the jogging project is 300, and the project training load corresponding to the cycling project is 200; further determine the recommended training parameters include training the jogging project for 5 hours and training the cycling project for 3 hours.

[0229] Optionally, the training load ratio may also be a preset training ratio corresponding to different training items to avoid allocating a higher or lower training load to a training item. The training load ratio may be determined based on exercise records of a large number of users.

[0230] For example, for low-intensity exercise, such as jogging, race walking, etc., due to the low amount of exercise, a higher training load ratio can be set; for high-intensity exercise, such as sprinting, cycling, etc., a lower training load ratio can be set.

[0231] In the embodiment of the present application, the target training load is divided according to the training items and training load ratio that the target user has trained in the historical training process; the project training load corresponding to each historical training item is determined; and the recommended training parameters of the target user are determined according to the project training load and output. It can ensure that the recommended training parameters are consistent with the exercise habits of the target user, and improve the accuracy and personalization of the recommended training parameters.

[0232] Optionally, the output recommended training parameters may be total training parameters within a preset period, and / or the training parameters corresponding to each day may be determined based on factors such as weather and temperature on different dates.

[0233] Optionally, during the training parameter recommendation process, the planned start date may be set by default to the current date, that is, recommended training parameters within one week after the current date are recommended to the target user; further, the planned start date may also be set by the user.

[0234] In one implementation, the current date and the planned start date are obtained; if the current date is before the planned start date, or the current date is consistent with the planned start date, the target training load is determined based on historical motion parameters or user attribute information; if the current date is after the planned start date, the recommended training parameters are output.

[0235] If the current date is before the planned start date, or is the same as the planned start date, indicating that there is no exercise data between the current date and the planned start date, the corresponding target training load determination method can be determined based on the target user's exercise duration, and the target training load can be determined in combination with the target user's historical exercise parameters or user attribute information; when the date reaches the planned start date, the corresponding recommended training parameters are determined based on the target training load and recommended to the target user. In this process, if it is detected that the historical exercise parameters or user attribute parameters are updated, the determined target training load can be updated based on the updated historical exercise parameters or user attribute information; or the updated target training load can be recalculated based on the updated data; when the date reaches the planned start date, the recommended training parameters are determined based on the latest target training load and recommended to the target user.

[0236] If the current date is after the planned start date, the recommended training parameters corresponding to the current date can be directly obtained and recommended to the target user, so that the target user can perform training according to the recommended training parameters of the current date.

[0237] It should be noted that after determining the target training load, the recommended training parameters pushed to the target user based on the target training load can also be combined with multiple influencing factors, such as season, weather, etc.; the embodiment of the present application does not limit the specific parameters of the recommended training parameters.

[0238] In an embodiment of the present application, the current date and the planned start date of the target setting are compared; when the current date is before the planned start date, or the current date is consistent with the planned start date, the target training load is continuously updated according to historical motion parameters or user attribute information, and dynamic adjustment of the target training load is achieved to ensure that the determined target training load is consistent with the dynamically changing physical state and motion conditions of the target user, and further improve the accuracy of the target training load; when the current date is after the planned start date, the recommended training parameters can be directly output, so that the target user can train according to the recommended training parameters, so as to ensure the training effect of the target user, and also reduce the risk of injury to the target user during training.

[0239] In summary, in the embodiment of the present application, the historical motion data of the target user is obtained; the method for determining the target training load is determined by judging the size relationship between the historical motion data and the preset threshold; when the historical motion data is greater than or equal to the preset threshold, the target training load is determined according to the historical motion parameters of the target user; when the historical motion data is less than the preset threshold, the target training load is determined according to the user attribute information of the target user; after determining the target training load, the corresponding recommended training parameters are further determined and recommended to the target user. The historical motion data reflects the amount of exercise of the user in the historical period. According to the different amounts of exercise of the target user, different training load calculation methods are determined to adapt to the actual needs of different users, improve the accuracy of the target training load, further ensure the accuracy of the recommended training parameters, realize the personalized customization of the training parameters, meet the differentiated needs of different users, optimize the training effect, and reduce the risk of injury to the user during training.

[0240] In order to facilitate understanding of the solution of the embodiment of the present application, Figure 3 The solution process of the embodiment of the present application is introduced in detail.

[0241] Figure 3 FIG. 1 is a schematic flow chart of another method for determining training parameters provided in an embodiment of the present application. It should be understood that the method can be applied to Figure 1 An electronic device 100 and / or a cloud platform 101 are shown.

[0242] For example, Figure 3 As shown, the method 300 includes:

[0243] S301, obtaining historical exercise data of a target user.

[0244] Among them, historical exercise data includes historical exercise duration and historical exercise intensity; historical exercise duration can indicate the number of exercise days or total exercise duration of the target user in the historical preset period; historical exercise intensity can indicate the exercise intensity of the target user's exercise training in the historical preset period; exercise intensity can be expressed using intensity levels or intensity indexes.

[0245] In the embodiment of the present application, historical exercise data is taken as an example of historical exercise duration.

[0246] Exemplarily, it is obtained that the historical exercise duration of the target user within the past 60 days is 20 days.

[0247] Optionally, the implementation of S301 can refer to Figure 2 The relevant description of S201 in the present application embodiment will not be repeated here.

[0248] S302, determine whether the historical exercise duration is greater than or equal to the duration threshold. If so, execute S303; if not, execute S307.

[0249] Exemplarily, the duration threshold is preset to 15 days. After the historical exercise duration of the target user is obtained, the historical exercise duration is compared with the duration threshold to determine whether the historical exercise duration is greater than or equal to the duration threshold of 15 days.

[0250] S303: Obtain a first training parameter, a second training parameter, and a third training parameter of the target user.

[0251] Among them, the first training parameter is used to represent the total training load of the target user within the historical preset time period; the second training parameter is used to represent the single training load of the target user during the historical training process; and the third training parameter is used to represent the physical state of the target user during the historical training process.

[0252] Specifically, in the embodiment of the present application, the first training parameter is the CTL value; the second training parameter is the TSS value; and the third training parameter is the TRS value.

[0253] Exemplarily, when the historical exercise duration is greater than the duration threshold, it is obtained that the first training parameter of the target user on the i-th day in the historical training process is 4, the second training parameter is 120, and the third training parameter is 73.

[0254] Optionally, the implementation of S303 can refer to Figure 2 The relevant description of S202 in the present application embodiment will not be repeated here.

[0255] S304: Determine a current physical fitness parameter of the target user based on the first training parameter and the second training parameter.

[0256] Exemplarily, after the first training parameter, the second training parameter, and the third training parameter of the target user are acquired, the current physical fitness parameter of the target user is calculated based on the first training parameter and the second training parameter.

[0257] Optionally, the implementation of S304 can refer to Figure 2 The relevant description of S202 in the present application embodiment will not be repeated here.

[0258] S305: Determine a first training load based on the current physical fitness parameters.

[0259] Exemplarily, after the current physical fitness parameters of the target user are calculated, the first training load is determined.

[0260] Optionally, the implementation of S305 can refer to Figure 2 The relevant description of S202 in the present application embodiment will not be repeated here.

[0261] S306: Determine a target training load based on the first training load and the third training parameter.

[0262] Exemplarily, after the first training load is calculated, the target training load of the target user is determined by combining the first training load and the third training parameter. At this time, the target training load is determined by combining multiple parameters of the target user in the historical training process, so the determined target training load can match the physical state and psychological state of the target user.

[0263] Optionally, the implementation of S306 can refer to Figure 2 The relevant description of S202 in the present application embodiment will not be repeated here.

[0264] S307, obtaining the age, gender, height and weight of the target user.

[0265] Exemplarily, when the historical exercise duration of the target user does not reach the duration threshold of 15 days, only basic information of the target user, such as age, gender, height and weight, is obtained.

[0266] Optionally, the implementation of S307 can refer to Figure 2 The relevant description of S203 in the present application embodiment will not be repeated here.

[0267] S308: Determine the body mass index of the target user based on the height and weight of the target user.

[0268] Exemplarily, the body mass index of the target user is calculated in combination with the height and weight of the target user, and the index can roughly reflect the obesity degree or body fat content of the target user.

[0269] Optionally, the implementation of S308 can refer to Figure 2 The relevant description of S203 in the present application embodiment will not be repeated here.

[0270] S309, standardizing the age and body mass index of the target user to obtain a standard age parameter and a standard index parameter.

[0271] For example, in order to facilitate data analysis and evaluation of age and body mass index, the age and body mass index of the target user may be standardized (or normalized), and the acquired age and body mass index may be converted into standard age parameters and standard index parameters.

[0272] Optionally, the implementation of S309 can refer to Figure 2 The relevant description of S203 in the present application embodiment will not be repeated here.

[0273] S310, determining target parameters based on the gender, standard age parameter and standard index parameter of the target user.

[0274] For example, the gender, age, height and weight in the basic information will affect the training load that the user can withstand; after the age and body mass index of the target user are standardized, the target parameters can be determined by combining the gender, standard age parameters and standard index parameters of the target user.

[0275] Optionally, the implementation of S310 can refer to Figure 2 The relevant description of S203 in the present application embodiment will not be repeated here.

[0276] S311, determining a target training load based on the target parameters.

[0277] Exemplarily, after the target parameters are calculated, the target training load that matches the target user can be determined in combination with the basic value of the training load.

[0278] Optionally, the implementation of S311 can refer to Figure 2 The relevant description of S203 in the present application embodiment will not be repeated here.

[0279] S312, obtaining the current date and the planned start date.

[0280] Exemplarily, the target user can set a planned start date for training parameters, and after determining the target training load of the target user based on the historical exercise parameters of the target user, the current date and the planned start date set by the user are obtained.

[0281] Optionally, the implementation of S312 can refer to Figure 2 The relevant description of S204 in the present application embodiment is not repeated here.

[0282] S313, determine whether the current date is after the planned start date. If so, execute S316; if not, execute S303.

[0283] For example, after obtaining the current date and the planned start date, it is determined whether the current date is after the planned start date. For example, if the current date is July 21 and the planned start date is July 19, it means that the current date is after the planned start date; if the current date is July 18, it means that the current date is before the planned start date.

[0284] Exemplarily, when it is determined that the current date is before the planned start date or is consistent with the planned start date, the first training parameter, the second training parameter and the third training parameter can be continuously updated before the planned start date is reached; and the target training load can be re-determined based on the updated data.

[0285] Optionally, the implementation of S313 can refer to Figure 2 The relevant description of S204 in the present application embodiment is not repeated here.

[0286] S314, obtaining the current date and the planned start date.

[0287] Exemplarily, the target user can set a planned start date for training parameters, and after determining the target training load of the target user according to the user attribute information of the target user, the current date and the planned start date set by the user are obtained.

[0288] Optionally, the implementation of S314 can refer to Figure 2 The relevant description of S204 in the present application embodiment is not repeated here.

[0289] S315, determine whether the current date is after the planned start date. If yes, execute S316; if not, execute S307.

[0290] For example, after obtaining the current date and the planned start date, it is determined whether the current date is after the planned start date. For example, if the current date is July 21 and the planned start date is July 19, it means that the current date is after the planned start date; if the current date is July 18, it means that the current date is before the planned start date.

[0291] Exemplarily, when it is determined that the current date is before the planned start date or is consistent with the planned start date, the height, weight, etc. of the target user may be updated before the planned start date is reached; the target training load can be re-determined based on the updated data.

[0292] Optionally, the implementation of S315 can refer to Figure 2 The relevant description of S204 in the present application embodiment is not repeated here.

[0293] S316: Based on the target training load, determine and output recommended training parameters for the target user.

[0294] Among them, the recommended training parameters may include training items, exercise duration, exercise distance, strength intensity, etc. that are suitable for the user.

[0295] Exemplarily, when it is detected that the current date is after the planned start date, recommended training parameters are determined based on the calculated target training load, and the recommended training parameters are output so that the user can perform exercise training with the recommended training parameters as a reference.

[0296] Optionally, the implementation of S316 can refer to Figure 2 The relevant description of S204 in the present application embodiment is not repeated here.

[0297] In the embodiment of the present application, taking the historical motion data as the historical motion duration as an example, after obtaining the historical motion duration of the target user, the size relationship between the historical motion duration and the duration threshold is determined; when the historical motion duration is greater than or equal to the duration threshold, it indicates that the target user has more historical motion data, then the target training load matching the target user can be determined according to the first training parameter, the second training parameter and the third training parameter of the target user to ensure the accuracy of the target training load, and the determined target training load can adapt to the long-term motion state of the target user; when the historical motion duration is less than the duration threshold, the target user has less historical motion data, if the training load is determined according to the historical motion data, it may lead to inaccurate training load, then the target training load can be determined according to the age, gender, height and weight of the target user; so that the physical state of the target user can withstand the target training load; and then determine the recommended training parameters according to the target training load, which can ensure the adaptability of the recommended training parameters to the target user, avoid excessive training of the user, so that the user can obtain the best training effect during the training process. And when the current date is before the planned start date, the target training load is continuously updated to ensure the real-time nature of the target training load.

[0298] Combination of the above Figures 1 to 3 The method for determining the training parameters provided in the embodiment of the present application is described in detail; Figure 4 and Figure 5 The device embodiments of the present application are described in detail. It should be understood that the device in the embodiments of the present application can execute the various methods of the aforementioned embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the aforementioned method embodiments.

[0299] Figure 4 It is a structural diagram of a device for determining training parameters provided in an embodiment of the present application.

[0300] For example, Figure 4 As shown, the determining device 400 includes:

[0301] The first acquisition module 401 is used to acquire the historical exercise data of the target user;

[0302] The first determination module 402 is used to obtain historical motion parameters of the target user if the historical motion data is greater than or equal to a preset threshold; and determine a target training load of the target user based on the historical motion parameters;

[0303] The second determination module 403 is used to obtain user attribute information of the target user if the historical exercise data is less than a preset threshold; and determine the target training load of the target user based on the user attribute information;

[0304] The first output module 404 is used to determine and output the recommended training parameters for the target user based on the target training load.

[0305] In one possible implementation, the historical motion parameters include a first training parameter, a second training parameter and a third training parameter; the first training parameter is used to represent the total training load of the target user within a historical preset time period; the second training parameter is used to represent the single training load of the target user during the historical training process; the third training parameter is used to represent the physical state of the target user during the historical training process; the first determination module 402 is also used to determine the current physical parameters of the target user based on the first training parameter and the second training parameter; and determine the target training load based on the current physical parameters and the third training parameter.

[0306] In a possible implementation, the first determination module 402 is further configured to determine a first training load based on the current physical fitness parameter; and determine a target training load based on the first training load and the third training parameter.

[0307] In one possible implementation, the user attribute information includes the target user's age, gender, height and weight; the second determination module 403 is further used to determine the target user's body mass index based on the target user's height and weight; and determine the target training load based on the target user's age, gender and body mass index.

[0308] In one possible implementation, the second determination module 403 is also used to standardize the age and body mass index of the target user to obtain standard age parameters and standard index parameters; determine target parameters based on the gender, standard age parameters and standard index parameters of the target user; and determine the target training load based on the target parameters.

[0309] In one possible implementation, the second determination module 403 is further used to determine the first parameter based on the standard age parameter and the standard index parameter; determine the second parameter based on the gender of the target user, the standard age parameter and the standard index parameter; and determine the minimum value of the first parameter and the second parameter as the target parameter.

[0310] In a possible implementation, the second determination module 403 is further configured to determine the minimum value of the standard age parameter and the standard index parameter as the first parameter.

[0311] In a possible implementation, the second determination module 403 is further configured to determine a target ratio based on a standard age parameter and a standard index parameter; and determine a second parameter based on the target ratio and the gender of the target user.

[0312] In a possible implementation, the determining device 400 further includes:

[0313] The second acquisition module is used to obtain the current date and the planned start date;

[0314] A third determination module is used to determine the target training load based on historical exercise parameters or user attribute information if the current date is before the planned start date or the current date is consistent with the planned start date;

[0315] The second output module is used to output recommended training parameters if the current date is after the planned start date.

[0316] In one possible implementation, the first output module 404 is also used to obtain the historical training projects and training load ratio of the target user; based on the historical training projects, training load ratio and target training load, determine the project training load corresponding to each historical training project; based on the project training load, determine and output the recommended training parameters for the target user.

[0317] In a possible implementation, the determining device 400 further includes:

[0318] The third acquisition module is used to obtain the historical training duration, standard training power and functional threshold power of the target user;

[0319] The fourth determination module is used to determine the intensity factor based on the standard training power and the functional threshold power; and to determine the second training parameter based on the historical training duration and the intensity factor.

[0320] In a possible implementation, the determining device 400 further includes:

[0321] A fourth acquisition module is used to acquire an index parameter of at least one physical index of the target user after exercise training, and a target weight value corresponding to each physical index;

[0322] The fifth determination module is used to determine the third training parameter based on the indicator parameter and the target weight value.

[0323] It should be noted that the above training parameter determination device is embodied in the form of a functional unit. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0324] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that support the described functions.

[0325] Therefore, the units of each example described in the embodiments of the present application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0326] Figure 5 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device can be Figure 1 The electronic device 100 and / or cloud platform 101 in.

[0327] For example, Figure 5 As shown, the electronic device 500 includes: a memory 501 and a processor 502, wherein the memory 501 stores an executable program code 503, and the processor 502 is used to call and execute the executable program code 503 to perform a method for determining a training parameter.

[0328] In this embodiment, the electronic device can be divided into functional modules according to the above method example. For example, each functional module can be corresponded to, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0329] In the case of dividing each functional module according to each function, the electronic device may include: an information acquisition module, a first load determination module, a second load determination module, and a training plan generation module, etc. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here.

[0330] The electronic device provided in this embodiment is used to execute the above-mentioned method for determining a training parameter, and thus can achieve the same effect as the above-mentioned implementation method.

[0331] In the case of an integrated unit, the electronic device may include a processing module and a storage module. The processing module may be used to control and manage the actions of the electronic device. The storage module may be used to support the electronic device in executing related program codes and data.

[0332] The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits shown in conjunction with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0333] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement a method for determining a training parameter in the above-mentioned embodiment.

[0334] This embodiment also provides a computer program product. When the computer program product runs on a computer, it enables the computer to execute the above-mentioned related steps to implement a method for determining a training parameter in the above-mentioned embodiment.

[0335] In addition, the electronic device provided in the embodiments of the present application may specifically be a chip, a component or a module, and the electronic device may include a connected processor and a memory; wherein the memory is used to store instructions, and when the electronic device is running, the processor may call and execute instructions so that the chip executes a method for determining a training parameter in the above-mentioned embodiment.

[0336] Among them, the electronic device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above and will not be repeated here.

[0337] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0338] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0339] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining training parameters, characterized in that: The method comprises: Obtain historical exercise data of target users; If the historical motion data is greater than or equal to a preset threshold, obtaining the historical motion parameters of the target user; and determining the target training load of the target user based on the historical motion parameters; If the historical exercise data is less than the preset threshold, obtaining user attribute information of the target user; and determining a target training load of the target user based on the user attribute information; Based on the target training load, recommended training parameters for the target user are determined and output.

2. The method according to claim 1, characterized in that The historical exercise parameters include a first training parameter, a second training parameter and a third training parameter; the first training parameter is used to represent the total training load of the target user within a historical preset period; The second training parameter is used to represent a single training load of the target user in a historical training process; The third training parameter is used to represent the physical state of the target user during the historical training process; The determining the target training load of the target user based on the historical exercise parameters includes: Determining a current physical fitness parameter of the target user based on the first training parameter and the second training parameter; The target training load is determined based on the current physical fitness parameter and the third training parameter.

3. The method according to claim 2, characterized in that The determining the target training load based on the current physical fitness parameter and the third training parameter includes: Determining a first training load based on the current physical fitness parameter; The target training load is determined based on the first training load and the third training parameter.

4. The method according to claim 1, characterized in that: The user attribute information includes the age, gender, height and weight of the target user; The determining, based on the user attribute information, a target training load of the target user includes: Determining a body mass index of the target user based on the height and weight of the target user; The target training load is determined based on the age, gender and body mass index of the target user.

5. The method according to claim 4, characterized in that The determining the target training load based on the age, gender and body mass index of the target user includes: Standardizing the age and body mass index of the target user to obtain a standard age parameter and a standard index parameter; Determining a target parameter based on the gender of the target user, the standard age parameter, and the standard index parameter; Based on the target parameters, the target training load is determined.

6. The method according to claim 5, characterized in that The determining the target parameter based on the gender of the target user, the standard age parameter and the standard index parameter includes: Determining a first parameter based on the standard age parameter and the standard index parameter; Determining a second parameter based on the gender of the target user, the standard age parameter, and the standard index parameter; The minimum value between the first parameter and the second parameter is determined as the target parameter.

7. The method according to claim 6, characterized in that The determining of the first parameter based on the standard age parameter and the standard index parameter comprises: The minimum value between the standard age parameter and the standard index parameter is determined as the first parameter.

8. The method according to claim 6, characterized in that The determining the second parameter based on the gender of the target user, the standard age parameter and the standard index parameter includes: Determining a target ratio based on the standard age parameter and the standard index parameter; The second parameter is determined based on the target ratio and the gender of the target user.

9. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: Get the current date and the planned start date; If the current date is before the planned start date, or the current date is consistent with the planned start date, determining the target training load based on the historical exercise parameters or the user attribute information; If the current date is after the planned start date, the recommended training parameters are output.

10. The method according to any one of claims 1 to 8, characterized in that: The determining and outputting the recommended training parameters for the target user based on the target training load includes: Obtaining historical training items and training load ratios of the target user; Determining a project training load corresponding to each historical training project based on the historical training projects, the training load ratio and the target training load; Based on the project training load, the recommended training parameters for the target user are determined and output.

11. The method according to claim 2, characterized in that The step of obtaining the second training parameter comprises: Obtaining the historical training duration, standard training power, and functional threshold power of the target user; determining an intensity factor based on the standard training power and the functional threshold power; The second training parameter is determined based on the historical training duration and the intensity factor.

12. The method according to claim 2, characterized in that: The step of obtaining the third training parameter comprises: Obtaining an indicator parameter of at least one physical indicator of the target user after exercise training, and a target weight value corresponding to each physical indicator; Based on the indicator parameter and the target weight value, the third training parameter is determined.

13. A device for determining training parameters, characterized in that: The device comprises: A first acquisition module is used to acquire historical sports data of a target user; A first determination module is configured to obtain historical motion parameters of the target user if the historical motion data is greater than or equal to a preset threshold; and determine a target training load of the target user based on the historical motion parameters; A second determination module is configured to obtain user attribute information of the target user if the historical exercise data is less than the preset threshold; and determine a target training load of the target user based on the user attribute information; The first output module is used to determine and output the recommended training parameters for the target user based on the target training load.

14. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory, so that the electronic device executes the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 12 is implemented.

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