Method and device for determining training plan, electronic equipment and storage medium

Abstract

The invention provides a method and device for determining a training plan, electronic equipment and a storage medium, the method is applied to the technical field of exercise training, and the method comprises the following steps: in response to a training plan generation request for a target competition, obtaining a current physical fitness parameter of a target user and a residual training duration of the target competition, the target physical fitness parameters of the target competition are obtained; determining a total training load according to the current physical fitness parameter, the target physical fitness parameter and the residual training duration; based on the total training load and the residual training duration, period training loads of a plurality of training periods in the residual training duration are determined; and generating a training plan of each training cycle based on the cycle training loads of the plurality of training cycles. According to the method, when the training plan is made for the user, the generated training plan has pertinence and meets the requirements of the target competition and the training requirements of the user, the user is prevented from being over-trained, and therefore the user can obtain the best training effect in the training process.

Description

Technical Field

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

[0002] At present, with the improvement of people's living standards and quality of life, health issues have become the focus of people's daily life. Based on this, more and more people begin to choose sports and participate in some sports competitions appropriately, which can enrich their daily lives and achieve good physical fitness.

[0003] With the continuous updating of various intelligent devices, people can wear some intelligent wearable devices (such as smart watches or smart bracelets) to monitor their body data and exercise data during running during exercise, so as to facilitate users to understand the exercise status during exercise and stimulate users' interest in exercise.

[0004] In the related art, in addition to monitoring the user's physical data and exercise data during exercise, smart wearable devices can also provide users with some training plans or training courses. Currently, the training plans and training courses provided by smart wearable devices usually use fixed templates, that is, the training plans pushed to all users are the same, lacking personalized training plan push solutions, and cannot meet the personalized training needs of different users. Summary of the invention

[0005] The present application provides a method, device, electronic device and storage medium for determining a training plan. The method can take into account the user's current physical condition and the physical fitness required for the current training when formulating a training plan for the user, so that the training plan finally generated is targeted and meets the requirements of the target competition and the user's training needs, avoiding overtraining of the user, thereby enabling the user to obtain the best training effect during the training process.

[0006] In a first aspect, a method for determining a training plan is provided, the method comprising: in response to a request for generating a training plan for a target game, obtaining current physical fitness parameters of a target user, a remaining training time for the target game, and target physical fitness parameters for the target game, the current physical fitness parameters being used to represent the current physical fitness condition of the target user, and the target physical fitness parameters being used to represent the physical fitness condition required for the target user to train for the target game; determining a total training load based on the current physical fitness parameters, the target physical fitness parameters, and the remaining training time, the total training load being used to represent the difficulty of the target user in training for the target game; determining a periodic training load of multiple training cycles within the remaining training time based on the total training load and the remaining training time; and generating a training plan for each of the training cycles based on the periodic training loads of the multiple training cycles.

[0007] In the above technical solution, when a user needs to train for a competition, the present application proposes a method for determining a training plan, which can determine the total training load during the training process by combining the user's current physical parameters, the remaining training time and the target physical parameters. The above process can be based on reality, taking into account the user's actual physical fitness level, so that the training plan is more in line with the user's personal actual situation. Furthermore, the total training load is distributed to multiple training cycles, which can ensure that the training load of each training cycle in the training process meets the training requirements of the stage, and ensures the scientificity and rationality of the training plan. Finally, a training plan is generated according to the cycle training load. Since the user's physical condition is taken into account during the generation of the training plan, the training plan finally generated can closely fit the user's actual training needs, ensure the personalized customization of the training plan during the training process, meet the differentiated needs of users of different levels, and improve the user experience.

[0008] In combination with the first aspect, in some possible implementations, the step of obtaining the target physical fitness parameters includes: obtaining the game duration, game type, training difficulty level and preset training duration interval of the target game, the game type is used to indicate the game form and game distance of the target game; determining the target physical fitness parameter interval based on the game type and the game duration; determining the target physical fitness parameters based on the remaining training time, the current physical fitness parameters, the target physical fitness parameter interval, the preset training duration interval and the training difficulty level.

[0009] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the target physical fitness parameters are determined based on the remaining training time, the current physical fitness parameters, the target physical fitness parameter interval, the preset training time interval and the training difficulty level, including: determining whether the training plan generation request meets the preset conditions based on the remaining training time, the current physical fitness parameters, the target physical fitness parameter interval and the preset training time interval; when the training plan generation request meets the preset conditions, determining the target physical fitness parameters based on the target physical fitness parameter interval and the training difficulty level.

[0010] In the above technical solution, in the process of determining the target physical fitness parameters, the remaining training time, current physical fitness parameters, target physical fitness parameter interval and preset training time interval are first used to determine whether the training plan request meets the preset conditions, and the target physical fitness parameters are determined when the training plan request meets the preset conditions. This can ensure that the final training plan is feasible and prevent the problem of being unable to be implemented due to insufficient time or mismatch of the current physical fitness level.

[0011] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the determination of whether the training plan generation request meets the preset conditions is made based on the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval and the preset training time interval, including: obtaining a subordinate physical fitness parameter interval of the target physical fitness parameter interval, the lower limit of the target physical fitness parameter interval being the upper limit of the subordinate physical fitness parameter interval; when the current physical fitness parameter is in the target physical fitness parameter interval or the subordinate physical fitness parameter interval, and the remaining training time is in the preset training time interval, determining that the training plan generation request meets the preset conditions; when the current physical fitness parameter is not in the target physical fitness parameter interval or the subordinate physical fitness parameter interval, or the remaining training time is not in the preset training time interval, determining that the training plan generation request does not meet the preset conditions.

[0012] In the above technical solution, if the current physical fitness parameter is in the target physical fitness parameter interval or lower than the next interval of the target physical fitness parameter, it means that the difference between the current physical fitness parameter and the target physical fitness parameter is not very large. This process can ensure that the goal of the training plan is reasonable, and the target physical fitness parameter and the current physical fitness parameter are not too large, resulting in a low user completion rate. In addition, when the remaining training time is in the preset training time interval, it means that the training plan is reasonable in terms of time arrangement. The above judgment of whether the training plan generation request meets the preset conditions can ensure the rationality of the training plan in terms of both time and intensity, help users obtain the best training effect, and avoid the problem of excessive difficulty and reduced experience during user training.

[0013] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the target physical fitness parameter is determined based on the target physical fitness parameter interval and the training difficulty level, including: when the training difficulty level is the first level, the target physical fitness parameter is determined to be the minimum value of the target physical fitness parameter interval; when the training difficulty level is the second level, the target physical fitness parameter is determined to be the average value of the target physical fitness parameter interval, and the training difficulty of the second level is higher than the training difficulty of the first level; when the training difficulty level is the third level, the target physical fitness parameter is determined to be the maximum value of the target physical fitness parameter interval, and the training difficulty of the third level is higher than the training difficulty of the second level.

[0014] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the periodic training load of multiple training cycles within the remaining training duration based on the total training load and the remaining training duration, the method also includes: obtaining a competition type of the target competition, where the competition type is used to indicate the competition format and competition distance of the target competition; and determining the periodic training load of multiple training cycles within the remaining training duration based on the total training load and the remaining training duration, including: determining the training load ratio of the multiple training cycles according to the remaining training duration and the competition type; determining the periodic training load of the multiple training cycles according to the total training load and the training load ratio of the multiple training cycles.

[0015] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the training plan for each training cycle is generated based on the periodic training load of the multiple training cycles, including: for any training cycle among the multiple training cycles, determining the weekly training load of at least one training week within the training cycle according to the periodic training load of the training cycle; for any training week among the multiple training weeks, determining the course training load of at least one training course within the training week according to the weekly training load of the training week; for any training course among the at least one training course, determining the target training parameters corresponding to the training course according to the course training load of the training course, the target training parameters being used to represent the training state that the target user needs to achieve to complete the training course; and generating the training plan for the training cycle according to the training course and the target training parameters.

[0016] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the weekly training load of at least one training week within the training cycle based on the periodic training load of the training cycle, the method also includes: obtaining the training load ratio corresponding to at least one training week within the training cycle; and determining the weekly training load of at least one training week within the training cycle based on the periodic training load of the training cycle includes: determining the weekly training load of the at least one training week based on the periodic training load of the training cycle and the training load ratio corresponding to the at least one training week.

[0017] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the course training load of at least one training course in the training week based on the weekly training load of the training week, the method also includes: obtaining the training load ratio of the at least one training course in the training week; and determining the course training load of at least one training course in the training week based on the weekly training load of the training week includes: determining the course training load of the at least one training course based on the weekly training load of the training week and the course load ratio of the at least one training course.

[0018] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the target training parameters corresponding to the training course according to the course training load of the training course, the method also includes: obtaining preset motion parameters corresponding to the training course, the preset motion parameters are used to represent the motion state that the target user needs to achieve to complete the training course; and determining the target training parameters corresponding to the training course according to the course training load of the training course includes: determining the target training parameters according to the preset motion parameters and the course training load.

[0019] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the preset motion parameters include multiple preset heart rate intervals, the target training parameters include target training duration, and the target training parameters are determined according to the preset motion parameters and the course training load, including: determining multiple preset heart rate ratios and multiple first preset intensity factors according to the multiple preset heart rate intervals, the preset heart rate ratios being used to indicate the weight of the course training load occupied by the corresponding preset heart rate intervals, and the first preset intensity factor being used to indicate the training intensity under the preset heart rate interval; dividing the course training load into multiple first sub-course training loads according to each of the preset heart rate ratios and the course training load; and determining the target training duration according to the multiple first sub-course training loads and the multiple first preset intensity factors.

[0020] In combination with the first aspect and the above-mentioned implementation manner, in some possible implementation manners, the preset motion parameter includes a preset average pace and a plurality of preset pace intensity intervals, the target training parameter includes a target training distance, and before determining the target training parameter according to the preset motion parameter and the course training load, the method further includes: obtaining the functional threshold power of the target user; and determining the target training parameter according to the preset motion parameter and the course training load, including: determining the standardized graded pace corresponding to the training course according to the preset average pace; determining a plurality of second preset intensity factors and a plurality of preset pace ratios and a plurality of standard The preset pace ratio is used to indicate the weight of the corresponding preset pace interval in the training load of the course, and the second preset intensity factor is used to indicate the training intensity under the preset pace interval; according to the standardized graded pace and the multiple second preset intensity factors, the standardized graded pace is divided into multiple sub-standardized graded paces; according to the course training load and the multiple preset pace ratios, the course training load is divided into multiple second sub-course training loads; according to the multiple second sub-course training loads, the functional threshold power, the multiple sub-standardized graded paces, the multiple second preset intensity factors and the multiple standardized graded pace ratios, the target training distance is determined.

[0021] In a second aspect, a device for determining a training plan is provided, the device comprising: an information acquisition module, for obtaining, in response to a request for generating a training plan for a target game, current physical fitness parameters of a target user, a remaining training time for the target game, and target physical fitness parameters for the target game, the current physical fitness parameters being used to represent the current physical fitness condition of the target user, and the target physical fitness parameters being used to represent the physical fitness condition required for the target user to train for the target game; a first load determination module, for determining a total training load based on the current physical fitness parameters, the target physical fitness parameters and the remaining training time, the total training load being used to represent the difficulty of the target user in training for the target game; a second load determination module, for determining, based on the total training load and the remaining training time, a periodic training load of a plurality of training cycles within the remaining training time; and a training plan generation module, for generating a training plan for each of the training cycles based on the periodic training loads of the plurality of training cycles.

[0022] In combination with the second aspect, in some possible implementations, the information acquisition module is specifically used to: obtain the game duration, game type, training difficulty level and preset training duration interval of the target game, the game type is used to indicate the game format and game distance of the target game; determine the target physical fitness parameter interval based on the game type and the game duration; determine the target physical fitness parameter based on the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval, the preset training duration interval and the training difficulty level.

[0023] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the information acquisition module is also used to: determine whether the training plan generation request meets the preset conditions based on the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval and the preset training time interval; if the training plan generation request meets the preset conditions, determine the target physical fitness parameter based on the target physical fitness parameter interval and the training difficulty level.

[0024] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the information acquisition module is also used to: obtain a lower-level physical fitness parameter interval of the target physical fitness parameter interval, the lower limit of the target physical fitness parameter interval being the upper limit of the lower-level physical fitness parameter interval; when the current physical fitness parameter is in the target physical fitness parameter interval or the lower-level physical fitness parameter interval, and the remaining training time is in the preset training time interval, determine that the training plan generation request meets the preset condition; when the current physical fitness parameter is not in the target physical fitness parameter interval or the lower-level physical fitness parameter interval, or the remaining training time is not in the preset training time interval, determine that the training plan generation request does not meet the preset condition.

[0025] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the information acquisition module is also used to: when the training difficulty level is the first level, determine the target physical fitness parameter as the minimum value of the target physical fitness parameter range; when the training difficulty level is the second level, determine the target physical fitness parameter as the average value of the target physical fitness parameter range, and the training difficulty of the second level is higher than the training difficulty of the first level; when the training difficulty level is the third level, determine the target physical fitness parameter as the maximum value of the target physical fitness parameter range, and the training difficulty of the third level is higher than the training difficulty of the second level.

[0026] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the periodic training load of multiple training cycles within the remaining training time based on the total training load and the remaining training time, the second load determination module is specifically used to: obtain the competition type of the target competition, which is used to indicate the competition form and competition distance of the target competition; and, based on the remaining training time and the competition type, determine the training load ratio of the multiple training cycles; based on the total training load and the training load ratio of the multiple training cycles, determine the periodic training load of the multiple training cycles.

[0027] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the training plan generation module is specifically used to: for any training cycle among the multiple training cycles, determine the weekly training load of at least one training week within the training cycle according to the periodic training load of the training cycle; for any training week among the multiple training weeks, determine the course training load of at least one training course within the training week according to the weekly training load of the training week; for any training course among the at least one training course, determine the target training parameters corresponding to the training course according to the course training load of the training course, and the target training parameters are used to represent the training state that the target user needs to achieve to complete the training course; and generate a training plan for the training cycle according to the training course and the target training parameters.

[0028] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the weekly training load of at least one training week within the training cycle based on the periodic training load of the training cycle, the training plan generation module is also used to: obtain the training load ratio corresponding to at least one training week within the training cycle; and determine the weekly training load of the at least one training week based on the periodic training load of the training cycle and the training load ratio corresponding to the at least one training week.

[0029] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the course training load of at least one training course in the training week based on the weekly training load of the training week, the training plan generation module is also used to: obtain the training load ratio of the at least one training course in the training week; and determine the course training load of the at least one training course based on the weekly training load of the training week and the course load ratio of the at least one training course.

[0030] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, before determining the target training parameters corresponding to the training course based on the course training load of the training course, the training plan generation module is also used to: obtain preset motion parameters corresponding to the training course, and the preset motion parameters are used to represent the motion state that the target user needs to achieve to complete the training course; and determine the target training parameters based on the preset motion parameters and the course training load.

[0031] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the preset motion parameters include multiple preset heart rate intervals, the target training parameters include target training duration, and the training plan generation module is also used to: determine multiple preset heart rate ratios and multiple first preset intensity factors based on the multiple preset heart rate intervals, the preset heart rate ratio is used to indicate the weight of the corresponding preset heart rate interval in the course training load, and the first preset intensity factor is used to indicate the training intensity under the preset heart rate interval; divide the course training load into multiple first sub-course training loads based on each of the preset heart rate ratios and the course training load; determine the target training duration based on the multiple first sub-course training loads and the multiple first preset intensity factors.

[0032] In combination with the second aspect and the above-mentioned implementation manner, in some possible implementation manners, the preset motion parameter includes a preset average pace and multiple preset pace intensity intervals, the target training parameter includes a target training distance, and the training plan generation module is further used to: obtain the functional threshold power of the target user; and, based on the preset average pace, determine the standardized graded pace corresponding to the training course; based on the multiple preset pace intensity intervals, determine multiple second preset intensity factors and multiple preset pace ratios and multiple standardized graded pace ratios, the preset pace ratio is used to indicate the weight of the course training load occupied by the corresponding preset pace interval, and the second preset intensity factor is used to indicate the training intensity under the preset pace interval; based on the standardized graded pace and the multiple second preset intensity factors, divide the standardized graded pace into multiple sub-standardized graded paces; based on the course training load and the multiple preset pace ratios, divide the course training load into multiple second sub-course training loads; based on the multiple second sub-course training loads, the functional threshold power, the multiple sub-standardized graded paces, the multiple second preset intensity factors and the multiple standardized graded pace ratios, determine the target training distance.

[0033] 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.

[0034] 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.

[0035] 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

[0036] Figure 1 It is a scenario diagram of a method for determining a training plan provided in an embodiment of the present application;

[0037] Figure 2 is a schematic flow chart of a method for determining a training plan provided in an embodiment of the present application;

[0038] Figure 3 is a schematic flow chart of another method for determining a training plan provided in an embodiment of the present application;

[0039] Figure 4 It is a structural schematic diagram of a device for determining a training plan provided in an embodiment of the present application;

[0040] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Training Stress Score (TSS): A metric used to quantify training load and intensity during training.

[0046] Functional Threshold Power (FTP): A measure of the maximum power output an athlete can maintain in a steady state.

[0047] 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.

[0048] Normalized Graded Pace (NGP): A term used to describe changes in pace adjustment during running training. It standardizes the pace by taking into account the influence of slope to ensure consistency of training intensity under different conditions.

[0049] The application scenarios of the embodiments of the present application are introduced below.

[0050] At present, with the improvement of people's living standards and health awareness, more and more people are gradually aware of the importance of health. Based on this, more and more sports competitions have become entertainment projects in people's daily lives. Before users participate in a sports competition, they often need to go through a long period of training.

[0051] During the user's training process, the user can select appropriate training items or competition items through the relevant applications installed in the smart terminal device. During the training process, the application in the smart terminal device can count the user's exercise time, distance and path in real time, and constantly encourage the user during the exercise to stimulate the user's interest in exercise.

[0052] In addition to smart terminal devices, various smart wearable devices (such as smart bracelets) have also entered people's lives based on the continuous updating of various smart devices. Smart wearable devices can obtain users' physical data (such as heart rate) during exercise, so as to monitor the user's physical condition during exercise.

[0053] In a possible implementation, during training, the user can connect the smart wearable device to the smart terminal device. The smart wearable device can synchronize and update the user's physical fitness data to the smart terminal device in real time, and then the smart terminal device sends it to the backend cloud server, so that the cloud server can analyze the user's physical fitness data in a timely manner.

[0054] In addition, in order to provide users with a better exercise experience, the cloud server can also push the corresponding training plan to the application based on the user's training project and synchronize it from the smart terminal device to the smart wearable device. Therefore, during exercise, users can view and execute the training plan in time through the smart wearable device or smart terminal device they wear.

[0055] Currently, the training plans pushed by cloud servers generally use fixed training plan templates. In other words, for different users, even if their physical fitness data is different, if they choose the same training project, the training plans pushed by the cloud server are the same. The problem brought about by this is that the training needs of different users cannot be met, resulting in a decline in user experience.

[0056] Based on the above problems, an embodiment of the present application provides a method for determining a training plan. When formulating a training plan for a user, the method can take into account the user's current physical condition and the physical fitness required for the current training, so that the final generated training plan is targeted and meets the requirements of the target competition and the user's training needs, thereby avoiding overtraining of the user, and enabling the user to obtain the best training effect during the training process.

[0057] Before introducing the method of the embodiment of the present application, the hardware device on which the method of the embodiment of the present application relies is first introduced.

[0058] Figure 1 It is a scenario diagram of a method for determining a training plan provided in an embodiment of the present application.

[0059] Exemplarily, when implementing the method for determining a training plan in the embodiment of the present application, the hardware devices involved include a cloud server 101, a smart terminal device 102 and a smart wearable device 103.

[0060] In a possible implementation, an application for training is installed in the smart terminal device 102. When the user is training, he can log in to the application in the smart terminal device 102 through his personal account. Thus, the smart terminal device 102 establishes a communication connection with the cloud server 101. In addition, when the user wears the smart wearable device 103, the user can also use a wireless connection (such as a Bluetooth connection) to enable communication between the smart terminal device 102 and the smart wearable device 103 to achieve sharing of training data.

[0061] Based on the communication process between the above devices, when implementing the method for determining a training plan in the embodiment of the present application, the working process of the above devices is roughly as follows:

[0062] When a user needs to train for a competition, he can configure the information related to the competition (such as competition type, competition duration, training difficulty, etc.) in the application. In response to the user's configuration operation, the smart terminal device 102 can obtain the configuration information and the user's current physical parameters and send them to the cloud server 101.

[0063] After receiving the current physical fitness parameters and configuration information, the cloud server 101 can generate a training plan for the competition and send it to the application of the smart terminal device 102, and then synchronized to the smart wearable device 103 by the smart terminal device 102.

[0064] Based on this, during the user's training process, the current training plan can be viewed on the smart terminal device 102 or the smart wearable device 103, and the user can train reasonably according to the training plan to achieve a good training effect. At the same time, during the training process, the smart wearable device 103 can collect the user's real-time physical data, and update it to the smart terminal device 102 synchronously, and then the smart terminal device 102 sends it to the cloud server 101, so that the cloud server 101 can analyze and process the user's physical data to determine whether the user's current training process is reasonable.

[0065] After introducing the hardware devices on which the embodiments of the present application rely, the methods of the embodiments of the present application are introduced below.

[0066] Figure 2 is a schematic flow chart of a method for determining a training plan provided in an embodiment of the present application. It should be understood that the method can be applied to Figure 1 The cloud server 101 in.

[0067] For example, Figure 2 As shown, the method 200 includes:

[0068] 201, in response to a request to generate a training plan for a target game, obtain the current physical fitness parameters of the target user, the remaining training time of the target game, and the target physical fitness parameters of the target game, where the current physical fitness parameters are used to represent the current physical fitness condition of the target user, and the target physical fitness parameters are used to represent the physical fitness condition required for the target user to train for the target game.

[0069] It should be understood that when implementing a method for determining a training plan provided in an embodiment of the present application, an application for training is installed in the user's smart terminal device, and the user can log in to the application through a personal account, so that the smart terminal device can establish a communication connection with the cloud server.

[0070] When the user needs to train for a target game, the training information related to the target game can be configured based on the training plan configuration interface of the application. After the smart terminal device detects the user's configuration operation, it can generate a training plan generation request for the target game and send the training plan generation request to the cloud server.

[0071] For example, when configuring the training information related to the target competition, the user can select the competition type to be configured (such as swimming, half marathon, full marathon, rope skipping, etc.) through a selection operation. After selecting the competition type, the smart terminal device displays the training information configuration interface of the current type of competition, which may include a competition duration configuration item, a competition date configuration item, a competition duration configuration item, a weekly training days configuration item, a training duration configuration item, and a training difficulty configuration item.

[0072] Based on the training information configuration interface, the user can configure the above different configuration items. The smart terminal device detects the user's configuration operation, determines the configuration information of the target game, generates a training plan generation request based on the configuration information, and sends it to the cloud server.

[0073] After receiving the training plan generation request, the cloud server can obtain the user's current physical parameters, the remaining training time of the target game, and the target physical parameters of the target game in the application based on the user's personal account to generate a training plan for the target game.

[0074] Among them, the current physical fitness parameter can represent the user's current physical condition. In the embodiment of the present application, the current physical fitness parameter specifically refers to the current CTL value.

[0075] For example, the current CTL value can be displayed in real time when the user logs into the application. Therefore, the cloud server can directly obtain the current CTL value based on the user's personal account.

[0076] For the remaining training time, when the user configures the training information related to the target game, after configuring the game date, based on the training plan generation request, the cloud server obtains the game date and can further calculate the remaining training time based on the current date and the game date.

[0077] The target physical parameters, namely the target CTL value, refer to the physical condition required by the target user to train for the target game, that is, the ideal physical parameters when the target user trains for the target game. The cloud server can obtain it indirectly through configuration information, and the specific acquisition process is as follows.

[0078] In a possible implementation, the step of obtaining the target physical fitness parameter includes:

[0079] Obtain the competition duration, competition type, training difficulty level, and preset training duration interval of the target competition. The competition type is used to indicate the competition format and competition distance of the target competition.

[0080] Determine the target physical parameters range according to the type and duration of the game;

[0081] Determine the target physical fitness parameters based on the remaining training time, current physical fitness parameters, target physical fitness parameter range, preset training time range and training difficulty level.

[0082] It should be understood that in the process of training for a competition, when the competition duration and competition type are different, the physical parameters are also different. Among them, the competition type is used to indicate the competition form and competition distance of the target competition. For example, the competition types include half marathon, full marathon, swimming, and sprinting, and different types of competitions correspond to different competition distances. In addition, half marathon, full marathon and sprinting are land-based competitions, while swimming is a water-based competition. The corresponding physical parameters for swimming competition training and half marathon competition training must be different; in addition, for both half marathons, when the competition duration is different, the required physical parameters are different. The shorter the competition duration, the less physical energy is consumed; on the contrary, the longer the competition duration, the greater the physical energy consumed.

[0083] Since different users have different basic physical fitness and potential, in order to ensure the applicability of the training plan, when determining the target physical fitness parameters, the cloud server can first determine a reasonable physical fitness parameter range based on the type and duration of the game, and provide a rough reference range.

[0084] In the embodiment of the present application, technicians can determine different game types and different game durations, determine the corresponding physical parameter ranges through scientific experiments and a large amount of actual data, and store multiple sets of mapping relationships corresponding to game type-game duration-physical parameter ranges in a cloud server.

[0085] Exemplarily, in combination with the foregoing, it can be seen that in the training information configuration interface of the target game, the user can configure the game duration, game type, and training difficulty level of the target game during the training process.

[0086] After receiving the training plan generation request, the cloud server can obtain the game duration and game type contained in the training plan request, and can look up the table to determine the corresponding target physical parameter range, that is, the target CTL range, according to the game duration and game type of the current target game.

[0087] After determining the target CTL interval, the cloud server needs to determine the final target physical parameters based on the user's current actual training needs, because each user's physical condition, selected training difficulty and selected training duration are different.

[0088] Exemplarily, for the training difficulty level, the cloud server may generate a request based on the training plan to obtain the training difficulty level configured by the user.

[0089] Optionally, the training difficulty level includes three difficulty levels: easy, moderate, and difficult.

[0090] For the preset training duration interval, after determining the type of the target race, different types of races allow different training durations. For example, the training duration for a half marathon is generally about 21 days to 84 days, and the training duration for a full marathon is generally about 42 days to 196 days.

[0091] Based on this, the technicians can store different types of games and corresponding training duration intervals in the cloud server in advance. After the user configures the game type of the target game in the application of the smart terminal device, the cloud server can obtain the preset training duration interval corresponding to the game type of the target game in response to the training plan generation request.

[0092] It should be understood that the cloud server obtains the preset training time, which can be used to determine whether the user's remaining training time can meet the training requirements of the target competition, that is, whether the user's training time is reasonable. For example, the training time for a half marathon is 21 days to 84 days. If the remaining training time is only 20 days, it means that the current remaining training time cannot support the user to complete the half marathon training, which means that the user's current configuration is unreasonable and a training plan cannot be generated; if the remaining training time is 50 days, it means that the current remaining training time can support the user to complete the half marathon training.

[0093] After determining the training difficulty level and the preset training duration, the cloud server can determine the target physical fitness parameters based on the remaining training duration, the current CTL value and the target CTL range.

[0094] In a possible implementation, the target physical fitness parameter is determined according to the remaining training time, the current physical fitness parameter, the target physical fitness parameter range, the preset training time range and the training difficulty level, including:

[0095] Determine whether the training plan generation request meets preset conditions according to the remaining training time, the current physical fitness parameters, the target physical fitness parameter range and the preset training time range;

[0096] When the training plan generation request meets the preset conditions, the target physical fitness parameters are determined according to the target physical fitness parameter range and the training difficulty level.

[0097] Specifically, when determining whether the user's training plan generation request meets the preset conditions based on the remaining training time, current physical fitness parameters, target physical fitness parameter range and preset training time range, the judgment is mainly divided into two aspects: on the one hand, it is to judge whether the remaining training time is within the preset training time range to judge whether the user's training time arrangement is reasonable; on the other hand, it is to judge whether the difference between the current physical fitness parameters and the target physical fitness parameter range is too large to determine whether the user can complete the training process.

[0098] Among them, judging whether the difference between the current physical fitness parameter and the target physical fitness parameter interval is too large specifically refers to judging which physical fitness parameter interval the current physical fitness parameter belongs to, so as to judge whether the user's physical fitness condition is improving across levels during training. When the user configures training information, the embodiment of the present application only allows the user's physical fitness condition to improve at the same level or step by step, and does not allow the user to improve across levels.

[0099] The meanings of the above-mentioned same-level progress, step-by-step progress or cross-level progress respectively refer to: if the physical parameter interval where the current physical fitness parameter is located is the target physical fitness parameter interval, since the target physical fitness parameter must be within the target physical fitness parameter interval. Therefore, when the physical parameter interval where the current physical fitness parameter is located is the target physical fitness parameter interval, it means that the current physical fitness parameter and the target physical fitness parameter belong to the same level, which is called "same-level progress"; if the physical parameter interval where the current physical fitness parameter is located does not belong to the target physical fitness parameter interval, but belongs to the lower-level physical fitness parameter interval of the target physical fitness parameter interval, it means that the current physical fitness parameter is one level lower than the target physical fitness parameter. When the user gradually trains from the current physical fitness parameter to the target physical fitness parameter, the change of the physical fitness parameter in this process is called "step-by-step progress". Among them, the meaning of the lower-level physical fitness parameter interval is specifically introduced below and will not be repeated here; on the contrary, if the physical parameter interval where the current physical fitness parameter is located does not belong to the target physical fitness parameter interval or the lower-level physical fitness parameter interval, it means that the current physical fitness parameter is at least two levels lower than the target physical parameter. When a user gradually trains from the current physical parameters to the target physical parameters, the change in physical parameters in this process is called "cross-level progress". In this case, the target physical parameters are much higher than the current physical parameters. Considering the user's physical condition, the cloud server will trigger a prompt that the training plan cannot be generated.

[0100] In the above technical solution, in the process of determining the target physical fitness parameters, the remaining training time, current physical fitness parameters, target physical fitness parameter interval and preset training time interval are first used to determine whether the training plan request meets the preset conditions, and the target physical fitness parameters are determined when the training plan request meets the preset conditions. This can ensure that the final training plan is feasible and prevent the problem of being unable to be implemented due to insufficient time or mismatch of the current physical fitness level.

[0101] The specific process of determining whether a training plan generation request meets the preset conditions is as follows.

[0102] In a possible implementation, determining whether the training plan generation request meets a preset condition based on the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval, and the preset training time interval includes:

[0103] Obtaining a lower level physical fitness parameter interval of the target physical fitness parameter interval, wherein the lower limit of the target physical fitness parameter interval is the upper limit of the lower level physical fitness parameter interval;

[0104] When the current physical fitness parameter is within the target physical fitness parameter interval or the lower physical fitness parameter interval, and the remaining training time is within the preset training time interval, determining that the training plan generation request meets the preset conditions;

[0105] When the current physical fitness parameter is not within the target physical fitness parameter interval or the lower-level physical fitness parameter interval, or the remaining training time is not within the preset training time interval, it is determined that the training plan generation request does not meet the preset conditions.

[0106] Exemplarily, assuming that the target physical fitness parameter interval obtained by the cloud server is (b, c], the corresponding lower-level physical fitness parameter interval is (a, b], where b is the lower limit of the target physical fitness parameter and also the upper limit of the lower-level physical fitness parameter interval.

[0107] If the interval where the current physical fitness parameter is located is (b, c] or (a, b], it means that the current physical fitness parameter and the target physical fitness parameter are at the same level or one level lower than the target physical fitness parameter, which belongs to the same level progress or step-by-step progress allowed by the embodiment of the present application; in addition, if the remaining training time is within the preset training time interval of the target game, it means that the user can complete the training process of the target game within the remaining training time. If both conditions are met, the cloud server determines that the training plan generation request meets the preset conditions and can generate a training plan.

[0108] On the contrary, if the current physical fitness parameters are not in (b, c] or (a, b], it means that the current physical fitness parameters may be at a lower level, and the difference with the target physical fitness parameters is large. In order to ensure the user's health during training and avoid excessive training of the user, the cloud server determines that the current training plan generation request is unreasonable. Alternatively, when the remaining training time is not in the preset training time interval, it means that the remaining training time is lower than the minimum value of the preset training time interval or greater than the maximum value of the preset training time interval. For example, the preset training time interval for a half marathon is 21 days-84 days, while the remaining training time is 10 days or 100 days, which does not meet the training time for a half marathon. Therefore, the cloud server can also determine that the current training plan generation request is unreasonable.

[0109] In the above technical solution, if the current physical fitness parameter is in the target physical fitness parameter interval or lower than the next interval of the target physical fitness parameter, it means that the difference between the current physical fitness parameter and the target physical fitness parameter is not very large. This process can ensure that the goal of the training plan is reasonable, and the target physical fitness parameter and the current physical fitness parameter are not too large, resulting in a low user completion rate. In addition, when the remaining training time is in the preset training time interval, it means that the training plan is reasonable in terms of time arrangement. The above judgment of whether the training plan generation request meets the preset conditions can ensure the rationality of the training plan in terms of both time and intensity, help users obtain the best training effect, and avoid the problem of excessive difficulty and reduced experience during user training.

[0110] When the cloud server determines that the training plan generation request meets the preset conditions, different requirements are set for the user's physical fitness parameters according to the different training difficulty levels. Specifically, the higher the training difficulty level, the greater the physical fitness parameter. Therefore, the cloud server can further determine the target physical fitness parameter based on the training difficulty level and the target physical fitness parameter range.

[0111] In a possible implementation, the target physical fitness parameter is determined according to the target physical fitness parameter interval and the training difficulty level, including:

[0112] When the training difficulty level is the first level, determining the target physical fitness parameter as the minimum value of the target physical fitness parameter range;

[0113] When the training difficulty level is the second level, the target physical fitness parameter is determined to be an average value of the target physical fitness parameter range, and the training difficulty of the second level is higher than that of the first level;

[0114] When the training difficulty level is the third level, the target physical fitness parameter is determined to be the maximum value of the target physical fitness parameter range. The training difficulty of the third level is higher than that of the second level.

[0115] Optionally, the training difficulty levels in the embodiment of the present application are divided into easy, moderate and difficult, which are respectively recorded as "first level, second level and third level".

[0116] Specifically, when the training difficulty level obtained by the cloud server is the first level, it means that the target CTL of the training process does not need to be too high. The cloud server can determine the target CTL as the minimum value of the target CTL interval, that is, b in the above.

[0117] When the training difficulty level obtained by the cloud server is the second level, it means that the target CTL of the training process is moderate and does not need to be too low or too high. The cloud server can determine the target CTL as the average value of the target CTL interval, that is, (b+c) / 2 in the above.

[0118] When the training difficulty level obtained by the cloud server is the third level, it means that the target CTL of the training process needs to be set higher. The cloud server can determine the target CTL as the maximum value of the target CTL interval, that is, c mentioned above.

[0119] Thus, through the above process, the cloud server can determine the target physical fitness parameters.

[0120] 202, determining a total training load according to the current physical fitness parameter, the target physical fitness parameter and the remaining training time, where the total training load is used to indicate the difficulty of the target user in the process of training for the target game.

[0121] After determining the target physical fitness parameters, the cloud server can determine the total training load, i.e., the total TSS value, required for the user to reach the target physical fitness parameters from the current physical fitness parameters during training based on the current physical fitness parameters, the target physical fitness parameters, and the remaining training time. This parameter represents the difficulty or pressure of the user's training process. The larger the total TSS value, the greater the difficulty and pressure of the user's training process; the smaller the total TSS value, the smaller the difficulty and pressure of the user's training process.

[0122] Specifically, the calculation process of the total training load can be expressed by the following formula (1).

[0123]

[0124] Wherein, in formula (1):

[0125] Y: total TSS value;

[0126] current ctl : Current physical parameters, that is, current CTL;

[0127] target ctl : Target physical parameters, i.e. target CTL;

[0128] week count : The number of weeks corresponding to the remaining training time.

[0129] Therefore, through the above formula (1), the cloud server can calculate the total TSS value required by the user during the training process.

[0130] 203. Determine the periodic training loads of multiple training periods within the remaining training time based on the total training load and the remaining training time.

[0131] After obtaining the total training load, in general, in order to optimize the training effect and ensure the gradual progress of the training process, the training and competition process can be divided into multiple cycles. In this case, after obtaining the total training load, the cloud server can determine the periodic training load of multiple training cycles within the remaining training time for the target competition based on the total training load and the remaining training time.

[0132] In a possible implementation, before determining the periodic training loads of a plurality of training periods within the remaining training duration based on the total training load and the remaining training duration, the method further includes:

[0133] Get the competition type of the target competition, where the competition type is used to indicate the competition form and competition distance of the target competition;

[0134] And, based on the total training load and the remaining training duration, determining the periodic training loads of multiple training periods within the remaining training duration, including:

[0135] Determine the training load ratio for multiple training cycles based on the remaining training time and competition type;

[0136] The periodic training loads of multiple training cycles are determined according to the total training load and the training load ratio of multiple training cycles.

[0137] It should be understood that for any competition, during the training process, common training cycle types include preparation period, basic period, establishment period or construction period, competition period or peak period, deload period or reduction period. According to different training progress, each training cycle occupies a different proportion in the training process, resulting in different period training loads when the total training load is distributed to each cycle.

[0138] Therefore, when determining the periodic training load, the cloud server can first determine the proportion of each period in the training process of the target competition training, that is, the training load ratio. Further, according to the total training load and the training load ratio corresponding to each training period, the periodic training load corresponding to each training period is determined.

[0139] Among them, the training load ratio of each training cycle represents the ratio of the training load in this training cycle to the total training load, that is, the training difficulty in this training cycle.

[0140] In the embodiment of the present application, the training load ratio of each training cycle is not only limited by the remaining training time, but also related to the type of target game. Therefore, in the process of determining the training load ratio, in addition to considering the remaining training time, it is also necessary to combine the type of game.

[0141] Specifically, when determining the training load ratio, the cloud server can generate a request based on the training plan, obtain the game type of the target game, and determine what the current target game is, so as to facilitate cycle allocation.

[0142] In the embodiment of the present application, the two main factors affecting the division of training cycles are the type of competition and the remaining training time. Specifically, when the remaining training time is the same, but the training competitions are different, due to the different requirements of the two competitions during the training process, the duration division of the corresponding training cycles and the training load ratio of each training cycle are different. When the training competitions are the same, but the remaining training time is different, it will also affect the duration division of the training cycle and the training load ratio of each training cycle.

[0143] Therefore, based on the periodization principle, progressive load principle, and specialization principle followed by scientific training, technicians can pre-set different competition types and remaining training time, as well as the periodic training time of multiple training cycles, and the corresponding relationship between the training load ratio of each training cycle, and store them in the cloud server. For example, assuming that the competition type is a half marathon, the remaining training time is 12 weeks. Then the corresponding preparation period can be 1 week, the basic period can be 2 weeks, the construction period can be 5 weeks, the competition period can be 2 weeks, and the deload period can be 2 weeks. Assuming that the competition type is a full marathon, the remaining training time is 12 weeks. Then the corresponding preparation period can be 1 week, the basic period can be 5 weeks, the construction period can be 4 weeks, the competition period can be 1 week, and the deload period can be 1 week.

[0144] After obtaining the game type and remaining training time of the current target game, the cloud server can look up the table to obtain the training time of each training cycle in multiple training cycles under the current game type and the corresponding remaining training time, as well as the training load ratio of each training cycle. The sum of the training time of multiple training cycles is the remaining training time.

[0145] Exemplarily, in combination with the above example, assuming that the competition type is a half marathon, the remaining training time is 12 weeks. Then the corresponding preparation period can be 1 week, the basic period can be 2 weeks, the construction period can be 5 weeks, the competition period can be 2 weeks, and the deload period can be 2 weeks. The corresponding training load ratio of the preparation period can be preset to 10%, the training load ratio of the basic period can be preset to 20%, the training load ratio of the construction period can be preset to 40%, the training load ratio of the competition period can be preset to 20%, and the training load ratio of the deload period can be preset to 10%.

[0146] After obtaining the training load ratio of each training cycle, the cloud server multiplies the total training load by the training load of each training cycle to obtain the cycle training load of each training cycle.

[0147] For example, if the total TSS is Y, then the periodic training load corresponding to the preparation period is 0.1Y; the periodic training load corresponding to the basic period is 0.2Y; the periodic training load corresponding to the construction period is 0.4Y; the periodic training load corresponding to the competition period is 0.2Y; and the periodic training load corresponding to the deload period is 0.1Y.

[0148] Therefore, through the above process, after obtaining the total TSS, the cloud server can reasonably distribute the total TSS to different training cycles according to different training cycles.

[0149] 204. Generate a training plan for each training cycle based on the periodic training loads of the multiple training cycles.

[0150] After obtaining the periodic training load of each training cycle based on step 203, the cloud server can obtain a training plan for each training cycle, that is, a training plan for the target competition, based on the periodic training loads of multiple training cycles.

[0151] It should be understood that when the cloud server generates training plans for each training cycle based on the periodic training load, for any training cycle, it can be subdivided into one or more training weeks, and each week corresponds to multiple training courses. During the actual training process of the user, each training process corresponds to a training course. In order to provide users with reasonable training suggestions during each training process of the user, when generating a training plan for each training cycle, the embodiment of the present application essentially first obtains a training plan for multiple training courses corresponding to each training cycle, thereby obtaining a training plan corresponding to each training cycle.

[0152] In a possible implementation, based on the periodic training loads of multiple training cycles, a training plan for each training cycle is generated, including:

[0153] For any training cycle among the plurality of training cycles, determining a weekly training load of at least one training week in the training cycle according to the periodic training load of the training cycle;

[0154] For any training week of the plurality of training weeks, determining a course training load of at least one training course in the training week according to the weekly training load of the training week;

[0155] For any training course in the at least one training course, determining a target training parameter corresponding to the training course according to the course training load of the training course, where the target training parameter is used to represent a training state that a target user needs to achieve to complete the training course;

[0156] Generate a training plan for the training cycle based on the training courses and target training parameters.

[0157] Specifically, for at least one training week in a training cycle, the weekly training load corresponding to each training week is different according to the different training intensities. Therefore, after obtaining the periodic training loads of multiple training cycles, for any training cycle in the multiple training cycles, the cloud server can first determine the weekly training load of at least one training week in the current training cycle, and then determine the course training loads of multiple training courses in any training week in the training cycle.

[0158] It should be understood that in the training process, users generally focus on the duration or distance of the training course, rather than the training load consumed by the training course. Therefore, after obtaining the training load of any training course, the cloud server can convert the training load of the training course into the user's target training parameters (such as training duration or training distance) during the training process, and generate the final training plan, so that the user can intuitively understand the training status of the current training course.

[0159] Specifically, the process of determining the weekly training load of any training week is as follows.

[0160] In a possible implementation, before determining the weekly training load of at least one training week in the training cycle according to the periodic training load of the training cycle, the method further includes:

[0161] Obtain the training load ratio corresponding to at least one training week in the training cycle;

[0162] And, determining the weekly training load of at least one training week in the training cycle according to the periodic training load of the training cycle, comprising:

[0163] The weekly training load of at least one training week is determined according to the ratio of the periodic training load of the training cycle and the training load corresponding to at least one training week.

[0164] It should be understood that the proportion of training load in at least one training week of each training cycle is usually closely related to the type of the target game. Therefore, after obtaining the remaining training time and the type of game, the cloud server can not only divide the periodic training time of each training cycle according to the remaining training time and the type of game, but also reasonably arrange the course schedule of multiple training weeks corresponding to each training cycle, as well as the proportion of training load in each training week in each training cycle, and the proportion of training load in each training course in each training week based on the current game type and course allocation principle.

[0165] For example, assuming that the target race type is a half marathon, combined with the above example, the cycle training load corresponding to the cloud preparation period is 0.1Y; the cycle training load corresponding to the basic period is 0.2Y; the cycle training load corresponding to the construction period is 0.4Y; the cycle training load corresponding to the competition period is 0.2Y; and the cycle training load corresponding to the deload period is 0.1Y.

[0166] After the cloud server determines the duration of multiple training cycles, it can determine the training load ratio of at least one training week in each training cycle and the training load ratio of at least one training course in each training week according to the preset course allocation principles and training needs.

[0167] Exemplarily, the number of weeks corresponding to the preparation period is 1 week, that is, M=1. In this case, the periodic training load corresponding to the preparation period is equal to the weekly training load of at least one training week in the preparation period, that is, 0.1Y. The number of weeks corresponding to the basic period is two weeks. Assuming that the cloud server determines that the training load of the first week in the basic period accounts for 60%, and the training load of the second week accounts for 40%. Then the weekly training loads of at least one training week in the basic period are 0.12Y and 0.08Y respectively. By analogy, the cloud server can determine the weekly training load of at least one training week contained in each training cycle.

[0168] Similarly, after obtaining the weekly training load of each training week, the cloud server can obtain the specific course training load of each training course according to the training load ratio of at least one training course in that week.

[0169] In a possible implementation, before determining the course training load of at least one training course in the training week according to the weekly training load of the training week, the method further includes:

[0170] Get the training load ratio of at least one training session in the training week;

[0171] and, determining a course training load of at least one training course in the training week according to the weekly training load of the training week, comprising:

[0172] A course training load of at least one training course is determined based on the weekly training load of the training week and the course load ratio of at least one training course.

[0173] Exemplarily, assume that during the preparation period for a half marathon, at least one training course per week includes four training courses: easy running, long-distance endurance running, strength training, and threshold running. In the above example, the number of weeks corresponding to the preparation period is 1 week, that is, M=1. The periodic training load corresponding to the preparation period is equal to the weekly training load of at least one training week in the preparation period, that is, 0.1Y. The course load ratios corresponding to each training course predetermined by the cloud server are 40%, 25%, 15%, and 20%, respectively. Then, after obtaining the weekly training load of the preparation period, the cloud server can calculate the course training load of each training course, which are 0.04Y, 0.025Y, 0.015Y, and 0.02Y, respectively.

[0174] In addition, let's take the basic period of the training cycle as an example. The number of weeks corresponding to the basic period is two weeks, the weekly training load of the first week is 0.12Y, and the weekly training load of the second week is 0.08Y.

[0175] Assume that during the basic period determined by the cloud server, at least one training course each week still includes four training courses: easy running, long-distance endurance running, strength training, and threshold running. However, the corresponding course training load ratios are different from the course training load ratios of at least one training course during the basic period, which are 30%, 35%, 15%, and 20%, respectively.

[0176] It should be understood that in each training cycle, the training process of multiple weeks is repeated. In other words, multiple training courses in each week of the same training cycle are the same. Moreover, the proportion of course training load occupied by at least one training course in each week is the same. However, since the weekly training load corresponding to each week in the same training cycle is not exactly the same, even if the proportion of course training load is the same, the final course training load is different.

[0177] Exemplarily, the weekly training loads corresponding to the two weeks of the basic period are 0.12Y and 0.08Y respectively. Among them, the course training load ratios corresponding to at least one training course in the two weeks are 30%, 35%, 15% and 20% respectively, then the cloud server can calculate that in the first week, the course training load ratios corresponding to at least one training course are: 0.036Y, 0.042Y, 0.018Y and 0.024Y respectively. In the corresponding second week, the course training load ratios corresponding to at least one training course are: 0.024Y, 0.028Y, 0.012Y and 0.016Y respectively.

[0178] Thus, through the above process, the cloud server can calculate the course training load of any one of the multiple training courses in the training plan for the target competition.

[0179] After obtaining the course training load of any training course, the cloud server can convert the course training load of the training course into a corresponding target training duration or target training distance.

[0180] In a possible implementation, before determining the target training parameters corresponding to the training course according to the course training load of the training course, the method further includes:

[0181] Obtaining preset motion parameters corresponding to the training course, where the preset motion parameters are used to represent the motion state that the target user needs to achieve when completing the training course;

[0182] And, according to the course training load of the training course, determine the target training parameters corresponding to the training course, including:

[0183] Determine target training parameters based on preset exercise parameters and course training load.

[0184] The target training parameter indicates the exercise state that the user needs to achieve when completing the training course. Optionally, the target training parameter includes a target training duration or a target training distance.

[0185] For any training course, in the process of determining the target training parameters, in addition to the course training load, the cloud server also needs to first obtain the preset motion parameters of the current training course, that is, the motion state achieved by the user when completing the training course.

[0186] Optionally, the preset sports parameters include a plurality of preset heart rate intervals, preset average paces, and preset pace intensity intervals. Different preset sports parameters correspond to different methods of determining training parameters.

[0187] Specifically, when generating a training plan, the embodiment of the present application provides two different training plan generation methods, one is to determine the training duration based on the heart rate intensity; the other is to determine the training distance based on the pace intensity.

[0188] During the process of generating a training plan, the user may manually configure the training plan generation method, or the cloud server may default to a training plan generation method, which is not limited in the embodiments of the present application.

[0189] Specifically, the process of determining training duration based on heart rate intensity is as follows.

[0190] In a possible implementation, the target training parameters are determined according to the preset motion parameters and the course training load, including:

[0191] According to the plurality of preset heart rate intervals, a plurality of preset heart rate ratios and a plurality of first preset intensity factors are determined, wherein the preset heart rate ratio is used to indicate the weight of the course training load corresponding to the preset heart rate interval, and the first preset intensity factor is used to indicate the training intensity under the preset heart rate interval;

[0192] According to each preset heart rate ratio and the course training load, the course training load is divided into a plurality of first sub-course training loads;

[0193] A target training duration is determined according to a plurality of first sub-course training loads and a plurality of first preset intensity factors.

[0194] When determining the training duration based on the heart rate intensity, generally during the training course, as the progress of the training course changes, the heart rate range that the user needs to reach also changes, and the corresponding training load also changes. For example, when a certain stage of the training course requires the user to have a higher heart rate, the training load corresponding to this stage will increase accordingly.

[0195] Therefore, for any training course of the target competition, in order to achieve a good training effect, the technician can pre-set the heart rate intervals corresponding to the different stages of each training course according to the experiment or the large amount of user data collected. Since the training loads corresponding to different heart rate intervals are also different, the technician can further set the training load ratio corresponding to different heart rate intervals, that is, the preset heart rate ratio, to indicate the weight or proportion of the course training load occupied by the preset heart rate interval.

[0196] Based on this, the cloud server can obtain the corresponding multiple preset heart rate intervals based on the current training course, and further obtain the preset heart rate ratio corresponding to each preset heart rate interval.

[0197] After obtaining multiple preset heart rate ratios, the cloud server can calculate the portion of the course training load occupied by each preset heart rate range respectively.

[0198] Specifically, the above process can be expressed by the following formula (2).

[0199] TSS zone =TSS·HR zone Ratio formula(2)

[0200] Wherein, in formula (2):

[0201] TSS: The training load of this training session;

[0202] HR zone Ratio: The preset heart rate ratio corresponding to each preset heart rate zone;

[0203] TSS zone : The training load of the first sub-course corresponding to each preset heart rate zone, that is, the training load of the partial course.

[0204] After obtaining multiple first sub-course training loads, the cloud server can determine the time required for the user to reach each first sub-course training load, and sum them up to obtain the target training duration, which can be specifically expressed by the following formula (3).

[0205]

[0206] Wherein, in formula (3):

[0207] Duration: the training duration corresponding to this training course, that is, the target training duration;

[0208] IF(i): The first preset intensity factor corresponding to each preset heart rate interval.

[0209] Among them, the preset intensity factor represents the training intensity under the preset heart rate range, has a one-to-one correspondence with the preset heart rate range, and can be obtained by looking up the corresponding table of the preset heart rate range.

[0210] Thus, through the above process, the cloud server can obtain the target training duration of the current training course.

[0211] Another process for determining training distance based on pace intensity is as follows.

[0212] In a possible implementation, before determining the target training parameters according to the preset motion parameters and the course training load, the method further includes:

[0213] Obtaining the functional threshold power of the target user;

[0214] And, according to the preset exercise parameters and course training load, determine the target training parameters, including:

[0215] Determine the standardized graded pace corresponding to the training course based on the preset average pace;

[0216] According to the plurality of preset pace intensity intervals, a plurality of second preset intensity factors, a plurality of preset pace ratios, and a plurality of standardized graded pace ratios are determined, wherein the preset pace ratio is used to indicate the weight of the course training load corresponding to the preset pace interval, and the second preset intensity factor is used to indicate the training intensity under the preset pace interval;

[0217] According to the standardized graded pace and a plurality of second preset intensity factors, the standardized graded pace is divided into a plurality of sub-standardized graded paces;

[0218] According to the course training load and a plurality of preset pace ratios, the course training load is divided into a plurality of second sub-course training loads;

[0219] The target training distance is determined according to the plurality of second sub-course training loads, the functional threshold power, the plurality of sub-standardized graded paces, the plurality of second preset intensity factors and the plurality of standardized graded pace ratios. Exemplarily, the cloud server can directly obtain the FTP of the target user through the application.

[0220] Similarly, when determining the training duration based on the pace intensity, generally during the completion of a training course, as the progress of the training course changes, the pace interval that the user needs to reach also changes, and the corresponding training load also changes. For example, when a certain stage of the training course requires a faster pace, the corresponding training load for this stage increases accordingly.

[0221] Therefore, for any training course of the target competition, in order to achieve a good training effect, the technician can pre-set the pace intervals and preset average paces corresponding to the different stages of each training course according to the experiment or the large amount of user data collected. Since the training loads corresponding to different pace intervals are also different, the technician can further set the training load ratio corresponding to different intensity intervals, that is, the preset pace ratio, to indicate the weight or proportion of the course training load occupied by the preset pace interval.

[0222] The specific process of solving the target training distance based on the above parameters can be expressed by the following formulas (4)-(8).

[0223]

[0224]

[0225] TSS zone =Pace zone Ratio×TSS formula (6)

[0226]

[0227]

[0228] Wherein, in formulas (4)-(8):

[0229] TSS: The training load of this training session;

[0230] Pace zone Ratio: The preset pace ratio corresponding to each preset pace interval;

[0231] TSS zone : The training load of the second sub-course corresponding to each preset pace interval;

[0232] duration: the training duration corresponding to this training course;

[0233] IF(i): the second preset intensity factor corresponding to each preset pace interval;

[0234] speed: preset average pace;

[0235] gradient: slope, which is set to 0 in the present embodiment. In other words, in the present embodiment, it is ideally considered that the user is exercising on a relatively flat road with no slope.

[0236] NGP: Normalized Graded Pace;

[0237] NGP zone(i): sub-standardized graded pace;

[0238] NGP zone Ratio(i): standardized graded pace ratio;

[0239] FTP: Functional Threshold Power.

[0240] Thus, through the above process, the cloud server can obtain the target training distance of the current training course.

[0241] After obtaining the target training duration or target training distance for each training course, the cloud server can generate a training plan for each training course based on the specific content of each training course and the target training duration or target training distance corresponding to each training course, thereby obtaining training plans for multiple training courses in each training cycle and ultimately obtaining a training plan for the target competition.

[0242] Furthermore, the cloud server can send the generated training plan to the smart terminal device and synchronize it to the smart wearable device by the smart terminal device, so that the user can complete the training according to the training plan during each training process.

[0243] In summary, when a user needs to train for a competition, the present application proposes a method for determining a training plan, which can determine the total training load during the training process by combining the user's current physical parameters, the remaining training time, and the target physical parameters. The above process can be based on reality, taking into account the user's actual physical fitness level, so that the training plan is more in line with the user's personal actual situation. Further, the total training load is allocated to multiple training cycles, which can ensure that the training load of each training cycle in the training process meets the training requirements of the stage, and ensures the scientificity and rationality of the training plan. Finally, a training plan is generated according to the cycle training load. Since the user's physical condition is taken into account during the generation of the training plan, the training plan finally generated can closely fit the user's actual training needs, ensure the personalized customization of the training plan during the training process, meet the differentiated needs of users of different levels, and improve the user experience.

[0244] 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.

[0245] Figure 3 It is a schematic flowchart of another method for determining a training plan provided in an embodiment of the present application.

[0246] For example, Figure 3 As shown, the method 300 includes:

[0247] 301 , in response to a request to generate a training plan for a target game, obtain current physical fitness parameters of a target user, remaining training time of the target game, and a target physical fitness parameter range.

[0248] 302 , determining whether the training plan generation request meets preset conditions according to the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval, and the preset training time interval.

[0249] When the training plan generation request meets the preset conditions, execute steps 304-307;

[0250] When the training plan generation request does not meet the preset condition, step 303 is executed.

[0251] 303, prompting the user to reconfigure the training information of the target game.

[0252] 304 , determining a plurality of training cycles for the target game according to the game type of the target game and the remaining training time.

[0253] 305, determining a plurality of training courses in each training cycle.

[0254] 306 , determining a target physical fitness parameter according to the target physical fitness parameter range and the training difficulty level.

[0255] 307 , determining a total training load according to the current physical fitness parameter, the target physical fitness parameter, and the remaining training time.

[0256] 308, determining the cycle training load of each cycle and the course training load of each training course.

[0257] 309 , determining target training parameters corresponding to the training course according to the course training load of the training course.

[0258] 310 , generating a training plan for a target game according to each training course and the target training parameters corresponding to each training course, and sending the plan to the smart terminal device.

[0259] 301-310 in the above method 300 and 201-204 in the method 200 have the same inventive concept, and the details can be found in the introduction of the method 200, which will not be repeated here.

[0260] Figure 4 It is a structural schematic diagram of a device for determining a training plan provided in an embodiment of the present application.

[0261] For example, Figure 4 As shown, the device 400 includes:

[0262] The information acquisition module 401 is used to obtain the current physical fitness parameters of the target user, the remaining training time of the target game, and the target physical fitness parameters of the target game in response to the training plan generation request for the target game, wherein the current physical fitness parameters are used to indicate the current physical fitness condition of the target user, and the target physical fitness parameters are used to indicate the physical fitness condition required for the target user to train for the target game;

[0263] A first load determination module 402 is used to determine a total training load according to the current physical fitness parameter, the target physical fitness parameter and the remaining training time, where the total training load is used to indicate the difficulty of the target user in training for the target game;

[0264] A second load determination module 403 is used to determine the periodic training loads of multiple training periods within the remaining training duration based on the total training load and the remaining training duration;

[0265] The training plan generating module 404 is used to generate a training plan for each training cycle based on the periodic training loads of the multiple training cycles.

[0266] In one possible implementation, the information acquisition module 401 is specifically used to: obtain the game duration, game type, training difficulty level and preset training duration interval of the target game, the game type is used to indicate the game form and game distance of the target game; determine the target physical fitness parameter interval based on the game type and the game duration; determine the target physical fitness parameter based on the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval, the preset training duration interval and the training difficulty level.

[0267] In one possible implementation, the information acquisition module 401 is also used to: determine whether the training plan generation request meets the preset conditions based on the remaining training time, the current physical fitness parameters, the target physical fitness parameter interval and the preset training time interval; if the training plan generation request meets the preset conditions, determine the target physical fitness parameters based on the target physical fitness parameter interval and the training difficulty level.

[0268] In a possible implementation, the information acquisition module 401 is also used to: obtain a lower-level physical fitness parameter interval of the target physical fitness parameter interval, the lower limit of the target physical fitness parameter interval being the upper limit of the lower-level physical fitness parameter interval; when the current physical fitness parameter is in the target physical fitness parameter interval or the lower-level physical fitness parameter interval, and the remaining training time is in the preset training time interval, determine that the training plan generation request meets the preset condition; when the current physical fitness parameter is not in the target physical fitness parameter interval or the lower-level physical fitness parameter interval, or the remaining training time is not in the preset training time interval, determine that the training plan generation request does not meet the preset condition.

[0269] In one possible implementation, the information acquisition module 401 is also used to: when the training difficulty level is the first level, determine the target physical fitness parameter as the minimum value of the target physical fitness parameter range; when the training difficulty level is the second level, determine the target physical fitness parameter as the average value of the target physical fitness parameter range, and the training difficulty of the second level is higher than the training difficulty of the first level; when the training difficulty level is the third level, determine the target physical fitness parameter as the maximum value of the target physical fitness parameter range, and the training difficulty of the third level is higher than the training difficulty of the second level.

[0270] In one possible implementation, before determining the periodic training loads of multiple training cycles within the remaining training duration based on the total training load and the remaining training duration, the second load determination module 403 is specifically used to: obtain the competition type of the target competition, where the competition type is used to indicate the competition format and competition distance of the target competition; and, based on the remaining training duration and the competition type, determine the training load ratio of the multiple training cycles; and determine the periodic training loads of the multiple training cycles based on the total training load and the training load ratio of the multiple training cycles.

[0271] In one possible implementation, the training plan generation module 404 is specifically used to: for any training cycle among the multiple training cycles, determine the weekly training load of at least one training week within the training cycle according to the periodic training load of the training cycle; for any training week among the multiple training weeks, determine the course training load of at least one training course within the training week according to the weekly training load of the training week; for any training course among the at least one training course, determine the target training parameters corresponding to the training course according to the course training load of the training course, the target training parameters are used to represent the training state that the target user needs to achieve to complete the training course; generate a training plan for the training cycle according to the training course and the target training parameters.

[0272] In one possible implementation, before determining the weekly training load of at least one training week within the training cycle based on the periodic training load of the training cycle, the training plan generation module 404 is further used to: obtain the training load ratio corresponding to at least one training week within the training cycle; and determine the weekly training load of the at least one training week based on the periodic training load of the training cycle and the training load ratio corresponding to the at least one training week.

[0273] In one possible implementation, before determining the course training load of at least one training course in the training week based on the weekly training load of the training week, the training plan generation module 404 is further used to: obtain the training load ratio of the at least one training course in the training week; and determine the course training load of the at least one training course based on the weekly training load of the training week and the course load ratio of the at least one training course.

[0274] In one possible implementation, before determining the target training parameters corresponding to the training course based on the course training load of the training course, the training plan generation module 404 is also used to: obtain preset motion parameters corresponding to the training course, the preset motion parameters are used to represent the motion state that the target user needs to achieve to complete the training course; and determine the target training parameters based on the preset motion parameters and the course training load.

[0275] In one possible implementation, the preset motion parameters include multiple preset heart rate intervals, the target training parameters include target training duration, and the training plan generation module 404 is further used to: determine multiple preset heart rate ratios and multiple first preset intensity factors based on the multiple preset heart rate intervals, the preset heart rate ratio is used to indicate the weight of the corresponding preset heart rate interval in the course training load, and the first preset intensity factor is used to indicate the training intensity under the preset heart rate interval; divide the course training load into multiple first sub-course training loads based on each of the preset heart rate ratios and the course training load; and determine the target training duration based on the multiple first sub-course training loads and the multiple first preset intensity factors.

[0276] In a possible implementation, the preset motion parameters include a preset average pace and a plurality of preset pace intensity intervals, the target training parameters include a target training distance, and the training plan generation module 404 is further used to: obtain the functional threshold power of the target user; and, according to the preset average pace, determine the standardized graded pace corresponding to the training course; according to the plurality of preset pace intensity intervals, determine a plurality of second preset intensity factors, a plurality of preset pace ratios, and a plurality of standardized graded pace ratios, the preset pace ratio being used to indicate the weight of the course training load occupied by the corresponding preset pace interval, and the second preset intensity factor being used to indicate the training intensity under the preset pace interval; according to the standardized graded pace and the plurality of second preset intensity factors, divide the standardized graded pace into a plurality of sub-standardized graded paces; according to the course training load and the plurality of preset pace ratios, divide the course training load into a plurality of second sub-course training loads; and according to the plurality of second sub-course training loads, the functional threshold power, the plurality of sub-standardized graded paces, the plurality of second preset intensity factors, and the plurality of standardized graded pace ratios, determine the target training distance.

[0277] Figure 5 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device is specifically Figure 1 The cloud server 101 in.

[0278] 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 5011, and the processor 502 is used to call and execute the executable program code 5011 to perform a method for determining a training plan.

[0279] 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.

[0280] 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.

[0281] The electronic device provided in this embodiment is used to execute the above-mentioned method for determining a training plan, and thus can achieve the same effect as the above-mentioned implementation method.

[0282] 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.

[0283] 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.

[0284] 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 plan in the above-mentioned embodiment.

[0285] 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 plan in the above-mentioned embodiment.

[0286] 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 the instructions so that the chip executes a method for determining a training plan in the above-mentioned embodiment.

[0287] 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.

[0288] 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.

[0289] 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.

[0290] 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 a training plan, characterized in that: The method comprises: In response to a request for generating a training plan for a target game, obtaining a current physical fitness parameter of a target user, a remaining training time for the target game, and a target physical fitness parameter of the target game, wherein the current physical fitness parameter is used to indicate a current physical fitness condition of the target user, and the target physical fitness parameter is used to indicate a physical fitness condition required for the target user to train for the target game; Determine a total training load according to the current physical fitness parameter, the target physical fitness parameter and the remaining training time, wherein the total training load is used to indicate the difficulty of the target user in training for the target game; Determine, based on the total training load and the remaining training duration, the periodic training loads of a plurality of training periods within the remaining training duration; Based on the periodic training loads of the plurality of training periods, a training plan for each of the training periods is generated.

2. The method according to claim 1, characterized in that The step of obtaining the target physical fitness parameter comprises: Obtaining the competition duration, competition type, training difficulty level, and preset training duration interval of the target competition, wherein the competition type is used to indicate the competition form and competition distance of the target competition; Determining a target physical fitness parameter interval according to the game type and the game duration; The target physical fitness parameter is determined according to the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval, the preset training time interval and the training difficulty level.

3. The method according to claim 2, characterized in that The determining the target physical fitness parameter according to the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval, the preset training time interval and the training difficulty level includes: Determining whether the training plan generation request meets a preset condition according to the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval and the preset training time interval; When the training plan generation request meets the preset condition, the target physical fitness parameter is determined according to the target physical fitness parameter interval and the training difficulty level.

4. The method according to claim 3, characterized in that The determining, according to the remaining training time, the current physical fitness parameter, the target physical fitness parameter interval and the preset training time interval, whether the training plan generation request meets a preset condition includes: Acquire a lower level physical fitness parameter interval of the target physical fitness parameter interval, wherein the lower limit of the target physical fitness parameter interval is the upper limit of the lower level physical fitness parameter interval; When the current physical fitness parameter is within the target physical fitness parameter interval or the lower-level physical fitness parameter interval, and the remaining training duration is within the preset training duration interval, determining that the training plan generation request satisfies the preset condition; When the current physical fitness parameter is not in the target physical fitness parameter interval or the lower-level physical fitness parameter interval, or the remaining training duration is not in the preset training duration interval, it is determined that the training plan generation request does not meet the preset condition.

5. The method according to claim 3, characterized in that: The step of determining the target physical fitness parameter according to the target physical fitness parameter interval and the training difficulty level includes: When the training difficulty level is the first level, determining the target physical fitness parameter to be the minimum value of the target physical fitness parameter interval; When the training difficulty level is the second level, determining the target physical fitness parameter as an average value of the target physical fitness parameter interval, and the training difficulty of the second level is higher than the training difficulty of the first level; When the training difficulty level is the third level, the target physical fitness parameter is determined to be the maximum value of the target physical fitness parameter interval, and the training difficulty of the third level is higher than the training difficulty of the second level.

6. The method according to claim 1, characterized in that Before determining the periodic training loads of a plurality of training periods within the remaining training duration based on the total training load and the remaining training duration, the method further includes: Acquire a competition type of the target competition, where the competition type is used to indicate a competition format and a competition distance of the target competition; And, determining the periodic training loads of a plurality of training periods within the remaining training duration based on the total training load and the remaining training duration includes: Determining the training load ratio of the multiple training cycles according to the remaining training time and the competition type; The periodic training loads of the multiple training cycles are determined according to the total training load and the ratio of the training loads of the multiple training cycles.

7. The method according to claim 1, characterized in that The step of generating a training plan for each of the training cycles based on the periodic training loads of the plurality of training cycles comprises: For any training cycle among the multiple training cycles, determining a weekly training load of at least one training week within the training cycle according to the periodic training load of the training cycle; For any training week among the plurality of training weeks, determining a course training load of at least one training course in the training week according to the weekly training load of the training week; For any training course of the at least one training course, determining a target training parameter corresponding to the training course according to the course training load of the training course, wherein the target training parameter is used to represent a training state that the target user needs to achieve to complete the training course; A training plan for the training cycle is generated according to the training course and the target training parameters.

8. The method according to claim 7, characterized in that Before determining the weekly training load of at least one training week in the training cycle according to the periodic training load of the training cycle, the method further includes: Obtaining a training load ratio corresponding to at least one training week within the training cycle; And, determining the weekly training load of at least one training week in the training cycle according to the periodic training load of the training cycle comprises: The weekly training load of the at least one training week is determined according to the ratio of the periodic training load of the training cycle and the training load corresponding to the at least one training week.

9. The method according to claim 7, characterized in that: Before determining the course training load of at least one training course in the training week according to the weekly training load of the training week, the method further includes: obtaining a training load ratio of the at least one training course in the training week; And, determining the course training load of at least one training course in the training week according to the weekly training load of the training week includes: The course training load of the at least one training course is determined according to the ratio of the weekly training load of the training week and the course load of the at least one training course.

10. The method according to claim 7, characterized in that Before determining the target training parameters corresponding to the training course according to the course training load of the training course, the method further includes: Acquire preset motion parameters corresponding to the training course, where the preset motion parameters are used to represent the motion state that the target user needs to achieve to complete the training course; And, determining the target training parameters corresponding to the training course according to the course training load of the training course includes: The target training parameters are determined according to the preset motion parameters and the course training load.

11. The method according to claim 10, characterized in that The preset motion parameters include a plurality of preset heart rate intervals, the target training parameters include a target training duration, and determining the target training parameters according to the preset motion parameters and the course training load includes: According to the plurality of preset heart rate intervals, a plurality of preset heart rate ratios and a plurality of first preset intensity factors are determined, wherein the preset heart rate ratio is used to indicate the weight of the course training load occupied by the corresponding preset heart rate interval, and the first preset intensity factor is used to indicate the training intensity under the preset heart rate interval; According to each of the preset heart rate ratios and the course training load, the course training load is divided into a plurality of first sub-course training loads; The target training duration is determined according to the plurality of first sub-course training loads and the plurality of first preset intensity factors.

12. The method according to claim 10, characterized in that The preset sports parameters include a preset average pace and a plurality of preset pace intensity intervals, the target training parameters include a target training distance, and before determining the target training parameters according to the preset sports parameters and the course training load, the method further includes: Acquiring a functional threshold power of the target user; And, determining the target training parameters according to the preset motion parameters and the course training load includes: Determining a standardized graded pace corresponding to the training course according to the preset average pace; Determine a plurality of second preset intensity factors, a plurality of preset pace ratios, and a plurality of standardized graded pace ratios according to the plurality of preset pace intensity intervals, wherein the preset pace ratios are used to indicate the weight of the course training load occupied by the corresponding preset pace intervals, and the second preset intensity factors are used to indicate the training intensity under the preset pace intervals; According to the standardized graded pace and the plurality of second preset intensity factors, the standardized graded pace is divided into a plurality of sub-standardized graded paces; According to the course training load and the plurality of preset pace ratios, the course training load is divided into a plurality of second sub-course training loads; The target training distance is determined according to the plurality of second sub-course training loads, the functional threshold power, the plurality of sub-standardized graded paces, the plurality of second preset intensity factors and the plurality of standardized graded pace ratios.

13. A device for determining a training plan, characterized in that: The device comprises: an information acquisition module, configured to, in response to a request for generating a training plan for a target game, acquire a current physical fitness parameter of a target user, a remaining training time for the target game, and a target physical fitness parameter for the target game, wherein the current physical fitness parameter is used to indicate a current physical fitness condition of the target user, and the target physical fitness parameter is used to indicate a physical fitness condition required for the target user to train for the target game; A first load determination module, configured to determine a total training load according to the current physical fitness parameter, the target physical fitness parameter and the remaining training time, wherein the total training load is used to indicate the difficulty of the target user in training for the target game; A second load determination module, configured to determine, based on the total training load and the remaining training duration, the periodic training loads of a plurality of training periods within the remaining training duration; The training plan generating module is used to generate a training plan for each of the training cycles based on the periodic training loads of the multiple training cycles.

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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