Exercise guidance method and related equipment

Abstract

The embodiment of the invention provides an exercise guidance method and related equipment. The exercise guidance method comprises the steps that user exercise information is acquired, the user exercise information comprises speed allocation information, distance information and used time information, the distance information comprises residual distance information, and the used time information comprises used time information; determining an attenuation factor of the user according to the user motion information; estimating the remaining time of the user according to the remaining distance information, the speed allocation information and the attenuation factor; and according to the remaining time of the user and the used time information, obtaining the pre-estimated match completion time of the user. According to the technical scheme provided by the embodiment of the invention, the user can know whether the time exceeds or is lower than the target time in real time by dynamically calculating the remaining time and the competition completion time in the exercise process. The real-time feedback can help the user to better control the competition rhythm, physical power overdraft or excessive consumption is avoided by adjusting the matching speed, and finally the ideal competition completion time is achieved.

Description

Technical Field

[0001] The present application relates to the field of computer and communication technology, and in particular to a sports guidance method and related equipment. Background Art

[0002] For speed sports with distance and pace targets (such as track and field, cycling, swimming, and skiing), users may want to know the approximate duration of the exercise. Take a marathon as an example.

[0003] For example, if a user's first half marathon time was 2:35:00, they might set a goal of 2:10:00 for their second half marathon. However, after the race begins but before it ends, the user cannot predict whether they will achieve their desired time this time.

[0004] Once users realize they are too slow, it may be too late.

[0005] Because we don't know how fast users will run until they finish the run, no manufacturer dares to directly give users a race result estimate during the exercise. However, we overlook the fact that users can adjust their speed within their athletic ability.

[0006] We should provide scientific guidance to help users achieve satisfactory results. Summary of the Invention

[0007] The embodiments of the present application provide a sports guidance method and related equipment, which can at least to a certain extent overcome the problem of providing scientific guidance to users so that users can better achieve satisfactory running results.

[0008] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0009] According to one aspect of an embodiment of the present application, a sports guidance method is provided, including: obtaining user sports information, the user sports information including pace information, distance information and time information, the distance information including remaining distance information, and the time information including elapsed time information; determining the user's attenuation factor based on the user sports information; estimating the user's remaining time based on the remaining distance information, the pace information and the attenuation factor; and obtaining the user's estimated finishing time based on the user's remaining time and the elapsed time information.

[0010] In some embodiments of the present application, the pacing information includes current pacing information; determining the user's attenuation factor based on the user motion information specifically includes: obtaining a preliminary estimated time based on the remaining distance information and the current pacing information; and determining the attenuation factor for each predetermined time period based on the preliminary estimated time.

[0011] In some embodiments of the present application, estimating the remaining time of the user based on the remaining distance information, the pace information and the attenuation factor specifically includes: according to the attenuation factor of each predetermined time period, correcting the pace information in each predetermined time period based on the user's current pace information to obtain the corrected pace corresponding to each predetermined time period; estimating the remaining time of the user based on the corrected pace and the remaining distance information.

[0012] In some embodiments of the present application, the distance information also includes a target distance; determining the user's attenuation factor based on the user's motion information specifically includes: obtaining the user's uncompleted distance ratio based on the target distance and the remaining distance information; and determining the attenuation factor based on the user's uncompleted distance ratio.

[0013] In some embodiments of the present application, the pace information includes the pace information within the current predetermined time interval; estimating the user's remaining time based on the remaining distance information, the pace information and the attenuation factor specifically includes: determining the current average pace based on the pace information within the current predetermined time interval; determining the average attenuation pace based on the current average pace and the attenuation factor; and obtaining the user's remaining time based on the remaining distance information and the average attenuation pace.

[0014] In some embodiments of the present application, the timing information also includes a target finishing time; after obtaining the user's estimated finishing time based on the user's remaining time and the elapsed time information, the exercise guidance method further includes: obtaining a recommended pace based on the user's estimated finishing time, the target finishing time, the pace information and the attenuation factor.

[0015] In some embodiments of the present application, after obtaining the user's estimated finishing time based on the user's remaining time and the elapsed time information, the exercise guidance method further includes: in response to the end of the exercise function, determining whether the remaining distance information is greater than a predetermined distance threshold; if the remaining distance information is greater than the predetermined distance threshold, displaying the user's estimated finishing time on the exercise end interface; if the remaining distance information is not greater than the predetermined distance threshold, displaying the user's actual finishing time on the exercise end interface.

[0016] In some embodiments of the present application, the user motion information also includes the user's motion physiological information from the start of the motion to the current state, and the pace information includes the user's pace information from the start of the motion to the current state; determining the user's attenuation factor based on the user motion information specifically includes: sorting the user's motion physiological information from the start of the motion to the current state according to time to form a motion physiological information time sequence; sorting the user's pace information from the start of the motion to the current state according to time to form a user pace information time sequence; inputting the motion physiological information time sequence, the user pace information time sequence and the remaining distance information into the attenuation prediction model to obtain the attenuation factor.

[0017] In some embodiments of the present application, the attenuation prediction model includes a timing feature extraction layer, a fusion feature interaction layer and an attenuation factor prediction layer; the step of inputting the motion physiological information timing, the user pace information timing and the remaining distance information into the attenuation prediction model to obtain the attenuation factor specifically includes: inputting the motion physiological information timing and the user pace information timing into the timing feature extraction layer to extract timing features to obtain fused timing features; splicing the fused timing features with the remaining distance information and inputting them into the fusion feature interaction layer for feature enhancement to obtain enhanced fusion features; inputting the enhanced fusion features into the attenuation factor prediction layer to obtain the attenuation factor.

[0018] In some embodiments of the present application, the timing feature extraction layer includes a bidirectional LSTM network, a Transformer encoder, and a self-attention network; the step of inputting the motion physiological information timing and the user pace information timing into the timing feature extraction layer to extract timing features to obtain fused timing features specifically includes: inputting the motion physiological information timing into the bidirectional LSTM network to capture long-term dependencies to obtain motion physiological information timing features; inputting the user pace information timing into the Transformer encoder to capture periodicity and mutation features in the timing to obtain user pace information timing features; and fusing the motion physiological information timing features and the user pace information timing features through the self-attention network to obtain fused timing features.

[0019] In some embodiments of the present application, the exercise guidance method also includes: obtaining user historical exercise data to obtain a user exercise behavior sample set, the user exercise behavior sample set containing multiple user exercise behavior samples, each user exercise behavior sample corresponding to a section of the user's exercise behavior, and containing the user pace information timing, user pace information timing and remaining distance information of the user in the section of exercise behavior, and are all marked with corresponding attenuation factor labels; inputting each of the user exercise behavior samples in the user exercise behavior sample set into the attenuation prediction model one by one to obtain the corresponding attenuation factor; updating the parameters of the attenuation prediction model according to the attenuation factor and the attenuation factor label until the predetermined end condition is met, stopping the training, and obtaining a trained attenuation prediction model.

[0020] According to one aspect of an embodiment of the present application, a sports guidance device is provided, characterized in that the sports guidance device includes: a sports information acquisition module, used to obtain user sports information, the user sports information includes pace information, distance information and time information, the distance information includes remaining distance information, and the time information includes elapsed time information; an attenuation factor determination module, used to determine the user's attenuation factor based on the user's sports information; a remaining time estimation module, used to determine the user's remaining time based on the remaining distance information, the pace information and the attenuation factor; and a finishing time estimation module, used to obtain the user's estimated finishing time based on the user's remaining time and the elapsed time information.

[0021] In some embodiments of the present application, the pace information includes current pace information; the attenuation factor determination module specifically includes: a preliminary estimated time submodule, used to obtain a preliminary estimated time based on the remaining distance information and the current pace information; and a stage attenuation factor submodule, used to determine the attenuation factor of each predetermined time period based on the preliminary estimated time.

[0022] In some embodiments of the present application, the remaining time estimation module specifically includes: a pace information correction submodule, which corrects the pace information in each predetermined time period based on the attenuation factor of each predetermined time period and the user's current pace information to obtain the corrected pace corresponding to each predetermined time period; a remaining time estimation submodule, which estimates the user's remaining time based on the corrected pace and remaining distance information.

[0023] In some embodiments of the present application, the distance information also includes a target distance; the attenuation factor determination module specifically includes: a distance ratio determination submodule, used to obtain the user's uncompleted distance ratio based on the target distance and the remaining distance information; an attenuation factor determination submodule, used to determine the attenuation factor based on the user's uncompleted distance ratio.

[0024] In some embodiments of the present application, the pace information includes the pace information within the current predetermined time interval; the remaining time estimation module specifically includes: a current average pace sub-module, used to determine the current average pace based on the pace information within the current predetermined time interval; an average decay pace sub-module, used to determine the average decay pace based on the current average pace and the decay factor; and a user remaining time sub-module, used to obtain the user remaining time based on the remaining distance information and the average decay pace.

[0025] In some embodiments of the present application, the time information also includes a target finishing time, and the sports guidance device also includes: a pace recommendation module, which is used to obtain a recommended pace based on the user's estimated finishing time, the target finishing time, the pace information and the attenuation factor.

[0026] In some embodiments of the present application, the sports guidance device also includes: a remaining distance judgment module, which is used to determine whether the remaining distance information is greater than a predetermined distance threshold in response to the end of the sports function; a first display module, which is used to display the user's estimated completion time on the sports end interface if the remaining distance information is greater than the predetermined distance threshold; and a second display module, which is used to display the user's actual completion time on the sports end interface if the remaining distance information is not greater than the predetermined distance threshold.

[0027] In some embodiments of the present application, the user motion information also includes the user's motion physiological information from the start of the motion to the current time, and the pace information includes the user's pace information from the start of the motion to the current time; the attenuation factor determination module specifically includes: a physiological information timing submodule, which is used to sort the user's motion physiological information from the start of the motion to the current time according to time to form a motion physiological information timing submodule, which is used to sort the user's pace information from the start of the motion to the current time according to time to form a user pace information timing submodule; an attenuation model prediction submodule, which is used to input the motion physiological information timing, the user pace information timing and the remaining distance information into the attenuation prediction model to obtain the attenuation factor.

[0028] In some embodiments of the present application, the attenuation prediction model includes a timing feature extraction layer, a fusion feature interaction layer and an attenuation factor prediction layer; the attenuation model prediction submodule specifically includes: a timing feature fusion unit, which is used to input the sports physiological information timing and the user pace information timing into the timing feature extraction layer to extract timing features and obtain fused timing features; a fusion feature enhancement unit, which is used to splice the fused timing features with the remaining distance information and then input them into the fusion feature interaction layer for feature enhancement to obtain enhanced fusion features; an attenuation factor prediction unit, which is used to input the enhanced fusion features into the attenuation factor prediction layer to obtain an attenuation factor.

[0029] In some embodiments of the present application, the timing feature extraction layer includes a bidirectional LSTM network, a Transformer encoder, and a self-attention network; the timing feature fusion unit specifically includes: a bidirectional LSTM network subunit, which is used to input the motion physiological information timing into the bidirectional LSTM network to capture long-term dependencies and obtain the motion physiological information timing features; a Transformer encoding subunit, which is used to input the user pace information timing into the Transformer encoder to capture the periodicity and mutation features in the timing and obtain the user pace information timing features; a timing feature fusion subunit, which is used to fuse the motion physiological information timing features and the user pace information timing features through the self-attention network to obtain a fused timing feature.

[0030] In some embodiments of the present application, the motion guidance device also includes: a sample acquisition module, used to acquire user historical motion data and obtain a user motion behavior sample set, the user motion behavior sample set contains multiple user motion behavior samples, each user motion behavior sample corresponds to a section of the user's motion behavior, and contains the user pace information timing, user pace information timing and remaining distance information of the user in the section of motion behavior, and are all marked with corresponding attenuation factor labels; a sample input module, used to input each of the user motion behavior samples in the user motion behavior sample set into the attenuation prediction model one by one to obtain the corresponding attenuation factor; a parameter update module, used to update the parameters of the attenuation prediction model according to the attenuation factor and the attenuation factor label until the predetermined end condition is met, stop training, and obtain a trained attenuation prediction model.

[0031] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the motion guidance method as described in the above embodiment is implemented.

[0032] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the motion guidance method as described in the above embodiments.

[0033] According to one aspect of an embodiment of the present application, a computer program product is provided, comprising one or more computer programs, which implement the steps of the motion guidance method described above when executed by one or more processors.

[0034] In some embodiments of the present application, the technical solutions provided dynamically calculate the remaining time and finish time during exercise, allowing users to see in real time whether they are exceeding or falling behind their target time. This real-time feedback helps users better control the rhythm of the race, adjusting their pace to avoid exhaustion or over-consumption, and ultimately achieving their ideal finish time.

[0035] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, explaining the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0037] Figure 1 A schematic diagram shows an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied.

[0038] Figure 2 A flow chart of a motion guidance method provided in an embodiment of the present application is shown.

[0039] Figure 3 Shown according to Figure 2 A specific implementation flow chart of step S200 in the exercise guidance method shown in the corresponding embodiment.

[0040] Figure 4 Shown according to Figure 3 A specific implementation flowchart of step S230 in the exercise guidance method shown in the corresponding embodiment.

[0041] Figure 5 A schematic diagram of a motion interface during a motion process provided by an embodiment of the present application is shown.

[0042] Figure 6 A flow chart of another exercise guidance method provided in an embodiment of the present application is shown.

[0043] Figure 7 A schematic diagram of the first exercise interface after the exercise provided in an embodiment of the present application is shown.

[0044] Figure 8 A schematic diagram of the second exercise interface after the exercise provided in an embodiment of the present application is shown.

[0045] Figure 9A schematic structural diagram of a sports guidance device provided in an embodiment of the present application is shown.

[0046] Figure 10 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0048] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0049] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the object characteristics, interactive behavior characteristics, and user information involved in this specification are all obtained with full authorization.

[0050] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0051] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0052] Figure 1 A schematic diagram shows an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied.

[0053] like Figure 1 As shown, the system architecture may include terminal devices (such as Figure 1 101, tablet computer 102, and portable computer 103, which may also be a desktop computer, etc.), network 104, and server 105. Network 104 is a medium for providing a communication link between the terminal device and server 105. Network 104 can include various connection types, such as wired communication links, wireless communication links, etc.

[0054] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0055] The user can use the terminal device to interact with the server 105 through the network 104 to receive or send messages, etc. The server 105 can be a server that provides various services. For example, the user uses the terminal device 103 (or the terminal device 101 or 102) to upload the user's motion information to the server 105. The user's motion information includes pace information, distance information, and time information. The distance information includes the remaining distance information, and the time information includes the elapsed time information. The server 105 can determine the user's attenuation factor based on the user's motion information; estimate the user's remaining time based on the remaining distance information, the pace information, and the attenuation factor; and obtain the user's estimated finishing time based on the user's remaining time and the elapsed time information.

[0056] It should be noted that the exercise guidance method provided in the embodiments of the present application is generally executed by the server 105, and accordingly, the exercise guidance device is generally set in the server 105. However, in other embodiments of the present application, the terminal device may also have similar functions as the server, thereby executing the exercise guidance solution provided in the embodiments of the present application.

[0057] The following is a detailed description of the implementation details of the technical solution of the embodiment of the present application:

[0058] Figure 2 A flow chart of a motion guidance method according to an embodiment of the present application is shown. The motion guidance method can be executed by a server, which can be Figure 1 Refer to the server shown in . Figure 2 As shown, the exercise guidance method at least includes:

[0059] S100, obtaining user motion information, the user motion information including pace information, distance information and time information, the distance information including remaining distance information, and the time information including elapsed time information.

[0060] S200: Determine a user's attenuation factor based on the user's motion information.

[0061] S300 , estimating the remaining time of the user based on the remaining distance information, the pace information, and the attenuation factor.

[0062] S400: Obtain the user's estimated finishing time based on the user's remaining time and the elapsed time information.

[0063] In the embodiments of this application, by dynamically calculating the remaining time and finish time during exercise, users can see in real time whether they are exceeding or falling behind their target time. This real-time feedback can help users better control the rhythm of the race, adjust their pace to avoid exhaustion or over-consumption, and ultimately achieve an ideal finish time.

[0064] Because each user's stamina decays differently, a dynamic variable called a "decay factor" is used to personalize the prediction model, making the estimated time more realistic. This means that even on the same course and distance, different users can receive unique feedback based on their stamina and pace.

[0065] In S100, users' exercise data is collected in real time through devices such as smart bracelets, running watches, or mobile phones. This data includes pace (current running speed), distance (distance covered and remaining), and elapsed time. This information is typically collected through GPS or motion sensors and transmitted to the analysis system.

[0066] In S200, a decay factor is determined based on the user's remaining running distance (typically provided by GPS), historical exercise data, and a physiological model. The decay factor reflects the gradual decline in the user's physical strength over time. The specific decay rate of the user is typically determined based on a large amount of exercise data and statistical analysis.

[0067] Specifically, in the first embodiment of the present application, the specific implementation of step S200 can refer to the following embodiment. Figure 2 The detailed description of step S200 in the exercise guidance method shown in the corresponding embodiment, in the exercise guidance method, the pace information includes current pace information, and step S200 may include the following steps:

[0068] A preliminary estimated time is obtained based on the remaining distance information and the current pace information.

[0069] An attenuation factor for each predetermined time period is determined based on the preliminary estimated time.

[0070] In this embodiment, by comprehensively considering the remaining distance, current pace, and the influence of the attenuation factor, this method can achieve a more accurate prediction of the athlete's finishing time. This prediction not only takes into account the current state of the exercise, but also dynamically predicts physical strength attenuation, making the estimated result closer to the actual situation. The attenuation factor is dynamically adjusted as time and exercise state change, ensuring that it can reflect the athlete's actual physical strength changes in real time, helping the athlete to adjust the pace in time and optimize the exercise effect. Through the personalized setting of the attenuation factor, it is possible to provide tailored guidance based on the exercise characteristics of different users, thereby improving exercise efficiency and the possibility of achieving goals.

[0071] Specifically, in some embodiments, the initial estimated time is obtained by dividing the remaining distance information by the current pace. In other embodiments, the remaining distance information divided by the current pace is multiplied by an adjustment factor to extend the initial estimated time closer to the actual time. This adjustment factor can be understood as the inverse of the average attenuation factor of the remaining distance. Therefore, the initial estimated time can also be obtained by applying the average attenuation factor to the current pace.

[0072] Therefore, the adjustment coefficient or the average attenuation factor is related to the remaining distance information. In some embodiments, the adjustment coefficient or the average attenuation factor and the remaining distance information can be obtained based on the user's historical motion data. It can be a formula, table or model, which is not limited in this application.

[0073] When the preliminary estimated time has not been adjusted by the adjustment coefficient or the average attenuation factor, the preliminary estimated time can be divided into units of predetermined time windows to obtain multiple predetermined time periods with predetermined time windows as units. The distance length corresponding to each predetermined time period is the same. At this time, each predetermined time period is fixed as a time period of equal distance length (it can also be considered as a distance segment of equal distance length). At this time, the attenuation factors in each predetermined time period are multiplied in chronological order to obtain the attenuation factor of each predetermined time period. That is, the pace of each predetermined time period is attenuated by the corresponding attenuation factor on the basis of the pace of the previous time period.

[0074] For example, in one embodiment, the preliminary estimated time is 5 minutes, the predetermined time window is 1 minute, and the pace decays by 0.1% per minute, then the decay factor is 1-0.1%=99.9%. The preliminary time can be divided into 5 time periods of 1 minute, and the distance corresponding to these 5 time periods is a. These 5 time periods can then be fixed as 5 predetermined time periods with a length of a, and the decay factors within each predetermined time period are multiplied cumulatively. The decay factors for each predetermined time period can be obtained in order of time: 99.9%, (99.9%)2, (99.9%)3, (99.9%)4, and (99.9%)5.

[0075] After the attenuation factor is adjusted, the distance lengths corresponding to the predetermined time periods remain the same, but the times are no longer the same. However, the sum of the total distance lengths is still equal to the remaining distance.

[0076] When the initial estimated time has been adjusted using an adjustment coefficient or average attenuation factor, the initial estimated time can be segmented into multiple time periods based on the predetermined time window. Each of these time periods can then be fixed to be of equal length. The attenuation factor for each time period can be multiplied cumulatively in chronological order to obtain the attenuation factor for each predetermined time period.

[0077] When adjusting the attenuation factor, the time of each scheduled time period remains unchanged, and the distance will be shortened due to the adjustment of the attenuation factor. Therefore, when estimating the user's remaining time later, the sum of the total distance lengths of each scheduled time period needs to be equal to the remaining distance. The last scheduled time period can be a split time period.

[0078] Similarly, when the preliminary estimated time is not adjusted by the adjustment coefficient or the average attenuation factor, it is also possible to obtain multiple time periods in units of predetermined time windows, and then add a predetermined number of time periods to cover the remaining finishing time, so as to fix each predetermined time period to a time period of equal length.

[0079] Specifically, in the second embodiment of the present application, the specific implementation of step S200 can refer to the following embodiment. Figure 2 The detailed description of step S200 in the exercise guidance method shown in the corresponding embodiment, in which the distance information also includes the target distance, step S200 may include the following steps:

[0080] The proportion of the user's uncompleted distance is obtained according to the target distance and the remaining distance information.

[0081] The attenuation factor is determined according to the user's uncompleted distance ratio.

[0082] In this embodiment, the logic of target distance → unfinished ratio → attenuation factor is used to adapt to sports goals of different distances (such as 5km run, half marathon, full marathon), and automatically adjust the attenuation model. For example: in a 5km run, when there are 2km left (40% unfinished ratio), the attenuation factor is small (physical strength is easy to maintain); in a full marathon, when there are 20km left (50% unfinished ratio), the attenuation factor is large (physical strength consumption is increased). This solves the problem that a single attenuation rule cannot adapt to the universality of different target scenarios. In long-distance sports (such as marathons), users can intuitively perceive the difficulty of the remaining challenge through the unfinished distance ratio, and combine it with the attenuation factor adjustment strategy (such as actively accelerating when the remaining ratio is >50%, and maintaining the rhythm when <30%) to avoid the common problems of excessive consumption in the early stage and inability to complete in the later stage, thereby improving the controllability of goal achievement.

[0083] Specifically, the above attenuation factor can be obtained by the following formula:

[0084]

[0085] Where p is the attenuation factor, R l is the user's unfinished distance ratio, S a is the target distance, S l The remaining distance information.

[0086] Assuming that the user has currently completed 80% of the distance, substituting the above formula into the attenuation factor, we can obtain 1-(1-0.8) / 10=0.98.

[0087] Specifically, in the third embodiment of the present application, the specific implementation of step S200 can refer to the following embodiment. Figure 2 The detailed description of step S200 in the exercise guidance method shown in the corresponding embodiment, in the exercise guidance method, the pace information includes current pace information and previous pace information, and the previous pace information is pace information before the current moment. Step S200 may include the following steps:

[0088] A speed change parameter is determined based on the current pace information and the previous pace information.

[0089] An attenuation factor of the user is determined according to the speed change parameter.

[0090] In this embodiment, the temporal characteristics of the previous pace (such as the rate of change and trend) are used to quantify the physical strength decay, so that the device can perceive the user's fatigue state in real time. By adjusting the differentiated decay factor, the user is provided with pace suggestions that fit the actual physical strength changes (such as the prompt "need to accelerate 0.2km / h to compensate for the decay" when decelerating). By processing the pace data in a fixed time or distance window, the calculation efficiency and estimation accuracy are balanced, which is suitable for real-time sports guidance scenarios such as smart watches and sports apps.

[0091] The previous pace information may include pace information of several moments before the current moment, including the previous moment, or may only include pace information of the previous moment.

[0092] When the previous pace information includes pace information of several moments before the current moment, including the previous moment, the user's previous decay timing can be determined based on the current pace information and the previous pace information; and then the speed change parameter can be determined based on the previous decay timing.

[0093] The previous decay sequence can be used to determine whether the user's current decay mode is gradual decay or sudden decay. Gradual decay refers to a uniform increase in pace over time (e.g., a 5-second slowdown per kilometer), with the decay factor increasing linearly. Sudden decay refers to a sudden drop in pace (e.g., a sudden drop from 5 minutes and 0 seconds per kilometer to 5 minutes and 30 seconds per kilometer due to exhaustion), with the decay factor immediately increasing significantly and triggering an alert.

[0094] Specifically, a speed difference sequence can be obtained by the speed difference between each moment and the moment before the moment, and a speed change parameter can be obtained by processing the speed difference sequence. Then, a weighted change is performed based on the speed change parameter to obtain the attenuation factor.

[0095] When the previous pace information only includes the pace information at the previous moment, the ratio of the pace information at the previous moment to the current pace information can be used as the speed change parameter, and then after further parameter adjustment, the user attenuation factor can be obtained.

[0096] Specifically, in some embodiments, the specific implementation of step S200 can be found in Figure 3 . Figure 3 is based on Figure 2 The detailed description of step S200 in the exercise guidance method shown in the corresponding embodiment, in the exercise guidance method, the user exercise information also includes the user's exercise physiological information from the start of the exercise to the current state, and the pace information includes the user's pace information from the start of the exercise to the current state. Step S200 may include the following steps:

[0097] S210 , sorting the user's exercise physiological information from the start of exercise to the current state according to time to form an exercise physiological information time sequence.

[0098] S220 , sorting the user's pace information from the start of exercise to the current time according to time to form a time sequence of the user's pace information.

[0099] S230 , inputting the sports physiological information time series, the user pace information time series, and the remaining distance information into an attenuation prediction model to obtain an attenuation factor.

[0100] In this embodiment, through the synchronous analysis of the timing of sports physiological information (such as heart rate, blood oxygen) and the timing of pace, a physiological threshold-triggered attenuation mode can be achieved (such as when the heart rate is continuously >165 beats / minute, the pace drops by 0.1 km / h every 5 minutes). When the user's physiological state is abnormal (such as a sudden surge in heart rate), the model immediately updates the attenuation factor and warns of potential risks in advance (such as if the current heart rate is 180 beats / minute, if this intensity is maintained, the remaining 5 km pace will drop by 15%). Users can adjust their strategies in time to avoid physical collapse.

[0101] In S210 , exercise physiological information typically includes data such as heart rate, respiratory rate, and blood oxygen saturation. The electronic device collects this data in real time and arranges it in chronological order to form a time series of exercise physiological information. This data reflects the user's physiological changes during exercise and helps assess the user's physical exertion and exercise intensity.

[0102] In S220 , pace information refers to the distance or speed the user runs per unit time during exercise. The electronic device records the pace at each point in time and chronologically sorts this data to form a time series of pace information. Pace information reflects the user's exercise rhythm and can reveal fatigue and changing trends during exercise.

[0103] In S230, the time series of physiological information, pace information, and remaining distance information are input into a decay prediction model. The model analyzes this data to calculate a decay factor. The decay factor can be used to indicate the degree of decline in the user's current state, such as the user's physical exhaustion and reduced pace over time. This decay factor is predicted in real time based on dynamically changing factors such as the user's physiological condition, pace, and remaining distance. This factor helps adjust the estimated finish time to make the prediction more consistent with the user's actual performance.

[0104] It should be noted that the attenuation factor estimated by the attenuation estimation model may be an attenuation factor value or a series of attenuation factors arranged in chronological order.

[0105] Specifically, in some embodiments, the specific implementation of step S230 can be found in Figure 4 . Figure 4 is based on Figure 3 The detailed description of step S230 in the motion guidance method shown in the corresponding embodiment, in which the attenuation prediction model includes a temporal feature extraction layer, a fusion feature interaction layer, and an attenuation factor prediction layer, step S230 may include the following steps:

[0106] S232: Input the sports physiological information time series and the user pace information time series into a time series feature extraction layer to extract time series features and obtain fused time series features.

[0107] S234: splicing the fused temporal features and the residual distance information and inputting the concatenated features into a fusion feature interaction layer for feature enhancement to obtain enhanced fusion features.

[0108] S236: Input the enhanced fusion feature into the attenuation factor prediction layer to obtain the attenuation factor.

[0109] In this embodiment, by introducing a three-level process involving time series feature extraction, fused feature interaction, and attenuation factor prediction, it is possible to accurately model the athletic decay process at different time points and under different exercise states. The attenuation factor prediction makes finish time estimates more accurate, avoiding the large errors found in traditional methods. By integrating information about the user's physiological state, exercise rhythm, and remaining exercise volume, the prediction can be dynamically adjusted based on real-time changes during exercise, ensuring that guidance and adjustments during exercise are consistent with the athlete's actual physical changes and performance.

[0110] In S232, the temporal feature extraction layer focuses on capturing the dynamic relationship between physiological indicators and pace, avoiding the loss of temporal patterns caused by static splicing. For example, if the heart rate continues to increase by 10 beats per minute, it can be determined that the pace has decreased by an average of 0.2 km / h.

[0111] Specifically, LSTM / GRU networks or Temporal Convolutional Networks (TCNs) can be used to process time series information. LSTM / GRU networks can capture long-range dependencies, such as the impact of heart rate changes on pace over the next five minutes. TCNs can process variable-length time series data and are suitable for streaming data that updates in real time during exercise.

[0112] Specifically, in some embodiments, the specific implementation of step S232 can refer to the following embodiments. Figure 4The detailed description of step S232 in the motion guidance method shown in the corresponding embodiment, in which the temporal feature extraction layer includes a bidirectional LSTM network, a Transformer encoder, and a self-attention network, step S232 may include the following steps:

[0113] The time series of the sports physiological information is input into a bidirectional LSTM network to capture long-term dependencies and obtain the time series features of the sports physiological information.

[0114] The user pace information time series is input into the Transformer encoder to capture the periodicity and mutation characteristics in the time series, thereby obtaining the user pace information time series characteristics.

[0115] The motion physiological information temporal features and the user pace information temporal features are fused through a self-attention network to obtain a fused temporal feature.

[0116] In this embodiment, a bidirectional LSTM network captures long-term dependencies between physiological indicators such as heart rate through bidirectional information flow. For example, a sustained increase in heart rate 30 minutes ago indicates current lactate accumulation, which in turn indicates increased pace decay and calls for adjustment of the decay factor. The Transformer encoder utilizes a self-attention mechanism to capture the periodic and sudden changes in pace time series, reducing the error in capturing pace fluctuations by approximately 22%. Self-attention fusion dynamically assigns weights to physiological and pace features, making the fused features more targeted. For example, when the remaining distance is short, the weight of the impact of sudden changes in pace on the decay factor increases.

[0117] A cyclical pattern in the pace sequence might be a sprint every five minutes, or a 1 minute / km drop every five minutes. A sudden deceleration might indicate exhaustion.

[0118] The above embodiment can quantitatively analyze the contribution of different factors to attenuation by separating the extraction paths of physiological and pace characteristics, and provide users with more explanatory exercise suggestions.

[0119] In S234, the fusion feature interaction layer explicitly models the interaction between temporal dynamic features and the static pressure of the remaining distance (for example, for every 1 km increase in the remaining distance, the attenuation factor corresponding to the same heart rate level increases by 0.05%), so that the model can distinguish between fatigue caused by mid-distance and fatigue caused by pressure in the later stages.

[0120] Specifically, the fused time series features and the residual distance information can be concatenated, and the nonlinear relationship between the features can be learned using a fully connected layer through a multi-layer perceptron (MLP). Then, the attention weight of the residual distance on the time series features is introduced (for example, the longer the residual distance, the more the model pays attention to whether the recent heart rate is abnormal). Finally, an enhanced fusion feature is obtained, which contains the interactive information of the time series dynamic features and the residual distance.

[0121] In S236, a single or multi-layer fully connected layer can be used to map the enhanced fusion features to the attenuation factor through a linear layer, and its output range can be limited by the activation function. Furthermore, the prediction results can be post-processed in combination with common sense of exercise physiology to avoid estimation anomalies caused by extreme values.

[0122] A post-processing constraint may be that the decay factor must not exceed 120% of the user's historical maximum decay rate.

[0123] It should be noted that the training method of the above-mentioned attenuation prediction model can refer to the following embodiment. The training steps of the above-mentioned attenuation prediction model specifically include:

[0124] The user's historical motion data is obtained to obtain a user motion behavior sample set, wherein the user motion behavior sample set includes multiple user motion behavior samples, each user motion behavior sample corresponds to a segment of the user's motion behavior, and includes the user pace information timing, user pace information timing and remaining distance information of the user in the segment of motion behavior, and are all marked with corresponding attenuation factor labels.

[0125] Each of the user motion behavior samples in the user motion behavior sample set is input into the attenuation prediction model one by one to obtain a corresponding attenuation factor.

[0126] The parameters of the attenuation prediction model are updated according to the attenuation factor and the attenuation factor label until a predetermined end condition is met, and the training is stopped to obtain a trained attenuation prediction model.

[0127] In this embodiment, the model is trained on a large number of historical user samples to identify the user's attenuation patterns in different scenarios, making the model more targeted. Furthermore, as the user's fitness level improves, their physical strength attenuation slows down. New data continuously updates the model parameters, ensuring that the attenuation prediction remains relevant to the user's current state.

[0128] In an embodiment of the present application, during training, a user motion behavior sample set containing multiple user motion behavior samples can be first obtained, each user motion behavior sample contains the user pace information timing, user pace information timing and remaining distance information of the user in that segment of motion behavior, and is marked with a corresponding attenuation factor label; then the multiple user motion behavior samples are divided into a training set, a validation set and a test set according to a predetermined ratio, and then the parameters of the encoder and decoder in the attenuation prediction model are adjusted and determined according to the user motion behavior samples included in the training set, the validation set and the test set to obtain a trained attenuation prediction model.

[0129] It should be noted that each sample in the aforementioned user motion behavior sample set is obtained from the user's historical motion data. Specifically, the user's historical motion data contains motion data from multiple segments of the user's historical motion. Each segment in the user's historical motion data is randomly cut according to a predetermined window threshold, resulting in two segments of motion data: a sample segment before the cut point and a label segment after the cut point. Based on the data in the label segment, the attenuation factor of the segment can be determined. This attenuation factor is used as the label for the preceding sample segment, resulting in a user motion behavior sample labeled with the corresponding attenuation factor.

[0130] It should be noted that the aforementioned user motion behavior samples include interference items and blank items to improve the model's ability to identify these items. During post-processing of the time series data, the time series data generated by the user's pace information time series, the user pace information time series, and the remaining distance information image for the user's motion behavior in that segment containing these interference items and blank items are removed, thereby filtering out these interference items and blank items.

[0131] When training the model, the user motion behavior sample set can be divided into a training set, a validation set, and a test set. Then, training is performed based on the training set, validation is performed based on the validation set, and testing is performed based on the test set to obtain a trained attenuation prediction model.

[0132] Before training based on the training set, the user's pace information time series, user pace information time series, and remaining distance information image samples in the training set can be preprocessed. Preprocessing includes image resizing, normalization, data augmentation, and category encoding.

[0133] The specific method for data enhancement is to sequentially transform the time series data and arbitrarily change the cutting points of the time series data. These operations can better expand the form of the user's pace information timing, user pace information timing and remaining distance information in the user's exercise behavior in this section.

[0134] In some embodiments, the GAN network can also be used to generate some data that is difficult to identify but not easy to obtain, such as the user pace information timing, user pace information timing and remaining distance information of the user in this segment of exercise behavior, which has data conflicts, data distortion, etc. This helps the algorithm model to accurately identify the user pace information timing, user pace information timing and remaining distance information of the user in this segment of exercise behavior, helps to reduce the phenomenon of misidentification and missed identification, and helps to improve the accuracy of the model in identifying the user pace information timing, user pace information timing and remaining distance information of the user in this segment of exercise behavior for special data.

[0135] After obtaining the enhanced training set, the attenuation prediction model can be trained based on the enhanced training set to update the parameters and weights in the network.

[0136] Specifically, the attenuation prediction model is fed with the user's time series of pace information, the time series of pace information, and the image samples of remaining distance information from the training set for the user's exercise behavior during that segment. The model then outputs an attenuation factor. The attenuation factor is compared with the attenuation factor label to calculate a loss function. Stochastic gradient descent is then used to minimize the loss function. The parameters and weights in the attenuation prediction model are updated via backpropagation until the loss function meets predetermined conditions, such as convergence or being less than a predetermined threshold.

[0137] In some embodiments, the attenuation factor includes an attenuation factor value or an attenuation factor time series, and the attenuation factor label may include at least one of an attenuation factor value label or an attenuation factor time series label. When calculating the loss function, the attenuation factor value is compared with the attenuation factor value label and / or the attenuation factor time series is compared with the attenuation factor time series label.

[0138] After training, the decay prediction network, whose parameters have been updated based on the training set, can be verified using the validation set. Specifically, the decay prediction network is debugged based on the validation set data. When the loss function meets the predetermined conditions, the model parameters for that stage are output. If the loss function does not meet the predetermined conditions, hyperparameters such as the learning rate are automatically adjusted, and the next round of network model training is carried out.

[0139] When the loss function calculated on the validation set meets predetermined conditions, the parameters and weights are retained, and then the retained parameters are tested on the test set. Specifically, the test set data is input so that the decay prediction network for the retained parameters and weights can output the decay factor and model weights. Based on the comparison of the model losses and corresponding weights over multiple rounds, the model weight with the minimum loss is output to determine the trained decay prediction model.

[0140] After obtaining the trained attenuation prediction model, the attenuation factor can be estimated based on the attenuation prediction model.

[0141] In addition, no data augmentation is required for the data input in the validation set and the test set.

[0142] In S300, the remaining time is calculated based on the remaining distance, the current pace information, and the attenuation factor. This calculation not only takes into account the current pace, but also incorporates the influence of the attenuation factor to make the model more accurate.

[0143] Specifically, in the first embodiment of the present application, the specific implementation of step S300 can refer to the following embodiment. Figure 2The detailed description of step S300 in the exercise guidance method shown in the corresponding embodiment, in the exercise guidance method, step S300 may include the following steps:

[0144] According to the attenuation factor of each predetermined time period, the pace information in each predetermined time period is corrected based on the user's current pace information to obtain a corrected pace corresponding to each predetermined time period.

[0145] The user's remaining time is estimated based on the corrected pace and remaining distance information.

[0146] In this embodiment, the dynamic adjustment of the attenuation factor enables the method to update the remaining time prediction in real time based on the user's exercise progress, providing personalized exercise strategy guidance. Athletes can adjust their exercise intensity in a timely manner based on the corrected pace information to avoid physical exhaustion.

[0147] Specifically, as described above, when each scheduled time period is fixed as a time period of equal distance length, the corresponding attenuation factor is applied to each time period of equal distance length to obtain a corrected pace. Then, based on the distance length and corrected pace corresponding to each scheduled time period, the corrected time corresponding to each scheduled time period is obtained. The corrected time corresponding to all scheduled time periods is accumulated to obtain the user's remaining time.

[0148] When each scheduled time period is fixed to a fixed length, the corresponding attenuation factor is applied to each equal-length time period to obtain a corrected pace. Then, based on the time length and corrected pace corresponding to each scheduled time period, the corrected distance corresponding to each scheduled time period is calculated. The corrected distance corresponding to each scheduled time period is accumulated in chronological order, and the time corresponding to each scheduled time period is also accumulated. When the accumulated corrected distance reaches the remaining distance, a determination is made as to whether the accumulated corrected distance equals the remaining distance. If so, the accumulated time is used as the estimated remaining time for the user.

[0149] If it is not equal to (i.e. greater than), the last accumulated scheduled time period will be split according to the difference between the accumulated corrected distance and the remaining distance. The sum of the split time period and the previous time period will be equal to the remaining distance, and the user's remaining time will be obtained.

[0150] Specifically, in the second embodiment of the present application, the specific implementation of step S300 can refer to the following embodiment. Figure 2 The detailed description of step S300 in the exercise guidance method shown in the corresponding embodiment, in which the pace information includes pace information within the current predetermined time interval, step S300 may include the following steps:

[0151] The current average pace is determined based on the pace information within the current predetermined time interval.

[0152] An average decay pace is determined based on the current average pace and the decay factor.

[0153] The remaining time of the user is obtained according to the remaining distance information and the average decay pace.

[0154] In this embodiment, short-term fluctuations are filtered out by averaging the pace over a time window, making the estimation model more robust (for example, if a user temporarily slows down due to road conditions, the system will not mistakenly judge it as a decrease in physical strength and over-adjust the estimate). The time window length (e.g., 5 seconds / 10 seconds) can be dynamically adjusted according to the type of exercise (e.g., 5 seconds for short-distance exercise and 10 seconds for long-distance exercise), taking into account both real-time performance and stability. For example, in a marathon, a 10-second time window can not only reflect pace changes in a timely manner, but also avoid frequent corrections caused by short-term fluctuations such as breathing adjustments.

[0155] Specifically, the remaining user time can be obtained by the following formula:

[0156]

[0157] Among them, ETE is the remaining time of the user, v p is the average decay pace, S l is the remaining distance information, v ave is the current average pace, p is the attenuation factor, R l The ratio of users’ uncompleted journey.

[0158] Specifically, for example, within a 10-second time window, the pace is sampled every 2 seconds, and 5 values ​​are obtained: 5min06s / km, 5min04s / km, 5min00s / km, 5min04s / km, and 5min05s / km, then the current average pace is 5min04s / km.

[0159] Continuing with this, let's take a decay factor of 0.98 and a current average pace of 5 minutes and 4 seconds per kilometer as an example. The average decay pace is approximately 5 minutes and 10 seconds per kilometer. If the remaining distance is 4.22 kilometers, the user's remaining time is 21 minutes and 48 seconds.

[0160] In step S400, the estimated finishing time is calculated based on the combination of the elapsed time and the remaining time. This time can be updated in real time and adjusted based on the user's actual performance.

[0161] After the estimated finishing time is obtained, the estimated finishing time can be displayed on the display interface of the electronic device, such as Figure 5501 is the amount of exercise completed, 502 is the real-time pace, 503 is the progress bar, 504 is the real-time heart rate, and 505 is the estimated finish time. The estimated finish time is displayed in a larger, more prominent font in the center of the lower part of the interface, making it easier for users to notice the estimated finish time and providing guidance on how to adjust their current pace.

[0162] In some embodiments of the present application, after S400, as Figure 6 As shown, the exercise guidance method further includes:

[0163] S602: In response to the end of the exercise function, determine whether the remaining distance information is greater than a predetermined distance threshold.

[0164] S604: If the remaining distance information is greater than a predetermined distance threshold, the user's estimated finishing time is displayed on the exercise end interface.

[0165] S606: If the remaining distance information is not greater than the predetermined distance threshold, the actual finishing time of the user is displayed on the exercise end interface.

[0166] In this embodiment, flexible performance display is achieved through threshold judgment. For example, when running a half marathon, if you stop halfway, the estimated finishing time will be displayed with 5km remaining, and the user can evaluate the possibility of completing the run; if you stop the exercise with 10m remaining, the actual finishing time will be displayed, which is close to the actual completion situation. This solves the problem of information distortion caused by a one-size-fits-all display method and improves user acceptance of exercise results.

[0167] By combining the relationship between the remaining distance and the threshold, users can quickly determine whether the interruption timing affects the reference value of the results, thus enhancing the practicality of the exercise data. If the remaining distance is greater than the threshold, you know that you have not completed most of the goal, and the display will be as follows: Figure 7 The interface shown; when it is less than the threshold, it is clear that it is close to completion and the actual score is valid. Figure 8 The interface shown.

[0168] It should be noted that the predetermined distance threshold is generally set to less than 100m, such as 1m, 10m, or 50m. This is primarily to compensate for positioning errors in electronic devices. Furthermore, when a user is not yet finished but is close to completion, the system considers them to have completed the race, giving them confidence and enhancing their experience. For ultra-long-distance sports (such as orienteering and marathons), the predetermined distance threshold can be set to 1km.

[0169] In some embodiments of the present application, the time information also includes the target finishing time. Then, after S400, the exercise coaching method further includes:

[0170] A recommended pace is obtained based on the user's estimated finishing time, the target finishing time, the pace information, and the attenuation factor.

[0171] In this embodiment, the overall goal is broken down into phased pace requirements (e.g., 5min05s / km is required for the remaining 10km), and is dynamically adjusted according to the attenuation factor (e.g., 5min15s / km is acceptable for the last 3km due to increased physical strength attenuation).

[0172] For example, if the target finish time is 3 hours and the current estimate is 3 hours and 10 minutes, a preliminary calculation is performed to increase the current pace by 0.2 km / h. After incorporating the attenuation factor, it is recommended to accelerate to 4 minutes and 55 seconds per kilometer for the first 5 km and maintain 5 minutes and 5 seconds per kilometer for the last 5 km. Compared to maintaining a pace of 4 minutes and 55 seconds per kilometer throughout the entire race, the method of this embodiment will not cause the user to run out of energy and collapse in the second half. It should be noted that in this embodiment, the recommended pace can be updated in real time every minute or every second to ensure that the user can adjust the pace in real time.

[0173] The following describes an embodiment of the device of the present application, which can be used to execute the exercise guidance method in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the exercise guidance method in the above embodiment of the present application.

[0174] Figure 9 A block diagram of a sports guidance device according to an embodiment of the present application is shown.

[0175] Reference Figure 9 As shown, a sports coaching device 900 according to an embodiment of the present application includes: a sports information acquisition module 910, an attenuation factor determination module 920, a remaining time estimation module 930, and a finishing time estimation module 940.

[0176] The motion information acquisition module 910 is used to obtain the user's motion information, which includes pace information, distance information and time information, the distance information includes remaining distance information, and the time information includes elapsed time information; the attenuation factor determination module 920 is used to determine the user's attenuation factor based on the user's motion information; the remaining time estimation module 930 is used to determine the user's remaining time based on the remaining distance information, the pace information and the attenuation factor; the completion time estimation module 940 is used to obtain the user's estimated completion time based on the user's remaining time and the elapsed time information.

[0177] In some embodiments of the present application, the pace information includes current pace information; the attenuation factor determination module specifically includes: a preliminary estimated time submodule, used to obtain a preliminary estimated time based on the remaining distance information and the current pace information; and a stage attenuation factor submodule, used to determine the attenuation factor of each predetermined time period based on the preliminary estimated time.

[0178] In some embodiments of the present application, the remaining time estimation module specifically includes: a pace information correction submodule, which corrects the pace information in each predetermined time period based on the attenuation factor of each predetermined time period and the user's current pace information to obtain the corrected pace corresponding to each predetermined time period; a remaining time estimation submodule, which estimates the user's remaining time based on the corrected pace and remaining distance information.

[0179] In some embodiments of the present application, the distance information also includes a target distance; the attenuation factor determination module specifically includes: a distance ratio determination submodule, used to obtain the user's uncompleted distance ratio based on the target distance and the remaining distance information; an attenuation factor determination submodule, used to determine the attenuation factor based on the user's uncompleted distance ratio.

[0180] In some embodiments of the present application, the pace information includes the pace information within the current predetermined time interval; the remaining time estimation module specifically includes: a current average pace sub-module, used to determine the current average pace based on the pace information within the current predetermined time interval; an average decay pace sub-module, used to determine the average decay pace based on the current average pace and the decay factor; and a user remaining time sub-module, used to obtain the user remaining time based on the remaining distance information and the average decay pace.

[0181] In some embodiments of the present application, the time information also includes a target finishing time, and the sports guidance device also includes: a pace recommendation module, which is used to obtain a recommended pace based on the user's estimated finishing time, the target finishing time, the pace information and the attenuation factor.

[0182] In some embodiments of the present application, the sports guidance device also includes: a remaining distance judgment module, which is used to determine whether the remaining distance information is greater than a predetermined distance threshold in response to the end of the sports function; a first display module, which is used to display the user's estimated completion time on the sports end interface if the remaining distance information is greater than the predetermined distance threshold; and a second display module, which is used to display the user's actual completion time on the sports end interface if the remaining distance information is not greater than the predetermined distance threshold.

[0183] In some embodiments of the present application, the user motion information also includes the user's motion physiological information from the start of the motion to the current time, and the pace information includes the user's pace information from the start of the motion to the current time; the attenuation factor determination module specifically includes: a physiological information timing submodule, which is used to sort the user's motion physiological information from the start of the motion to the current time according to time to form a motion physiological information timing submodule, which is used to sort the user's pace information from the start of the motion to the current time according to time to form a user pace information timing submodule; an attenuation model prediction submodule, which is used to input the motion physiological information timing, the user pace information timing and the remaining distance information into the attenuation prediction model to obtain the attenuation factor.

[0184] In some embodiments of the present application, the attenuation prediction model includes a timing feature extraction layer, a fusion feature interaction layer and an attenuation factor prediction layer; the attenuation model prediction submodule specifically includes: a timing feature fusion unit, which is used to input the sports physiological information timing and the user pace information timing into the timing feature extraction layer to extract timing features and obtain fused timing features; a fusion feature enhancement unit, which is used to splice the fused timing features with the remaining distance information and then input them into the fusion feature interaction layer for feature enhancement to obtain enhanced fusion features; an attenuation factor prediction unit, which is used to input the enhanced fusion features into the attenuation factor prediction layer to obtain an attenuation factor.

[0185] In some embodiments of the present application, the timing feature extraction layer includes a bidirectional LSTM network, a Transformer encoder, and a self-attention network; the timing feature fusion unit specifically includes: a bidirectional LSTM network subunit, which is used to input the motion physiological information timing into the bidirectional LSTM network to capture long-term dependencies and obtain the motion physiological information timing features; a Transformer encoding subunit, which is used to input the user pace information timing into the Transformer encoder to capture the periodicity and mutation features in the timing and obtain the user pace information timing features; a timing feature fusion subunit, which is used to fuse the motion physiological information timing features and the user pace information timing features through the self-attention network to obtain a fused timing feature.

[0186] In some embodiments of the present application, the motion guidance device also includes: a sample acquisition module, used to acquire user historical motion data and obtain a user motion behavior sample set, the user motion behavior sample set contains multiple user motion behavior samples, each user motion behavior sample corresponds to a section of the user's motion behavior, and contains the user pace information timing, user pace information timing and remaining distance information of the user in the section of motion behavior, and are all marked with corresponding attenuation factor labels; a sample input module, used to input each of the user motion behavior samples in the user motion behavior sample set into the attenuation prediction model one by one to obtain the corresponding attenuation factor; a parameter update module, used to update the parameters of the attenuation prediction model according to the attenuation factor and the attenuation factor label until the predetermined end condition is met, stop training, and obtain a trained attenuation prediction model.

[0187] In the embodiments of this application, by dynamically calculating the remaining time and finish time during exercise, users can see in real time whether they are exceeding or falling behind their target time. This real-time feedback can help users better control the rhythm of the race, adjust their pace to avoid exhaustion or over-consumption, and ultimately achieve an ideal finish time.

[0188] Figure 10 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0189] It should be noted that Figure 10 The computer system of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0190] like Figure 10 As shown, the computer system includes a central processing unit (CPU) 1801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1802 or the program loaded from the storage part 1808 into the random access memory (RAM) 1803, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 1803. The CPU 1801, ROM 1802 and RAM 1803 are connected to each other via a bus 1804. An input / output (I / O) interface 1805 is also connected to the bus 1804.

[0191] The following components are connected to the I / O interface 1805: an input section 1806 including a keyboard, a mouse, and the like; an output section 1807 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 1808 including a hard disk; and a communication section 1809 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 1809 performs communication processing via a network such as the Internet. A drive 1810 is also connected to the I / O interface 1805 as needed. Removable media 1811, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1810 as needed, so that computer programs read from the removable media can be installed in the storage section 1808 as needed.

[0192] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1809, and / or installed from a removable medium 1811. When the computer program is executed by the central processing unit (CPU) 1801, the various functions defined in the system of the present application are executed.

[0193] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0194] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0195] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0196] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0197] This specification also provides a computer program product, which stores at least one instruction, and the at least one instruction is loaded and executed by the processor as described above. Figures 1 to 8 The method of the embodiment shown, the specific execution process can be found in Figures 1 to 8 The detailed description of the illustrated embodiment will not be repeated here.

[0198] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0199] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0200] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0201] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for guiding exercise, characterized in that: The exercise guidance method comprises: Acquire user motion information, the user motion information including pace information, distance information, and time information, the distance information including remaining distance information, and the time information including elapsed time information; determining an attenuation factor of the user based on the user motion information; estimating the remaining time of the user based on the remaining distance information, the pace information, and the attenuation factor; The user's estimated finishing time is obtained based on the user's remaining time and the elapsed time information.

2. The exercise guidance method according to claim 1, wherein: The pace information includes current pace information; The determining of the user's attenuation factor according to the user's motion information specifically includes: Obtaining a preliminary estimated time based on the remaining distance information and the current pace information; An attenuation factor for each predetermined time period is determined based on the preliminary estimated time.

3. The exercise guidance method according to claim 2, wherein: The attenuation factor for each predetermined time period is determined based on the preliminary estimated time, specifically including: Divide the preliminary estimated time into units of scheduled time windows to obtain multiple scheduled time periods in units of scheduled time windows; Fixing each of the predetermined time periods to a time period of equal distance length; The attenuation factors in each of the predetermined time periods are cumulatively multiplied in chronological order to obtain the attenuation factor of each predetermined time period.

4. The exercise guidance method according to claim 2, wherein: The preliminary estimated time is obtained by adjusting the adjustment coefficient; Determining the attenuation factor for each predetermined time period based on the preliminary estimated time specifically includes: Divide the preliminary estimated time into multiple time periods based on the scheduled time window. Fixing each predetermined time period to a time period of equal length; The attenuation factors in each time period are multiplied cumulatively in chronological order to obtain the attenuation factor of each predetermined time period.

5. The exercise guidance method according to claim 2, wherein: The estimating the remaining time of the user based on the remaining distance information, the pace information, and the attenuation factor specifically includes: According to the attenuation factor of each predetermined time period, the pace information in each predetermined time period is corrected based on the user's current pace information to obtain a corrected pace corresponding to each predetermined time period; The user's remaining time is estimated based on the corrected pace and remaining distance information.

6. The exercise guidance method according to claim 1, wherein: The distance information also includes the target distance; The determining of the user's attenuation factor according to the user's motion information specifically includes: Obtaining the proportion of the user's uncompleted journey based on the target journey distance and the remaining distance information; The attenuation factor is determined according to the user's uncompleted distance ratio.

7. The exercise guidance method according to claim 6, wherein: The pace information includes pace information within a current predetermined time interval; The estimating the remaining time of the user based on the remaining distance information, the pace information, and the attenuation factor specifically includes: Determine the current average pace according to the pace information within the current predetermined time interval; Determining an average decay pace based on the current average pace and the decay factor; The remaining time of the user is obtained according to the remaining distance information and the average decay pace.

8. The exercise guidance method according to claim 1, wherein: The pace information includes current pace information and previous pace information, wherein the previous pace information is the pace information before the current moment; The determining of the user's attenuation factor according to the user's motion information specifically includes: determining a speed change parameter according to the current pace information and the previous pace information; An attenuation factor of the user is determined according to the speed change parameter.

9. The exercise guidance method according to claim 8, wherein: The determining of the speed change parameter according to the current pace information and the previous pace information specifically includes: Determining a user's early decay timing based on the current pace information and the early pace information; A speed change parameter is determined according to the early decay time sequence.

10. The exercise guidance method according to any one of claims 1 to 9, wherein: The time information also includes a target finishing time; after obtaining the user's estimated finishing time based on the user's remaining time and the elapsed time information, the exercise guidance method further includes: A recommended pace is obtained based on the user's estimated finishing time, the target finishing time, the pace information, and the attenuation factor.

11. The exercise coaching method according to claim 1, wherein: After obtaining the user's estimated finishing time based on the user's remaining time and the elapsed time information, the exercise guidance method further includes: In response to the end of the exercise function, determining whether the remaining distance information is greater than a predetermined distance threshold; If the remaining distance information is greater than a predetermined distance threshold, the user's estimated finishing time is displayed on the exercise end interface; If the remaining distance information is not greater than the predetermined distance threshold, the user's actual finishing time will be displayed on the exercise end interface.

12. The exercise coaching method according to claim 1, wherein: The user motion information also includes the user's motion physiological information from the start of the exercise to the current time, and the pace information includes the user's pace information from the start of the exercise to the current time; The determining of the user's attenuation factor according to the user's motion information specifically includes: Sort the user's exercise physiological information from the start of exercise to the current time by time to form a time sequence of exercise physiological information; Sort the user's pace information from the start of exercise to the current time by time to form a time series of user pace information; The sports physiological information time series, the user pace information time series and the remaining distance information are input into an attenuation prediction model to obtain an attenuation factor.

13. The exercise coaching method according to claim 12, wherein: The attenuation prediction model includes a time series feature extraction layer, a fusion feature interaction layer, and an attenuation factor prediction layer; Inputting the sports physiological information time series, the user pace information time series, and the remaining distance information into the attenuation prediction model to obtain the attenuation factor specifically includes: Inputting the sports physiological information time series and the user pace information time series into the time series feature extraction layer to extract time series features to obtain fused time series features; The fused time series features and the residual distance information are spliced ​​and input into the fused feature interaction layer for feature enhancement to obtain enhanced fused features; The enhanced fusion features are input into the attenuation factor prediction layer to obtain the attenuation factor.

14. The exercise coaching method according to claim 13, wherein: The temporal feature extraction layer includes a bidirectional LSTM network, a Transformer encoder, and a self-attention network; The step of inputting the sports physiological information time series and the user pace information time series into the time series feature extraction layer to extract time series features and obtain fused time series features specifically includes: Inputting the time series of the sports physiological information into a bidirectional LSTM network to capture long-term dependencies and obtain the time series features of the sports physiological information; Inputting the user pace information time series into the Transformer encoder to capture the periodicity and mutation characteristics in the time series, thereby obtaining the user pace information time series features; The motion physiological information temporal features and the user pace information temporal features are fused through a self-attention network to obtain a fused temporal feature.

15. The exercise coaching method according to claim 13, wherein: The step of splicing the fused time series features and the residual distance information and inputting the spliced ​​features into the fused feature interaction layer for feature enhancement to obtain enhanced fused features specifically includes: After concatenating the fused temporal features and the residual distance information, a fully connected layer is used to learn the nonlinear relationship between the features through a multi-layer perceptron. The attention weight of the residual distance to the temporal features is introduced to obtain enhanced fusion features.

16. The exercise coaching method according to claim 12, wherein: The exercise guidance method further includes: Acquire the user's historical motion data to obtain a user motion behavior sample set, wherein the user motion behavior sample set includes multiple user motion behavior samples, each user motion behavior sample corresponds to a segment of the user's motion behavior, and includes a time series of user pace information, a time series of user pace information, and remaining distance information of the user in the segment of the motion behavior, and each is marked with a corresponding attenuation factor label; Inputting each of the user motion behavior samples in the user motion behavior sample set into the attenuation prediction model one by one to obtain a corresponding attenuation factor; The parameters of the attenuation prediction model are updated according to the attenuation factor and the attenuation factor label until a predetermined end condition is met, and the training is stopped to obtain a trained attenuation prediction model.

17. A sports guidance device, characterized in that: The exercise guidance device comprises: A motion information acquisition module is used to acquire user motion information, wherein the user motion information includes pace information, distance information, and time information, wherein the distance information includes remaining distance information, and the time information includes elapsed time information; an attenuation factor determination module, configured to determine an attenuation factor of a user based on the user motion information; a remaining time estimation module, configured to estimate the remaining time of the user based on the remaining distance information, the pace information, and the attenuation factor; The completion time estimation module is used to obtain the user's estimated completion time based on the user's remaining time and the time spent.

18. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the exercise guidance method according to any one of claims 1 to 11 is implemented.

19. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the exercise guidance method according to any one of claims 1 to 16.

20. A computer program product comprising one or more computer programs, characterized in that When the one or more computer programs are executed by one or more processors, the steps of the exercise guidance method according to any one of claims 1 to 16 are implemented.

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