Intelligent exercise training method and device based on Internet of Things and electronic equipment

Abstract

The invention belongs to the technical field of data processing, and relates to an intelligent exercise training method based on Internet of Things, which comprises the following steps: firstly, acquiring exercise state information and heart rate state information selected by a user, and transmitting the heart rate state information and the exercise state information to edge computing equipment; secondly, the edge computing device constructs a heart rate prediction model according to the real-time heart rate state information and the motion state information of the user, and meanwhile the edge computing device feeds back heart rate prediction information output by the heart rate prediction model in real time to the user in real time; meanwhile, the edge computing device evaluates the fatigue degree of the user according to the heart rate prediction information and the heart rate state information of the user at the time pointed by the heart rate prediction information; and finally, when the fatigue degree of the user in the motion process is abnormal, the edge computing device outputs a fatigue alarm to the user. By means of the technical scheme, real-time data collection, analysis and feedback in the exercise training process are achieved; meanwhile, the invention further designs a device for improving the training effect and electronic equipment.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to an intelligent sports training method, device and electronic equipment based on the Internet of Things. Background Art

[0002] With rising health awareness in modern society, more and more people are engaging in exercise, and the demand for exercise training is also showing a significant growth trend. Particularly in the health and fitness sectors, more and more users are seeking personalized, scientific exercise training programs to improve their performance and avoid the health risks of overtraining. Simultaneously, with technological advancements, particularly the rapid development of the Internet of Things (IoT), smart training devices that use sensors to monitor a user's exercise status in real time, collect and analyze data, and provide instant feedback are becoming a mainstream market trend.

[0003] In this context, traditional exercise training methods can no longer meet the personalized needs of users. Existing training methods rarely consider users' real-time physiological data (such as heart rate, fatigue, etc.) during the execution of training plans, and there is no flexible mechanism to adjust training content in real time based on this data. During training, users may incur unnecessary health risks due to excessive exercise, or fail to achieve the expected results due to too low intensity. At the same time, the exercise plan adjustment methods in the existing technology often ignore the factor of fatigue level and lack a mechanism to monitor and adjust training through real-time heart rate and exercise intensity.

[0004] Based on this, it is urgent to improve the existing sports training methods by combining them with the Internet of Things to solve the technical defects of the existing technical solutions. Summary of the Invention

[0005] One of the purposes of the present invention is to address the shortcomings of the existing technology and provide an intelligent sports training method based on the Internet of Things combined with Internet of Things technology to achieve real-time data collection, analysis and feedback during sports training, dynamically adjust training intensity and evaluate the user's fatigue level in real time.

[0006] In order to achieve the above-mentioned invention objectives, this application implements the following technical solutions:

[0007] An intelligent sports training method based on the Internet of Things includes the following steps:

[0008] S101. Obtaining exercise status information selected by a user and original heart rate information of the user during exercise, obtaining heart rate status information by preprocessing the original heart rate information, and transmitting the heart rate status information and exercise status information to an edge computing device via a remote communication protocol;

[0009] S201: The edge computing device builds a heart rate prediction model based on the user's real-time heart rate status information and exercise status information, and the edge computing device feeds back the heart rate prediction information output by the heart rate prediction model to the user in real time;

[0010] S301. The edge computing device evaluates the user's fatigue level based on the heart rate prediction information and the user's heart rate status information at the time indicated by the heart rate prediction information.

[0011] S401. When the user's fatigue level during exercise becomes abnormal, the edge computing device outputs a fatigue alarm to the user.

[0012] The above technical solution produces the following technical effects:

[0013] The application acquires the user's real-time heart rate information, combines it with their exercise status information, and uses IoT technology to transmit it to an edge computing device in real time, continuously monitoring the user's heart rate changes during exercise. If the heart rate deviates from the expected range, the edge computing device uses a heart rate prediction model to predict the heart rate in real time and output training recommendations, dynamically adjusting training intensity. Compared to traditional fixed training plans, this solution can adjust training content in real time based on the user's actual physiological responses, avoiding overtraining or undertraining.

[0014] At the same time, this application also takes into account the impact of user fatigue. By combining heart rate prediction information with the user's real-time heart rate status information, the edge computing device can assess the user's fatigue level in real time. When an abnormal level of fatigue is detected, the system will immediately issue a warning, prompting the user to rest or reduce the intensity of training. Compared with existing technologies, this application not only relies on predetermined exercise intensity, but also accurately assesses the user's fatigue state through real-time data monitoring and analysis to avoid sports injuries.

[0015] As a further improvement to the intelligent sports training method based on the Internet of Things of the present invention, the heart rate status information includes resting heart rate information, maximum heart rate information, and real-time heart rate information;

[0016] The edge computing device builds a heart rate prediction model based on resting heart rate information, maximum heart rate information, and exercise status information;

[0017] The edge computing model evaluates the user's fatigue level based on the heart rate prediction information calculated by the heart rate prediction model and the real-time heart rate information pointing to the same time as the heart rate prediction information.

[0018] As a further improvement to the intelligent sports training method based on the Internet of Things of the present invention, the heart rate prediction model calculates the heart rate prediction information as follows:

[0019] HR pred (t) = HR rest+(HR max -HR rest )×(1-e -kt )

[0020] Among them, HR pred (t) is the heart rate prediction information; HR rest HR is the resting heart rate information; max is the maximum heart rate information; k is the exercise intensity adjustment factor.

[0021] As a further improvement to the intelligent sports training method based on the Internet of Things of the present invention, the method for calculating the user's fatigue level is:

[0022]

[0023] Among them, Fatigue is the user's fatigue level, HR current Heart rate prediction information HR pred (t) points to the real-time heart rate information HR at the same time current (t), HR pred Predicted HR information for heart rate monitor pred (t) Information at a certain moment of prediction.

[0024] 5. As a further improvement to the IoT-based intelligent exercise training method of the present invention, the exercise intensity adjustment factor is adjusted as follows:

[0025] k=k0·f(HR current , HR rest , ΔHR, Fatigue, Environment)

[0026] Where k0 is the standard factor, which is determined by the exercise state information selected by the user, Environment is the environmental parameter information during the user's exercise, and ΔHR is the amplitude of the user's heart rate change.

[0027] As a further improvement of the present invention's intelligent sports training method based on the Internet of Things, the user's heart rate change amplitude is the real-time heart rate information HR of two users in adjacent one unit time. current The difference in (t).

[0028] As a further improvement to the intelligent sports training method based on the Internet of Things of the present invention, the calculation method of the environmental parameter information Environment is:

[0029]

[0030] Among them, T env is the real-time temperature parameter during the user's exercise, T optimalis the standard temperature parameter, the standard temperature parameter T optimal The value range is: 20℃-25℃.

[0031] As a further improvement to the IoT-based intelligent sports training method of the present invention, the method in which the edge computing device outputs a fatigue alarm to the user is as follows:

[0032] When the user's fatigue level is greater than a first threshold, the edge computing device drives the display device to prompt the user to reduce the intensity of exercise;

[0033] When the user's fatigue level is greater than a second threshold, the edge computing device drives the display device to prompt the user to reduce the intensity of exercise and shorten the time of exercise training;

[0034] When the user's fatigue level is greater than the third threshold, the edge computing device drives the display device to prompt the user to stop exercising.

[0035] The second purpose of the present invention is to provide an intelligent sports training device based on the Internet of Things to address the shortcomings of the existing technology, so as to realize real-time data collection, analysis and feedback during sports training, dynamically adjust the training intensity and evaluate the user's fatigue level in real time.

[0036] In order to achieve the above-mentioned invention objectives, this application implements the following technical solutions:

[0037] An intelligent sports training device based on the Internet of Things includes a data acquisition device and an edge computing device. The edge computing device performs the steps of any of the above methods, and the data acquisition device is used to collect the exercise status information selected by the user and the original heart rate information of the user during exercise.

[0038] The above technical solution produces the following technical effects:

[0039] The intelligent sports training device in this application uses a data acquisition device to obtain real-time information about the user's exercise status and heart rate, and uses edge computing equipment for data processing and analysis, thus realizing an intelligent sports training process. The high precision and real-time performance of the data acquisition device ensures the accuracy of the data, while the powerful computing power of the edge computing equipment ensures fast and efficient data processing. The design of this device not only improves the scientific nature and personalization of sports training, but also effectively reduces the health risks caused by overtraining.

[0040] The third purpose of the present invention is to provide an intelligent sports training electronic device based on the Internet of Things to address the shortcomings of the existing technology, so as to realize real-time data collection, analysis and feedback during sports training, dynamically adjust the training intensity and evaluate the user's fatigue level in real time.

[0041] In order to achieve the above-mentioned invention objectives, this application implements the following technical solutions:

[0042] An intelligent sports training device based on the Internet of Things includes a memory, an edge computing device, and a computer program stored in the memory and executable on the edge computing device. When the edge computing device executes the computer program, the steps of any of the above methods are implemented.

[0043] The above technical solution produces the following technical effects:

[0044] This application's intelligent sports training electronic device achieves comprehensive intelligent sports training capabilities through integrated memory, edge computing devices, and computer programs. The edge computing device, serving as the core of data processing, leverages its computing power to analyze the user's heart rate information, exercise status, and environmental parameters in real time, rapidly constructing a heart rate prediction model and assessing the user's fatigue level. The computer program, serving as an instruction set guiding the operation of the edge computing device, automates the entire process from data acquisition and processing to feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0046] Figure 1 This is a schematic diagram of the workflow of Example 2 of the present invention;

[0047] Figure 2 This is one of the structural diagrams of Example 2 of the present invention;

[0048] Figure 3 This is the second structural diagram of Example 2 of the present invention;

[0049] in:

[0050] 100-data acquisition device;

[0051] 200-edge computing devices;

[0052] 210-Memory. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0054] Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the claims. Any person skilled in the art may make several possible changes and modifications without departing from the concept of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0055] The present invention will be further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0056] Example 1

[0057] It is known that conventional exercise training methods in the prior art are unable to meet the personalized needs of users. This is primarily manifested in: a one-size-fits-all approach, disregard for physiological feedback during exercise, and outdated equipment and technology. Specifically, traditional training methods often use the same training plan, lacking personalized adjustments for different users (e.g., age, physical fitness, athletic ability, etc.). These training methods typically set training plans based solely on exercise time or intensity, ignoring changes in physiological feedback during exercise, particularly changes in heart rate and fatigue status. Especially during high-intensity or long-term training, a user's physical condition can change significantly, making real-time monitoring and adjustment particularly important. Consequently, existing exercise training equipment and methods primarily rely on preset exercise parameters and lack intelligent technical means to automatically adjust training content based on real-time physiological data (such as heart rate, exercise intensity, etc.). However, with technological advancements, particularly the rapid development of the Internet of Things (IoT), intelligent training devices that can monitor a user's exercise status in real time through sensors, collect and analyze data, and provide instant feedback are becoming increasingly mainstream. Therefore, how to dynamically adjust training plans based on a user's real-time physiological data, reduce training risks, and improve training effectiveness, has become an urgent issue.

[0058] like Figure 1 As shown, in order to solve the above technical defects in the prior art, specifically, the present application provides an intelligent sports training method based on the Internet of Things, comprising the following steps:

[0059] S101, obtaining exercise status information selected by a user and original heart rate information of the user during exercise, obtaining heart rate status information by preprocessing the original heart rate information, and transmitting the heart rate status information and exercise status information to the edge computing device 200 via a remote communication protocol;

[0060] S201: The edge computing device 200 builds a heart rate prediction model based on the user's real-time heart rate status information and exercise status information, and the edge computing device 200 feeds back the heart rate prediction information output by the heart rate prediction model to the user in real time;

[0061] S301. The edge computing device 200 evaluates the user's fatigue level based on the heart rate prediction information and the user's heart rate state information at the time indicated by the heart rate prediction information.

[0062] S401. When the user's fatigue level during exercise becomes abnormal, the edge computing device 200 outputs a fatigue alarm to the user.

[0063] Specifically, the working principle of the above technical solution is that when a user performs exercise training, they first select exercise status information (such as exercise type, target exercise time, target exercise intensity, etc.) through the data acquisition device 100 (such as a wearable device), and collect the user's raw heart rate information during exercise in real time. This raw heart rate information is pre-processed and then transmitted to the edge computing device 200 along with the exercise status information via a remote communication protocol (such as Wi-Fi, Bluetooth, etc.).

[0064] Furthermore, after receiving this information, the edge computing device 200 (mainly used for data collation and processing on a device close to the user) will immediately build a heart rate prediction model based on the user's real-time heart rate status information and motion status information. This model can predict the user's heart rate change trend in the future, and feed back this prediction information to the user in real time, so that the user can understand his or her heart rate status and adjust the exercise rhythm accordingly. At the same time, the edge computing device 200 will also dynamically adjust the user's real-time motion status by feeding back to the user the heart rate prediction information and motion status information output by the heart rate prediction model. For example, when it is predicted that the user's heart rate is about to exceed the safe range, the device will automatically reduce the exercise intensity or give corresponding prompts to avoid health risks to the user due to excessive exercise.

[0065] In addition, the edge computing device 200 assesses the user's fatigue level in real time based on the predicted heart rate information and the user's heart rate status information at the time indicated by the predicted heart rate information. If the user's fatigue level is abnormal, the system will immediately issue a warning, prompting the user to rest or reduce the intensity of training to protect the user's health.

[0066] In summary, the IoT-based intelligent sports training method, device, and electronic device provided by this invention achieve intelligent sports training by collecting and analyzing the user's exercise status and heart rate information in real time. This intelligent training approach not only improves the scientific nature and personalization of sports training, but also effectively reduces the health risks associated with overtraining, providing users with a safer and more effective sports training experience.

[0067] Example 2

[0068] In order to further improve the construction of the heart rate prediction model and the accuracy of heart rate prediction in the intelligent sports training method based on the Internet of Things in this application, further, the heart rate state information includes resting heart rate information, maximum heart rate information, and real-time heart rate information; the edge computing device 200 constructs a heart rate prediction model based on the resting heart rate information, the maximum heart rate information and the sports state information. As a result, the heart rate prediction model can more comprehensively consider the user's personal heart rate characteristics, thereby improving the accuracy of heart rate prediction. In Example 2, the resting heart rate information reflects the user's basal heart rate in a resting state and is an important indicator for evaluating the user's cardiovascular health; the maximum heart rate information reflects the user's heart rate upper limit in an extreme exercise state, which helps to determine the user's exercise intensity range. Combining these two types of heart rate information with the user's real-time heart rate state information, the edge computing device 200 can more accurately construct a heart rate prediction model and predict the user's heart rate change trend in real time under different exercise states.

[0069] Furthermore, the edge computing model evaluates the user's fatigue level based on the heart rate prediction information calculated by the heart rate prediction model and the real-time heart rate information with the same time as the heart rate prediction information. The heart rate prediction model calculates the heart rate prediction information as follows:

[0070] HR pred (t) = HR rest +(HR max -HR rest )×(1-e -kt )

[0071] Among them, HR pred (t) is the heart rate prediction information; HR rest HR is the resting heart rate information; max is the maximum heart rate information; k is the exercise intensity adjustment factor.

[0072] Specifically, the various heart rate information collected in this application is a key physiological indicator of the human body reflecting physical exertion and metabolic burden during exercise. During exercise, heart rate is usually positively correlated with exercise intensity. When the exercise intensity increases, the heart must beat faster to meet the needs of muscles and other organs for oxygen and nutrients. In the heart rate prediction model mentioned in this embodiment, the maximum heart rate is usually related to age. In theory, the closer to the maximum heart rate, the higher the exercise intensity. Conventional training methods do not take into account the impact of age on training methods, so the collection and prediction of heart rate are not taken into account. Resting heart rate reflects the basic health level of the user, and people with lower heart rates are generally healthier. Based on these data, the basic framework of the heart rate prediction model of this application is supported.

[0073] Furthermore, as a further improvement to the intelligent sports training method based on the Internet of Things of the present invention, the method for calculating the user's fatigue level is:

[0074]

[0075] Among them, Fatigue is the user's fatigue level, HR current Heart rate prediction information HR pred (t) points to the real-time heart rate information HR at the same time current (t), HR pred Predicted HR information for heart rate monitor pred (t) Information at a certain moment of prediction.

[0076] Furthermore, when the predicted heart rate deviates significantly from the actual heart rate, it is inferred that the user has reached a certain level of fatigue. This is because if the heart rate rises rapidly and deviates from the predicted value, it indicates that the user may be in high-intensity exercise, but at this time the heart and body's tolerance has begun to decline, manifesting as the heart rate failing to respond quickly to the expected change in exercise intensity, which is often a precursor to fatigue. If the heart rate increases slowly, it indicates that the user has entered a state of exercise fatigue and lacks the physical strength to maintain the original exercise intensity, so the heart rate fails to increase according to the predicted pattern.

[0077] Therefore, the fatigue degree calculation method of the present application combines the deviation analysis of heart rate prediction information and real-time heart rate information, which can more accurately assess the user's fatigue state. When the edge computing device 200 detects that this deviation exceeds the preset range, it will immediately trigger the fatigue alarm mechanism, and issue a warning to the user through a display device or other communication means, suggesting that the user adjust the exercise intensity or suspend training to avoid health risks caused by excessive fatigue. This method not only improves the safety and effectiveness of exercise training, but also enhances the user experience, allowing users to plan their exercise training plans more scientifically and reasonably.

[0078] In the specific implementation process, assume that the user is 35 years old and participates in moderate aerobic training. The resting heart rate information is 65bpm, the maximum heart rate information is 185bpm, and the predicted heart rate information at time t is 145bpm. The user's real-time heart rate information at time t is 155bpm. In summary, during the exercise, the user's fatigue level at time t is calculated as:

[0079]

[0080] The fatigue level of 0.083 is relatively low, and the current training intensity is appropriate. Training can continue without an alarm.

[0081] Furthermore, the method by which the edge computing device 200 outputs a fatigue alarm to the user is:

[0082] When the user's fatigue level is greater than a first threshold, the edge computing device 200 drives the display device to prompt the user to reduce the intensity of exercise; when the user's fatigue level is greater than a second threshold, the edge computing device 200 drives the display device to prompt the user to reduce the intensity of exercise and shorten the time of exercise training; when the user's fatigue level is greater than a third threshold, the edge computing device 200 drives the display device to prompt the user to stop exercising.

[0083] Specifically, the first threshold corresponds to a mild fatigue warning, the second threshold corresponds to a moderate fatigue warning, and the third threshold corresponds to a severe fatigue warning. If the first threshold is 0.2, then when the fatigue level parameter Fatigue ≥ 0.2, it means that the user's heart rate deviation is large and the training intensity needs to be slightly reduced. Prompt the user to take a proper rest, reduce the intensity of exercise, or provide corresponding heart rate control suggestions. If the first threshold is 0.4, then when the fatigue level parameter Fatigue ≥ 0.4, the user's heart rate significantly exceeds the predicted heart rate parameter, and there is a risk of excessive fatigue. The training plan should be adjusted to reduce the intensity of exercise and shorten the exercise time. If the first threshold is 0.6, then when the fatigue level parameter Fatigue ≥ 0.6, the user's heart rate significantly exceeds the predicted heart rate parameter, and there is a risk of excessive fatigue. The current exercise should be forced to stop and it is recommended to rest immediately. At this time, the edge computing device 200 drives the display device to prompt the user to stop exercising.

[0084] Other details that are the same as those in Example 1 are not described in detail in this example.

[0085] Example 3

[0086] The difference from Example 2 is that, in order to further improve the accuracy of the heart rate prediction parameters of this application, the exercise intensity adjustment factor is further adjusted as follows:

[0087] k=k0·f(HR current , HR rest, ΔHR, Fatigue, Environment)

[0088] Here, k0 is a standard factor determined by the user's selected exercise state, Environment is the environmental parameter information during the user's exercise, and ΔHR is the amplitude of the user's heart rate change. By adjusting the exercise intensity adjustment factor above, we can more comprehensively consider the user's individual characteristics and real-time environmental conditions, thereby further improving the accuracy of heart rate prediction.

[0089] Specifically, the standard factor reflects the basic influence of the user's selected exercise status information on heart rate prediction, such as exercise type, target exercise time, target exercise intensity, etc. Environmental parameter information includes temperature, humidity, air pressure, etc., all of which may affect the user's heart rate changes. For example, in a high temperature environment, the user's heart rate may increase due to increased body temperature. The amplitude of the user's heart rate change reflects the fluctuation of the user's heart rate during exercise, which is also an important basis for assessing the user's fatigue level and adjusting the exercise intensity. By comprehensively considering these factors, the exercise intensity adjustment factor in this application can more accurately reflect the user's actual exercise status, thereby improving the accuracy of heart rate prediction.

[0090] Furthermore, the user heart rate change range is the real-time heart rate information HR of two users in adjacent unit time. current (t) difference. Thus, the changing trend of the user's heart rate can be captured more accurately, providing more refined data support for the heart rate prediction model. In embodiment 3, by fine-tuning the exercise intensity adjustment factor, not only the exercise state information selected by the user is taken into account, but also the real-time environmental parameters and the user's heart rate variation range are incorporated. This enables the heart rate prediction model to be closer to the user's actual exercise situation, thereby improving the accuracy and practicality of the prediction. For example, in a high temperature and high humidity environment, the user's heart rate may accelerate due to environmental pressure. At this time, by adjusting the exercise intensity adjustment factor, the training plan can be adjusted in real time to avoid the user from having health risks due to environmental discomfort. At the same time, the introduction of the user's heart rate variation range also enables the system to more sensitively capture signs of user fatigue, issue warnings in time, and protect the user's health.

[0091] In the specific implementation process, preferably, the calculation method of the environmental parameter information Environment is:

[0092]

[0093] Among them, T env is the real-time temperature parameter during the user's exercise, T optimal is the standard temperature parameter, the standard temperature parameter T optimal The value range is: 20℃-25℃.

[0094] Other details that are the same as those in Example 2 are not described in detail in this example.

[0095] Example 4

[0096] like Figure 2-3 As shown, in order to further improve the Xu Lian method based on the Internet of Things technology, further, this embodiment provides an intelligent sports training device based on the Internet of Things, including a data acquisition device 100 and an edge computing device 200. The edge computing device 200 performs the steps of any of the above methods, and the data acquisition device 100 is used to collect the user's selected motion state information and the original heart rate information during the user's exercise. Thus, the intelligent sports training device obtains the user's motion state information and heart rate information in real time through the data acquisition device 100, providing data support for the edge computing device 200. The data acquisition device 100 includes but is not limited to wearable devices, such as smart bracelets, heart rate belts, etc. These devices can fit closely to the user's body and accurately collect physiological data such as heart rate. At the same time, the data acquisition device 100 may also include a user interaction interface, allowing the user to select motion state information, such as exercise type, target exercise time and intensity, so that the system can formulate and adjust the training plan according to the user's personalized needs.

[0097] As the core of data processing and analysis, the edge computing device 200 will immediately build a heart rate prediction model based on the information transmitted by the data acquisition device 100 after receiving it, and predict the user's heart rate change trend in real time. By comparing the predicted heart rate with the actual heart rate, the edge computing device 200 can assess the user's fatigue level and trigger a fatigue alarm mechanism when necessary to protect the user's physical health. In addition, the edge computing device 200 can also dynamically adjust the user's exercise status based on real-time heart rate information and exercise status information to ensure that the training process is both scientific and effective.

[0098] Furthermore, this embodiment also provides an intelligent sports training device based on the Internet of Things, including a memory 210, an edge computing device 200, and a computer program stored in the memory 210 and executable on the edge computing device 200. When the edge computing device 200 executes the computer program, the steps of any of the above methods are implemented. Thus, when using the device, the user only needs to select his or her own motion status information through a simple operation interface, and the device can automatically tailor a training plan for the user based on this information. At the same time, the device will also collect the user's heart rate information in real time, and by comparing the predicted heart rate with the actual heart rate, accurately assess the user's fatigue level to ensure the safety and effectiveness of the training process.

[0099] In practice, the smart sports training device may also be equipped with a display or other communication means to display training plans, heart rate prediction information, fatigue assessment results, and fatigue warning information to the user. This allows users to not only intuitively understand their exercise status but also adjust their training plans in a timely manner based on the device's prompts, thereby avoiding health risks caused by excessive fatigue.

[0100] Furthermore, this intelligent sports training device is highly scalable and flexible, allowing for customization based on user needs. For example, users can select different training modes, exercise intensities, and durations based on their preferences, and the device will automatically adjust the training plan accordingly. The device also supports connectivity and interaction with other smart devices, such as smart bracelets and smartwatches, enabling data sharing and synchronization, providing users with a more convenient and comprehensive sports training experience.

[0101] Other details that are the same as those in Example 1 are not described in detail in this example.

[0102] The above are merely preferred embodiments of the present application and are not intended to limit the present application. Persons skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. An intelligent sports training method based on the Internet of Things, characterized in that: The steps include: S101, obtaining the exercise state information selected by the user and the original heart rate information of the user during exercise, obtaining the heart rate state information by preprocessing the original heart rate information, and transmitting the heart rate state information and the exercise state information to an edge computing device through a remote communication protocol; S201, the edge computing device constructs a heart rate prediction model according to the real-time heart rate state information and the motion state information of the user, and the edge computing device feeds back the heart rate prediction information output by the heart rate prediction model in real time to the user in real time; S301, the edge computing device evaluates the user's fatigue level according to the heart rate prediction information and the user's heart rate state information at the time indicated by the heart rate prediction information; S401. When the fatigue level of the user during exercise becomes abnormal, the edge computing device outputs a fatigue alarm to the user.

2. The method of intelligent sports training based on the Internet of Things according to claim 1, characterized in that: The heart rate status information includes resting heart rate information, maximum heart rate information, and real-time heart rate information; The edge computing device constructs the heart rate prediction model according to the resting heart rate information, the maximum heart rate information and the motion state information; The edge computing model evaluates the user's fatigue level based on the heart rate prediction information calculated by the heart rate prediction model and the real-time heart rate information pointing to the same time as the heart rate prediction information.

3. The method of intelligent sports training based on the Internet of Things according to claim 2, characterized in that: The heart rate prediction model calculates the heart rate prediction information as follows: HR pred (t)=HR rest +(HR max -HR rest )×(1-e -kt ) Among them, HR pred (t) is the heart rate prediction information; rest is the resting heart rate information; HR max is the maximum heart rate information; k is the exercise intensity adjustment factor.

4. The method of intelligent sports training based on the Internet of Things according to claim 3, characterized in that: The calculation method of the user fatigue degree is: Among them, Fatigue is the user's fatigue level, HR current The heart rate prediction information HR pred (t) refers to the real-time heart rate information HR at the same time current (t), HR pred Predicted information HR for the heart rate monitor pred (t) Information about the predicted time.

5. The method of intelligent sports training based on the Internet of Things according to claim 4, characterized in that: The exercise intensity adjustment factor is adjusted as follows: k=k0·f(HR current ,HR rest ,ΔHR,Fatigue,Environment) Wherein, k0 is a standard factor determined by the exercise state information selected by the user, Environment is the environmental parameter information during the user's exercise, and ΔHR is the amplitude of the user's heart rate change.

6. The method of intelligent sports training based on the Internet of Things according to claim 5, characterized in that: The user's heart rate change amplitude is the real-time heart rate information HR of two users in adjacent unit time. current The difference of (t).

7. The method of intelligent sports training based on the Internet of Things according to claim 5, characterized in that: The calculation method of the environmental parameter information Environment is: Among them, T env is the real-time temperature parameter of the user during exercise, T optimal is the standard temperature parameter, the standard temperature parameter T optimal The value range is: 20℃-25℃.

8. The method of intelligent sports training based on the Internet of Things according to claim 4, characterized in that: The method in which the edge computing device outputs a fatigue alarm to the user is: When the user's fatigue level is greater than a first threshold, the edge computing device drives the display device to prompt the user to reduce exercise intensity; When the user's fatigue level is greater than a second threshold, the edge computing device drives the display device to prompt the user to reduce the intensity of exercise and to shorten the time of the exercise training; When the user's fatigue level is greater than a third threshold, the edge computing device drives the display device to prompt the user to stop exercising.

9. An intelligent sports training device based on the Internet of Things, including a data acquisition device and an edge computing device, characterized in that: The edge computing device executes the steps of the method as described in any one of claims 1 to 8, and the data acquisition device is used to collect the exercise state information selected by the user and the original heart rate information of the user during exercise.

10. An intelligent sports training electronic device based on the Internet of Things, comprising a memory, an edge computing device, and a computer program stored in the memory and executable on the edge computing device, characterized in that: When the edge computing device executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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