Heart rate pectoral girdle using process management method and system based on monitoring data feedback

Abstract

The invention discloses a heart rate pectoral girdle using process management method and system based on monitoring data feedback, and relates to the technical field of heart rate pectoral girdle using process management, and the method specifically comprises the following steps: obtaining motion state information generated by a user wearing a heart rate pectoral girdle in an aerobic motion process in real time, respectively generating a heart rate fluctuation intensity coefficient and an exercise load fluctuation index; and constructing an exercise load adjustment evaluation model for the generated heart rate fluctuation intensity coefficient and the exercise load fluctuation index, generating an exercise load adjustment evaluation coefficient, and evaluating the exercise load monitoring demand degree of the user wearing the heart rate chest strap in continuous long-time aerobic exercise. And the conditions are divided into three types of conditions of low demand degree, medium demand degree and high demand degree. The heart rate chest strap solves the problem that an existing heart rate chest strap cannot accurately and dynamically adjust the monitoring strategy in the aerobic exercise process of a user, and through real-time feedback and periodic optimization, the exercise load evaluation accuracy and the equipment performance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heart rate chest strap use process management, and in particular to a heart rate chest strap use process management method and system based on monitoring data feedback. Background Art

[0002] A heart rate chest strap is a wearable device that usually continuously monitors and records the wearer's heart rate data through a sensor on the chest. It is widely used in sports health management, fitness training, and medical monitoring. Since the changes in heart rate during exercise can directly reflect the individual's physical state, exercise intensity, and health status, the use process management of the heart rate chest strap is crucial. Relying solely on traditional heart rate data collection, it is often difficult to make dynamic adjustments based on the user's real-time changes, and it is also impossible to provide users with timely and effective health guidance. In order to improve the use effect of the heart rate chest strap, it is necessary to combine intelligent algorithms with data collection to analyze and feedback the heart rate changes in real time, so as to adjust the exercise intensity, rest cycle, etc. according to the user's personalized needs. This process management based on monitoring data feedback can not only ensure that the user is in the best heart rate range during exercise, but also avoid the health risks caused by excessive exercise or excessive stillness, and ensure the maximization of exercise effects. Through this intelligent and personalized management, the heart rate chest strap can play a higher role, help users achieve health management goals, and at the same time improve the intelligence level of the device and user experience.

[0003] The existing heart rate chest strap use process management technology based on monitoring data feedback usually collects the wearer's heart rate data in real time and transmits it to a smart device or cloud platform for processing. The system analyzes the heart rate data and combines it with the user's exercise history, health status and goal setting to provide personalized exercise suggestions. During use, when the user's heart rate reaches the preset high and low thresholds, the system will issue feedback in real time to remind the user to adjust the exercise intensity or take rest measures to prevent excessive or insufficient exercise from affecting health. In addition, management based on data feedback also includes dynamically adjusting the monitoring frequency or alarm settings of the chest strap to better adapt to the user's exercise status and physical changes. For example, when the user is doing high-intensity exercise, the system may increase the frequency of data collection to provide more accurate heart rate monitoring; when the exercise intensity is low, the system may reduce the monitoring frequency and extend the use time of the device. Through these intelligent adjustments and feedback, the existing technology can ensure the efficiency and safety of the heart rate chest strap throughout the use process, help users maintain the optimal heart rate range, and maximize the exercise effect and health management goals.

[0004] The prior art has the following deficiencies: When performing long-term aerobic exercise (such as jogging or cycling), the user's heart rate usually shows a relatively gentle fluctuation. In this case, the existing heart rate chest strap use process management technology based on monitoring data feedback is usually unable to effectively identify this low-amplitude, continuous heart rate fluctuation pattern, resulting in the inability to dynamically adjust the monitoring frequency and accuracy of the device according to the actual exercise state. Since the fixed high-frequency data acquisition mode of the device during long-term exercise will lead to too much redundant data, and lack of real-time adaptive adjustment to heart rate fluctuations, the data acquisition efficiency is low, and the monitoring load cannot be optimized, resulting in insufficient monitoring accuracy. This leads to the fact that in the low-fluctuation heart rate area, the high-frequency sampling of the monitoring device may not only increase energy consumption, but also make the device unable to efficiently collect key exercise data, affecting the real-time feedback of the exercise state and the accuracy of load assessment. The existing technology cannot dynamically adjust the operating frequency of the device according to this low-fluctuation heart rate pattern, which may make it impossible to obtain enough effective data to accurately assess the user's exercise load during exercise, thereby affecting the effect and accuracy of training, and ultimately may cause the user to be unable to make adjustments based on real-time feedback during exercise, reducing the personalization and scientificity of the exercise effect.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for managing the use process of a heart rate chest strap based on monitoring data feedback, so as to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a heart rate chest strap usage process management method based on monitoring data feedback, specifically comprising the following steps: When the user wearing the heart rate chest strap is exercising, it is detected in real time whether the user wearing the heart rate chest strap is doing aerobic exercise, and when it is detected that the user wearing the heart rate chest strap is doing aerobic exercise for a long time, the dynamic adjustment mechanism is triggered; Real-time acquisition of the exercise status information generated by users wearing heart rate chest straps during aerobic exercise, and analysis after acquisition to generate heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively; The generated heart rate fluctuation intensity coefficient and exercise load fluctuation index are used to construct an exercise load adjustment evaluation model, generate an exercise load adjustment evaluation coefficient, and analyze it after generation to evaluate the exercise load monitoring needs of users wearing heart rate chest belts during long-term aerobic exercise, and divide them into three categories: low need, medium need, and high need; According to the evaluation results, different adjustment measures are taken for the heart rate chest belt in the cases of low demand, medium demand and high demand. Continuously monitor the exercise status information of users wearing heart rate chest straps, and dynamically adjust the operating frequency and accuracy of the device based on real-time feedback. At the same time, match the adjusted device parameters with the user's exercise load requirements, and perform periodic optimization to improve data collection efficiency and the accuracy of exercise load assessment.

[0008] Preferably, the exercise state information generated by the user wearing a heart rate chest belt during aerobic exercise is obtained in real time, and analyzed after acquisition to generate a heart rate fluctuation intensity coefficient and an exercise load fluctuation index, respectively, which specifically includes the following steps: Real-time acquisition of exercise status information generated by users wearing heart rate chest straps during aerobic exercise, and pre-processing after acquisition; Extracting heart rate trend information and load stability information from the preprocessed exercise state information; The extracted heart rate trend information and load stability information are analyzed to generate the heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively.

[0009] Preferably, the logic for obtaining the heart rate fluctuation intensity coefficient is as follows: Extract the heart rate trend information from the preprocessed exercise status information, including the heart rate values ​​at different times during a period of time when the user wearing the heart rate chest strap is doing aerobic exercise, the heart rate change rate, and the standard deviation of the heart rate values ​​of the user wearing the heart rate chest strap during the period of time, and calibrate them as , and , Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. Heart rate value at the moment, Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The heart rate change rate at any time, Indicates the standard deviation of the heart rate values ​​of users wearing a heart rate chest strap during this time period. , is a positive integer; The heart rate values ​​of users wearing heart rate chest straps at different times during a period of aerobic exercise are constructed into a set, and the maximum and minimum values ​​in the set are marked as and ; Calculate the average heart rate values ​​of users wearing heart rate chest straps during aerobic exercise at different times over a period of time , according to the formula: ; Calculate the heart rate fluctuation intensity coefficient. The specific calculation formula is as follows: In the formula, is the heart rate fluctuation intensity coefficient.

[0010] Preferably, the acquisition logic of the exercise load fluctuation index is as follows: The load stability information in the preprocessed exercise state information is extracted, including the frequency of heart rate fluctuations at different times during a period of time during aerobic exercise for users wearing heart rate chest straps, the amplitude of changes in heart rate fluctuation intensity, and the time required for the heart rate of users wearing heart rate chest straps to recover from the maximum fluctuation to a stable state, and they are calibrated as , and , Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The frequency of heart rate fluctuations at all times, Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The magnitude of the change in the intensity of heart rate fluctuations at each moment, Indicates the time required for the heart rate of a user wearing a heart rate chest monitor to recover from the maximum fluctuation to a stable state. , is a positive integer; Calculate the exercise load fluctuation index. The specific calculation formula is as follows: In the formula, It is the exercise load fluctuation index.

[0011] Preferably, constructing an exercise load adjustment evaluation model for the generated heart rate fluctuation intensity coefficient and exercise load fluctuation index to generate an exercise load adjustment evaluation coefficient specifically includes the following steps: Collect several heart rate fluctuation intensity coefficients, exercise load fluctuation indexes and corresponding exercise load adjustment evaluation coefficients generated in the past period of time, and calibrate them as , and , Indicates the numbers of several heart rate fluctuation intensity coefficients, exercise load fluctuation indexes and corresponding exercise load adjustment evaluation coefficients generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; The multivariate regression model was selected as the exercise load adjustment evaluation model, and the historical data set was used for training to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Using the final regression coefficient, the constructed exercise load adjustment evaluation model is input with the heart rate fluctuation intensity coefficient generated in real time. and exercise load fluctuation index , real-time generation of exercise load adjustment evaluation coefficients .

[0012] Preferably, the generated exercise load adjustment evaluation coefficient The pre-set exercise load adjustment evaluation coefficient threshold range The comparison was conducted and the demand for exercise load monitoring of users wearing heart rate chest straps during long-term aerobic exercise was evaluated based on the comparison results. The users were divided into three categories: low demand, medium demand and high demand. The specific comparison and analysis are as follows: like , the demand for exercise load monitoring during long-term aerobic exercise for users wearing heart rate chest straps is low; like , the demand for exercise load monitoring during long-term aerobic exercise for users wearing heart rate chest straps is medium; like Users who wear heart rate chest straps have a high demand for exercise load monitoring during long-term aerobic exercise.

[0013] Preferably, according to the evaluation results, different adjustment measures are respectively taken for the heart rate chest belt in the cases where the evaluation results are low demand, medium demand and high demand, specifically: If the evaluation result is low demand, the adjustment measures for the heart rate chest strap are: maintain the existing configuration and parameter settings of the heart rate chest strap, maintain the existing monitoring strategy, do not frequently update the heart rate data, and continue to monitor the user's heart rate status and exercise load changes; If the evaluation result is a medium demand, the adjustment measures for the heart rate chest strap are: dynamically adjust the heart rate data collection and monitoring strategy according to the real-time feedback of the exercise load changes, adjust the sensor sensitivity of the heart rate chest strap to improve the collection accuracy of heart rate and exercise load data; when the exercise load fluctuates, increase the data update frequency and perform fluctuation analysis, and adjust the monitoring strategy in time to cope with different load changes; If the evaluation result is a high demand, the adjustment measures for the heart rate chest strap are: immediately calibrate the accuracy of the heart rate chest strap sensor, increase the data transmission frequency, and improve the data collection frequency and update speed; at the same time, start the exercise load adjustment mechanism, adjust the monitoring strategy in real time, and improve the monitoring accuracy.

[0014] Preferably, the heart rate chest strap use process management system based on monitoring data feedback includes a user motion state detection module, a motion state information collection and analysis module, a motion load adjustment evaluation module, a heart rate chest strap adjustment strategy module, and a device dynamic adjustment and optimization module; The user exercise state detection module detects in real time whether the user wearing the heart rate chest strap is doing aerobic exercise during exercise, and triggers the dynamic adjustment mechanism when it is detected that the user wearing the heart rate chest strap is doing aerobic exercise for a long time; The exercise status information collection and analysis module obtains the exercise status information generated by users wearing heart rate chest straps during aerobic exercise in real time, and analyzes it after acquisition to generate the heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively; The exercise load adjustment assessment module constructs an exercise load adjustment assessment model based on the generated heart rate fluctuation intensity coefficient and exercise load fluctuation index, generates an exercise load adjustment assessment coefficient, and analyzes it after generation to assess the exercise load monitoring needs of users wearing heart rate chest straps during long-term aerobic exercise, and divides them into three categories: low need, medium need, and high need; The heart rate chest strap adjustment strategy module takes different adjustment measures for the heart rate chest strap in the three cases of low demand, medium demand and high demand according to the evaluation results; The device dynamic adjustment and optimization module continuously monitors the exercise status information of users wearing heart rate chest straps, and dynamically adjusts the operating frequency and accuracy of the device based on real-time feedback. At the same time, it matches the adjusted device parameters with the user's exercise load requirements and performs periodic optimization to improve data collection efficiency and the accuracy of exercise load assessment.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention can accurately assess the user's exercise load requirements by real-time monitoring of the user's exercise status information, especially the heart rate fluctuations and load changes during aerobic exercise, and make dynamic adjustments based on this. The introduction of this mechanism enables the heart rate chest strap to adaptively adjust its operating frequency and accuracy to cope with different exercise loads. In traditional heart rate monitoring equipment, it is often impossible to track changes in exercise load in real time, resulting in inaccurate data collection or failure to adapt to user needs in a timely manner. The present invention not only enhances the accuracy of data collection through real-time feedback and dynamic adjustment, but also optimizes the performance of the equipment under various load conditions, greatly improving the monitoring accuracy of the heart rate chest strap and greatly improving the effect of sports health management.

[0016] 2. The present invention introduces a highly quantitative data analysis method by generating a heart rate fluctuation intensity coefficient and an exercise load fluctuation index. This method helps evaluate the load changes of users during exercise by accurately analyzing key parameters such as the frequency, intensity, and recovery time of the user's heart rate fluctuations during exercise. Unlike the simple heart rate monitoring of traditional equipment, it can analyze the dynamic characteristics of heart rate fluctuations in detail, and generate corresponding exercise load assessment coefficients based on these data, making the classification of user exercise load requirements more accurate, and being able to divide users into three categories of low, medium, and high requirements, thereby providing personalized adjustment strategies. This method significantly improves the accuracy of exercise load assessment and can better match the exercise needs of different users.

[0017] 3. Based on the dynamically adjusted device parameters, the system of the present invention can continuously monitor and periodically optimize the device parameters according to feedback data. This adaptive mechanism can adjust the operating frequency and accuracy of the heart rate chest belt in real time according to changes in the user's exercise load, thereby ensuring that the device can maintain optimal performance in any state of exercise. In addition, as the device is continuously optimized, it can more accurately match the individual differences and exercise needs of users, improve data collection efficiency, reduce device power consumption and reduce unnecessary excessive monitoring, thereby improving the overall performance of the system and user experience. Through this periodic optimization, the monitoring capabilities of the device are continuously improved, providing users with more accurate and personalized exercise monitoring services. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0019] Figure 1 The present invention is a flowchart of a method and system for managing the use of a heart rate chest strap based on monitoring data feedback.

[0020] Figure 2 It is a module schematic diagram of the heart rate chest strap usage process management method and system based on monitoring data feedback of the present invention. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0022] The present invention provides Figure 1 The heart rate chest strap usage management method based on monitoring data feedback shown in the figure specifically includes the following steps: When the user wearing the heart rate chest strap is exercising, it is detected in real time whether the user wearing the heart rate chest strap is doing aerobic exercise, and when it is detected that the user wearing the heart rate chest strap is doing aerobic exercise for a long time, the dynamic adjustment mechanism is triggered; When it comes to real-time detection of whether a user wearing a heart rate chest strap is doing aerobic exercise, it is first necessary to collect real-time heart rate data through the heart rate chest strap and analyze it in combination with the exercise status data. Specifically, this can be achieved in the following ways: by monitoring the user's heart rate fluctuation range, it is possible to identify whether the heart rate is in a typical aerobic exercise range (for example, the heart rate is between 60% and 80% of the maximum heart rate). At the same time, it can be combined with other exercise data, such as the exercise mode information provided by the accelerometer and gyroscope, to determine whether the user is in a continuous aerobic exercise state (for example, jogging or cycling). These sensors can capture the user's exercise intensity and stability, and by analyzing the change patterns of acceleration and angular velocity, it can be distinguished whether the user is in a typical aerobic exercise stage. Finally, the heart rate and exercise mode data are fused, and machine learning or rule engines are used to determine whether the user is in a continuous aerobic exercise state.

[0023] Once it is detected that the user wearing a heart rate chest strap enters a state of continuous aerobic exercise, the user's exercise time and heart rate changes can be analyzed to determine whether the exercise has reached the "continuous long-term" standard. For example, a threshold can be set, and when the user's heart rate remains within the aerobic exercise range for more than a certain time (such as 15 minutes or 30 minutes), the dynamic adjustment mechanism is triggered. The dynamic adjustment mechanism includes adjusting the sampling frequency and accuracy of the device. For example, when it is detected that the user is in a low-fluctuation heart rate range, the sampling frequency is reduced to reduce redundant data collection, thereby optimizing battery use and improving data collection efficiency. At the same time, the system can further optimize the monitoring accuracy of the device according to the user's exercise pattern to avoid redundant data caused by excessive sampling of the device.

[0024] The implementation of this step can effectively solve the problems in the prior art: during long-term aerobic exercise, the heart rate fluctuates less, and the traditional fixed high-frequency sampling mode will lead to the accumulation of redundant data, resulting in energy waste and reduced data collection efficiency. By monitoring in real time whether the user is in an aerobic exercise state and triggering a dynamic adjustment mechanism, the sampling frequency and accuracy of the device can be accurately controlled, thereby maximizing the energy efficiency of the device and ensuring the validity of the data. In the low-fluctuation heart rate range, reducing the sampling frequency not only reduces battery consumption, but also avoids the generation of redundant data, thereby improving data collection efficiency. This flexible dynamic adjustment mechanism can not only improve the accuracy of the data, but also make personalized adjustments according to the user's exercise needs, ultimately providing a more accurate exercise load assessment, helping users optimize exercise effects, and improving the scientificity and personalization of training.

[0025] Real-time acquisition of the exercise status information generated by users wearing heart rate chest straps during aerobic exercise, and analysis after acquisition to generate heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively; In this embodiment, the exercise state information generated by the user wearing a heart rate chest strap during aerobic exercise is obtained in real time, and analyzed after acquisition to generate a heart rate fluctuation intensity coefficient and an exercise load fluctuation index, respectively, which specifically includes the following steps: Real-time acquisition of exercise status information generated by users wearing heart rate chest straps during aerobic exercise, and pre-processing after acquisition; In order to obtain real-time information about the exercise status of users wearing heart rate chest straps during aerobic exercise, the internal sensor of the heart rate chest strap can be connected to smart devices (such as mobile phones or sports watches) via Bluetooth or other wireless communication protocols to transmit the user's physiological data (such as heart rate, cadence, exercise duration, etc.) to the device in real time. The heart rate chest strap collects exercise status information by detecting physiological signals such as the wearer's heart rate, exercise intensity, and breathing rate. In addition, the wearer's exercise method (such as jogging, cycling, etc.) and changes in exercise mode can be obtained through the built-in accelerometer or gyroscope to further accurately determine the user's exercise type and status. The software side collects these physiological data in real time and stores the data for subsequent analysis and processing. The frequency and accuracy of data collection can be adjusted through the application or the device's firmware to ensure the accuracy and timeliness of the data.

[0026] Preprocessing is to improve the accuracy and data quality of subsequent analysis, especially in real-time monitoring, the data collected by the sensor may be affected by noise, interference or incomplete data. Therefore, preprocessing can help eliminate these errors and improve the validity of the data. The specific preprocessing process includes: first, remove the noise in the original data, such as smoothing the data through filtering algorithms (such as Kalman filtering, low-pass filtering, etc.), and remove small fluctuations in a short period of time to ensure the continuity and stability of the heart rate data. Secondly, synchronize the data and align the data from different sensors (such as heart rate data, acceleration data, etc.) by timestamp to ensure data consistency. Furthermore, standardize or normalize the data, especially when there is fluctuation in the heart rate data, convert the data into standard units or relative values ​​to avoid deviations caused by different measurement units or ranges. Finally, segment processing is performed according to the user's exercise type. For example, in aerobic exercise such as jogging or cycling, extract the data of the heart rate stable period and the fluctuation period, so as to more accurately calculate the heart rate fluctuation intensity coefficient and exercise load fluctuation index later. After preprocessing, the data is more suitable for efficient calculation and analysis, reducing errors and improving the accuracy of evaluation.

[0027] Extracting heart rate trend information and load stability information from the preprocessed exercise state information; When extracting the heart rate trend information and load stability information from the preprocessed exercise state information, it can be achieved through time series analysis of the heart rate data. First, the heart rate trend information can be extracted through sliding window analysis of the heart rate data. In the sliding window, the heart rate data is smoothed by applying a time series algorithm (such as moving average, exponential smoothing, etc.) to identify the long-term and short-term trends of the heart rate over time, such as rising, falling or stable trends. By setting a threshold, it can be determined whether there is a significant rising or falling trend in the heart rate, which reflects the change in the user's exercise intensity. Secondly, the load stability information is extracted by analyzing the stability of the heart rate fluctuation. The standard deviation or variance of the heart rate data can be calculated to quantify the amplitude and frequency of the heart rate fluctuation, thereby obtaining the stability of the load. If the standard deviation is low, it means that the user's exercise load is relatively stable; if the standard deviation is high, it means that the exercise load fluctuates greatly. In order to more accurately evaluate the load stability, other data, such as step frequency or acceleration, can also be combined for joint analysis to further determine whether the exercise load is stable. Through these analysis methods, heart rate trend information and load stability information can be extracted from the preprocessed heart rate data, providing basic data for the subsequent calculation of the heart rate fluctuation intensity coefficient and exercise load fluctuation index.

[0028] The extracted heart rate trend information and load stability information are analyzed to generate the heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively.

[0029] In this embodiment, the logic for obtaining the heart rate fluctuation intensity coefficient is as follows: Extract the heart rate trend information from the preprocessed exercise status information, including the heart rate values ​​at different times during a period of time when the user wearing the heart rate chest strap is doing aerobic exercise, the heart rate change rate, and the standard deviation of the heart rate values ​​of the user wearing the heart rate chest strap during the period of time, and calibrate them as , and , Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. Heart rate value at the moment, Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The heart rate change rate at any time, Indicates the standard deviation of the heart rate values ​​of users wearing a heart rate chest strap during this time period. , is a positive integer; To obtain quantitative data such as "heart rate value", "heart rate change rate" and "heart rate value standard deviation" generated by users wearing heart rate chest straps during aerobic exercise in real time, this can be done through the sensors and monitoring modules built into the heart rate chest strap. These devices usually use photoelectric volume pulse wave sensing technology (PPG) or electrophysiological signal monitoring technology (such as ECG) to collect the user's heart rate data in real time. During exercise, the chest strap continuously records the user's heart rate value and transmits the data to the supporting mobile device or cloud platform. Specifically, the heart rate value can be obtained by measuring the time interval between each heartbeat in real time, and the heart rate per second or per minute is calculated and recorded in real time. The heart rate change rate is obtained by calculating the speed of change of the heart rate value in adjacent time periods. The difference in the heart rate value in each time interval can be calculated to obtain the value of the change rate. As for the heart rate value standard deviation, it can be statistically analyzed by the heart rate values ​​collected over a period of time, and the standard deviation of the heart rate value in the period of time is calculated to reflect the amplitude of heart rate fluctuations. All these data are transmitted to the relevant application or platform through wireless connection, and data processing and analysis are performed in real time to provide support for subsequent parameter calculations.

[0030] The heart rate values ​​of users wearing heart rate chest straps at different times during a period of aerobic exercise are constructed into a set, and the maximum and minimum values ​​in the set are marked as and ; Calculate the average heart rate values ​​of users wearing heart rate chest straps during aerobic exercise at different times over a period of time , according to the formula: ; Calculate the heart rate fluctuation intensity coefficient. The specific calculation formula is as follows: In the formula, is the heart rate fluctuation intensity coefficient.

[0031] Heart rate fluctuation intensity coefficient The calculation formula is a series of calculation steps to comprehensively evaluate the intensity of heart rate fluctuations during aerobic exercise for users wearing a heart rate chest monitor. First, the maximum heart rate value is calculated. and minimum value It is to measure the fluctuation range of the user's heart rate during exercise. The larger the range, the more drastic the heart rate fluctuation. Then, the average heart rate is calculated. This is to provide a balanced baseline to help assess the relative strength of heart rate fluctuations. To reflect the rapid change of heart rate fluctuations. The greater the rate of change, the more drastic the heart rate fluctuations. It is used to reflect the stability of heart rate fluctuations. The larger the standard deviation, the more unstable the fluctuations. Finally, the entire calculation formula combines the fluctuation range, heart rate change rate and standard deviation in a weighted average manner to obtain a comprehensive fluctuation intensity coefficient. This coefficient can fully reflect the intensity of the user's heart rate fluctuations during aerobic exercise, thereby providing a basis for subsequent load adjustments.

[0032] Heart rate fluctuation intensity coefficient There is a direct relationship between the size of and "assessing the need for exercise load monitoring during long-term aerobic exercise for users wearing heart rate chest straps". Specifically, The larger the value, the more drastic the wearer's heart rate fluctuations, the larger the heart rate change rate and standard deviation, indicating a higher exercise intensity and more obvious load fluctuations. In this case, the user's exercise load requirement is also correspondingly higher, because their exercise status requires more detailed and more frequent monitoring to ensure exercise safety and optimize exercise results. On the contrary, A smaller value indicates that the heart rate fluctuation is relatively stable, the exercise load is low, and the monitoring requirement is low. The change of the value can dynamically evaluate the user's load monitoring needs during exercise, and adjust the monitoring frequency and accuracy according to this demand, ensuring more frequent and more accurate data collection under high load, and reducing unnecessary monitoring under low load, saving energy and improving monitoring efficiency.

[0033] In this embodiment, the logic for obtaining the exercise load fluctuation index is as follows: The load stability information in the preprocessed exercise state information is extracted, including the frequency of heart rate fluctuations at different times during a period of time during aerobic exercise for users wearing heart rate chest straps, the amplitude of changes in heart rate fluctuation intensity, and the time required for the heart rate of users wearing heart rate chest straps to recover from the maximum fluctuation to a stable state, and they are calibrated as , and , Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The frequency of heart rate fluctuations at all times, Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The magnitude of the change in the intensity of heart rate fluctuations at each moment, Indicates the time required for the heart rate of a user wearing a heart rate chest monitor to recover from the maximum fluctuation to a stable state. , is a positive integer; To obtain the heart rate fluctuation frequency, the amplitude of the heart rate fluctuation intensity, and the time required for the heart rate to recover from the maximum fluctuation to a stable state at different times during a period of time during aerobic exercise for users wearing a heart rate chest strap in real time, the user's real-time heart rate data can first be directly obtained through the heart rate chest strap. The heart rate chest strap usually has a built-in sensor that can continuously record heart rate changes and transmit data to the supporting application or device. Specifically, the frequency of heart rate fluctuations can be determined by analyzing the rate of change of heart rate data. The frequency of heart rate fluctuations can be obtained by calculating the number of cycles of heart rate changes per unit time. The amplitude of the change in heart rate fluctuation intensity is quantified by calculating the difference between the maximum and minimum values ​​of the heart rate data within a certain time window, reflecting the amplitude of fluctuations during exercise. As for the time for the heart rate to recover from the maximum fluctuation to a stable state, it can be obtained by detecting the recovery process of the heart rate after the fluctuation. The specific method is to track the time period when the heart rate drops and stabilizes to a predetermined threshold after the heart rate reaches the maximum value, thereby measuring the time required for recovery. All of these data are collected through the heart rate chest strap sensor and obtained after real-time processing and analysis. Through data collection, processing algorithms and time marking, the software can accurately extract this information and conduct dynamic monitoring to provide real-time feedback on the user's exercise status.

[0034] Calculate the exercise load fluctuation index. The specific calculation formula is as follows: In the formula, It is the exercise load fluctuation index.

[0035] The calculation formula of the exercise load fluctuation index combines the heart rate fluctuation frequency, fluctuation intensity change amplitude and heart rate recovery time, aiming to comprehensively evaluate the load fluctuation of users wearing heart rate chest belts during aerobic exercise. First, It represents the frequency of heart rate fluctuations, reflecting the frequency of heart rate fluctuations during exercise. The higher the frequency, the more severe the load fluctuation, so it needs to be weighted in the calculation. It indicates the amplitude of the fluctuation of heart rate at each moment. The larger the fluctuation amplitude, the stronger the fluctuation intensity of load. Therefore, logarithmic transformation is performed on it. , which can amplify larger fluctuations and thus increase the sensitivity of the load fluctuation index. It indicates the time required to recover from the maximum fluctuation to a stable state. The longer the recovery time, the more unstable the load recovery process is. It needs to be weighted in the index to reflect the recovery ability of the exercise load. Finally, these three factors are combined and calculated by dividing by the heart rate fluctuation frequency. The value of 1 is added to ensure the comprehensive balance of the load fluctuation index at each moment, so that the exercise load fluctuation index can accurately reflect the load fluctuation and recovery status of users wearing heart rate chest belts during aerobic exercise.

[0036] The size of the exercise load fluctuation index is closely related to the relationship between "evaluating the degree of exercise load monitoring needs of users wearing heart rate chest straps during long-term aerobic exercise". Specifically, the higher the exercise load fluctuation index, the more severe the heart rate fluctuation, the stronger the load change, and the longer the recovery time during aerobic exercise. This indicates that the user's exercise load is more intense and difficult to stabilize, requiring more monitoring and adjustment to avoid excessive fatigue or potential health risks. Therefore, when the exercise load fluctuation index is high, the system needs to monitor and dynamically adjust the exercise load more frequently to ensure the user's safety and exercise effect. On the contrary, if the index is low, it means that the user's exercise load fluctuates less and recovers faster, indicating that the user's exercise load is in a relatively stable state and the monitoring needs are relatively low. By evaluating the exercise load fluctuation index, it is possible to accurately determine whether the user needs closer monitoring, and dynamically adjust the exercise load monitoring strategy based on the evaluation results.

[0037] The generated heart rate fluctuation intensity coefficient and exercise load fluctuation index are used to construct an exercise load adjustment evaluation model, generate an exercise load adjustment evaluation coefficient, and analyze it after generation to evaluate the exercise load monitoring needs of users wearing heart rate chest belts during long-term aerobic exercise, and divide them into three categories: low need, medium need, and high need; In this embodiment, an exercise load adjustment evaluation model is constructed based on the generated heart rate fluctuation intensity coefficient and exercise load fluctuation index to generate an exercise load adjustment evaluation coefficient, which specifically includes the following steps: Collect several heart rate fluctuation intensity coefficients, exercise load fluctuation indexes and corresponding exercise load adjustment evaluation coefficients generated in the past period of time, and calibrate them as , and , Indicates the numbers of several heart rate fluctuation intensity coefficients, exercise load fluctuation indexes and corresponding exercise load adjustment evaluation coefficients generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; To collect the heart rate fluctuation intensity coefficient, exercise load fluctuation index and corresponding exercise load adjustment evaluation coefficient generated in the past period of time, it can be achieved by integrating the exercise data of users wearing heart rate chest straps. These data can be recorded in real time through the exercise tracking system or health management application. First, when the user wearing a heart rate chest strap performs aerobic exercise, the device will monitor and record heart rate data, exercise duration, exercise intensity and other information in real time. These data are sent to the cloud platform or local database for storage. After the data is stored, the system will automatically calculate the heart rate fluctuation intensity coefficient and exercise load fluctuation index in each time period according to the set time interval and the user's exercise record. These calculation results will be stored together with the exercise load adjustment evaluation coefficient and associated with the user's personal account or health record. In order to collect historical data, the system needs to set a data collection cycle, such as automatically saving the heart rate fluctuation intensity coefficient and exercise load fluctuation index after each exercise, and regularly reviewing and archiving these data. Past exercise records can be queried and downloaded through the health management platform to form a historical data set. The historical data set will be updated regularly to ensure that it contains the latest user exercise load data for subsequent training of the exercise load adjustment evaluation model.

[0038] limited The reason why the value range of is a positive integer greater than or equal to 3 is mainly to ensure that enough equations can be formed to accurately calculate the regression coefficient. According to the requirements of the least squares regression model, in order to accurately estimate the regression coefficient ( , and ), there needs to be enough data points to ensure that the solution to the regression equation is unique and reliable. Each sample data point provides an equation for the regression equation, so at least three sample data points are needed to generate three independent equations, thus forming a system of equations to solve the three regression coefficients. If If the value range of is less than 3, there will be insufficient data points, and the regression model may not be able to accurately fit the data, resulting in the inability to calculate the regression coefficient or unstable calculation results. The value range of is a positive integer greater than or equal to 3, which ensures that the model can be trained and optimized based on sufficient data points, so as to accurately calculate the regression coefficient and obtain an effective exercise load adjustment evaluation coefficient.

[0039] The multivariate regression model was selected as the exercise load adjustment evaluation model, and the historical data set was used for training to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; A multiple regression model is a statistical analysis method used to explore the relationship between multiple independent variables (explanatory variables) and a dependent variable (response variable). In a multiple regression model, the variation in the dependent variable is explained by the linear combination of multiple independent variables. In this scenario, the exercise load adjustment assessment coefficient As the dependent variable, the heart rate fluctuation intensity coefficient and exercise load fluctuation index As the independent variable, this linear relationship is established to predict the need for exercise load adjustment. The reason for choosing a multivariate regression model as the exercise load adjustment evaluation model is that we need to consider the impact of multiple factors on exercise load adjustment. By training the model with a historical data set, the model can self-adjust the regression coefficient to ensure that it can accurately evaluate and predict exercise load requirements in practical applications. Historical data provides the actual relationship between independent variables and dependent variables under realistic conditions, allowing the regression model to optimize predictions based on these relationships.

[0040] In the regression formula, , and are the regression coefficients, and their meanings are as follows: (Intercept term): represents the baseline value of the exercise load adjustment evaluation coefficient (AEC) when all independent variables (HIC and EFI) are zero. It reflects the basic load demand when there is no influence of other factors.

[0041] : Indicates the degree of influence of the heart rate fluctuation intensity coefficient (HIC) on the exercise load adjustment evaluation coefficient (AEC). The size of determines the contribution of each unit change of HIC to AEC.

[0042] : Indicates the influence of the exercise load fluctuation index (EFI) on the exercise load adjustment evaluation coefficient (AEC). The size of determines the contribution of each unit change of EFI to AEC.

[0043] By training the model and determining the values ​​of these regression coefficients, we can ensure that the model can provide reasonable exercise load adjustment suggestions under different exercise load scenarios.

[0044] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Optimizing the value of the regression coefficient by minimizing the error between the predicted value and the actual value is to ensure the accuracy and reliability of the regression model. This process is called "least squares method" or "error minimization". The core idea is to adjust the regression coefficient so that the gap (ie, error) between the value predicted by the model and the actual observed value is as small as possible, so that the model can accurately reflect the true relationship between the independent variables (HIC and EFI) and the dependent variable (AEC). Specifically, during the training process, by inputting the heart rate fluctuation intensity coefficient (HIC) and exercise load fluctuation index (EFI) in the historical data set and the corresponding exercise load adjustment evaluation coefficient (AEC), the model will calculate the predicted AEC value and compare it with the actual value. Next, the algorithm adjusts the regression coefficient by calculating the error (ie, the sum of the squares of the difference between the predicted value and the actual value) and using an optimization algorithm (such as gradient descent or matrix operations). , and , until the error is minimized. Finally, the determined regression coefficient is the optimal coefficient that minimizes the error when the model predicts future data, ensuring the accuracy and adaptability of the model.

[0045] Using the final regression coefficient, the constructed exercise load adjustment evaluation model is input with the heart rate fluctuation intensity coefficient generated in real time. and exercise load fluctuation index , real-time generation of exercise load adjustment evaluation coefficients .

[0046] In this embodiment, the generated exercise load adjustment evaluation coefficient The pre-set exercise load adjustment evaluation coefficient threshold range The comparison was conducted and the demand for exercise load monitoring of users wearing heart rate chest straps during long-term aerobic exercise was evaluated based on the comparison results. The users were divided into three categories: low demand, medium demand and high demand. The specific comparison and analysis are as follows: like , the demand for exercise load monitoring during long-term aerobic exercise for users wearing heart rate chest straps is low; This situation means that the user wearing a heart rate chest strap has a relatively light exercise load during aerobic exercise, and the amplitude and change of heart rate fluctuations are not large. Specifically, this situation usually occurs in stages with low exercise intensity, such as easy aerobic training or recovery period. In this case, the user's exercise load monitoring demand is rated as low, indicating that the system believes that too much intervention or adjustment is not required. For the system, this can reduce the need for real-time intervention and adjustment, thereby reducing resource consumption, while allowing users to independently control the rhythm of exercise and avoid excessive monitoring.

[0047] like , the demand for exercise load monitoring during long-term aerobic exercise for users wearing heart rate chest straps is medium; This situation indicates that the user's exercise load is at a moderate level. At this time, the amplitude and variation of heart rate fluctuations are within the normal range, which may be within the normal intensity range of aerobic exercise, and the user is in a suitable training state. This situation usually means that the user is doing moderate-intensity exercise, neither too easy nor too intense. The evaluation results of medium demand show that the system should moderately monitor the user's exercise load, promptly identify and prevent the risk of excessive or insufficient exercise load, and ensure that the user is within a safe exercise intensity range. In this state, the system can make timely adjustment suggestions, such as increasing or decreasing exercise intensity, to help users optimize training effects and prevent excessive or insufficient exercise.

[0048] like Users who wear heart rate chest straps have a high demand for exercise load monitoring during long-term aerobic exercise.

[0049] This situation means that the user's exercise load is high, the heart rate fluctuates greatly, and the user may be in a high-intensity aerobic exercise stage, or there are great fluctuations in exercise intensity. This situation usually means that the user is undergoing intense aerobic training or is in a state of excessive load, which may cause physical fatigue, excessive strain or excessive heart rate. At this time, the system assesses it as a high demand, indicating that the user's exercise load monitoring needs are very high, and the system needs to monitor the user's physiological state in real time, adjust the training plan in time, or take protective measures to prevent health risks caused by excessive exercise. The system may prompt the user to slow down the intensity of exercise, adjust the rest time, or take other protective measures to ensure exercise safety and avoid sports injuries.

[0050] The "pre-set threshold interval of the exercise load adjustment evaluation coefficient" can be determined through data analysis and mining of historical training data. First, collect a large number of exercise load adjustment evaluation coefficients of users wearing heart rate chest belts under different exercise load conditions, and perform statistical analysis on these data to calculate the average, maximum and minimum values ​​under various exercise load conditions. Through cluster analysis of these data, identify the commonalities under different exercise intensities or load conditions, and thus determine the reasonable interval of the exercise load adjustment evaluation coefficient. In order to ensure the scientificity and adaptability of the threshold interval, users with different exercise levels can be grouped through data mining techniques (such as K-means clustering or regression analysis), and the evaluation coefficient thresholds corresponding to different groups can be set. In this process, the interval range can be refined according to the user's exercise habits, heart rate response and training goals to adapt to diverse exercise needs. In addition, the threshold interval should be continuously optimized and adjusted according to real-time monitoring data, and the threshold interval should be dynamically updated using machine learning algorithms to make it more in line with the personalized needs of different users, thereby improving the accuracy and applicability of the evaluation model.

[0051] According to the evaluation results, different adjustment measures are taken for the heart rate chest belt in the cases of low demand, medium demand and high demand. In this embodiment, according to the evaluation results, different adjustment measures are respectively taken for the heart rate chest belt in the cases where the evaluation results are low demand, medium demand and high demand, specifically: If the evaluation result is low demand, the adjustment measures for the heart rate chest strap are: maintain the existing configuration and parameter settings of the heart rate chest strap, maintain the existing monitoring strategy, do not frequently update the heart rate data, and continue to monitor the user's heart rate status and exercise load changes; For users whose evaluation results are low demand, the adjustment measures for the heart rate chest strap mainly include maintaining the existing configuration and parameter settings, reducing frequent heart rate data updates, and continuously monitoring the user's heart rate and exercise load changes. In order to achieve this adjustment, the stability of the user's exercise load and heart rate changes can be monitored through intelligent algorithms, and the frequency of data updates can be reduced when it is confirmed that the changes are not significant. Specifically, the software system can set a low-frequency data update strategy that triggers updates only when large changes are detected. This approach not only saves device energy consumption by reducing the frequency of calculation and data transmission, but also extends the battery life of the heart rate chest strap, while avoiding excessive monitoring and data redundancy. The reason for this approach is that in low-demand situations, frequent data updates are of limited help in monitoring the user's exercise status, so there is no need for complex dynamic adjustments.

[0052] If the evaluation result is a medium demand, the adjustment measures for the heart rate chest strap are: dynamically adjust the heart rate data collection and monitoring strategy according to the real-time feedback of the exercise load changes, adjust the sensor sensitivity of the heart rate chest strap to improve the collection accuracy of heart rate and exercise load data; when the exercise load fluctuates, increase the data update frequency and perform fluctuation analysis to accurately capture the user's exercise status, and adjust the monitoring strategy in time to cope with different load changes; When the evaluation result is a medium demand, it is necessary to dynamically adjust the heart rate data collection and monitoring strategy, increase the data update frequency, and dynamically adjust the monitoring accuracy according to the change of exercise load. The software implements this measure by collecting exercise load change data (for example, exercise intensity and heart rate fluctuation amplitude) in real time, and using a rule-based dynamic adjustment algorithm to automatically determine the improvement of sensor sensitivity and the increase of data update frequency. For example, the data collection frequency can be adjusted according to the heart rate fluctuation amplitude and change frequency. When the load fluctuation is large, the system automatically adjusts to a higher sampling frequency to ensure more accurate exercise load and heart rate data recording. This adjustment ensures that the system can respond in time and ensure the accuracy of the data under medium load demand by monitoring the user's exercise pattern and load fluctuation. Through this dynamic adaptation, the system can improve its monitoring ability for medium-demand users, ensure that their exercise load changes are not missed, and avoid unnecessary calculations and excessive data redundancy.

[0053] If the evaluation result is a high demand, the adjustment measures for the heart rate chest strap are: immediately calibrate the accuracy of the heart rate chest strap sensor, increase the data transmission frequency, and improve the data collection frequency and update speed to ensure more detailed monitoring of heart rate fluctuations under high load conditions; at the same time, start the exercise load adjustment mechanism, adjust the monitoring strategy in real time, improve monitoring accuracy, and respond to exercise load fluctuations under high demand conditions.

[0054] For users with high demands, adjustment measures require precision calibration and enhanced data collection frequency to ensure accurate monitoring under high load conditions. This measure is achieved by using intelligent algorithms to monitor the amplitude and frequency of user heart rate fluctuations and automatically trigger the sensor precision calibration procedure. When the system detects extreme exercise load fluctuations, the sensor sampling frequency is immediately adjusted through software, and the data transmission channel is optimized to increase the data update speed. In addition, the software can also start the exercise load adjustment mechanism to improve the capture accuracy of heart rate fluctuation data by adjusting the monitoring strategy in real time. For example, the system can adjust the heart rate data collection cycle based on the amplitude of load fluctuations and the frequency of heart rate changes, and continue to increase the monitoring frequency under extreme loads to cope with the increase in exercise intensity. This process is automated through algorithms to minimize manual intervention, ensure accurate capture of heart rate and exercise load data, and meet the actual needs of high-demand users. In this way, it can be ensured that under high load conditions, the system will not miss key heart rate change information, ensuring accurate exercise load monitoring for users.

[0055] Continuously monitor the exercise status information of users wearing heart rate chest straps, and dynamically adjust the operating frequency and accuracy of the device based on real-time feedback. At the same time, match the adjusted device parameters with the user's exercise load requirements, and perform periodic optimization to improve data collection efficiency and the accuracy of exercise load assessment.

[0056] To continuously monitor the user's exercise status information, it is first necessary to collect heart rate data, exercise load data and other physiological parameters (such as cadence, exercise intensity, etc.) in real time through a heart rate chest strap. The software system can use low-power Bluetooth (BLE) or other wireless communication technologies to continuously collect data from sensors and analyze the data in real time through intelligent algorithms. For example, the software can use time series data analysis methods to determine the user's exercise status based on information such as the user's exercise mode and exercise intensity. The monitoring frequency can be dynamically adjusted according to different needs to ensure that sufficient data can be collected in time when the exercise load changes greatly. This process not only requires real-time data transmission, but also relies on algorithms to judge the current state and automatically trigger the accuracy adjustment of the equipment to ensure the stability and effectiveness of the monitoring data. Through real-time monitoring, the system can continuously evaluate the user's exercise load and provide a basis for subsequent adjustment measures.

[0057] Dynamically adjusting the operating frequency and accuracy of the device based on real-time feedback mainly relies on data analysis and feedback mechanisms. The software can set a dynamic adjustment algorithm that automatically determines the frequency of data collection and the accuracy of the sensor according to changes in the wearer's motion state. For example, when the user's motion load is low and stable, the device can reduce the frequency of data collection and reduce the accuracy of the sensor to save battery and computing resources; while in high-load, high-fluctuation motion states, the device automatically increases the frequency of data collection and enhances the sensitivity of the sensor to ensure more accurate monitoring of heart rate and other physiological data. This process is achieved by adjusting the device parameters in real time through software. The algorithm automatically optimizes the device's working mode based on the amplitude of load fluctuations, the frequency of heart rate fluctuations, and the complexity of the motion state. This method ensures that the device can provide optimal monitoring performance under different motion loads through intelligent adjustment, avoiding resource waste or data distortion caused by over-adjustment.

[0058] The matching of equipment parameters with the user's exercise load requirements is achieved through a continuous feedback mechanism and a periodic optimization strategy. First, the software system adjusts the equipment parameters according to the exercise load conditions monitored each time (such as exercise intensity, heart rate changes, continuous exercise time, etc.), and dynamically optimizes according to the personalized needs and exercise habits of each user (for example, whether to participate in high-intensity training or long-term continuous exercise, etc.). This process is achieved by building a model or rule engine based on machine learning. The system learns the user's exercise pattern and feedback information, and adjusts the parameters of the heart rate chest belt to match the user's exercise load requirements. For example, if a user's exercise load requirements change, the system automatically adjusts the equipment parameters based on historical data and current status to adapt to the new requirements. In addition, through periodic evaluation and feedback, the system can continuously optimize parameter settings to ensure that the best monitoring accuracy and data collection efficiency can be maintained when tracking the user's exercise status for a long time. This method based on periodic feedback and optimization can greatly improve the adaptability and accuracy of the equipment, thereby providing users with continuous and high-quality exercise load monitoring services.

[0059] like Figure 2 The heart rate chest strap usage process management system based on monitoring data feedback shown includes a user motion state detection module, a motion state information collection and analysis module, a motion load adjustment evaluation module, a heart rate chest strap adjustment strategy module, and a device dynamic adjustment and optimization module; The user exercise state detection module detects in real time whether the user wearing the heart rate chest strap is doing aerobic exercise during exercise, and triggers the dynamic adjustment mechanism when it is detected that the user wearing the heart rate chest strap is doing aerobic exercise for a long time; The exercise status information collection and analysis module obtains the exercise status information generated by users wearing heart rate chest straps during aerobic exercise in real time, and analyzes it after acquisition to generate the heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively; The exercise load adjustment assessment module constructs an exercise load adjustment assessment model based on the generated heart rate fluctuation intensity coefficient and exercise load fluctuation index, generates an exercise load adjustment assessment coefficient, and analyzes it after generation to assess the exercise load monitoring needs of users wearing heart rate chest straps during long-term aerobic exercise, and divides them into three categories: low need, medium need, and high need; The heart rate chest strap adjustment strategy module takes different adjustment measures for the heart rate chest strap in the three cases of low demand, medium demand and high demand according to the evaluation results; The device dynamic adjustment and optimization module continuously monitors the exercise status information of users wearing heart rate chest straps, and dynamically adjusts the operating frequency and accuracy of the device based on real-time feedback. At the same time, it matches the adjusted device parameters with the user's exercise load requirements and performs periodic optimization to improve data collection efficiency and the accuracy of exercise load assessment.

[0060] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0061] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0062] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0063] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0064] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0065] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0066] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A heart rate chest strap usage management method based on monitoring data feedback, characterized in that: The specific steps include: When the user wearing the heart rate chest strap is exercising, it is detected in real time whether the user wearing the heart rate chest strap is doing aerobic exercise, and when it is detected that the user wearing the heart rate chest strap is doing aerobic exercise for a long time, the dynamic adjustment mechanism is triggered; Real-time acquisition of the exercise status information generated by users wearing heart rate chest straps during aerobic exercise, and analysis after acquisition to generate heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively; The generated heart rate fluctuation intensity coefficient and exercise load fluctuation index are used to construct an exercise load adjustment evaluation model, generate an exercise load adjustment evaluation coefficient, and analyze it after generation to evaluate the exercise load monitoring needs of users wearing heart rate chest belts during long-term aerobic exercise, and divide them into three categories: low need, medium need, and high need; According to the evaluation results, different adjustment measures are taken for the heart rate chest belt in the cases of low demand, medium demand and high demand. Continuously monitor the exercise status information of users wearing heart rate chest straps, and dynamically adjust the operating frequency and accuracy of the device based on real-time feedback. At the same time, match the adjusted device parameters with the user's exercise load requirements, and perform periodic optimization to improve data collection efficiency and the accuracy of exercise load assessment.

2. The heart rate chest strap usage management method based on monitoring data feedback according to claim 1, characterized in that: The exercise status information generated by a user wearing a heart rate chest strap during aerobic exercise is obtained in real time, and analyzed after acquisition to generate a heart rate fluctuation intensity coefficient and an exercise load fluctuation index, respectively, which specifically includes the following steps: Real-time acquisition of exercise status information generated by users wearing heart rate chest straps during aerobic exercise, and pre-processing after acquisition; Extracting heart rate trend information and load stability information from the preprocessed exercise state information; The extracted heart rate trend information and load stability information are analyzed to generate the heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively.

3. The heart rate chest strap usage management method based on monitoring data feedback according to claim 2, characterized in that: The logic for obtaining the heart rate fluctuation intensity coefficient is as follows: Extract the heart rate trend information from the preprocessed exercise status information, including the heart rate values ​​at different times during a period of time when the user wearing the heart rate chest strap is doing aerobic exercise, the heart rate change rate, and the standard deviation of the heart rate values ​​of the user wearing the heart rate chest strap during the period of time, and calibrate them as , and , Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. Heart rate value at the moment, Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The heart rate change rate at any time, Indicates the standard deviation of the heart rate values ​​of users wearing a heart rate chest strap during this time period. , is a positive integer; The heart rate values ​​of users wearing heart rate chest straps at different times during a period of aerobic exercise are constructed into a set, and the maximum and minimum values ​​in the set are marked as and ; Calculate the average heart rate values ​​of users wearing heart rate chest straps during aerobic exercise at different times over a period of time , according to the formula: ; Calculate the heart rate fluctuation intensity coefficient. The specific calculation formula is as follows: In the formula, is the heart rate fluctuation intensity coefficient.

4. The heart rate chest strap usage management method based on monitoring data feedback according to claim 3, characterized in that: The acquisition logic of the exercise load fluctuation index is as follows: The load stability information in the preprocessed exercise state information is extracted, including the frequency of heart rate fluctuations at different times during a period of time during aerobic exercise for users wearing heart rate chest straps, the amplitude of changes in heart rate fluctuation intensity, and the time required for the heart rate of users wearing heart rate chest straps to recover from the maximum fluctuation to a stable state, and they are calibrated as , and , Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The frequency of heart rate fluctuations at all times, Indicates that users wearing heart rate chest straps have a certain period of time during aerobic exercise. The magnitude of the change in the intensity of heart rate fluctuations at each moment, Indicates the time required for the heart rate of a user wearing a heart rate chest monitor to recover from the maximum fluctuation to a stable state. , is a positive integer; Calculate the exercise load fluctuation index. The specific calculation formula is as follows: In the formula, It is the exercise load fluctuation index.

5. The heart rate chest strap usage management method based on monitoring data feedback according to claim 4, characterized in that: Constructing an exercise load adjustment evaluation model for the generated heart rate fluctuation intensity coefficient and exercise load fluctuation index to generate an exercise load adjustment evaluation coefficient specifically includes the following steps: Collect several heart rate fluctuation intensity coefficients, exercise load fluctuation indexes and corresponding exercise load adjustment evaluation coefficients generated in the past period of time, and calibrate them as , and , Indicates the numbers of several heart rate fluctuation intensity coefficients, exercise load fluctuation indexes and corresponding exercise load adjustment evaluation coefficients generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; The multivariate regression model was selected as the exercise load adjustment evaluation model, and the historical data set was used for training to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Using the final regression coefficient, the constructed exercise load adjustment evaluation model is input with the heart rate fluctuation intensity coefficient generated in real time. and exercise load fluctuation index , real-time generation of exercise load adjustment evaluation coefficients .

6. The heart rate chest strap usage management method based on monitoring data feedback according to claim 5, characterized in that: The generated exercise load adjustment evaluation factor The pre-set exercise load adjustment evaluation coefficient threshold range The comparison was conducted and the demand for exercise load monitoring of users wearing heart rate chest straps during long-term aerobic exercise was evaluated based on the comparison results. The users were divided into three categories: low demand, medium demand and high demand. The specific comparison and analysis are as follows: like , the demand for exercise load monitoring during long-term aerobic exercise for users wearing heart rate chest straps is low; like , the demand for exercise load monitoring during long-term aerobic exercise for users wearing heart rate chest straps is medium; like Users who wear heart rate chest straps have a high demand for exercise load monitoring during long-term aerobic exercise.

7. The heart rate chest strap usage management method based on monitoring data feedback according to claim 6, characterized in that: According to the evaluation results, different adjustment measures are taken for the heart rate chest belt in the three cases of low demand, medium demand and high demand, specifically: If the evaluation result is low demand, the adjustment measures for the heart rate chest strap are: maintain the existing configuration and parameter settings of the heart rate chest strap, maintain the existing monitoring strategy, do not frequently update the heart rate data, and continue to monitor the user's heart rate status and exercise load changes; If the evaluation result is a medium demand, the adjustment measures for the heart rate chest strap are: dynamically adjust the heart rate data collection and monitoring strategy according to the real-time feedback of the exercise load changes, and adjust the sensor sensitivity of the heart rate chest strap to improve the collection accuracy of heart rate and exercise load data; When the exercise load fluctuates, increase the frequency of data updates and perform fluctuation analysis, and adjust the monitoring strategy in time to cope with different load changes; If the evaluation result is a high demand, the adjustment measures for the heart rate chest strap are: immediately calibrate the accuracy of the heart rate chest strap sensor, increase the data transmission frequency, and improve the data collection frequency and update speed; at the same time, start the exercise load adjustment mechanism, adjust the monitoring strategy in real time, and improve the monitoring accuracy.

8. A heart rate chest strap usage process management system based on monitoring data feedback, used to implement the heart rate chest strap usage process management method based on monitoring data feedback as described in any one of claims 1 to 7, characterized in that: It includes user motion state detection module, motion state information collection and analysis module, motion load adjustment evaluation module, heart rate chest strap adjustment strategy module and equipment dynamic adjustment and optimization module; The user exercise state detection module detects in real time whether the user wearing the heart rate chest strap is doing aerobic exercise during exercise, and triggers the dynamic adjustment mechanism when it is detected that the user wearing the heart rate chest strap is doing aerobic exercise for a long time; The exercise status information collection and analysis module obtains the exercise status information generated by users wearing heart rate chest straps during aerobic exercise in real time, and analyzes it after acquisition to generate the heart rate fluctuation intensity coefficient and exercise load fluctuation index respectively; The exercise load adjustment assessment module constructs an exercise load adjustment assessment model based on the generated heart rate fluctuation intensity coefficient and exercise load fluctuation index, generates an exercise load adjustment assessment coefficient, and analyzes it after generation to assess the exercise load monitoring needs of users wearing heart rate chest straps during long-term aerobic exercise, and divides them into three categories: low need, medium need, and high need; The heart rate chest strap adjustment strategy module takes different adjustment measures for the heart rate chest strap in the three cases of low demand, medium demand and high demand according to the evaluation results; The device dynamic adjustment and optimization module continuously monitors the exercise status information of users wearing heart rate chest straps, and dynamically adjusts the operating frequency and accuracy of the device based on real-time feedback. At the same time, it matches the adjusted device parameters with the user's exercise load requirements and performs periodic optimization to improve data collection efficiency and the accuracy of exercise load assessment.

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