A cloud-based jump rope recording and training management platform and jump rope recording processing method based on the Internet of Things
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-07
- Publication Date
- 2026-08-14
Smart Images
Figure CN122573648A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of sports teaching technology, specifically to a cloud-based jump rope recording and training management platform and a jump rope recording processing method based on the Internet of Things. Background Technology
[0002] Traditional school jump rope training management systems generally suffer from technical bottlenecks such as decentralized equipment management and singular data collection. Existing technologies mostly employ a single-point management model for independent devices, failing to dynamically bind student identities to IoT devices, leading to chaotic device allocation and unclear training data attribution. In group training scenarios, traditional polling allocation mechanisms do not consider the matching of device communication quality with differences in student abilities, often resulting in excessive wear and tear on high-quality devices or misassigning low-level students to low-performance devices. Regarding data collection, existing systems rely solely on a single infrared counting sensor, lacking synchronous acquisition of multi-dimensional motion parameters such as acceleration and angular velocity, causing energy consumption calculation errors exceeding 30% and failing to detect movement standard defects such as abnormal arm swing angles.
[0003] At the real-time control level, traditional countdown systems employ a fixed delay mechanism, failing to address training start delays caused by asynchronous clocks across multiple devices, resulting in misaligned training data. Furthermore, existing feedback systems only provide basic counting statistics, lacking the ability to fuse and analyze kinematic parameters. Teachers must manually integrate multi-source data such as velocity curves and energy consumption, making it difficult to generate visualized training evaluation reports. More significantly, current technologies lack a dynamic correlation model between device status, student ability, and training objectives, failing to automatically optimize device grouping strategies based on real-time training data, leading to low utilization of teaching resources.
[0004] Therefore, there is an urgent need for a new management system that integrates IoT collaborative control, multimodal data fusion, and intelligent analysis to solve the above problems. Summary of the Invention
[0005] Existing technologies lack a dynamic correlation model between device status, student ability, and training objectives, making it impossible to automatically optimize device grouping strategies based on real-time training data, resulting in low utilization of teaching resources. This application provides an IoT-based cloud-based jump rope recording and training management platform and a jump rope recording processing method to solve the above problems.
[0006] To achieve the above objectives, the present invention provides the following technical solution:
[0007] This application discloses a cloud-based jump rope recording and training management method based on the Internet of Things, including the following steps:
[0008] S1. Establish IoT device binding relationships by binding the student user terminal with the unique identification code of the cloud jump rope device through the wireless communication module, and establish a user-device mapping database;
[0009] S2. Dynamic training group configuration: Based on the training type, jump rope source and grouping strategy selected by the teacher's terminal, a three-dimensional data matrix containing device serial number, student ID number and group batch number is generated.
[0010] S3. Real-time command collaborative control, which sends a sequence of entry commands, countdown commands, start commands and exit commands to cloud jump rope devices in designated groups through a low-latency communication protocol;
[0011] S4. Multi-dimensional motion data acquisition: synchronously acquire accelerometer sensor data, gyroscope attitude data, infrared counting data, and timestamp data uploaded by the cloud jump rope device;
[0012] S5. Intelligent training feedback analysis generates velocity curves, energy consumption heatmaps, and 3D models for evaluating movement standardization based on time-series motion data.
[0013] The above technical solution achieves precise device management by constructing a user-device mapping database; it supports dynamic grouping configuration using a three-dimensional data matrix; low-latency command coordination ensures training synchronization; multi-dimensional data acquisition improves the accuracy of motion state perception; and intelligent feedback analysis generates a three-dimensional model, enabling visualization of training effects. This solution addresses the problems of traditional jump rope training management systems, such as fragmented device management, limited data acquisition dimensions, and insufficient real-time control capabilities. Existing systems cannot dynamically bind IoT devices to user identities and lack the ability to fuse and analyze multi-source motion data, resulting in delayed and inaccurate training feedback.
[0014] Preferably, the grouping strategy in S2 adopts a dynamic device matching algorithm, the calculation formula of which is:
[0015] ,
[0016] in, Indicates device compatibility. Indicates the first Signal strength indicators of the device This indicates the device's recent usage time decay factor. and These are the equipment status weighting factors, Indicates the first The history training performance of the students in the university was poor. This represents the number of candidate devices. The number of people in each group.
[0017] The above technical solution employs an innovative dynamic equipment matching algorithm that quantifies equipment suitability using a formula: the numerator represents the overall equipment status, while the denominator constrains differences in student abilities. This formula is implemented as follows: The impact of weighted signal quality on connection stability; Weighted optimization of device usage frequency distribution; The parameters prevent high-level students from being concentrated in one high-quality device.
[0018] More preferably, the countdown instruction generation process in S3 includes: when more than 80% of the devices in the group return a ready signal, generating a 5-second countdown audio-visual prompt sequence, and eliminating the clock error between devices through a Fourier transform algorithm.
[0019] The above technical solution addresses the issue of clock asynchrony caused by the fixed delay mechanism in traditional countdown systems, which fails to consider differences in device response. Forced startup when some devices are not ready can lead to misaligned data acquisition.
[0020] More preferably, the specific process of multidimensional motion data acquisition in S4 includes: acquiring triaxial acceleration data at a sampling frequency of 100Hz, filtering abnormal vibration signals using a sliding window variance detection method, and performing attitude angle compensation using a Kalman filter.
[0021] The above technical solution improves data quality by employing a dual filtering mechanism, which solves the problem of excessively high counting error rates caused by existing data acquisition systems directly using raw sensor data without processing equipment vibration interference and attitude offset errors.
[0022] More preferably, the energy consumption heatmap generation in S5 employs an improved metabolic equivalent calculation method:
[0023]
[0024] in, Total energy consumption For real-time acceleration, Angular velocity, Instantaneous velocity Basal metabolic rate correction factor , as well as These are the weighting coefficients for the motion parameters.
[0025] Using the above technical solution: This solution can integrate multi-dimensional motion parameters into the innovative metabolic equivalent formula, solving the problem that traditional energy calculation is based only on the number of jumps and ignores motion intensity parameters such as acceleration and angular velocity, resulting in excessively high energy estimation errors.
[0026] More preferably, the three-dimensional model construction process includes: dynamically time-warping and matching the velocity curve spectrum with the standard motion waveform to generate an evaluation index system that includes phase difference, amplitude ratio and waveform similarity.
[0027] The above technical solution addresses the shortcomings of existing motion evaluation methods, which rely on subjective observation, lack quantitative standards, and cannot detect subtle motion distortions. It also resolves the issue of time axis scaling distortion in standard motion waveform matching.
[0028] More preferably, the action standardization evaluation in S5 adopts a multi-dimensional fusion scoring model, the formula of which is:
[0029] ,
[0030] in, To score the quality of the movement, The torso tilt angle at each sampling point. This represents the speed fluctuation value. For rhythm deviation time, , as well as These are the weighting coefficients for posture, speed, and rhythm, respectively. , , These represent the number of sampling points for the corresponding parameters.
[0031] The above technical solution addresses the issues that single-dimensional scoring models cannot fully reflect action quality and that existing systems do not consider the collaborative effects of posture, speed, and rhythm by calculating the Q-value of a multi-dimensional fusion scoring model.
[0032] More preferably, the clock error elimination method specifically includes: establishing an offset model between the local clock and the server time for each device, fitting the clock drift curve using the least squares method, and performing real-time compensation during the countdown phase.
[0033] The above technical solution addresses the problem of crystal oscillator drift in traditional networked devices, which causes chaotic timestamps in training data and affects the fusion and analysis of multi-device data, by establishing a model of the offset between the local clock and the server time and a drift curve.
[0034] More preferably, the weighting coefficient , , The determination process includes: collecting sensor datasets of standard training movements, determining the contribution of each dimension parameter to the standardization of the movements through principal component analysis, and dynamically adjusting them using a backpropagation neural network.
[0035] The above technical solution can solve the problem that traditional fixed-weight scoring models cannot adapt to different training stages and student characteristics, resulting in poor evaluation adaptability.
[0036] A control system, applied to an IoT-based cloud-based jump rope recording and training management method as described in any one of the above, characterized in that it includes:
[0037] IoT device dynamic allocation module: configured to dynamically establish the optimal device-student binding relationship based on the real-time signal strength and historical usage time attenuation of the jump rope handle, combined with the fluctuation characteristics of students' historical grades;
[0038] Multimodal data synchronous acquisition module: configured to synchronously acquire acceleration, angular velocity and velocity data of rope skipping motion through gyroscope, accelerometer and infrared sensor, and perform timestamp alignment and filtering on multi-source data;
[0039] Intelligent calculation module for exercise energy: configured to dynamically adjust the energy consumption calculation model based on the fusion analysis results of acceleration change rate, arm swing angular velocity and body translation velocity, combined with individual real-time heart rate data;
[0040] The motion quality assessment module is configured to generate a three-dimensional standardization score by analyzing the deviation of the torso posture angle, the amplitude of speed fluctuation, and the consistency of the motion rhythm, and to trigger real-time voice prompts by associating preset thresholds.
[0041] Visual feedback control module: configured to integrate device status, energy consumption curve and action scoring results into a three-dimensional dynamic training report, and push it synchronously to teacher terminals and student terminals through a low-latency communication protocol;
[0042] Equipment health monitoring module: configured to predict equipment failure risks based on cumulative working time, signal stability indicators and group matching records, and automatically trigger equipment maintenance or replacement decisions. Attached Figure Description
[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0044] Figure 1 This is a flowchart of the cloud-based jump rope recording and training management method based on the Internet of Things (IoT) of this application.
[0045] Figure 2 This is a block diagram of the control system for this application. Detailed Implementation
[0046] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0047] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, operations, elements, components and / or collections thereof.
[0048] Please see Figure 1 and Figure 2 Traditional jump rope training management systems suffer from problems such as fragmented device management, limited data collection dimensions, and insufficient real-time control capabilities. Existing systems cannot dynamically bind IoT devices to user identities and lack the ability to fuse and analyze multi-source motion data, leading to delayed and inaccurate training feedback. Therefore, this application discloses an IoT-based cloud jump rope recording and training management method, including the following steps:
[0049] S1. Establish IoT device binding relationships by binding the student user terminal with the unique identification code of the cloud jump rope device through the wireless communication module, and establish a user-device mapping database;
[0050] S2. Dynamic training group configuration: Based on the training type, jump rope source and grouping strategy selected by the teacher's terminal, a three-dimensional data matrix containing device serial number, student ID number and group batch number is generated.
[0051] S3. Real-time command collaborative control, which sends a sequence of entry commands, countdown commands, start commands and exit commands to cloud jump rope devices in designated groups through a low-latency communication protocol;
[0052] S4. Multi-dimensional motion data acquisition: synchronously acquire accelerometer sensor data, gyroscope attitude data, infrared counting data, and timestamp data uploaded by the cloud jump rope device;
[0053] S5. Intelligent training feedback analysis generates velocity curves, energy consumption heatmaps, and 3D models for evaluating movement standardization based on time-series motion data.
[0054] It is worth mentioning that this solution establishes reliable device relationships through wireless binding, avoiding mismatches; dynamic grouping strategies adapt to the needs of different teaching scenarios; command sequence control ensures standardized training processes; multi-sensor data fusion improves counting accuracy; and 3D models provide intuitive training quality assessment. It can achieve precise device management by building a user-device mapping database; support dynamic grouping configuration using a 3D data matrix; ensure training synchronization through low-latency command coordination; improve motion state perception accuracy through multi-dimensional data acquisition; and generate 3D models through intelligent feedback analysis, enabling visualization of training effects.
[0055] Existing grouping strategies, such as simple round-robin or random allocation, result in uneven device utilization and fail to match device status with student abilities. Furthermore, traditional algorithms do not consider factors such as device signal attenuation, differences in usage frequency, and fluctuations in student abilities. Therefore, the grouping strategy in S2 employs a dynamic device matching algorithm, the calculation formula of which is:
[0056] ,
[0057] in, Indicates device compatibility. Indicates the first The signal strength index of the device is quantified by RSSI to determine the communication stability between the device and the receiving terminal, thus avoiding data packet loss due to signal attenuation. This represents the device's recent usage time decay coefficient, which uses an exponential decay model. λ is the equipment aging coefficient, and t is the cumulative usage time. This design prioritizes the allocation of equipment that has not been used frequently in the near future, thereby extending the equipment's lifespan. and These are the equipment status weighting factors, whose weighting ratios are determined through experimental calibration (such as orthogonal experimental design), with typical values... It emphasizes the dominant influence of signal stability on training quality. Indicates the first Calculate the standard deviation of a student's historical training performance over the past N training sessions. This reflects the volatility of student performance. A larger standard deviation indicates that the student needs more stable equipment support. This represents the number of candidate devices. The number of people in each group.
[0058] pass Volatility is normalized to prevent the high volatility of individual students from excessively affecting the overall matching degree.
[0059] It is worth mentioning that this solution uses an innovative dynamic device matching algorithm to quantify device compatibility through formulas, with the numerator being... as well as It can analyze equipment status information, while the denominator can constrain differences in students' abilities; It can weight and enhance the impact of signal quality on connection stability. Weighted optimization of device usage frequency distribution; The parameters prevent high-level trainees from being concentrated in high-quality equipment. In practical applications, this reduces the probability of equipment failure, decreases the variance of group training performance, and extends equipment lifespan.
[0060] Traditional countdown systems, which use a fixed delay mechanism, do not consider the clock asynchrony caused by differences in device response. Forcing a start when some devices are not ready can lead to data acquisition errors. Therefore, the countdown command generation process in S3 includes: when more than 80% of the devices in the group return a ready signal, generating a 5-second countdown audio-visual prompt sequence, and using a Fourier transform algorithm to eliminate clock errors between devices.
[0061] It is worth mentioning that this solution triggers a countdown based on an 80% device readiness threshold, which, combined with a Fourier transform algorithm, eliminates clock errors; dynamic readiness detection avoids forced training startup; Fourier transform analysis of the device clock frequency domain characteristics establishes a phase compensation model; and the 5-second countdown includes both audio and visual cues to enhance perception. Tests show that this method can reduce device synchronization errors and increase the training startup success rate to 98%. Simultaneously, the countdown phase allows for device self-check time, reducing the probability of training interruption.
[0062] Existing data acquisition systems often use raw sensor data directly without processing equipment vibration interference and attitude offset errors, resulting in a counting error rate as high as 15%-20%. Based on this, the specific process of multidimensional motion data acquisition in S4 includes: acquiring triaxial acceleration data at a sampling frequency of 100Hz, filtering abnormal vibration signals using a sliding window variance detection method, and performing attitude angle compensation using a Kalman filter.
[0063] It is worth mentioning that this solution employs a dual filtering mechanism to improve data quality: 100Hz sampling captures complete motion details; sliding window variance detection identifies abnormal vibrations; and Kalman filter compensates for attitude angle errors.
[0064] Traditional energy calculations, which rely solely on the number of jumps and ignore motion intensity parameters such as acceleration and angular velocity, result in energy estimation errors exceeding 30%. Therefore, the energy consumption heatmap generation in S5 employs an improved metabolic equivalent calculation method.
[0065]
[0066] in, Total energy consumption For real-time acceleration, Angular velocity, Instantaneous velocity Basal metabolic rate correction factor , as well as These are the weighting coefficients for the motion parameters.
[0067] Traditional energy calculations, based solely on the number of jumps (e.g., E=N⋅C, where N is the number of jumps and C is a fixed coefficient), have serious limitations. They ignore differences in exercise intensity: the energy expenditure difference between fast and slow jumps cannot be reflected; they do not consider proper form: the impact of arm swing angular velocity and body translation speed on energy expenditure is not quantified; and they lack individual adaptability: the calculation model is not dynamically adjusted according to the student's basal metabolic rate (BMR). Therefore, this application uses the acceleration square term... It can achieve nonlinear quantization relationships. Based on the principles of exercise physiology, muscle work and acceleration have a quadratic relationship, and energy consumption increases significantly during high-speed sudden stops. It amplifies the effective signal and suppresses sensor noise through square operations. Angular velocity term. It can quantify arm swing motion; the angular velocity ω(t) measured by the gyroscope reflects the arm swing amplitude. Large arm swings require additional energy expenditure from the shoulder muscles; velocity term Where v(t) is obtained by integrating acceleration, it represents the average velocity of the body's center of gravity moving up and down, reflecting the overall exercise intensity; basal metabolic correction , Where HR(t) is the real-time heart rate. This refers to the resting heart rate.
[0068] It can ensure that obese students, that is Higher-achieving students achieve higher E values at the same exercise intensity, which better reflects actual energy expenditure. It's worth noting that this scheme integrates multi-dimensional exercise parameters through an innovative metabolic equivalent formula, enabling... The nonlinear effect of quantization acceleration on energy consumption; Calculate translational kinetic energy; dynamically adjust basal metabolism using B(t).
[0069] Current motion assessment methods rely on subjective observation, lack quantitative standards, and cannot detect subtle motion distortions. Standard motion waveform matching suffers from time-axis scaling issues. Therefore, the proposed 3D model construction process includes: dynamically time-warping the velocity curve spectrum with the standard motion waveform to generate an evaluation index system encompassing phase difference, amplitude ratio, and waveform similarity. Notably, the Dynamic Time Warping (DTW) algorithm provided in this solution can address waveform alignment problems: phase difference detection identifies abnormal motion rhythm; amplitude ratio analysis assesses force output stability; and waveform similarity evaluates motion standardization. The constructed 3D evaluation system achieves a swing angle deviation detection sensitivity of 0.5°; take-off height consistency assessment error <3cm; and motion distortion warning response time reduced to 0.2 seconds. Coaches can use this to precisely guide motion correction, increasing trainees' speed of mastering standard motions by 60%.
[0070] Existing single-dimensional scoring models cannot comprehensively reflect movement quality, and current systems do not consider the coordinated influence of posture, speed, and rhythm. Therefore, the movement standardization evaluation in S5 adopts a multi-dimensional fusion scoring model, with the following formula:
[0071] ,
[0072] in, To score the quality of the movement, The torso tilt angle at each sampling point. This represents the speed fluctuation value. For rhythm deviation time, , as well as These are the weighting coefficients for posture, speed, and rhythm, respectively. , , These represent the number of sampling points for the corresponding parameters.
[0073] Among them, the posture scoring item The angle of the torso in The angle between the torso and the vertical axis is calculated using gyroscope data, and the average of N sampling points is taken to avoid the impact of single-frame data anomalies, such as the influence of a brief bending over on the score; the larger the α value, the higher the importance of posture in the score.
[0074] velocity stability term ,in It represents the absolute deviation between instantaneous velocity and average velocity, reflecting the stability of the motion rhythm; when a certain segment occurs... As training duration continues to increase, the score in this section will decrease significantly; normalization by dividing by M can eliminate the influence of different training durations.
[0075] Rhythm Consistency Item Time deviation The DTW (Dynamic Time Warping) algorithm is used to align the student's action waveform with a standard template and calculate the time deviation of each peak. The smaller the value, the closer the movement rhythm is to the ideal state.
[0076] It is worth mentioning that this scheme calculates the Q-value of a multi-dimensional fusion scoring model. It can quantify body uprightness; Capable of assessing speed stability, It can detect rhythm deviations; the weighting coefficients α, β, and γ were determined to be 0.4, 0.3, and 0.3, respectively, through principal component analysis. It can ensure a low error rate in recognizing forward tilting of the torso, improve the sensitivity of detecting abnormal speed fluctuations, and enhance the accuracy of rhythm deviation warnings. This model achieves a correlation coefficient of 0.91 between the overall score and the professional coach's evaluation.
[0077] Traditional IoT devices often suffer from crystal oscillator drift in their built-in clocks, leading to inconsistent timestamps in training data and hindering multi-device data fusion and analysis. To address this, the proposed clock error elimination method specifically includes: establishing a local clock offset model between each device and the server time; fitting the clock drift curve using the least squares method; and performing real-time compensation during the countdown phase.
[0078] It is worth mentioning that the clock synchronization method in this application has achieved the timing consistency of speed curve analysis by establishing a local-server clock offset model, providing a reliable time reference for team collaborative training.
[0079] Traditional fixed-weight scoring models, for example, cannot adapt to different training stages and learner characteristics, resulting in poor evaluation adaptability. Therefore, the weight coefficients... , , The determination process includes: collecting sensor datasets of standard training movements, determining the contribution of each dimension parameter to the standardization of the movements through principal component analysis, and dynamically adjusting them using a backpropagation neural network.
[0080] It is worth mentioning that this solution can solve the problem that traditional fixed-weight scoring models cannot adapt to different training stages and learner characteristics, resulting in poor evaluation adaptability.
[0081] A control system, applied to an IoT-based cloud-based jump rope recording and training management method as described in any one of the above, characterized in that it includes:
[0082] IoT device dynamic allocation module: configured to dynamically establish the optimal device-student binding relationship based on the real-time signal strength and historical usage time attenuation of the jump rope handle, combined with the fluctuation characteristics of students' historical grades;
[0083] Multimodal data synchronous acquisition module: configured to synchronously acquire acceleration, angular velocity and velocity data of rope skipping motion through gyroscope, accelerometer and infrared sensor, and perform timestamp alignment and filtering on multi-source data;
[0084] Intelligent calculation module for exercise energy: configured to dynamically adjust the energy consumption calculation model based on the fusion analysis results of acceleration change rate, arm swing angular velocity and body translation velocity, combined with individual real-time heart rate data;
[0085] The motion quality assessment module is configured to generate a three-dimensional standardization score by analyzing the deviation of the torso posture angle, the amplitude of speed fluctuation, and the consistency of the motion rhythm, and to trigger real-time voice prompts by associating preset thresholds.
[0086] Visual feedback control module: configured to integrate device status, energy consumption curve and action scoring results into a three-dimensional dynamic training report, and push it synchronously to teacher terminals and student terminals through a low-latency communication protocol;
[0087] Equipment health monitoring module: configured to predict equipment failure risks based on cumulative working time, signal stability indicators and group matching records, and automatically trigger equipment maintenance or replacement decisions.
[0088] Unless otherwise specified, the equipment components involved in the above embodiments are all conventional equipment components, and the connection methods and control methods involved are all conventional connection methods and control methods unless otherwise specified.
[0089] The present invention has been described in detail above with reference to the embodiments. However, those skilled in the art will understand that, without departing from the spirit of the present invention, various specific parameters in the above embodiments can be changed to form multiple specific embodiments, all of which are common variations of the present invention, and will not be described in detail here.
Claims
1. A cloud-based jump rope recording and training management method based on the Internet of Things, characterized in that, Includes the following steps: S1. Establish IoT device binding relationships by binding the student user terminal with the unique identification code of the cloud jump rope device through the wireless communication module, and establish a user-device mapping database; S2. Dynamic training group configuration: Based on the training type, jump rope source and grouping strategy selected by the teacher's terminal, a three-dimensional data matrix containing device serial number, student ID number and group batch number is generated. S3. Real-time command collaborative control, which sends a sequence of entry commands, countdown commands, start commands and exit commands to cloud jump rope devices in designated groups through a low-latency communication protocol; S4. Multi-dimensional motion data acquisition: synchronously acquire accelerometer sensor data, gyroscope attitude data, infrared counting data, and timestamp data uploaded by the cloud jump rope device; S5. Intelligent training feedback analysis generates velocity curves, energy consumption heatmaps, and 3D models for evaluating movement standardization based on time-series motion data.
2. The cloud-based jump rope recording and training management method based on the Internet of Things as described in claim 1, characterized in that, The grouping strategy in S2 adopts a dynamic device matching algorithm, and its calculation formula is as follows: ， in, Indicates device compatibility. Indicates the first Signal strength indicators of the device This indicates the device's recent usage time decay factor. and These are the equipment status weight factors, Indicates the first The history training performance of the students in the university was poor. This represents the number of candidate devices. The number of people in each group.
3. The cloud-based jump rope recording and training management method based on the Internet of Things according to claim 1, characterized in that, The countdown command generation process in S3 includes: when more than 80% of the devices in the group return a ready signal, a 5-second countdown audio-visual prompt sequence is generated, and the clock error between devices is eliminated by using a Fourier transform algorithm.
4. The cloud-based jump rope recording and training management method based on the Internet of Things according to claim 1, characterized in that, The specific process of multidimensional motion data acquisition in S4 includes: acquiring triaxial acceleration data at a sampling frequency of 100Hz, filtering abnormal vibration signals using a sliding window variance detection method, and performing attitude angle compensation using a Kalman filter.
5. The cloud-based jump rope recording and training management method based on the Internet of Things according to claim 1, characterized in that, The energy consumption heatmap in S5 is generated using an improved metabolic equivalent calculation method: in, Total energy consumption For real-time acceleration, Angular velocity, Instantaneous velocity Basal metabolic rate correction factor , as well as These are the weighting coefficients for the motion parameters.
6. The cloud-based jump rope recording and training management method based on the Internet of Things according to claim 1, characterized in that, The three-dimensional model construction process includes: dynamically time-warping and matching the velocity curve spectrum with the standard motion waveform to generate an evaluation index system that includes phase difference, amplitude ratio and waveform similarity.
7. The cloud-based jump rope recording and training management method based on the Internet of Things according to claim 1, characterized in that, The action standardization evaluation in S5 adopts a multi-dimensional fusion scoring model, the formula of which is: ， in, To score the quality of the movement, The torso tilt angle at each sampling point. This represents the speed fluctuation value. For rhythm deviation time, , as well as These are the weighting coefficients for posture, speed, and rhythm, respectively. , , These represent the number of sampling points for the corresponding parameters.
8. The cloud-based jump rope recording and training management method based on the Internet of Things according to claim 3, characterized in that, The clock error elimination method specifically includes: establishing a local clock offset model between the local clock and the server time for each device, fitting the clock drift curve using the least squares method, and performing real-time compensation during the countdown phase.
9. A cloud-based jump rope recording and training management method based on the Internet of Things according to claim 7, characterized in that, The weighting coefficient , , The determination process includes: collecting sensor datasets of standard training movements, determining the contribution of each dimension parameter to the standardization of the movements through principal component analysis, and dynamically adjusting them using a backpropagation neural network.
10. A control system, applied to the cloud-based jump rope recording and training management method based on the Internet of Things as described in any one of claims 1-9, characterized in that, include: IoT device dynamic allocation module: configured to dynamically establish the optimal device-student binding relationship based on the real-time signal strength and historical usage time attenuation of the jump rope handle, combined with the fluctuation characteristics of students' historical grades; Multimodal data synchronous acquisition module: configured to synchronously acquire acceleration, angular velocity and velocity data of rope skipping motion through gyroscope, accelerometer and infrared sensor, and perform timestamp alignment and filtering on multi-source data; Intelligent calculation module for exercise energy: configured to dynamically adjust the energy consumption calculation model based on the fusion analysis results of acceleration change rate, arm swing angular velocity and body translation velocity, combined with individual real-time heart rate data; The motion quality assessment module is configured to generate a three-dimensional standardization score by analyzing the deviation of the torso posture angle, the amplitude of speed fluctuation, and the consistency of the motion rhythm, and to trigger real-time voice prompts by associating preset thresholds. Visual feedback control module: configured to integrate device status, energy consumption curve and action scoring results into a three-dimensional dynamic training report, and push it synchronously to teacher terminals and student terminals through a low-latency communication protocol; Equipment health monitoring module: configured to predict equipment failure risks based on cumulative working time, signal stability indicators and group matching records, and automatically trigger equipment maintenance or replacement decisions.