Primary and secondary school track and field training equipment motion risk assessment system based on big data analysis

Abstract

This invention relates to the field of sports risk assessment technology and discloses a sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis. The system includes a multi-source acquisition module, an equipment compatibility assessment module, a load accumulation analysis module, a risk propagation prediction module, and an intelligent early warning module. The system acquires student individual characteristic parameters, equipment size parameters, and training management data through the multi-source acquisition module. The equipment compatibility assessment module analyzes the matching relationship between student individual characteristics and training equipment to reduce the risk of injury caused by poor fit. The load accumulation analysis module assesses the cumulative change pattern of load on local tissues during long-term training, identifying latent fatigue accumulation and chronic sports injury risks in advance. The risk propagation prediction module analyzes the degree of transmission and diffusion of sports risks along the lower limb kinetic chain, predicting the risk of multi-joint cascading injuries. The intelligent early warning module enables graded management and precise early warning of sports risks.

Description

Technical Field

[0001] This invention relates to the field of sports risk assessment technology, specifically a sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis. Background Technology

[0002] Core strength is the core foundation of track and field in primary and secondary schools, directly determining the quality of students' running, jumping, and throwing techniques. It plays a crucial role in cultivating students' body coordination and balance control, correcting force compensation patterns, and preventing acute and chronic sports injuries. Currently, core strength training in primary and secondary schools still has many shortcomings. Existing training equipment is mostly generic, failing to adapt to the physical development characteristics and individual fitness differences of primary and secondary school students, making it difficult to accurately control the safe boundaries of training intensity. Training models are also highly homogenized, failing to provide tiered and personalized guidance for students with different fitness levels, and thus failing to meet the diverse training and improvement needs of students.

[0003] Currently, traditional track and field training equipment risk assessment systems generally treat equipment performance as a long-term, stable, and fixed condition, lacking continuous analysis of the gradual performance degradation during equipment use. They also fail to comprehensively consider the coupling effect between equipment condition evolution and student growth changes. During long-term use, the mechanical properties of the equipment will undergo subtle changes, while students' height, weight, and body proportions continue to develop. Both factors together lead to dynamic adjustments in the force relationship between the human body and the equipment. Although these changes may not cause obvious damage in the short term, they will have a long-term impact on the force distribution of the lumbar spine, shoulder joint stability, pelvic control ability, and knee and ankle coordination, causing training risks to gradually accumulate and increasing the probability of chronic sports injuries. Traditional systems cannot identify potential sports risks that have accumulated over a long period of time, and they also cannot provide a scientific basis for the maintenance, replacement, and personalized training program adjustments of training equipment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis. It has advantages such as reducing the risk of injury caused by poor fit and classifying and managing sports risks. It solves the problem that traditional track and field training equipment sports risk assessment systems do not comprehensively consider the coupling effect between the evolution of equipment status and changes in student growth, and cannot identify potential sports risks that have accumulated over a long period of time.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis, comprising: The multi-source acquisition module is used to acquire individual student characteristic parameters, equipment size parameters, and training management data, and classify them into student datasets, equipment datasets, and training datasets. The equipment compatibility assessment module analyzes the matching relationship between individual student characteristics and training equipment, identifies potential sports risks caused by equipment mismatch from multiple dimensions, and quantifies the compatibility index. ; The cumulative load analysis module is used to assess the cumulative changes in the load on local tissues of the body during long-term training, identify the risk of latent fatigue accumulation and chronic sports injuries in advance, and quantify the cumulative injury index. ; The risk propagation prediction module is used to analyze the transmission and evolution of sports risks between different parts of the human body and quantify the risk propagation index. ; The intelligent early warning module is set with a fixed range of adaptation thresholds. Cumulative damage threshold range and risk transmission threshold range It is used to determine the student's fit level with the equipment, the cumulative injury level of long-term training, and the sports risk level, and outputs the corresponding judgment results and response measures.

[0006] Preferably, the student dataset includes the student's height, arm length, total leg length, baseline height and baseline weight for the first time using the adapter.

[0007] Preferably, the equipment dataset includes the equipment's support height, support width, and working surface depth.

[0008] Preferably, the training dataset includes the support force at each test point when using the equipment, the total number of test points, the action angle during the sampling process, the total number of samples for a single action, the maximum allowable angle deviation of the action, the average force on the body parts, the total number of historical training sessions, the training duration, the peak pressure when the core is stressed, the peak pressure when the hip joint is stressed, the peak pressure when the knee joint is stressed, and the peak pressure when the ankle joint is stressed.

[0009] Preferably, the adaptation index The calculation process is as follows: S11. Based on the student dataset, equipment dataset, and training dataset, extract students. Individual characteristic parameters, equipment Size data and students Use of equipment Training management data at the time; S12. Calculate the student's dimensions. and equipment Dimensional deviation ; S13. Calculate the student's mechanics dimension. and equipment Force balance coefficient ; S14. Calculate the student's action trajectory dimension. and equipment Action stability coefficient ; S15. Calculate the student's growth adaptation dimension. and equipment Growth Adaptability Coefficient ; S16. Based on S11-S15, calculate the students' scores using a weighted method. and equipment Adaptability Index .

[0010] Preferably, the cumulative damage index The calculation process is as follows: S21. Based on the training dataset, extract students. Use of equipment Training management data at the time; S22, Calculate the first Single training load of a training session ; S23, Calculate the first Recovery reduction factor for each training session ; S24, Calculate students Historical cumulative load during training ; S25, Regarding students Historical cumulative load during training Perform dimensionless normalization; S26. Based on S21-S25, calculate the students' scores using a weighted method. Cumulative injury index during training .

[0011] Preferably, the risk transmission index The calculation process is as follows: S31. Based on the training dataset, extract students. Use of equipment Training management data at the time; S32, Calculate students Mean risk of equivalent nodes in the transmission ; S33. Based on S31-S32, calculate the students' scores using a weighted method. Use of equipment Transmission risk index at that time .

[0012] Preferably, the adaptation level evaluation process is as follows: Let the upper limit of the adaptation threshold range be denoted as The lower limit of the adaptation threshold range is denoted as ; If a student and equipment Adaptability Index > This indicates a high degree of match between the student's individual characteristics and the training equipment, with a suitability level of 1. Response measures include allowing the equipment to be used for all training movements and load intensities. During regular training, a full review of movement postures will be conducted every two weeks, and a monthly on-site equipment status inspection will be carried out. During intensive training, a full review of movement postures will be conducted weekly, and an on-site inspection will be carried out every three days. ≤Students and equipment Adaptability Index ≤ This indicates a moderate match between the student's individual characteristics and the training equipment, with a suitability level of 2. Response measures include allowing the equipment to be used in basic strength and routine technique training scenarios. Before training, personalized fine-tuning of equipment parameters and pre-verification of movement postures must be completed. During the regular training phase, a comprehensive review of movement postures will be conducted weekly, and an on-site visual inspection will be conducted every 3 days. During the intensive training phase, a comprehensive review of movement postures will be conducted every 3 days, and an on-site inspection will be conducted daily. If the student... and equipment Adaptability Index < This indicates a low match between the student's individual characteristics and the training equipment, with a fit level of 3. Response measures include strictly prohibiting its use in formal training, immediately stopping its use and sealing it off, organizing professional personnel to conduct a review of individual characteristics and a retest of all equipment parameters, readjusting equipment size, counterweight, damping, or changing equipment models, and conducting a new full-item fit test and fit level assessment after optimization.

[0013] Preferably, the cumulative damage level assessment process is as follows: The upper limit of the cumulative damage threshold range is denoted as... The lower limit of the cumulative damage threshold interval is denoted as ; If a student Use of equipment Cumulative damage index < This indicates that the student's long-term training has resulted in a low level of localized load accumulation and a low risk of chronic sports injury, with a cumulative injury level of 1. Response measures include allowing full-load routine training and intensive training. During the routine training phase, comprehensive monitoring of localized load and movement posture will be conducted every two weeks, with a monthly check-up on physical fatigue status. During the intensive training phase, comprehensive monitoring of localized load and movement posture will be conducted weekly, with a fatigue check-up every three days. ≤Students Use of equipment Cumulative damage index ≤ This indicates that the student's cumulative local load from long-term training is moderate, posing a certain risk of chronic sports injury, with a cumulative injury level of 2. Response measures include allowing de-load routine training, prohibiting high-intensity intensive training, requiring complete local warm-up activation and pre-verification of movement posture before training, conducting comprehensive monitoring of local load and movement posture once a week during routine training, and checking physical fatigue status every 3 days during intensive training, and conducting comprehensive monitoring of local load and movement posture every 3 days during intensive training, and checking fatigue status daily. If the student... Use of equipment Cumulative damage index > This indicates that the student has a high level of local load accumulation from long-term training, a high risk of chronic sports injury, and a cumulative injury level of 3. Response measures include strictly prohibiting formal training, immediately stopping training for rest, organizing coaches and rehabilitation therapists to conduct a review of training load and a comprehensive retest of physical functions, readjusting training frequency, load intensity and movement combination programs, and intervening with local rehabilitation and relaxation measures. After optimization, load monitoring and cumulative injury level assessment will be carried out again.

[0014] Preferably, the exercise risk level assessment process is as follows: The upper limit of the risk transmission threshold range is denoted as The lower limit of the risk transmission threshold range is denoted as ; If a student Use of equipment Risk transmission index at time < This indicates that the transmission and diffusion of sports risks between multiple joints is weak, with a low risk of associated chronic injuries, resulting in a sports risk level of 1. Response measures include allowing normal full-event track and field training, maintaining regular training load and frequency, conducting a comprehensive sports risk reassessment once per semester, and conducting monthly on-site inspections of movement standardization. ≤Students Use of equipment Risk transmission numbers at time ≤ This indicates a moderate degree of transmission and diffusion of exercise risk across multiple joints, with a potential risk of cascading chronic injury. The exercise risk level is 2. Response measures include allowing regular training but limiting the proportion of high-load explosive movements, prioritizing optimization of equipment parameters to reduce local stress, conducting joint stress monitoring every two months, carrying out on-site inspections of movement compliance every two weeks, and simultaneously providing basic stretching and joint stability training. If students... Use of equipment Risk transmission numbers at time > This indicates that the risk of exercise is strongly transmitted and spread between multiple joints, with a high risk of chronic injury. The exercise risk level is 3. The response measures include immediately suspending high-intensity specialized training on the corresponding equipment, reducing the training load and duration, organizing coaches and school doctors to conduct a review of movement patterns and retest equipment compatibility, adjusting equipment parameters, optimizing training movement standards, and recalculating the risk transmission index and level assessment after rectification. Training can only be resumed after the standard is confirmed to be met.

[0015] Compared with existing technologies, this invention provides a sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis, which has the following beneficial effects: 1. The multi-source acquisition module of this invention acquires individual student characteristic parameters, equipment size parameters, and training management data, and classifies them into student datasets, equipment datasets, and training datasets. This provides unified and complete data support for subsequent multi-dimensional risk assessment, ensuring the comprehensiveness and consistency of the assessment data. The equipment compatibility assessment module analyzes the matching relationship between individual student characteristics and training equipment, identifying potential sports risks caused by equipment mismatch based on four dimensions: size, mechanics, movement trajectory, and growth compatibility. It considers both immediate compatibility and long-term compatibility with student growth and development, and quantitatively outputs a compatibility index. This reduces the risk of damage caused by poor compatibility.

[0016] 2. This invention uses a load accumulation analysis module to assess the cumulative changes in the load borne by local tissues during long-term training, identify the risk of latent fatigue accumulation and chronic sports injuries in advance, and quantify the cumulative injury index. This provides data support for dynamic adjustment of training load and fatigue prevention, avoiding long-term excessive load injuries. The risk propagation prediction module is used to analyze the degree of transmission and diffusion of exercise risks along the lower limb kinetic chain and quantify the output of the risk propagation index. It can predict the risk of multi-joint cascading injuries, provide quantitative basis for targeted intervention and blocking the transmission of risks, and improve the foresight of risk prevention and control. The intelligent early warning module automatically determines the student's and equipment's compatibility level, the cumulative injury level of long-term training, and the sports risk level, and outputs differentiated response measures to achieve graded management and accurate early warning of sports risks. It can take into account training efficiency while ensuring training safety, and facilitate the implementation of graded safety management. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example Please see Figure 1 Table 1 shows the experimental data of the fit index, Table 2 shows the experimental data of the cumulative injury index, and Table 3 shows the experimental data of the risk propagation index. This invention provides a sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis, including a multi-source acquisition module, an equipment fit assessment module, a load accumulation analysis module, a risk propagation prediction module, and an intelligent early warning module. The multi-source acquisition module is used to acquire individual student characteristic parameters, equipment size parameters, and training management data, and classify them into student datasets, equipment datasets, and training datasets. The student dataset includes students' height, arm length, total leg length, baseline height and weight for first-time adapter use; The equipment dataset includes the equipment's support height, support width, and working surface depth. The training dataset includes the support force at each test point when using the equipment, the total number of test points, the movement angle during the sampling process, the total number of samples for a single movement, the maximum allowable angle deviation of the movement, the average force on the body parts, the total number of training sessions in history, the training duration, the peak pressure when the core is stressed, the peak pressure when the hip joint is stressed, the peak pressure when the knee joint is stressed, and the peak pressure when the ankle joint is stressed. The equipment compatibility assessment module analyzes the matching relationship between individual student characteristics and training equipment, identifies potential sports risks caused by equipment mismatch from multiple dimensions, and quantifies the compatibility index. ; Adaptability Index The calculation process is as follows: S11. Based on the student dataset, equipment dataset, and training dataset, extract students... Individual characteristic parameters, equipment Size data and students Use of equipment Training management data at the time; S12. Calculate the student's dimensions. and equipment Dimensional deviation The calculation formula is as follows: In the formula, Students Current height Indicating equipment The support height, Indicates the relative deviation at the altitude level. Students The arm length at the current moment, Indicating equipment Support width, Indicates the relative deviation at the width level. Students Current leg length Indicating equipment Working face depth Indicates the relative deviation of the working face depth level, when When the upper limit of the value is truncated to 1; Specifically, target equipment The equipment includes starting blocks, a complete set of high jump equipment, a complete set of pole vault equipment, and standard hurdles. The starting blocks are designed to adjust the height difference of the starting blocks according to the height of the runners, adapt the grip posture to the upper body arm length, and set the front-to-back depth of the two starting blocks according to the overall leg length. The high jump equipment is designed to adjust the height of the crossbar according to the height of the runners, determine the left-to-right distance between the two uprights with reference to the arm length, and match the front-to-back depth of the foam landing pad according to the leg length. The pole vault equipment must be matched to the three human body dimensions of height, arm length, and leg length. The standard hurdles are designed to adjust the height of the hurdles according to the height of the runners, set the front-to-back depth of the base according to the leg length, and limit the lateral width occupied by the hurdles based on the range of arm swing. S13. Calculate the student's mechanics dimension. and equipment Force balance coefficient The calculation formula is as follows: In the formula, Students Use of equipment At that time, the first The support force at each test point , This indicates the total number of test points. Indicates average support force; In the formula, This represents the overall standard deviation of the support strength at each testing point. Indicates the average support force, when or When the lower limit of the value is truncated to 0, the force equilibrium coefficient is... A larger value indicates a more balanced force distribution; S14. Calculate the student's action trajectory dimension. and equipment Action stability coefficient The calculation formula is as follows: In the formula, Students In the Equipment used in the second sampling process The corresponding action angle at that time , This represents the total number of samples for a single action. Indicates the use of equipment The corresponding standard value of the action angle at that time This indicates the maximum permissible angular deviation for the corresponding action. When the lower limit of the value is truncated to 0, the action stability coefficient... The higher the value, the better the stability of the movement; S15. Calculate the student's growth adaptation dimension. and equipment Growth Adaptability Coefficient The calculation formula is as follows: In the formula, Students First adapter The baseline height at that time Students Current weight Students First adapter At the baseline weight, when When the lower limit of the value is truncated to 0, the growth fit coefficient is increased. The higher the value, the better the long-term compatibility of the equipment; S16. Based on S11-S15, calculate the students' scores using a weighted method. and equipment Adaptability Index The calculation formula is as follows: In the formula, Denotes the weight coefficients, and satisfies ; Specifically, the compatibility index The higher the value, the better the fit between the equipment and the student, and the lower the risk of exercise. The following is the experimental data for the fit index, as shown in Table 1: Table 1: Experimental Data for the Fit Index In Table 1, the experimental data of the fit index were selected. A 14-year-old junior high school male track and field athlete A was selected as the experimental subject, and the standard hurdles used for junior high school male training were used as the experimental equipment. Adaptability Index The weights are set as follows: , , , ; The intelligent early warning module is set with a fixed range of adaptation thresholds. It is used to quickly determine the compatibility level between students and equipment, and the compatibility threshold range. The calibration method is as follows: This study utilizes a sports training intelligent monitoring database to screen student-equipment matching samples at different fit levels, covering categories such as excellent fit (high fit), moderate fit (average fit), and poor fit (low fit). Core data on individual student characteristics (e.g., height and weight, limb dimensions, muscle strength, movement pattern characteristics, basic motor skills), equipment parameter configuration records, and post-training monitoring results (e.g., movement standardization, injury rate, changes in training efficiency) are extracted from the samples. Different candidate threshold ranges are set, and in each calibration experiment, student-equipment samples are classified into fit levels based on these candidate threshold ranges. The matching degree between the classification results and the actual professional fit assessment conclusions is recorded. Combined with long-term dynamic monitoring data of student training, the sample fit index is simulated under different training intensities and equipment adjustment schemes. The changing trends were analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple verification experiments were conducted to record the impact of threshold interval settings on fit level assessment and subsequent training program development. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times low-fitness samples were misclassified as high-fitness samples due to improper interval settings (counted as over-assessment), the number of times high-fitness samples were misclassified as low-fitness samples (counted as under-assessment), and the degree of fit between the fit level classification results and the subsequent training implementation effectiveness (such as movement achievement rate, injury prevention effect, training efficiency improvement, equipment use safety, etc.) were calculated. Finally, the interval range that minimizes both over-assessment and under-assessment rates and has the highest degree of fit with the subsequent training implementation effectiveness was selected as the fit threshold interval. The preferred range; Table 1 shows the adaptation threshold range in the experimental data of the adaptation index. The optimal range was set at 0.4-0.8. Based on the assessment, the compatibility index between 14-year-old junior high school male track and field athlete A and the standard hurdles used in junior high school male training is [missing information]. A score >0.8 indicates a high degree of matching between the student's individual characteristics and the training equipment, with an adaptation level of 1. Response measures include allowing the equipment to be used for all training movements and load intensity scenarios. During the regular training phase, a full review of movement postures will be conducted every 2 weeks, and an on-site equipment status inspection will be conducted monthly. During the intensive training phase, a full review of movement postures will be conducted weekly, and an on-site inspection will be conducted every 3 days. The cumulative load analysis module is used to assess the cumulative changes in the load on local tissues of the body during long-term training, identify the risk of latent fatigue accumulation and chronic sports injuries in advance, and quantify the cumulative injury index. ; Cumulative damage index The calculation process is as follows: S21. Based on the training dataset, extract students. Use of equipment Training management data at the time; S22, Calculate the first Single training load of a training session Its expression is as follows: In the formula, Students In the In this training session, the equipment used The average force on the corresponding parts of the body at that time , This represents the total number of training sessions in history. Indicates the duration of the corresponding training; S23, Calculate the first Recovery reduction factor for each training session Its expression is as follows: In the formula, This represents the recovery attenuation coefficient, which is the coefficient of performance when the recovery time is in days. The unit is When the unit is hour, it should be recalibrated to , Indicates the first The recovery time from the end of the training session to the current moment; the longer the recovery time, the smaller the impact of historical load on the current moment. S24, Calculate students Historical cumulative load during training Its expression is as follows: S25, Regarding students Historical cumulative load during training Dimensionless normalization is performed to eliminate dimensional and numerical scale differences between different parameters. The quantile truncation method is then used to suppress extreme value interference. Its expression is as follows: In the formula, This represents the 95th percentile of the cumulative historical load sample. hour, , This represents the 5th percentile of the cumulative historical load sample. hour, ; S26. Based on S21-S25, calculate the students' scores using a weighted method. Cumulative injury index during training Its expression is as follows: In the formula, Denotes the weight coefficients, and satisfies ; Specifically, cumulative damage index The higher the value, the better the student. Long-term use equipment The higher the risk of chronic sports injuries during training; The following are the experimental data for the cumulative damage index, as shown in Table 2: Table 2: Experimental Data for the Cumulative Damage Index In Table 2, the cumulative injury experimental data were also selected as the experimental subjects, and the standard hurdles used for junior high school boys' training were used as the experimental equipment. Table 2 shows the 5th percentile of the historical cumulative load sample in step S25. N·min, the 95th percentile of the historical cumulative load sample. N·min, historical cumulative load The normalized value is 1; Cumulative damage index The weights are set as follows: , ; The intelligent early warning module is set with a fixed range of cumulative damage threshold intervals. Used to determine the cumulative damage level of students' long-term training, and the cumulative damage threshold range. The calibration method is as follows: Using a smart sports training monitoring database, student-equipment training samples with different cumulative injury levels were screened, covering situations such as high injury risk (heavy cumulative load), medium injury risk (moderate cumulative load), and low injury risk (light cumulative load). Core monitoring data for student training (such as local stress level, training duration, movement and posture deviations, and muscle fatigue levels), training program implementation records, and subsequent physical function monitoring results (such as chronic injury incidence, local functional recovery level, and cumulative injury index) were extracted from the samples. (Changes, etc.) Different candidate threshold ranges were set. In each calibration experiment, the cumulative injury level of the student-equipment sample was classified according to the candidate threshold range. The matching degree between the classification results and the actual sports injury professional assessment conclusions was recorded. Then, combined with the long-term training dynamic monitoring data of students, the cumulative injury index of the sample was simulated under different training load adjustments and rehabilitation intervention intensities. The changing trend was analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple sets of verification experiments were conducted to record the impact of threshold interval settings on cumulative injury risk assessment and subsequent training program development. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times high-risk injury samples were misclassified as low-risk injury samples due to improper interval settings (counted as over-assessment), the number of times low-risk injury samples were misclassified as high-risk injury samples (counted as under-assessment), and the degree of fit between the cumulative injury level classification results and the subsequent training implementation effectiveness (such as injury prevention and control effects, training efficiency improvement, and physical function recovery level) were counted. Finally, the interval range that minimizes both over-assessment and under-assessment rates and has the highest degree of fit with the subsequent training implementation effectiveness was selected as the cumulative injury threshold interval. The preferred range; Table 2 shows the cumulative damage threshold range in the cumulative damage experimental data. The optimal range was set at 0.3-0.7. Based on this, the cumulative injury index of a 14-year-old male junior high school track and field athlete (A) during training was determined to be... A score >0.7 indicates that the student has a high level of local load accumulation from long-term training, a high risk of chronic sports injury, and a cumulative injury level of 3. Response measures include prohibiting formal training, immediately stopping training and resting, organizing coaches and rehabilitation therapists to conduct a review of training load and a comprehensive retest of physical functions, readjusting training frequency, load intensity and movement combination programs, and intervening with local rehabilitation and relaxation measures. After optimization, load monitoring and cumulative injury level assessment will be carried out again. The risk propagation prediction module is used to analyze the transmission and evolution of sports risks between different parts of the human body and quantify the risk propagation index. ; Risk transmission index The calculation process is as follows: S31. Based on the training dataset, extract students. Use of equipment Training management data at the time; S32, Calculate students Mean risk of equivalent nodes in the transmission Its expression is as follows: In the formula, Students Peak pressure under core stress Students Peak pressure when the hip joint is under stress Students Peak pressure when the knee joint is under stress. Students Peak pressure when the ankle joint is subjected to force. Denotes the transmission weight coefficient, and satisfies... ; Specifically, the transmission weight coefficient is determined by the force transmission characteristics of adjacent joints in the track and field chain, reflecting the amplification effect of risk transmission between multiple parts, and the weight value is set differently according to the type of equipment. S33. Based on S31-S32, calculate the students' scores using a weighted method. Use of equipment Risk transmission index at time Its expression is as follows: In the formula, Denotes the weight coefficients, and satisfies ; Specifically, the risk transmission index The higher the value, the stronger the transmission and diffusion of movement risks between multiple joints, and the higher the risk of chronic injury. The following is the experimental data for the risk transmission index, as shown in Table 3: Table 3: Experimental Data for the Risk Transmission Index In Table 3, the experimental data of the risk transmission index were also selected as the experimental subjects, and the standard hurdles used for junior high school boys' training were used as the experimental equipment. In step S32 of Table 3, the upper limit of the peak pressure at each node is uniformly set to 1000 kPa. The normalized value = measured peak pressure ÷ upper limit of reference. The transmission weighting coefficient is set as follows: , , , ; Risk transmission index The weights are set as follows: , , ; The intelligent early warning module is set with a fixed range of risk propagation threshold intervals. Used to determine the student's sports risk level and risk transmission threshold range. The calibration method is as follows: Using a sports training movement chain monitoring database, student-equipment training samples with different sports risk levels were screened, covering various situations such as weak risk transmission and diffusion (low risk of cascading injuries), moderate risk transmission and diffusion (potential for cascading injuries), and strong risk transmission and diffusion (high risk of cascading injuries). Core monitoring data on multi-joint stress (such as load on the main stress joints, compensatory stress on adjacent joints, and degree of movement chain force line deviation), equipment adaptation parameter adjustment records, and subsequent injury development monitoring results (such as the incidence of cascading chronic injuries, the degree of joint function degeneration, and the risk transmission index) were extracted from the samples. (Changes, etc.) Different candidate threshold ranges are set. In each calibration experiment, the student-equipment samples are classified into motion risk levels based on the candidate threshold range. The matching degree between the classification results and the actual professional assessment conclusions of the movement chain risk is recorded. Then, combined with the dynamic monitoring data of students' long-term training, the sample risk propagation index is simulated under different equipment parameter optimization and movement pattern correction schemes. The changing trend was analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple verification experiments were conducted to record the impact of threshold interval settings on the assessment of sports risk transmission and the formulation of subsequent prevention and control plans. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times high-risk samples were misclassified as low-risk samples due to improper interval settings (counted as over-assessment), the number of times low-risk samples were misclassified as high-risk samples (counted as under-assessment), and the degree of fit between the sports risk level classification results and the subsequent risk prevention and control effectiveness (such as the effect of comorbidity injury prevention, the improvement of action chain stability, and the level of training safety assurance) were counted. Finally, the interval range that minimizes both the over-assessment rate and the under-assessment rate and has the highest degree of fit with the subsequent risk prevention and control effectiveness was selected as the risk transmission threshold interval. The preferred range; Table 3 shows the risk transmission threshold range in the experimental data of the risk transmission index. The preferred range was defined as 0.4-0.7. It was determined that 0.4 < the risk transmission index when a 14-year-old junior high school male track and field athlete, A, uses a standard junior high school male training hurdle. <0.7 indicates that the transmission and diffusion of exercise risk among multiple joints is moderate, with potential risks of chronic injuries. The exercise risk level is 2. Response measures include allowing regular training but limiting the proportion of high-load explosive movements, prioritizing the optimization of equipment adaptation parameters to reduce local stress, conducting joint stress monitoring once every 2 months, conducting on-site inspection of movement standardization once every half month, and simultaneously providing basic stretching and joint stability training. The intelligent early warning module is set with a fixed range of adaptation thresholds. Cumulative damage threshold range and risk transmission threshold range It is used to determine the student's compatibility level with the equipment, the cumulative injury level of long-term training, and the sports risk level, and output the corresponding judgment results and response measures. The compatibility level assessment process is as follows: Let the upper limit of the adaptation threshold range be denoted as The lower limit of the adaptation threshold range is denoted as ; If a student and equipment Adaptability Index > This indicates a high degree of match between the student's individual characteristics and the training equipment, with a suitability level of 1. Response measures include allowing the equipment to be used for all training movements and load intensities. During regular training, a full review of movement postures will be conducted every two weeks, and a monthly on-site equipment status inspection will be carried out. During intensive training, a full review of movement postures will be conducted weekly, and an on-site inspection will be carried out every three days. ≤Students and equipment Adaptability Index ≤ This indicates a moderate match between the student's individual characteristics and the training equipment, with a suitability level of 2. Response measures include allowing the equipment to be used in basic strength and routine technique training scenarios. Before training, personalized fine-tuning of equipment parameters and pre-verification of movement postures must be completed. During the regular training phase, a comprehensive review of movement postures will be conducted weekly, and an on-site visual inspection will be conducted every 3 days. During the intensive training phase, a comprehensive review of movement postures will be conducted every 3 days, and an on-site inspection will be conducted daily. If the student... and equipment Adaptability Index < This indicates that the student's individual characteristics are not well matched with the training equipment, with an adaptation level of 3. The response measures include strictly prohibiting the use of the equipment in formal training, immediately stopping its use and storing it, organizing professional personnel to conduct a review of individual characteristics and a retest of all parameters of the equipment, readjusting the equipment size, counterweight, damping or changing the equipment model, and after optimization, conducting a new full-item adaptation test and adaptation level assessment. The cumulative damage level assessment process is as follows: The upper limit of the cumulative damage threshold range is denoted as... The lower limit of the cumulative damage threshold interval is denoted as ; If a student Use of equipment Cumulative damage index < This indicates that the student's long-term training has resulted in a low level of localized load accumulation and a low risk of chronic sports injury, with a cumulative injury level of 1. Response measures include allowing full-load routine training and intensive training. During the routine training phase, comprehensive monitoring of localized load and movement posture will be conducted every two weeks, with a monthly check-up on physical fatigue status. During the intensive training phase, comprehensive monitoring of localized load and movement posture will be conducted weekly, with a fatigue check-up every three days. ≤Students Use of equipment Cumulative damage index ≤ This indicates that the student's cumulative local load from long-term training is moderate, posing a certain risk of chronic sports injury, with a cumulative injury level of 2. Response measures include allowing de-load routine training, prohibiting high-intensity intensive training, requiring complete local warm-up activation and pre-verification of movement posture before training, conducting comprehensive monitoring of local load and movement posture once a week during routine training, and checking physical fatigue status every 3 days during intensive training, and conducting comprehensive monitoring of local load and movement posture every 3 days during intensive training, and checking fatigue status daily. If the student... Use of equipment Cumulative damage index > This indicates that the student has a high degree of local load accumulation due to long-term training, a high risk of chronic sports injury, and a cumulative injury level of 3. The response measures include strictly prohibiting formal training, immediately stopping training and resting, organizing coaches and rehabilitation therapists to conduct training load review and full retest of physical functions, readjusting training frequency, load intensity and movement combination plan, and intervening with local rehabilitation and relaxation measures. After optimization, load monitoring and cumulative injury level assessment will be carried out again. The process for assessing the risk level of exercise is as follows: The upper limit of the risk transmission threshold range is denoted as The lower limit of the risk transmission threshold range is denoted as ; If a student Use of equipment Risk transmission index at time < This indicates that the transmission and diffusion of sports risks between multiple joints is weak, with a low risk of associated chronic injuries, resulting in a sports risk level of 1. Response measures include allowing normal full-event track and field training, maintaining regular training load and frequency, conducting a comprehensive sports risk reassessment once per semester, and conducting monthly on-site inspections of movement standardization. ≤Students Use of equipment Risk transmission index at time ≤ This indicates a moderate degree of transmission and diffusion of exercise risk across multiple joints, with a potential risk of cascading chronic injury. The exercise risk level is 2. Response measures include allowing regular training but limiting the proportion of high-load explosive movements, prioritizing optimization of equipment parameters to reduce local stress, conducting joint stress monitoring every two months, carrying out on-site inspections of movement compliance every two weeks, and simultaneously providing basic stretching and joint stability training. If students... Use of equipment Risk transmission index at time > This indicates that the risk of exercise is strongly transmitted and spread between multiple joints, with a high risk of chronic injury. The exercise risk level is 3. The response measures include immediately suspending high-intensity specialized training on the corresponding equipment, reducing the training load and duration, organizing coaches and school doctors to conduct a review of movement patterns and retest equipment compatibility, adjusting equipment parameters, optimizing training movement standards, and recalculating the risk transmission index and level assessment after rectification. Training can only be resumed after the standard is confirmed to be met.

[0020] In this embodiment, a multi-source acquisition module acquires student individual characteristic parameters, equipment size parameters, and training management data, which are then categorized into student datasets, equipment datasets, and training datasets. This provides unified and comprehensive data support for subsequent multi-dimensional risk assessments, ensuring the comprehensiveness and consistency of the assessment data. The equipment compatibility assessment module analyzes the matching relationship between student individual characteristics and training equipment, identifying potential sports risks caused by equipment mismatch based on four dimensions: size, mechanics, movement trajectory, and growth compatibility. It considers both immediate compatibility and long-term compatibility with student growth and development, quantifying and outputting a compatibility index. To reduce the risk of injury due to poor adaptation, the load accumulation analysis module is used to assess the cumulative changes in the load on local tissues of the body during long-term training, identify the risk of latent fatigue accumulation and chronic sports injuries in advance, and quantify the output of the cumulative injury index. This provides data support for dynamic adjustment of training load and fatigue prevention, avoiding long-term excessive load injuries. The risk propagation prediction module is used to analyze the degree of transmission and diffusion of exercise risks along the lower limb kinetic chain and quantify the output of the risk propagation index. It can predict the risk of multi-joint cascading injuries, provide quantitative basis for targeted intervention and blocking the transmission of risks, and improve the foresight of risk prevention and control. The intelligent early warning module automatically determines the student's and equipment's compatibility level, the cumulative injury level of long-term training, and the sports risk level, and outputs differentiated response measures to achieve graded management and accurate early warning of sports risks. It can take into account training efficiency while ensuring training safety, and facilitate the implementation of graded safety management.

[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis, characterized in that, include: The multi-source acquisition module is used to acquire individual student characteristic parameters, equipment size parameters, and training management data, and classify them into student datasets, equipment datasets, and training datasets. The equipment compatibility assessment module analyzes the matching relationship between individual student characteristics and training equipment, identifies potential sports risks caused by equipment mismatch from multiple dimensions, and quantifies the compatibility index. ; The cumulative load analysis module is used to assess the cumulative changes in the load on local tissues of the body during long-term training, identify the risk of latent fatigue accumulation and chronic sports injuries in advance, and quantify the cumulative injury index. ; The risk propagation prediction module is used to analyze the transmission and evolution of sports risks between different parts of the human body and quantify the risk propagation index. ; The intelligent early warning module is set with a fixed range of adaptation thresholds. Cumulative damage threshold range and risk transmission threshold range It is used to determine the student's fit level with the equipment, the cumulative injury level of long-term training, and the sports risk level, and outputs the corresponding judgment results and response measures.

2. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 1, characterized in that: The student dataset includes students' height, arm length, total leg length, baseline height and baseline weight for their first fitting.

3. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 2, characterized in that: The equipment dataset includes the equipment's support height, support width, and working surface depth.

4. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 3, characterized in that: The training dataset includes the support force at each test point when using the equipment, the total number of test points, the action angle during the sampling process, the total number of samples for a single action, the maximum allowable angle deviation of the action, the average force on the body parts, the total number of historical training sessions, the training duration, the peak pressure when the core is stressed, the peak pressure when the hip joint is stressed, the peak pressure when the knee joint is stressed, and the peak pressure when the ankle joint is stressed.

5. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 4, characterized in that: The compatibility index The calculation process is as follows: S11. Based on the student dataset, equipment dataset, and training dataset, extract students. Individual characteristic parameters, equipment Size data and students Use of equipment Training management data at the time; S12. Calculate the student's dimensions. and equipment Dimensional deviation ; S13. Calculate the student's mechanics dimension. and equipment Force balance coefficient ; S14. Calculate the student's action trajectory dimension. and equipment Action stability coefficient ; S15. Calculate the student's growth adaptation dimension. and equipment Growth Adaptability Coefficient ; S16. Based on S11-S15, calculate the students' scores using a weighted method. and equipment Adaptability Index .

6. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 5, characterized in that: The cumulative damage index The calculation process is as follows: S21. Based on the training dataset, extract students. Use of equipment Training management data at the time; S22, Calculate the first Single training load of a training session ; S23, Calculate the first Recovery reduction factor per training session ; S24, Calculate students Historical cumulative load during training ; S25, Regarding students Historical cumulative load during training Perform dimensionless normalization; S26. Based on S21-S25, calculate the students' scores using a weighted method. Cumulative injury index during training .

7. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 6, characterized in that: The risk transmission index The calculation process is as follows: S31. Based on the training dataset, extract students. Use of equipment Training management data at the time; S32, Calculate students Mean risk of equivalent nodes in the transmission ; S33. Based on S31-S32, calculate the students' scores using a weighted method. Use of equipment Risk transmission index at time .

8. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 7, characterized in that: The adaptation level assessment process is as follows: Let the upper limit of the adaptation threshold range be denoted as The lower limit of the adaptation threshold range is denoted as ; If a student and equipment Adaptability Index > This indicates a high degree of match between the student's individual characteristics and the training equipment, with a suitability level of 1. Response measures include allowing the equipment to be used for all training movements and load intensities. During regular training, a full review of movement postures will be conducted every two weeks, and a monthly on-site equipment status inspection will be carried out. During intensive training, a full review of movement postures will be conducted weekly, and an on-site inspection will be carried out every three days. ≤Students and equipment Adaptability Index ≤ This indicates a moderate match between the student's individual characteristics and the training equipment, with a suitability level of 2. Response measures include allowing the equipment to be used in basic strength and routine technique training scenarios. Before training, personalized fine-tuning of equipment parameters and pre-verification of movement postures must be completed. During the regular training phase, a comprehensive review of movement postures will be conducted weekly, and an on-site visual inspection will be conducted every 3 days. During the intensive training phase, a comprehensive review of movement postures will be conducted every 3 days, and an on-site inspection will be conducted daily. If the student... and equipment Adaptability Index < This indicates a low match between the student's individual characteristics and the training equipment, with a fit level of 3. Response measures include strictly prohibiting its use in formal training, immediately stopping its use and sealing it off, organizing professional personnel to conduct a review of individual characteristics and a retest of all equipment parameters, readjusting equipment size, counterweight, damping, or changing equipment models, and conducting a new full-item fit test and fit level assessment after optimization.

9. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 8, characterized in that: The cumulative damage level assessment process is as follows: The upper limit of the cumulative damage threshold range is denoted as... The lower limit of the cumulative damage threshold interval is denoted as ; If a student Use of equipment Cumulative damage index < This indicates that the student's long-term training has resulted in a low level of localized load accumulation and a low risk of chronic sports injury, with a cumulative injury level of 1. Response measures include allowing full-load routine training and intensive training. During the routine training phase, comprehensive monitoring of localized load and movement posture will be conducted every two weeks, with a monthly check-up on physical fatigue status. During the intensive training phase, comprehensive monitoring of localized load and movement posture will be conducted weekly, with a fatigue check-up every three days. ≤Students Use of equipment Cumulative damage index ≤ This indicates that the student's cumulative local load from long-term training is moderate, posing a certain risk of chronic sports injury, with a cumulative injury level of 2. Response measures include allowing de-load routine training, prohibiting high-intensity intensive training, requiring complete local warm-up activation and pre-verification of movement posture before training, conducting comprehensive monitoring of local load and movement posture once a week during routine training, and checking physical fatigue status every 3 days during intensive training, and conducting comprehensive monitoring of local load and movement posture every 3 days during intensive training, and checking fatigue status daily. If the student... Use of equipment Cumulative damage index > This indicates that the student has a high level of local load accumulation from long-term training, a high risk of chronic sports injury, and a cumulative injury level of 3. Response measures include strictly prohibiting formal training, immediately stopping training for rest, organizing coaches and rehabilitation therapists to conduct a review of training load and a comprehensive retest of physical functions, readjusting training frequency, load intensity and movement combination programs, and intervening with local rehabilitation and relaxation measures. After optimization, load monitoring and cumulative injury level assessment will be carried out again.

10. The sports risk assessment system for primary and secondary school track and field training equipment based on big data analysis according to claim 9, characterized in that: The process for assessing the risk level of exercise is as follows: The upper limit of the risk transmission threshold range is denoted as The lower limit of the risk transmission threshold range is denoted as ; If a student Use of equipment Risk transmission index at time < This indicates that the transmission and diffusion of sports risks between multiple joints is weak, with a low risk of associated chronic injuries, resulting in a sports risk level of 1. Response measures include allowing normal full-event track and field training, maintaining regular training load and frequency, conducting a comprehensive sports risk reassessment once per semester, and conducting monthly on-site inspections of movement standardization. ≤Students Use of equipment Risk transmission index at time ≤ This indicates a moderate degree of transmission and diffusion of exercise risk across multiple joints, with a potential risk of cascading chronic injury. The exercise risk level is 2. Response measures include allowing regular training but limiting the proportion of high-load explosive movements, prioritizing optimization of equipment parameters to reduce local stress, conducting joint stress monitoring every two months, carrying out on-site inspections of movement compliance every two weeks, and simultaneously providing basic stretching and joint stability training. If students... Use of equipment Risk transmission index at time > This indicates that the risk of exercise is strongly transmitted and spread between multiple joints, with a high risk of chronic injury. The exercise risk level is 3. The response measures include immediately suspending high-intensity specialized training on the corresponding equipment, reducing the training load and duration, organizing coaches and school doctors to conduct a review of movement patterns and retest equipment compatibility, adjusting equipment parameters, optimizing training movement standards, and recalculating the risk transmission index and level assessment after rectification. Training can only be resumed after the standard is confirmed to be met.

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