Track and field training management system based on big data

Abstract

The invention relates to the technical field of track and field training management, and discloses a big data-based track and field training management system, which comprises a training acquisition module and an intelligent management module. According to the system, management data of all athletes, management data of a training field and environment sensing data are acquired through a training acquisition module and are classified to form a data set, and an intelligent management module accurately quantifies individual competitive ability and generates ability indexes for speed-strength type, speed-endurance type and endurance type athletes of different items; data support is provided for personalized training, multi-dimensional evaluation accuracy is high, an intelligent management module evaluates the degree of influence of site conditions on athletic performance of athletes, generates influence indexes, avoids poor training effect or sports injury caused by site problems, evaluates the degree of influence of environmental factors on athletic performance of athletes, generates environmental scores, and improves training efficiency. And then corresponding track and field training management suggestions are output, various safety risks caused by adverse environments are avoided, and the targeted management training effect is good.

Description

Technical Field

[0001] This invention relates to the field of track and field training management technology, specifically a track and field training management system based on big data. Background Technology

[0002] Track and field training is a systematic training process that combines individual athlete traits, venue conditions, and environmental factors, through scientific planning and dynamic adjustments to improve specific athletic abilities such as speed, endurance, and explosive power. Track and field training management is the core support for ensuring scientific, efficient, and safe training, and its importance is reflected in multiple dimensions. From the perspective of training effectiveness, by systematically collecting three key data points—physical fitness, venue conditions, and environmental parameters—it is possible to accurately identify athletes' strengths and weaknesses, providing solid data support for the development of personalized training programs and avoiding inefficiency caused by blind training. Regarding training safety, real-time monitoring of athletes' dynamic physiological indicators such as heart rate and environmental parameters can promptly identify potential risks such as abnormal heart rates or environments exceeding suitable ranges, and trigger adjustment suggestions to avoid sports injuries. In terms of training continuity, the dynamic adjustment module can flexibly optimize training content, intensity, and duration based on data feedback, ensuring that the training plan always adapts to changes in the athlete's condition and fluctuations in external conditions. Furthermore, standardized training management can accumulate complete training data archives, providing a reliable basis for subsequent training optimization and performance improvement analysis, and is an important guarantee for supporting the long-term development of athletes.

[0003] Currently, traditional track and field training management systems mostly rely on manual recording or single-device data collection, lacking comprehensive integration of data on physical fitness, venue, and environment. Static and dynamic data are disconnected, making it difficult to form a complete dataset to support analysis. In addition, the specific impact of venue parameters on different events is not fully considered, resulting in analysis results lacking practical guidance. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a track and field training management system based on big data. It has advantages such as high accuracy in multidimensional assessment and excellent targeted management and training effects, and solves the problems of traditional track and field training management systems lacking a mechanism for integrating multidimensional data and lacking practical guidance significance in the analysis results.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a track and field training management system based on big data, including a training data acquisition module and an intelligent management module; The training data acquisition module connects to a database, oxygen uptake analyzer, 3D motion capture equipment, force table, hardness tester, impact absorption tester, and environmental sensing device via network to acquire management data of all athletes, management data of training venues, and environmental sensing data, and classifies them into personnel datasets, venue datasets, and environmental datasets. The intelligent management module consists of an ability assessment unit, a venue assessment unit, an environment assessment unit, and a training management unit. The ability assessment unit evaluates the competitive ability of each athlete based on the athlete dataset and generates a corresponding ability index. The venue assessment unit evaluates the impact of venue conditions on athletes' competitive performance based on athlete datasets and venue datasets, and generates a corresponding impact index. The environmental assessment unit evaluates the impact of environmental factors on athletes' competitive performance based on the environmental dataset and generates corresponding environmental scores. The training management unit is set with a fixed capability threshold value. and the threshold of influence Combined with the ability index Impact Index and environmental rating It provides corresponding suggestions for track and field training management.

[0006] Preferably, the athlete dataset includes each athlete's training program category, lean body mass, height-to-weight ratio, maximum oxygen uptake, standard deviation of all normal heart rate intervals, test scores, and deviation of center of gravity projection landing point. The program categories include speed-strength, speed-endurance, and endurance.

[0007] Preferably, the field dataset includes the track hardness of each training field, the maximum gap measured with a 3-meter ruler, and the rebound coefficient of the jump zone.

[0008] Preferably, the environmental dataset includes temperature, humidity, air pressure, light intensity, and PM2.5 index.

[0009] Preferably, the capability index The calculation process is as follows: Based on the athlete dataset, extract the first... The management data of each athlete, and the first athlete's data. The athlete's lean body mass is denoted as... , will the The height-to-weight ratio of each athlete is recorded as follows: , will the The athlete's maximum oxygen uptake is recorded as , will the The standard deviation of all normal heart rate intervals for each athlete is denoted as . , will the The latest test score of each athlete is recorded as follows: ; If the first The athlete's training program is categorized as speed-strength type. In the formula, This represents the dimensionless conversion factor for lean body mass. This indicates the weight of the fat-free body mass after dimensionless processing. This represents the dimensionless conversion factor for the height-to-weight ratio. This indicates the weight of the height-to-weight ratio after dimensionless processing. This represents the conversion factor for dimensionless treatment maximum oxygen uptake. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the dimensionless conversion factor for the standard deviation of all normal heartbeat intervals. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the conversion factor for dimensionless processing of test scores. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , Indicates the first An athlete's ability index in speed-strength training programs; If the first The athlete's training program is classified as speed-endurance type. In the formula, This indicates the weight of the fat-free body mass after dimensionless processing. This indicates the weight of the height-to-weight ratio after dimensionless processing. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , , Indicates the first An athlete's ability index in speed endurance training programs; If the first The athlete's training program is endurance-based. In the formula, This indicates the weight of the fat-free body mass after dimensionless processing. This indicates the weight of the height-to-weight ratio after dimensionless processing. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , , Indicates the first An athlete's ability index in endurance training programs.

[0010] Preferably, the influence index The calculation process is as follows: Based on the athlete dataset, the first The athlete conducted the first... The deviation of the center of gravity projection landing point during the testing of each training item is denoted as . , Indicates the first The total number of time points at which the deviation was recorded for each training item test; Based on the site dataset, extract the first... Management data for training venues, known to be the first training venue. The athlete was in the... The training venue completed the first The training project was tested, and the first training item was tested. The track hardness of each training venue is recorded as follows: , will the The maximum gap measured with a 3-meter straightedge at each training area is recorded as follows: , will the The rebound coefficient of the jump zone in each training area is denoted as... ; In the formula, Indicates the first The athlete in the The training venue was used for the first time. Average deviation of the center of gravity projection landing point during the testing of each training item; In the formula, This represents the conversion factor for the average deviation of the centroid projection landing point in dimensionless processing. The weight representing the average deviation of the centroid projection point after dimensionless processing. This represents the standard value used to measure the hardness of a running track. This represents the conversion factor for the absolute difference between the dimensionless processed runway hardness and the standard value. This represents the weight of the absolute difference between the dimensionless processed runway hardness and the standard value. This represents the standard value used to measure the maximum gap when measured with a 3-meter straightedge. This represents the conversion factor for the absolute difference between the maximum gap measured by a dimensionless 3-meter straightedge and the standard value. This represents the weight of the absolute difference between the maximum gap measured by a 3-meter straightedge after dimensionless processing and the standard value. This represents the standard value used to measure the rebound coefficient in the jump zone. This represents the conversion factor between the dimensionless springback coefficient in the jump zone and the absolute difference of the standard value. This represents the weight of the absolute difference between the springback coefficient in the jump zone after dimensionless processing and the standard value. , , and All are constants, and , Indicates the first The athlete in the The training venue was used for the first time. The impact index when testing each training item.

[0011] Preferably, the environmental score The evaluation process is as follows: Environmental rating The initial value is set to 5 points, and then the first value is extracted based on the environmental dataset. Environmental sensor data from each training venue; If the first If the temperature of the training venue is >32℃ or <5℃, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first If the humidity of the training venue is >80% or <30%, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first The air pressure at the training venue is <950 hPa or >1030 hPa. Environmental rating of each training venue Decrease by 1 point; If the first If the light intensity of a training venue is >50,000 lux or <10,000 lux, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first The PM2.5 index at the training venue was >75 μg / m³, which will affect the first training venue. Environmental rating of each training venue Decrease by 1 point.

[0012] Preferably, the capability index ≤ Capability Threshold When the time is right, it indicates that the athlete's competitive ability has not met the standard. It is recommended that the athlete supplement muscle mass and strength reserves, ensure sufficient sleep, and adjust the training plan to improve cardiopulmonary function and aerobic metabolism, and optimize the details of competitive movements.

[0013] Preferably, the influence index ≥Influence threshold When the condition of the track is negatively impacting the athletes' performance, it is recommended that the athletes change the track for training, and the management personnel should be reminded to replace or repair the track materials in a timely manner.

[0014] Preferably, the environmental score A score below 5 indicates that environmental factors have negatively impacted the athlete's performance, and it is recommended that the athlete change training venues or adjust training times.

[0015] Compared with existing technologies, this invention provides a track and field training and management system based on big data, which has the following beneficial effects: 1. This invention connects a database, oxygen uptake analyzer, 3D motion capture equipment, force table, hardness tester, impact absorption tester, and environmental sensing device through a training acquisition module. It acquires management data for all athletes, training venue management data, and environmental sensing data, classifying them into personnel datasets, venue datasets, and environmental datasets. Based on the athlete datasets, the intelligent management module precisely quantifies individual competitive abilities for athletes in different sports—speed-strength type, speed-endurance type, and endurance type—and generates corresponding ability indices. It breaks through the limitations of single-performance evaluation, provides data support for personalized training, and has high accuracy in multi-dimensional assessment.

[0016] 2. This invention, through an intelligent management module, assesses the impact of venue conditions on athletes' competitive performance based on athlete and venue datasets, and generates a corresponding impact index. It accurately quantifies the impact of venue conditions on athletes' competitive performance, intuitively presents whether the venue is suitable for training needs, and avoids poor training results or sports injuries due to venue problems. The intelligent management module assesses the impact of environmental factors on athletes' competitive performance based on environmental datasets and generates corresponding environmental scores. The intelligent management module quickly determines whether the training environment is suitable and sets a fixed threshold value for the capability. and the threshold of influence Then, corresponding track and field training management suggestions are provided to avoid various safety risks caused by unfavorable environments, and targeted management of training results are better. 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 Based on the experimental data of the ability index in Table 1 and the experimental data of the influence index in Table 2, this invention provides a track and field training management system based on big data, including a training data acquisition module and an intelligent management module. The training data acquisition module connects to a database, oxygen uptake analyzer, 3D motion capture equipment, force table, hardness tester, impact absorption tester, and environmental sensing device via network to acquire management data of all athletes, management data of training venues, and environmental sensing data, and classifies them into personnel datasets, venue datasets, and environmental datasets. The athlete dataset includes each athlete's training program category, lean body mass, height-to-weight ratio, maximum oxygen uptake, standard deviation of all normal heart rate intervals, test scores, and deviation of center of gravity projection landing point. The program categories include speed-strength, speed-endurance, and endurance. Speed-strength training programs include 100m, 200m, and 400m; speed-endurance training programs include 800m, 1500m, and 3000m steeplechaser; and endurance training programs include marathon and race walking. The dataset includes the track hardness of each training field, the maximum gap measured by a 3-meter ruler, and the rebound coefficient of the jump zone. The 3-meter ruler is laid flat along the longitudinal direction of the track, and a feeler gauge is inserted into the maximum gap between the ruler and the ground. The maximum gap value can be obtained by reading the scale of the feeler gauge. The rebound coefficient of the jump zone is the ratio of the rebound energy to the incident energy after the field is impacted, which can intuitively reflect its elastic recovery ability. The environmental dataset includes temperature, humidity, air pressure, light intensity, and PM2.5 index; The intelligent management module consists of an ability assessment unit, a venue assessment unit, an environment assessment unit, and a training management unit. The ability assessment unit evaluates each athlete's competitive ability based on the athlete dataset and generates a corresponding ability index. The calculation process is as follows: Based on the athlete dataset, extract the first... The management data of each athlete, and the first athlete's data. The athlete's lean body mass is denoted as... , will the The height-to-weight ratio of each athlete is recorded as follows: , will the The athlete's maximum oxygen uptake is recorded as , will the The standard deviation of all normal heart rate intervals for each athlete is denoted as . , will the The latest test score of each athlete is recorded as follows: ; If the first The athlete's training program is categorized as speed-strength type. In the formula, This represents the dimensionless conversion factor for lean body mass. This indicates the weight of the fat-free body mass after dimensionless processing. This represents the dimensionless conversion factor for the height-to-weight ratio. This indicates the weight of the height-to-weight ratio after dimensionless processing. This represents the conversion factor for dimensionless treatment maximum oxygen uptake. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the dimensionless conversion factor for the standard deviation of all normal heartbeat intervals. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the conversion factor for dimensionless processing of test scores. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , Indicates the first An athlete's ability index in speed-strength training programs; If the first The athlete's training program is classified as speed-endurance type. In the formula, This indicates the weight of the fat-free body mass after dimensionless processing. This indicates the weight of the height-to-weight ratio after dimensionless processing. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , , Indicates the first An athlete's ability index in speed endurance training programs; If the first The athlete's training program is endurance-based. In the formula, This indicates the weight of the fat-free body mass after dimensionless processing. This indicates the weight of the height-to-weight ratio after dimensionless processing. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , , Indicates the first An athlete's ability index in endurance training programs; Specifically, lean body mass not only reflects the total amount of functional tissues such as muscle and bone, but also the solidity of an athlete's strength foundation. The higher the height-to-weight ratio, the more obvious the height advantage per unit weight. With comparable muscle strength, athletes can complete acceleration, take-off, or hurdling maneuvers faster, and their explosive power output efficiency is also higher. Maximum oxygen uptake reflects the body's limit of aerobic metabolism; the higher the value, the stronger the athlete's energy supply capacity for sustained exercise over a long period. A higher standard deviation of all normal heart rate intervals usually indicates better physical recovery and a stronger ability to adapt to training load, allowing for an appropriate increase in training intensity. Lean body mass, height-to-weight ratio, maximum oxygen uptake, standard deviation of all normal heart rate intervals, and training performance are interconnected, constructing a complete evaluation chain around the athlete's morphological conditions, physical conditions, recovery ability, and performance ability. The following is the experimental data for the ability index, as shown in Table 1: Table 1: Experimental Data for the Ability Index Table 1 shows the experimental data for the ability index. Athlete A (speed-strength type), athlete B (speed-endurance type), and athlete C (endurance type) were selected as experimental subjects. The conversion coefficients were... to All are 0.01, representing the weights for athlete A. , 1, , , Weights for athlete B , , , , Weights for athlete A , , , , ; Capability threshold Used to measure an athlete's competitive ability, the ability threshold is shown in Table 1, which contains experimental data on the ability index. The optimal value is 0.4. Based on this, athlete A's ability index is... <Ability Threshold This indicates that Athlete A's competitive ability has not met the standard. It is recommended that Athlete A supplement muscle mass and strength reserves, ensure sufficient sleep, and adjust the training plan to improve cardiopulmonary function and aerobic metabolism, and optimize the details of competitive movements. The venue assessment unit evaluates the impact of venue conditions on athletes' competitive performance based on athlete and venue datasets, and generates a corresponding impact index. The calculation process is as follows: Based on the athlete dataset, the first The athlete conducted the first... The deviation of the center of gravity projection landing point during the testing of each training item is denoted as . , Indicates the first The total number of time points at which the deviation was recorded for each training item test; Based on the site dataset, extract the first... Management data for training venues, known to be the first training venue. The athlete was in the... The training venue completed the first The training project was tested, and the first training item was tested. The track hardness of each training venue is recorded as follows: , will the The maximum gap measured with a 3-meter straightedge at each training area is recorded as follows: , will the The rebound coefficient of the jump zone in each training area is denoted as... ; In the formula, Indicates the first The athlete in the The training venue was used for the first time. Average deviation of the center of gravity projection landing point during the testing of each training item; In the formula, This represents the conversion factor for the average deviation of the centroid projection landing point in dimensionless processing. The weight representing the average deviation of the centroid projection point after dimensionless processing. This represents the standard value used to measure the hardness of a running track. This represents the conversion factor for the absolute difference between the dimensionless processed runway hardness and the standard value. This represents the weight of the absolute difference between the dimensionless processed runway hardness and the standard value. This represents the standard value used to measure the maximum gap when measured with a 3-meter straightedge. This represents the conversion factor for the absolute difference between the maximum gap measured by a dimensionless 3-meter straightedge and the standard value. This represents the weight of the absolute difference between the maximum gap measured by a 3-meter straightedge after dimensionless processing and the standard value. This represents the standard value used to measure the rebound coefficient in the jump zone. This represents the conversion factor between the dimensionless springback coefficient in the jump zone and the absolute difference of the standard value. This represents the weight of the absolute difference between the springback coefficient in the jump zone after dimensionless processing and the standard value. , , and All are constants, and , Indicates the first The athlete in the The training venue was used for the first time. The impact index of each training item during testing; The following is the experimental data on the influence index, as shown in Table 2: Table 2: Experimental Data on the Influence Index Table 2 shows the experimental data for the influence index. Running track 1 in the training area was selected as the experimental subject. Every 20 meters, the deviation between the athlete's center of gravity projection and the foot landing point (D) was recorded, along with the conversion coefficient. Conversion factor Conversion factor Conversion factor , , , , ; Influence threshold Used to measure the impact of venue conditions on athletes' competitive performance, the impact threshold is shown in Table 2, the experimental data of the impact index. The optimal value is 0.1. Based on the assessment, the influence index of athlete D during the 100-meter test on track 1 of the training ground is [value missing]. >Influence threshold When the venue conditions are negatively impacting athlete D's performance, it is recommended that athlete D change the track for training, and the management personnel are reminded to replace or repair the track materials in a timely manner. The environmental assessment unit evaluates the impact of environmental factors on athletes' competitive performance based on the environmental dataset and generates corresponding environmental scores. The evaluation process is as follows: Environmental rating The initial value is set to 5 points, and then the first value is extracted based on the environmental dataset. Environmental sensor data from each training venue; If the first If the temperature of the training venue is >32℃ or <5℃, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first If the humidity of the training venue is >80% or <30%, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first The air pressure at the training venue is <950 hPa or >1030 hPa. Environmental rating of each training venue Decrease by 1 point; If the first If the light intensity of a training venue is >50,000 lux or <10,000 lux, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first The PM2.5 index at the training venue was >75 μg / m³, which will affect the first training venue. Environmental rating of each training venue Decrease by 1 point; Environmental rating A score below 5 indicates that environmental factors have negatively impacted the athlete's performance, and it is recommended that the athlete change training venues or adjust training schedules. The training management unit has a fixed capability threshold. and the threshold of influence Combined with the ability index Impact Index and environmental rating It provides corresponding suggestions for track and field training management.

[0020] In this embodiment, the training acquisition module obtains management data of all athletes, management data of the training venue, and environmental sensor data, and classifies them into personnel datasets, venue datasets, and environmental datasets. The intelligent management module accurately quantifies individual competitive abilities for athletes in different sports, such as speed-strength, speed-endurance, and endurance sports, breaking through the limitations of single performance evaluation and using ability indices. Provides data support for personalized training, influencing the index It can accurately quantify the impact of venue conditions on athletes' competitive performance, intuitively present whether the venue is suitable for training needs, and avoid poor training results or sports injuries due to venue problems. Environmental rating It can quickly determine whether the training environment is suitable and avoid various safety risks caused by unfavorable environments.

[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 big data based track and field training management system characterized in that: The training acquisition module and the intelligent management module are included; The training acquisition module connects a database, an oxygen uptake analyzer, a three-dimensional motion capture device, a force platform, a durometer, an impact absorption tester and an environmental sensing device through a network, acquires management data of all athletes, management data of training sites and environmental sensing data, and classifies and forms personnel data sets, site data sets and environmental data sets; The intelligent management module consists of an ability assessment unit, a venue assessment unit, an environment assessment unit, and a training management unit. The ability assessment unit evaluates the competitive ability of each athlete based on the athlete dataset and generates a corresponding ability index. The venue assessment unit evaluates the impact of venue conditions on athletes' competitive performance based on athlete datasets and venue datasets, and generates a corresponding impact index. The environmental assessment unit evaluates the impact of environmental factors on athletes' competitive performance based on the environmental dataset and generates corresponding environmental scores. The training management unit is set with a fixed capability threshold value. and the threshold of influence Combined with the ability index Impact Index and environmental rating It provides corresponding suggestions for track and field training management.

2. The big data based athletic training management system as claimed in claim 1, wherein: The athlete data set includes the training project category, the lean body weight, the height-weight ratio, the maximum oxygen uptake, the standard deviation of all normal heartbeat intervals, the test results and the deviation of the gravity projection landing point of each athlete, wherein the project category includes speed strength type, speed endurance type and endurance type.

3. The big data based athletic training management system of claim 2, wherein: The site data set includes the track hardness, the 3-meter straight-line measurement maximum gap and the jumping area rebound coefficient of each training site.

4. The big data based athletic training management system of claim 3, wherein: The environmental data set includes temperature, humidity, air pressure, light intensity and PM2.5 index.

5. The big data based athletic training management system of claim 4, wherein: The capability index The calculation proceeds as follows: According to the athlete data set, the management data of the first athlete is extracted, and the lean body mass of the first athlete is recorded as , the height-weight ratio of the first athlete is recorded as , the maximum oxygen uptake of the first athlete is recorded as , the standard deviation of all normal heart intervals of the first athlete is recorded as , and the test result of the first athlete at the latest time point is recorded as ; If the training item category of the first athlete is a speed-strength type, the second athlete is selected as the target athlete. In the formula, This represents the dimensionless conversion factor for lean body mass. This indicates the weight of the fat-free body mass after dimensionless processing. This represents the dimensionless conversion factor for the height-to-weight ratio. This indicates the weight of the height-to-weight ratio after dimensionless processing. This represents the conversion factor for dimensionless treatment maximum oxygen uptake. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the dimensionless conversion factor for the standard deviation of all normal heartbeat intervals. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the conversion factor for dimensionless processing of test scores. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , Indicates the first An athlete's ability index in speed-strength training programs; If the training item category of the first athlete is a speed endurance type, the second training item is a speed endurance type. In the formula, This indicates the weight of the fat-free body mass after dimensionless processing. This indicates the weight of the height-to-weight ratio after dimensionless processing. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , , Indicates the first An athlete's ability index in speed endurance training programs; If the training item category of the first athlete is endurance type, the second athlete is endurance type, In the formula, This indicates the weight of the fat-free body mass after dimensionless processing. This indicates the weight of the height-to-weight ratio after dimensionless processing. The weights representing the maximum oxygen uptake after dimensionless treatment. This represents the weight of the standard deviation of all normal heartbeat intervals after dimensionless processing. This represents the weight of the test score after dimensionless processing. , , , and All are constants, and , , Indicates the first An athlete's ability index in endurance training programs.

6. The big data based athletic training management system of claim 5, wherein: The impact index The calculation proceeds as follows: According to the athlete dataset, the deviation of the center of gravity projection landing point of the first athlete when performing the first training item test is recorded as , , , , , represents the total number of deviation recording time points of the first training item test. According to the venue dataset, the management data of the first training venue is extracted, the first athlete is known to complete the first training item test at the first training venue, and the track hardness of the first training venue is recorded as , the 3-meter straight ruler maximum gap of the first training venue is recorded as , and the rebound coefficient of the first training venue is recorded as ; In the formula, represents the average deviation of the projection point of the center of gravity of the athletes in the training ground in the training project test; In the formula, is a conversion factor for the average deviation of the non-dimensional processed barycentric projection landing point, is a weight for the average deviation of the non-dimensional processed barycentric projection landing point, is a standard value for measuring the runway hardness, is a conversion factor for the absolute difference between the non-dimensional processed runway hardness and the standard value, is a weight for the absolute difference between the non-dimensional processed runway hardness and the standard value, is a standard value for measuring the maximum gap measured by the 3-meter ruler, is a conversion factor for the absolute difference between the non-dimensional processed maximum gap measured by the 3-meter ruler and the standard value, is a weight for the absolute difference between the non-dimensional processed maximum gap measured by the 3-meter ruler and the standard value, is a standard value for measuring the rebound coefficient of the jumping area, is a conversion factor for the absolute difference between the non-dimensional processed rebound coefficient of the jumping area and the standard value, is a weight for the absolute difference between the non-dimensional processed rebound coefficient of the jumping area and the standard value, , , and are constants, and , represents the influence index of the th athlete in the th training venue for the th training item test.

7. The big data based athletic training management system of claim 6, wherein: The environmental score The assessment procedure is as follows: Environmental rating The initial value is set to 5 points, and then the first value is extracted based on the environmental dataset. Environmental sensor data from each training venue; If the first If the temperature of the training venue is >32℃ or <5℃, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first If the humidity of the training venue is >80% or <30%, the first training venue will be... Environmental rating of each training venue Decrease by 1 point; If the first The air pressure at the training venue is <950 hPa or >1030 hPa. Environmental rating of each training venue Decrease by 1 point; If the first The light intensity of each training venue is >50000 lux or <10000 lux. Environmental rating of each training venue Decrease by 1 point; If the first The PM2.5 index at the training venue was >75 μg / m³, which will affect the first training venue. Environmental rating of each training venue Decrease by 1 point.

8. The big data based athletic training management system of claim 7, wherein: The ability index ≤ ability threshold When the value is less than the ability threshold, it indicates that the athlete's competitive ability is not up to standard, and the athlete is recommended to supplement muscle mass and strength reserves, ensure adequate sleep, and adjust the training plan to improve cardiorespiratory function and aerobic metabolism ability, and optimize the details of competitive movements.

9. The big data based athletic training management system of claim 8, wherein: The influence index ≥ influence threshold value When the influence index is greater than the influence threshold value, it indicates that the track conditions have a negative impact on the athlete's performance, and it is recommended that the athlete change the track for training, and remind the management personnel to replace or repair and maintain the track materials in a timely manner.

10. The big data based athletic training management system of claim 9, wherein: The environmental score When the score is below 5, it indicates that the environmental factors have a negative impact on the athlete's performance, and it is recommended that the athlete change the training site or adjust the training period.

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