A middle school sports load evaluation teaching system based on physical data processing

Abstract

The present application belongs to the technical field of sports load evaluation, and discloses a middle school student sports load evaluation teaching system based on physical data processing, which comprises a student physical basic data acquisition module, a sports process data acquisition module, a physical data standardization processing module, a sports load calculation module, an individual difference correction module, a teaching adaptation regulation module, a safety early warning feedback module and a teaching report generation module. Through the linkage of student physical basic data acquisition, sports process data acquisition, physical data standardization processing, sports load calculation, individual difference correction, teaching adaptation regulation, safety early warning feedback and teaching report generation, the middle school student classroom sports load quantitative evaluation, individual difference correction, hierarchical teaching adjustment, safety risk early warning and after-school teaching review are realized, and the problems of traditional sports classroom relying on experience to judge load, being difficult to take into account student differences, sports risk discovery lagging behind and teaching adjustment lacking data basis are solved.

Description

Technical Field

[0001] This invention belongs to the field of sports load assessment technology, and in particular relates to a teaching system for assessing sports load for middle school students based on physical fitness data processing. Background Technology

[0002] Physical education for middle school students needs to achieve physical training goals such as speed, endurance, strength, flexibility, and coordination, while also taking into account the different physical fitness levels, heart rate adaptability, fatigue status, and risk of sports injuries of different students. In traditional classrooms, teachers usually judge the intensity of exercise based on the content of the activity, students' performance, and experience, making it difficult to grasp the actual exercise load of each student in real time. This can easily lead to situations where some students have insufficient load, some students have excessive load, there are large differences in classroom training effects, and safety risks are detected late. Therefore, there is a need for a middle school student exercise load assessment teaching system that can collect students' basic physical fitness data and exercise process data, perform standardized processing, load calculation, individual correction, teaching control, and safety early warning.

[0003] Current management of physical education in middle schools focuses on recording physical test scores and post-class statistics, but lacks integration and processing of dynamic data such as heart rate sequence, exercise duration, exercise distance, number of movements, interval recovery, and subjective fatigue during classroom exercise. Furthermore, the data dimensions, strengths and weaknesses, and evaluation criteria are inconsistent among different grades, genders, and sports, making it difficult for teachers to convert physical test data, classroom exercise data, and real-time heart rate data into comparable exercise load results.

[0004] Current exercise load assessments often rely on uniform class-wide exercise volume or a single heart rate indicator as a reference, failing to adequately incorporate students' basic physical fitness index, resting heart rate, heart rate recovery ability, past sports injury records, subjective fatigue feedback, and historical load adaptation trends for individualized adjustments. As a result, assessment results are difficult to directly translate into adjustments to the number of exercises, exercise intensity, rest intervals, teaching group suggestions, and warnings to stop exercising. Consequently, there is a lack of data to support the regulation of physical education classes, and sports safety warnings and post-class reviews are not timely enough. Summary of the Invention

[0005] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a teaching system for assessing exercise load in middle school students based on physical fitness data processing. This system integrates student basic physical fitness data collection, exercise process data collection, standardized processing of physical fitness data, exercise load calculation, individual difference correction, teaching adaptation and adjustment, safety warning feedback, and teaching report generation. It enables quantitative assessment of classroom exercise load in middle school students, correction of individual differences, adjustment of differentiated instruction, safety risk warning, and post-class teaching review. This solves the problems of traditional physical education classes relying on experience to judge load, difficulty in addressing student differences, delayed detection of exercise risks, and lack of data basis for teaching adjustments.

[0006] The technical solution adopted in this invention is as follows: a teaching system for assessing exercise load for middle school students based on physical fitness data processing, including a student basic physical fitness data acquisition module, an exercise process data acquisition module, a physical fitness data standardization processing module, an exercise load calculation module, an individual difference correction module, a teaching adaptation and control module, a safety early warning feedback module, and a teaching report generation module; The student physical fitness data collection module collects students' basic physical fitness data and establishes student physical fitness profiles based on this data. The exercise process data acquisition module collects students' exercise process data and links the students' exercise process data with their physical fitness records; The physical fitness data standardization processing module standardizes students' basic physical fitness data and students' exercise process data to generate standardized physical fitness data. The exercise load calculation module generates classroom exercise load values ​​based on standardized physical fitness data and student exercise process data; The individual difference correction module corrects the classroom exercise load value based on the individual student difference data and generates the individual corrected exercise load value; the teaching adaptation and control module generates the teaching adaptation and control result based on the individual corrected exercise load value and the teaching adaptation constraint data. The safety early warning feedback module generates exercise load early warning results based on individual corrected exercise load values ​​and safety risk correlation data; The teaching report generation module generates a teaching report on exercise load based on students' physical fitness records, classroom exercise load values, individual adjusted exercise load values, teaching adaptation and control results, and exercise load early warning results.

[0007] As a preferred technical solution of this scheme, the student physical fitness basic data collection module includes a student identity binding unit, a physical test data collection unit, a resting state data collection unit, a health risk data collection unit, a daily exercise habit data collection unit, and a student physical fitness file generation unit. The student physical fitness basic data includes student identity data, physical test data, resting state data, health risk data, and daily exercise habit data. The student identity data includes student ID, class, grade, and gender. The physical test data includes height, weight, vital capacity, 50-meter sprint time, standing long jump time, sit-and-reach time, and endurance running time. The resting state data includes resting heart rate and pre-class fatigue status. The health risk data includes records of past sports injuries, sports contraindications, and recent physical discomfort records. The daily exercise habit data includes weekly exercise frequency, exercise duration, and sports preferences.

[0008] As a preferred technical solution of this scheme, the exercise process data acquisition module includes an exercise identification unit, an exercise duration acquisition unit, an exercise distance acquisition unit, a number of movements acquisition unit, a heart rate sequence acquisition unit, an interval state acquisition unit, and a subjective fatigue acquisition unit; the student exercise process data includes exercise data, exercise duration data, exercise distance data, number of movements data, heart rate sequence data, interval recovery data, and subjective fatigue data; the exercise data includes running, jumping, ball games, strength training, flexibility training, and comprehensive physical fitness training; the exercise duration data includes continuous exercise duration and cumulative exercise duration; the exercise distance data includes moving distance and sprint distance; the number of movements data includes the number of jumps, the number of backtracking, the number of throwing, and the number of strength training; and the interval recovery data includes the interval duration and the recovery duration.

[0009] As a preferred technical solution of this scheme, the physical fitness data standardization processing module includes a data cleaning unit, an outlier identification unit, a missing value completion unit, a grade and gender stratification unit, a project direction correction unit, a physical fitness standardization unit, and a standardization result output unit. The physical fitness data standardization processing module performs duplicate data deletion, data unit unification, outlier marking, missing value completion, grade and gender stratification, project direction correction, and standardization processing on students' basic physical fitness data and students' exercise process data to generate standardized physical fitness data.

[0010] As a preferred technical solution of this scheme, the exercise load calculation module includes a heart rate load calculation unit, an external load calculation unit, an interval recovery calculation unit, an exercise density calculation unit, a classroom exercise load fusion unit, and a load interval judgment unit. The heart rate load calculation unit generates heart rate load results based on the pre-class resting heart rate, the average heart rate in class, the peak heart rate, and the heart rate recovery speed. The external load calculation unit generates external load results based on the exercise distance, the number of sprints, the number of movements, the speed change, and the acceleration change. The classroom exercise load fusion unit generates classroom exercise load values ​​based on the heart rate load results, the external load results, the recovery load results, and the exercise density results.

[0011] As a preferred technical solution of this scheme, the individual difference correction module includes a basic physical fitness correction unit, a heart rate adaptation correction unit, a fatigue state correction unit, an injury risk correction unit, a historical adaptation correction unit, and an individual modified exercise load output unit; the student's individual difference data includes the student's basic physical fitness index, resting heart rate, peak heart rate, heart rate recovery ability score, pre-class fatigue state, subjective fatigue score, previous sports injury records, movement abnormality markers, and historical load adaptation score; the individual difference correction module corrects the classroom exercise load value according to the student's individual difference data and generates an individual modified exercise load value.

[0012] As a preferred technical solution of this scheme, the teaching adaptation and control module includes a teaching objective matching unit, a load stratification and grouping unit, a project intensity adjustment unit, a practice repetition adjustment unit, an interval time adjustment unit, and a teaching plan output unit; the teaching adaptation constraint data includes course teaching objectives, target load range, student group status, remaining class time, student physical fitness index, and individual modified exercise load value; the teaching adaptation and control results include suggestions for adjusting sports projects, practice repetitions, practice intensity, interval time, and teaching grouping.

[0013] As a preferred technical solution of this scheme, the safety early warning feedback module includes a heart rate abnormality early warning unit, an overload early warning unit, a fatigue abnormality early warning unit, a movement abnormality early warning unit, an injury risk early warning unit, and a stop-exercise early warning unit; the teaching report generation module includes an individual load report generation unit, a class load distribution report generation unit, a teaching goal achievement analysis unit, a course adjustment record unit, a safety incident record unit, and a continuous teaching archive generation unit; the exercise load teaching report includes a student individual exercise load report, a class exercise load distribution report, and a physical education teaching adjustment report.

[0014] The beneficial effects of this invention are as follows: (1) By utilizing the principle of hierarchical processing of students' basic physical fitness data, students' exercise process data and standardized physical fitness data, students' physical fitness files are established through the student physical fitness data collection module, classroom exercise process data is bound through the exercise process data collection module, and then data cleaning, outlier identification, missing value filling, grade and gender stratification, project direction correction and standardization are carried out through the physical fitness data standardization processing module. This realizes the unified comparison between data from different students, different projects and different sources, and solves the technical problems of scattered physical education class exercise load assessment data, inconsistent evaluation benchmarks and difficulty in participating in real-time classroom judgment of physical fitness test data.

[0015] (2) By utilizing the principle of integrating heart rate load, external load, exercise density and recovery ability, the exercise load calculation module generates classroom exercise load values ​​based on standardized physical fitness data, heart rate changes, exercise distance, number of movements, acceleration changes, effective exercise duration and heart rate recovery ability. Then, the individual difference correction module generates individual corrected exercise load values ​​based on students' basic physical fitness index, subjective fatigue score, injury risk score and historical load adaptation score. This realizes the quantitative expression of the real load differences of different students under the same classroom exercise volume, and solves the technical problems that teachers cannot accurately judge exercise intensity by observation and that it is difficult to adapt the uniform exercise volume to the whole class.

[0016] (3) By utilizing the principle of closed-loop linkage between exercise load assessment results and physical education teaching regulation, the teaching adaptation regulation module generates suggestions for adjusting teaching groups, project intensity, number of exercises and rest time based on individual exercise load values. The safety warning feedback module generates warnings for abnormal heart rate, high load, fatigue abnormality, movement abnormality, injury risk and cessation of exercise. The teaching report generation module generates individual student exercise load reports, class exercise load distribution reports and physical education teaching adjustment reports. This realizes real-time classroom regulation, safety risk reminders and post-class teaching review, and solves the technical problems of physical education teachers having difficulty adjusting classroom arrangements in a timely manner, delayed discovery of exercise risks and lack of data support for teaching improvement. Attached Figure Description

[0017] The accompanying drawings are provided to further understand the present invention and form part of the specification. They are used together with the embodiments of the present invention to explain the invention and do not constitute a limitation thereof.

[0018] Figure 1 This is a schematic diagram of the teaching system for assessing exercise load in middle school students proposed in this invention. Figure 2 This is a flowchart of the data processing for the middle school student exercise load assessment teaching system proposed in this invention. Detailed Implementation

[0019] Example 1, see Figures 1-2 The present invention provides a teaching system for assessing exercise load for middle school students based on physical fitness data processing, including a student physical fitness basic data acquisition module, an exercise process data acquisition module, a physical fitness data standardization processing module, an exercise load calculation module, an individual difference correction module, a teaching adaptation and control module, a safety early warning feedback module, and a teaching report generation module.

[0020] The student physical fitness data collection module collects basic physical fitness data from students, including their grade, gender, height, weight, vital capacity, resting heart rate, 50-meter sprint time, standing long jump time, sit-and-reach time, endurance running time, previous sports injury records, and daily exercise habits. Based on the student's basic physical fitness data, a student physical fitness profile is established.

[0021] The exercise process data collection module collects exercise process data of students in physical education classes, recess training, physical fitness tests and after-school exercise. The exercise process data includes exercise items, exercise duration, exercise distance, number of exercise sessions, heart rate sequence, cadence, speed, acceleration, interval duration and subjective fatigue feedback, and links the exercise process data with the student's physical fitness record.

[0022] The physical fitness data standardization processing module performs missing value completion, outlier removal, unit standardization, grade and gender stratification, and physical test item direction correction and standardization processing on students' basic physical fitness data and exercise process data to generate standardized physical fitness data.

[0023] The exercise load calculation module generates classroom exercise load values ​​based on standardized physical fitness data, heart rate sequence, exercise duration, exercise intensity, and interval duration, and determines the degree of exercise stimulation for students in the current course based on the classroom exercise load values.

[0024] The individual difference correction module adjusts the classroom exercise load value individually based on students' basic physical fitness level, resting heart rate, historical load adaptation, previous sports injury records, and subjective fatigue feedback, generating an individualized adjusted exercise load value.

[0025] The teaching adaptation and control module generates suggestions for adjusting the exercise program, the number of exercises, the intensity of the exercise, the rest interval, and the teaching group based on individual exercise load values, course teaching objectives, student group status, and remaining class time.

[0026] The safety warning and feedback module generates low load alerts, appropriate load alerts, high load warnings, and stop exercise warnings based on individual corrected exercise load values, abnormal heart rate status, fatigue feedback status, movement completion status, and previous exercise injury records.

[0027] The teaching report generation module generates individual student exercise load reports, class exercise load distribution reports, and physical education teaching adjustment reports based on student physical fitness records, classroom exercise load values, individual modified exercise load values, teaching adaptation and control results, and safety warning results.

[0028] Through the above system, this invention links and processes middle school students' basic physical fitness data, classroom exercise process data, heart rate changes, project completion status, individual differences, and teaching objectives, enabling physical education teachers to grasp students' exercise load levels and load adaptation status in real time. This solves the problems of traditional physical education classes, which mainly rely on teachers' experience to judge exercise intensity, make it difficult to take into account students' physical fitness differences, make it difficult to detect exercise risks in a timely manner, and lack of basis for teaching adjustments.

[0029] Example 2: Based on all the above examples, the student physical fitness basic data collection module specifically includes a student identity binding unit, a physical test data collection unit, a resting state collection unit, a health risk collection unit, a daily exercise habit collection unit, and a student physical fitness record generation unit.

[0030] The student identity binding unit binds student ID, class, grade, and gender. The physical fitness test data collection unit collects student height, weight, vital capacity, 50-meter sprint time, standing long jump time, sit-and-reach test time, and endurance running time. The resting state collection unit collects resting heart rate and pre-class fatigue status. The health risk collection unit collects past sports injury records, sports contraindication reminders, and recent physical discomfort records. The daily exercise habit collection unit collects weekly exercise frequency, exercise duration, and sports preferences. The student physical fitness file generation unit generates student physical fitness files based on the above data.

[0031] The student's basic physical fitness index is calculated using the following formula:

[0032] In the formula, This represents the student's basic physical fitness index. This indicates the number of physical fitness events included in the calculation. This represents the standardized score of the j-th physical fitness item. This represents the weight coefficient of the j-th physical fitness item.

[0033] When the 50-meter sprint and endurance run times are events where a smaller value indicates better performance, the physical fitness test data acquisition unit first performs directional correction before generating standardized scores. When vital capacity, standing long jump, and sit-and-reach are events where a larger value indicates better performance, the physical fitness test data acquisition unit directly performs positive standardization. When there are records of previous sports injuries, the health risk acquisition unit generates risk correction markers.

[0034] Regarding parameter adjustments: The weight coefficients for physical fitness items are set according to grade, teaching stage, and course objectives. Physical fitness improvement courses increase the corresponding weights of endurance running and vital capacity, speed quality courses increase the corresponding weights of 50-meter sprint, and flexibility and coordination courses increase the corresponding weights of sit-and-reach and standing long jump.

[0035] Through the above processing, the student physical fitness basic data collection module can unify students' physical test results, resting state, health risks and daily exercise habits into a student physical fitness profile, solving the problem that physical education classes cannot accurately grasp the differences in students' initial physical fitness.

[0036] Example 3: Based on all the above examples, the motion process data acquisition module specifically includes a motion identification unit, a motion duration acquisition unit, a motion distance acquisition unit, a number of movements acquisition unit, a heart rate sequence acquisition unit, an interval state acquisition unit, and a subjective fatigue acquisition unit.

[0037] The sports activity identification unit identifies the types of sports activities in the classroom, including running, jumping, ball games, strength training, flexibility training, and comprehensive physical fitness training. The sports duration collection unit collects students' continuous sports duration and cumulative sports duration. The sports distance collection unit collects the distance traveled and the distance sprinted. The number of movements collection unit collects the number of jumps, turns, throws, and strength training movements. The heart rate sequence collection unit collects the heart rate before class, the heart rate during exercise, and the heart rate during the recovery period. The interval state collection unit collects the interval duration and the recovery time. The subjective fatigue collection unit collects students' self-rated fatigue level.

[0038] The classroom exercise intensity index is calculated using the following formula:

[0039] In the formula, Indicates the classroom exercise intensity index. This represents the average heart rate during class. This indicates the resting heart rate before class. This indicates the upper limit of a student's heart rate. Indicates the effective exercise duration. Indicates the total class time. Indicates the distance traveled. This indicates the upper limit of the calibration distance for movement. Indicates the number of times the action is completed. This indicates the upper limit of the number of actions. Indicates the duration of intermittent recovery. , , , and These represent the weighting coefficients of the corresponding motion factors.

[0040] When students' average heart rate increases and the proportion of effective exercise time increases, the classroom exercise intensity index increases. When the interval recovery time increases, the classroom exercise intensity index decreases. When students complete significantly fewer movements than their peers and their heart rate increases, the exercise process data acquisition module generates a marker indicating insufficient movement efficiency.

[0041] Regarding parameter adjustments: Running courses increase the weight of average heart rate and distance; strength training courses increase the weight of the number of repetitions; recovery courses increase the weight of interval recovery time; and ball games adjust the weight of distance and number of repetitions in sync with the rhythm of the sport.

[0042] Through the above processing, the exercise process data acquisition module can uniformly convert classroom exercise items, exercise time, exercise distance, number of movements, heart rate changes and recovery intervals into exercise intensity data, solving the problem that judging exercise intensity based solely on the name of the exercise in physical education classes is inaccurate.

[0043] Example 4: Based on all the above examples, the physical fitness data standardization processing module specifically includes a data cleaning unit, an outlier identification unit, a missing value completion unit, a grade and gender stratification unit, a project direction correction unit, a physical fitness standardization unit, and a standardization result output unit.

[0044] The data cleaning unit removes duplicate data and standardizes data units; the outlier identification unit identifies data such as sudden heart rate jumps, sudden speed changes, abnormal number of movements, and physical test scores exceeding the reasonable range; the missing value completion unit completes missing values ​​based on adjacent sampled data of the same student and statistical data of students in the same group; the grade and gender stratification unit establishes stratification benchmarks according to grade and gender; the project direction correction unit unifies the advantages and disadvantages of different physical test projects; and the physical fitness standardization unit generates physical fitness standardization data.

[0045] Standardized scores for physical fitness events are calculated using the following formula:

[0046] In the formula, This represents the standardized score of the j-th physical fitness item. This represents the raw data for the j-th physical fitness item for the student. Let $j$ represent the mean of the j-th physical fitness item for a group of the same grade and gender. This represents the standard deviation of the j-th physical fitness item for a group of the same grade and gender.

[0047] When the project direction correction unit identifies that the project belongs to the time-based performance category, the physical fitness standardization unit reverse-processes the raw data and then calculates the standardized score. When the outlier identification unit detects a sudden heart rate jump but the motion data and speed data do not change synchronously, the physical fitness data standardization processing module marks the corresponding heart rate sampling point as low-confidence data. When the proportion of missing values ​​exceeds the missing threshold, the standardization result output unit generates a retest prompt.

[0048] Through the above processing, the physical fitness data standardization module can eliminate the impact of grade, gender, project direction, and collection anomalies on physical fitness evaluation, and solve the problem of incomparability between different students, different projects, and different data sources.

[0049] Example 5: Based on all the above examples, the exercise load calculation module specifically includes a heart rate load calculation unit, an external load calculation unit, an interval recovery calculation unit, an exercise density calculation unit, a classroom exercise load fusion unit, and a load interval judgment unit.

[0050] The heart rate load calculation unit generates heart rate load results based on pre-class resting heart rate, average heart rate during class, peak heart rate, and heart rate recovery speed. The external load calculation unit generates external load results based on exercise distance, number of sprints, number of movements, speed changes, and acceleration changes. The interval recovery calculation unit generates recovery load results based on interval duration and heart rate recovery speed. The exercise density calculation unit generates exercise density results based on effective exercise duration and total class time. The classroom exercise load fusion unit merges the heart rate load results, external load results, recovery load results, and exercise density results to generate the classroom exercise load value.

[0051] Classroom exercise load values ​​are calculated using the following formula:

[0052] In the formula, This represents the classroom exercise load value. Indicates the classroom exercise intensity index. Indicates peak heart rate during class. This indicates the resting heart rate before class. This indicates the upper limit of a student's heart rate. This represents the cumulative value of the acceleration change. Indicates the upper limit of the acceleration change calibration. Indicates the effective exercise duration. Indicates the total class time. This indicates a heart rate recovery ability score. , , , and These represent the weighting coefficients of the corresponding load factors.

[0053] When the classroom exercise load value is lower than the target load lower limit, the load interval judgment unit generates a low load result. When the classroom exercise load value is within the target load interval, the load interval judgment unit generates a suitable load result. When the classroom exercise load value is higher than the target load upper limit, the load interval judgment unit generates a high load result.

[0054] Regarding parameter adjustments: In the new lesson phase, lower the upper limit of the classroom exercise load target; in the consolidation practice phase, increase the corresponding weight of the effective exercise duration; in the physical fitness enhancement phase, increase the corresponding weight of the peak heart rate and cumulative acceleration change value; and in the recovery and conditioning phase, increase the corresponding weight of the heart rate recovery ability score.

[0055] Through the above processing, the exercise load calculation module can uniformly calculate the heart rate load, external exercise load, exercise density, and recovery capacity into a classroom exercise load value, solving the problem that exercise load in physical education teaching is estimated solely based on teachers' observation and experience.

[0056] Example 6: This example is based on all the above examples. The individual difference correction module specifically includes a basic physical fitness correction unit, a heart rate adaptation correction unit, a fatigue state correction unit, an injury risk correction unit, a historical adaptation correction unit, and an individual corrected exercise load output unit.

[0057] The basic physical fitness correction unit adjusts the classroom exercise load value based on the student's basic physical fitness index; the heart rate adaptation correction unit adjusts the classroom exercise load value based on resting heart rate, peak heart rate, and heart rate recovery ability score; the fatigue state correction unit adjusts the classroom exercise load value based on pre-class fatigue state and subjective fatigue feedback; the injury risk correction unit adjusts the classroom exercise load value based on past sports injury records and movement abnormality markers; and the historical adaptation correction unit adjusts the classroom exercise load value based on the student's continuous course load changes and load adaptation trends.

[0058] Individual corrected exercise load values ​​are calculated using the following formula:

[0059] In the formula, This represents the individual's corrected exercise load value. This represents the classroom exercise load value. This represents the student's basic physical fitness index. This represents the average basic physical fitness index of a group of the same grade and gender. Indicates subjective fatigue rating. Indicates the damage risk score, This indicates the historical load adaptation score. , , and These represent the weight coefficients of the corresponding individual difference factors.

[0060] When a student's basic physical fitness index is lower than the group average and their subjective fatigue score is higher, the individual difference correction module increases the risk expression of the individual's modified exercise load value. When a student's historical load adaptation score is higher and their heart rate recovery ability is better, the individual difference correction module decreases the risk expression of the individual's modified exercise load value. When the injury risk score exceeds the injury threshold, the individual difference correction module generates a protective load reduction marker.

[0061] Regarding parameter adjustments: For lower-grade students, increase the weight of subjective fatigue score and injury risk score; for graduating students in the intensive physical fitness test phase, increase the weight of historical load adaptation score; for obese students, increase the weight of heart rate adaptation correction; and for students with a history of knee or ankle injuries, increase the weight of injury risk score.

[0062] Through the above processing, the individual difference correction module can reflect the differences in physical fitness, fatigue status, injury risk and load adaptation of different students on the basis of the same classroom exercise load, and solve the problem that it is difficult to adapt the uniform exercise volume arrangement to all students in the class.

[0063] Example 7: This example is based on all the above examples. The teaching adaptation and control module specifically includes a teaching objective matching unit, a load stratification and grouping unit, a project intensity adjustment unit, a practice number adjustment unit, an interval time adjustment unit, and a teaching plan output unit.

[0064] The teaching objective matching unit determines the target load range based on the course objectives. The load stratification and grouping unit generates low load group, appropriate load group, high load group and key focus group based on individual modified exercise load values ​​and students' basic physical fitness index. The project intensity adjustment unit generates suggestions for adjusting the intensity of the exercise project based on different groups. The exercise repetition adjustment unit generates suggestions for increasing, maintaining and decreasing the number of exercise repetitions. The rest time adjustment unit generates suggestions for shortening, maintaining and extending the rest time. The teaching plan output unit outputs the grouped teaching plan to the teacher.

[0065] When students are in the low-load group and there are no safety risk markers, the instructional adaptation and control module generates suggestions to increase the number of exercises or shorten the rest intervals. When students are in the high-load group, the instructional adaptation and control module generates suggestions to reduce the exercise intensity or extend the rest intervals. When students are in the focus group, the instructional adaptation and control module generates suggestions for alternative activities and teacher attention prompts.

[0066] Through the above processing, the teaching adaptation and control module can directly transform the exercise load assessment results into executable teaching grouping and practice adjustment plans for physical education classes, solving the problem that physical education teachers have difficulty in quickly adjusting teaching arrangements based on real-time data.

[0067] Example 8: This example is based on all the above examples. The safety warning feedback module and the teaching report generation module together form a closed loop for exercise load teaching feedback.

[0068] The safety warning feedback module specifically includes a heart rate abnormality warning unit, an overload warning unit, a fatigue abnormality warning unit, a movement abnormality warning unit, an injury risk warning unit, and a stop-exercise warning unit. The heart rate abnormality warning unit generates a heart rate abnormality warning when the student's heart rate exceeds the heart rate safety threshold or the heart rate recovery rate is lower than the recovery threshold. The overload warning unit generates a high load warning when the individual's corrected exercise load value exceeds the individual's upper limit. The fatigue abnormality warning unit generates a fatigue abnormality warning when the subjective fatigue score continuously increases. The movement abnormality warning unit generates a movement abnormality warning when the number of times the movement is completed suddenly decreases, the speed suddenly decreases, or the acceleration is abnormal. The injury risk warning unit generates an injury risk warning when both a previous exercise injury record and a movement abnormality marker exist. The stop-exercise warning unit generates a stop-exercise warning when the heart rate abnormality warning, the high load warning, and the injury risk warning are all met simultaneously.

[0069] The teaching report generation module specifically includes an individual load report generation unit, a class load distribution report generation unit, a teaching objective achievement analysis unit, a course adjustment record unit, a safety incident record unit, and a continuous teaching archive generation unit. The individual load report generation unit generates individual student exercise load reports. The class load distribution report generation unit generates the number of students and group changes in different load intervals. The teaching objective achievement analysis unit generates teaching objective achievement results by adjusting exercise load values ​​based on target load intervals and actual individuals. The course adjustment record unit records adjustments to item intensity, number of exercises, and rest intervals. The safety incident record unit records abnormal heart rate, abnormal fatigue, abnormal movement, and warnings of stopping exercise. The continuous teaching archive generation unit writes the results of multiple courses into the student's physical fitness archive.

[0070] When the number of students in the high-load group in a class exceeds the class proportion threshold, the teaching report generation module generates suggestions to reduce the overall load in the next class. When the number of students in the low-load group exceeds the class proportion threshold, the teaching report generation module generates suggestions to increase the intensity of classroom activities. When the same student has a high-load warning in multiple consecutive classes, the teaching report generation module generates individualized review suggestions.

[0071] Through the above processing, the safety early warning feedback module can promptly detect excessive exercise load, insufficient recovery, abnormal fatigue, and risk of injury in physical education classes. The teaching report generation module can transform real-time classroom data into a basis for continuous teaching improvement, solving the problem of the lack of real-time safety early warning and post-class data review in the physical education teaching process for middle school students.

Claims

1. A teaching system for assessing exercise load in middle school students based on physical fitness data processing, characterized in that: It includes modules for collecting basic student physical fitness data, collecting exercise process data, standardizing physical fitness data processing, calculating exercise load, correcting individual differences, adjusting and regulating teaching, providing safety warnings and feedback, and generating teaching reports. The student physical fitness data collection module collects students' basic physical fitness data and establishes student physical fitness profiles based on this data. The exercise process data acquisition module collects students' exercise process data and links the students' exercise process data with their physical fitness records; The physical fitness data standardization processing module standardizes students' basic physical fitness data and students' exercise process data to generate standardized physical fitness data. The exercise load calculation module generates classroom exercise load values ​​based on standardized physical fitness data and student exercise process data; The individual difference correction module corrects the classroom exercise load value based on the individual differences of students, and generates an individual corrected exercise load value; The instructional adaptation and control module generates instructional adaptation and control results based on individual modified exercise load values ​​and instructional adaptation constraint data; The safety early warning feedback module generates exercise load early warning results based on individual corrected exercise load values ​​and safety risk correlation data; The teaching report generation module generates a teaching report on exercise load based on students' physical fitness records, classroom exercise load values, individual adjusted exercise load values, teaching adaptation and control results, and exercise load early warning results.

2. The teaching system for assessing exercise load in middle school students based on physical fitness data processing according to claim 1, characterized in that: The student physical fitness basic data collection module includes a student identity binding unit, a physical test data collection unit, a resting state data collection unit, a health risk data collection unit, a daily exercise habit data collection unit, and a student physical fitness file generation unit. The student physical fitness basic data includes student identity data, physical test data, resting state data, health risk data, and daily exercise habit data. Student identity data includes student ID, class, grade, and gender. Physical test data includes height, weight, vital capacity, 50-meter sprint time, standing long jump time, sit-and-reach time, and endurance running time. Resting state data includes resting heart rate and pre-class fatigue status. Health risk data includes past sports injury records, sports contraindication warnings, and recent physical discomfort records. Daily exercise habit data includes weekly exercise frequency, exercise duration, and sports preferences.

3. The teaching system for assessing exercise load in middle school students based on physical fitness data processing according to claim 1, characterized in that: The exercise process data acquisition module includes an exercise identification unit, an exercise duration acquisition unit, an exercise distance acquisition unit, a number of movements acquisition unit, a heart rate sequence acquisition unit, an interval state acquisition unit, and a subjective fatigue acquisition unit; student exercise process data includes exercise data, exercise duration data, exercise distance data, number of movements data, heart rate sequence data, interval recovery data, and subjective fatigue data; exercise data includes running, jumping, ball games, strength training, flexibility training, and comprehensive physical fitness training; exercise duration data includes continuous exercise duration and cumulative exercise duration; exercise distance data includes moving distance and sprint distance; number of movements data includes the number of jumps, the number of backtracking, the number of throws, and the number of strength training exercises; and interval recovery data includes interval duration and recovery duration.

4. The teaching system for assessing exercise load in middle school students based on physical fitness data processing according to claim 1, characterized in that: The physical fitness data standardization processing module includes a data cleaning unit, an outlier identification unit, a missing value completion unit, a grade and gender stratification unit, a project direction correction unit, a physical fitness standardization unit, and a standardization result output unit. The data cleaning unit deletes duplicate data and standardizes data units. The outlier identification unit identifies data such as sudden heart rate jumps, sudden speed changes, abnormal number of movements, and physical fitness test scores that exceed the reasonable range. The missing value completion unit completes missing values ​​based on adjacent sampled data of the same student and statistical data of students in the same group. The grade and gender stratification unit establishes stratification benchmarks according to grade and gender. The project direction correction unit standardizes the advantages and disadvantages of different physical fitness test projects. The physical fitness standardization unit generates standardized physical fitness data.

5. The teaching system for assessing exercise load in middle school students based on physical fitness data processing according to claim 1, characterized in that: The exercise load calculation module includes a heart rate load calculation unit, an external load calculation unit, an interval recovery calculation unit, an exercise density calculation unit, a classroom exercise load fusion unit, and a load interval judgment unit; The heart rate load calculation unit generates heart rate load results based on pre-class resting heart rate, average heart rate during class, peak heart rate, and heart rate recovery speed. The external load calculation unit generates external load results based on exercise distance, number of sprints, number of movements, speed changes, and acceleration changes. The interval recovery calculation unit generates recovery load results based on interval duration and heart rate recovery speed. The exercise density calculation unit generates exercise density results based on effective exercise duration and total class time. The classroom exercise load fusion unit merges the heart rate load results, external load results, recovery load results, and exercise density results to generate the classroom exercise load value.

6. The teaching system for assessing exercise load in middle school students based on physical fitness data processing according to claim 1, characterized in that: The individual difference correction module includes a basic physical fitness correction unit, a heart rate adaptation correction unit, a fatigue state correction unit, an injury risk correction unit, a historical adaptation correction unit, and an individualized exercise load output correction unit. Student individual difference data includes students' basic physical fitness index, resting heart rate, peak heart rate, heart rate recovery ability score, pre-class fatigue state, subjective fatigue score, previous sports injury records, movement abnormality markers, and historical load adaptation scores. The basic physical fitness correction unit corrects the classroom exercise load value based on students' basic physical fitness index; the heart rate adaptation correction unit corrects the classroom exercise load value based on resting heart rate, peak heart rate, and heart rate recovery ability score; the fatigue state correction unit corrects the classroom exercise load value based on pre-class fatigue state and subjective fatigue feedback; the injury risk correction unit corrects the classroom exercise load value based on previous sports injury records and movement abnormality markers; and the historical adaptation correction unit corrects the classroom exercise load value based on students' continuous course load changes and load adaptation trends.

7. The teaching system for assessing exercise load in middle school students based on physical fitness data processing according to claim 1, characterized in that: The teaching adaptation and control module includes a teaching objective matching unit, a load stratification and grouping unit, a project intensity adjustment unit, a practice repetition adjustment unit, an interval time adjustment unit, and a teaching plan output unit; the teaching adaptation constraint data includes course teaching objectives, target load range, student group status, remaining class time, student physical fitness index, and individual modified exercise load value; The load stratification and grouping unit generates low-load, appropriate-load, high-load, and key-focus groups based on individual modified exercise load values ​​and students' basic physical fitness index. The project intensity adjustment unit generates exercise intensity adjustment suggestions based on different groups. The exercise repetition adjustment unit generates suggestions for increasing, maintaining, and decreasing the number of exercise repetitions. The rest time adjustment unit generates suggestions for shortening, maintaining, and extending the rest time. The teaching plan output unit outputs grouped teaching plans to teachers.

8. The teaching system for assessing exercise load in middle school students based on physical fitness data processing according to claim 1, characterized in that: The safety early warning and feedback module includes an abnormal heart rate warning unit, an excessive load warning unit, an abnormal fatigue warning unit, an abnormal movement warning unit, an injury risk warning unit, and a stop-exercise warning unit. The teaching report generation module includes an individual load report generation unit, a class load distribution report generation unit, a teaching goal achievement analysis unit, a course adjustment record unit, a safety incident record unit, and a continuous teaching archive generation unit. The Heart Rate Abnormality Warning Unit generates a heart rate abnormality warning when a student's heart rate exceeds the heart rate safety threshold or the heart rate recovery rate is lower than the recovery threshold. The Overload Warning Unit generates a high load warning when the individual's corrected exercise load value exceeds the individual's upper limit. The Fatigue Abnormality Warning Unit generates a fatigue abnormality warning when the subjective fatigue score continuously increases. The Injury Risk Warning Unit generates an injury risk warning when both a previous sports injury record and a movement abnormality marker are present. The Individual Load Report Generation Unit generates an individual student exercise load report. The Class Load Distribution Report Generation Unit generates the number of students and group changes for different load intervals. The Teaching Objective Achievement Analysis Unit generates the teaching objective achievement results based on the target load interval and the actual individual corrected exercise load value.

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