University sports and health education teaching quality analysis system

Abstract

The invention belongs to the technical field of physical education teaching management, and provides a university physical education and health education teaching quality analysis system which comprises an input statistics module, a personalized exercise module, a common project module, a comparative analysis module, an improvement module, an educational administration system module, an exercise review module and a doctor feedback module. By designing a plurality of functional modules such as an input statistics module, a personalized exercise module, a general project module, a comparative analysis module, an improvement module, an educational administration system module, an exercise review module and a doctor feedback module, comprehensive input, personalized analysis, intelligent improvement and health management of student exercise conditions are realized; the modules work cooperatively, so that the accuracy and efficiency of data analysis are improved, more scientific and effective exercise guidance and improvement schemes are provided for students, potential exercise injury risks can be found and prevented in time, the physical health of the students is guaranteed, and teaching management work is more efficient and convenient.

Description

Technical Field

[0001] The invention belongs to the technical field of physical education teaching management, in particular to a university physical education and health education teaching quality analysis system. Background Art

[0002] In the current university sports and health education teaching environment, the evaluation and management of teaching quality still face many challenges. The existing sports teaching management system also has deficiencies in data processing and analysis, such as inaccurate data analysis, insufficient grasp of data trends, and weak data correlation, which directly affect the accuracy and effectiveness of teaching quality analysis.

[0003] To this end, those skilled in the art have proposed a university physical education and health education teaching quality analysis system to solve the problems raised by the background technology. Summary of the invention

[0004] In order to solve the above-mentioned technical problems, the present invention provides a university physical education and health education teaching quality analysis system to solve the deficiencies in data processing and analysis of the physical education teaching management system in the prior art, such as inaccurate data analysis, insufficient grasp of data trends, weak data correlation, etc., which directly affect the accuracy and effectiveness of teaching quality analysis.

[0005] A university physical education and health education teaching quality analysis system, comprising:

[0006] Input statistics module, used to input the sports situation of each student;

[0007] Personalized sports module, used to collect students' heart rate values;

[0008] Common project module, used to manage data of common sports projects;

[0009] A comparative analysis module is used to receive and compare different data line charts;

[0010] Improvement module, used to generate improvement plans based on comparative analysis results;

[0011] The teaching system module is used to receive and manage students’ sports performance and physical condition data;

[0012] The exercise review module is used to record the pain location and pain time caused by the corresponding sports after the students exercise;

[0013] The doctor feedback module is used to receive students' pain information and provide treatment feedback, and generate sports files for individual students' corresponding projects.

[0014] Preferably, in the personalized sports module, the student's heart rate value is collected by a heart rate collection device, and a Kalman filter algorithm is introduced to reduce noise interference to improve the stability of heart rate collection.

[0015] Preferably, in the personalized sports module, in order to improve the accuracy of heart rate data analysis, a dynamic time warping (DTW) algorithm is introduced to compare and analyze the heart rate change trends of different students.

[0016] Preferably, in the comparative analysis module, in order to more accurately analyze data trends and differences, the Pearson correlation coefficient is introduced to calculate the correlation or similarity between different data.

[0017] Preferably, in the improvement module, in order to generate an improvement plan more intelligently, a decision tree algorithm is introduced to classify and analyze data, thereby generating targeted improvement suggestions.

[0018] Preferably, in the sports review module and the doctor feedback module, in order to prevent sports injuries, a Naive Bayes algorithm is introduced to establish a sports injury prediction model.

[0019] Preferably, it also includes a sports feedback module for recording the sports events and pain conditions corresponding to boys and girls of the same age group, and transmitting the information to the teaching system module and the doctor feedback module for comparative analysis.

[0020] A processor is configured to execute the above-mentioned university sports and health education teaching quality analysis system.

[0021] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned university sports and health education teaching quality analysis system.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The present invention realizes the comprehensive entry and personalized analysis of students' sports conditions through the design of multiple modules such as the input statistics module, the personalized sports module, and the common project module; among them, the personalized sports module collects the students' heart rate values ​​and introduces the Kalman filter algorithm and the dynamic time warping algorithm to improve the stability and accuracy of heart rate data collection, thereby providing students with more personalized sports guidance.

[0024] 2. The present invention introduces the Pearson correlation coefficient in the comparative analysis module to calculate the correlation or similarity between different data, so as to more accurately analyze data trends and differences; it helps teachers to more accurately grasp the students' exercise status, discover potential problems in time, and provide strong support for teaching improvement.

[0025] 3. The present invention introduces a decision tree algorithm in the improvement module to classify and analyze data, thereby generating targeted improvement suggestions; this intelligent improvement method not only improves the efficiency and accuracy of teaching improvement, but also provides students with a more scientific and effective sports improvement plan.

[0026] 4. The present invention introduces the Naive Bayes algorithm into the sports review module and the doctor feedback module to establish a sports injury prediction model; it helps to timely discover and prevent potential sports injury risks and ensure the physical health of students.

[0027] 5. The present invention realizes comprehensive management of students' sports performance and physical condition data through the teaching system module, making teaching management more efficient and convenient; at the same time, the design of the sports feedback module also provides students with a feedback channel, which helps to collect students' opinions and suggestions and further improve the teaching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a framework diagram of the university physical education and health education teaching quality analysis system of the present invention. DETAILED DESCRIPTION

[0029] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0030] Embodiment: The present invention provides a university sports and health education teaching quality analysis system, such as Figure 1 As shown, including:

[0031] Input statistics module, used to input the sports situation of each student;

[0032] Personalized sports module, used to collect students' heart rate values;

[0033] Common project module, used to manage data of common sports projects;

[0034] A comparative analysis module is used to receive and compare different data line charts;

[0035] Improvement module, used to generate improvement plans based on comparative analysis results;

[0036] The teaching system module is used to receive and manage students’ sports performance and physical condition data;

[0037] The exercise review module is used to record the pain location and pain time caused by the corresponding sports after the students exercise;

[0038] The doctor feedback module is used to receive students' pain information and provide treatment feedback, and generate sports files for individual students' corresponding projects.

[0039] From the above, we can see that by designing multiple functional modules such as input statistics module, personalized sports module, common project module, comparative analysis module, improvement module, teaching system module, sports review module and doctor feedback module, comprehensive input, personalized analysis, intelligent improvement and health management of students' sports conditions are achieved; these modules work together, which not only improves the accuracy and efficiency of data analysis, but also provides students with more scientific and effective sports guidance and improvement plans, while helping to timely discover and prevent potential sports injury risks, ensuring students' physical health, and making teaching management more efficient and convenient.

[0040] Furthermore, in the personalized sports module, the heart rate value of the student is collected by a heart rate collection device, and a Kalman filter algorithm is introduced to reduce noise interference to improve the stability of heart rate collection. The formula of the Kalman filter algorithm is as follows:

[0041] x k =A k x k-1 +B k u k-1 +w k-1 ;

[0042] z k =H k x k +v k ;

[0043] Among them, x k is the current state vector, A k is the state transfer matrix, B k is the control matrix, u k-1 is the control vector, w k-1 is the process noise, z k is the observation vector, H k is the observation matrix, v k is the observation noise.

[0044] From the above, we can see that by collecting students' heart rate values ​​through heart rate acquisition equipment and introducing the Kalman filter algorithm to reduce noise interference, the stability of heart rate acquisition can be significantly improved; it can ensure the accuracy and reliability of heart rate data, thereby providing students with more precise and personalized exercise guidance, avoiding unreasonable exercise plans or increased risk of sports injuries due to data errors; at the same time, this also provides a solid foundation for subsequent data analysis and improvement.

[0045] Furthermore, in the personalized sports module, in order to improve the accuracy of heart rate data analysis, the dynamic time warping (DTW) algorithm is introduced to compare and analyze the heart rate change trends of different students. The formula of the dynamic time warping algorithm is as follows:

[0046]

[0047] Among them, X and Y are two time series, A is a collection of time maps, and d(x i ,y ai ) is the distance between two points and N is the length of the time series.

[0048] From the above, we can see that in the personalized exercise module, the dynamic time warping (DTW) algorithm is introduced to compare and analyze the heart rate change trends of different students, which greatly improves the accuracy of heart rate data analysis; the algorithm can flexibly handle the nonlinear changes of time series data and accurately calculate the similarity between the heart rate data of different students, which helps to have a deeper understanding of the heart rate characteristics of students and provides strong support for the formulation of more accurate and personalized exercise guidance plans.

[0049] Furthermore, in the comparative analysis module, in order to more accurately analyze data trends and differences, the Pearson correlation coefficient is introduced to calculate the correlation or similarity between different data. The formula of the Pearson correlation coefficient is as follows:

[0050]

[0051] Among them, x i and i are the i-th values ​​in the two data sets, respectively. and are the means of the two data sets, and n is the number of data points.

[0052] From the above, we can see that in the comparative analysis module, the introduction of the Pearson correlation coefficient to calculate the correlation or similarity between different data can more accurately analyze data trends and differences; it helps teachers to more accurately grasp the students' exercise status, discover potential problems in time, and provide strong support for teaching improvement; through the Pearson correlation coefficient, teachers can quantify the relationship between different data, so as to formulate teaching strategies and exercise plans more scientifically, and further improve teaching quality and effectiveness.

[0053] Furthermore, in the improvement module, in order to generate an improvement plan more intelligently, a decision tree algorithm is introduced to classify and analyze data, thereby generating targeted improvement suggestions. The formula of the decision tree algorithm is as follows:

[0054]

[0055] Where D is the dataset, K is the number of categories, and p k is the probability of the kth category.

[0056] From the above, we can see that in the improvement module, the decision tree algorithm is introduced to classify and analyze data, so that targeted improvement suggestions can be generated more intelligently; the system can automatically mine the rules and patterns in the data set and provide customized sports improvement plans for each student; through the decision tree algorithm, the system can clearly display the process and results of data classification, so that teachers can more easily understand the students' sports status and formulate more accurate and effective teaching strategies based on this, thereby further improving the quality and personalization of physical education teaching.

[0057] Furthermore, in the sports review module and the doctor feedback module, in order to prevent sports injuries, the Naive Bayes algorithm can be introduced to establish a sports injury prediction model. The formula of the Naive Bayes algorithm is as follows:

[0058]

[0059] Where C is the category, X is the feature vector, P(C|X) is the posterior probability, P(X|C) is the likelihood probability, P(C) is the prior probability, and P(X) is the probability of the feature vector.

[0060] From the above, we can see that introducing the naive Bayes algorithm to establish a sports injury prediction model in the sports review module and the doctor feedback module can effectively prevent the occurrence of sports injuries; the algorithm can accurately predict the potential sports injury risks by analyzing the students' sports data and physical condition, and provide timely warnings and interventions for teachers and students; it helps to protect the physical health of students, improve the safety and effectiveness of physical education, and further promote the scientific and intelligent development of physical education.

[0061] Furthermore, it also includes a sports feedback module, which is used to input the sports items and pain conditions corresponding to boys and girls of the same age group, and transmit the information to the teaching system module and the doctor feedback module for comparative analysis. The clustering algorithm (including the K-means algorithm) is introduced for data analysis. The formula of the clustering algorithm is as follows:

[0062]

[0063] Among them, J is the clustering loss function, K is the number of clusters, and C i is the i-th cluster, x is the cluster C i The data points in μ i It is cluster C i The mean vector of .

[0064] From the above, we can see that analyzing these data through clustering algorithms (such as K-means algorithm) can help discover the commonalities and differences in sports selection and pain conditions among students of different genders, and provide a more detailed and targeted comparative analysis basis for the teaching system module and the doctor feedback module; through the clustering algorithm, the system can automatically classify similar data, making the analysis process more efficient and accurate, and providing strong support for further optimizing physical education teaching strategies and enhancing students' sports experience.

[0065] Working principle: The system works together through multiple functional modules, including statistical input module, personalized sports module, common project module, comparative analysis module, improvement module, teaching system module, sports review module, doctor feedback module and sports feedback module; each module collects, processes and analyzes students' sports data, heart rate values, physical conditions, pain after exercise and other information, and uses a variety of algorithms (such as Kalman filtering, dynamic time warping, Pearson correlation coefficient, decision tree and naive Bayes, etc.) for data processing and prediction, and finally generates personalized sports guidance and improvement plans to improve the quality and effectiveness of physical education teaching, while preventing the occurrence of sports injuries.

[0066] Furthermore, the university sports and health education teaching quality analysis system of the embodiment is compared with the existing sports teaching management system (comparative example), and the following table is obtained:

[0067]

[0068]

[0069] It can be seen from the above table that the university sports and health education teaching quality analysis system of this embodiment is superior to the current existing technology in terms of data entry and management, personalized analysis, data comparison analysis, intelligent improvement, sports injury prevention, feedback and optimization, and overall effect. The system is widely promoted and applied.

[0070] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned university sports and health education teaching quality analysis system, including:

[0071] Memory, used to protect computer programs and data;

[0072] Processor, used to run system programs.

[0073] The embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned university sports and health education teaching quality analysis system, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0074] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0075] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0079] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0080] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0081] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0082] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A university physical education and health education teaching quality analysis system, characterized by: include: Input statistics module, used to input the sports situation of each student; Personalized sports module, used to collect students' heart rate values; Common project module, used to manage data of common sports projects; A comparative analysis module is used to receive and compare different data line charts; Improvement module, used to generate improvement plans based on comparative analysis results; The teaching system module is used to receive and manage students’ sports performance and physical condition data; The exercise review module is used to record the pain location and pain time caused by the corresponding sports after the students exercise; The doctor feedback module is used to receive students' pain information and provide treatment feedback, and generate sports files for individual students' corresponding projects.

2. A university physical education and health education teaching quality analysis system as claimed in claim 1, characterized in that: In the personalized sports module, students' heart rate values ​​are collected through heart rate acquisition equipment, and the Kalman filter algorithm is introduced to reduce noise interference.

3. A university physical education and health education teaching quality analysis system as claimed in claim 1, characterized in that: In the personalized sports module, in order to improve the accuracy of heart rate data analysis, a dynamic time warping algorithm is introduced to compare and analyze the heart rate change trends of different students.

4. A university physical education and health education teaching quality analysis system as claimed in claim 1, characterized in that: In the comparative analysis module, in order to more accurately analyze data trends and differences, the Pearson correlation coefficient is introduced to calculate the correlation or similarity between different data.

5. A university physical education and health education teaching quality analysis system as claimed in claim 1, characterized in that: In the improvement module, in order to generate improvement plans more intelligently, a decision tree algorithm is introduced to classify and analyze data, thereby generating targeted improvement suggestions.

6. A university physical education and health education teaching quality analysis system as claimed in claim 1, characterized in that: In the sports review module and doctor feedback module, in order to prevent sports injuries, the naive Bayes algorithm is introduced to establish a sports injury prediction model.

7. A university physical education and health education teaching quality analysis system as claimed in claim 1, characterized in that: It also includes a sports feedback module, which is used to record the sports events and pain conditions corresponding to boys and girls of the same age group, and transmit the information to the teaching system module and the doctor feedback module for comparative analysis.

8. A processor, characterized in that: The system is configured to execute a university sports and health education teaching quality analysis system according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a university sports and health education teaching quality analysis system as described in any one of claims 1 to 7 is implemented.

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