Digital management method, platform and storage medium for campus sports

Abstract

The present invention belongs to the field of campus sports management technology, and specifically relates to a campus sports digital management method, platform and storage medium, and the method includes: S1, an intelligent sports module collects students' sports data, uploads the students' sports data to a server module for analysis and processing, and obtains analysis results about the students; S2, a teacher checks the analysis results through a teacher module, formulates sports homework for students, and the teacher module sends the sports homework to a parent module, and at the same time, the teacher enters the test scores of the students' sports items into the teacher module, and the teacher module sends the test scores to the parent module and the server module; S3, the parent enters the completion information of the student's sports homework into the parent module, and the parent module uploads the completion information to the server module, and the server module generates sports guidance information for specific students. The present invention can generate sports guidance information to help specific students exercise sports items.

Description

Technical Field

[0001] The present invention belongs to the technical field of campus sports management, and in particular relates to a campus sports digital management method, platform and storage medium. Background Art

[0002] School physical education courses can improve students' physical fitness and health level and are an indispensable part of school education. With the continuous development of computer application technology, intelligent management methods for campus sports are becoming more and more common.

[0003] A similar prior art includes a Chinese patent application with publication number CN113256466A, which discloses a physical education teaching management method for smart campus construction based on artificial intelligence. The method comprises the following steps: counting the number of students who have pre-registered for various physical education courses in campus classes, comparing and screening various physical education courses with excessive pre-registration numbers, counting the number of registered students who have studied corresponding courses in various physical education courses with excessive pre-registration numbers, and comparing them with the standard number of registered students for the corresponding physical education courses. If the number is greater than the standard number, the academic performance of each registered student who has studied the corresponding course in various physical education courses with excessive pre-registration numbers is counted, and the students are screened in sequence. If the number is less than the standard number, the gender matching degree and physical fitness test value of each remaining registered student in various physical education courses with excessive pre-registration numbers are counted, the compliance matching coefficient of each remaining registered student in various physical education courses with excessive pre-registration numbers is calculated, and the students are supplemented in sequence. In addition, similar prior art includes a Chinese patent application with publication number CN115271621A, which proposes a campus sports and health comprehensive management platform, including an AI behavior recognition system, an intelligent Internet of Things system, a big data management system, a smart operation management system, and a C-end user service system. Among them, the AI ​​behavior recognition system includes an AI motion capture perception module, an action error correction module, an AI health algorithm module, an AI motion posture recognition module, and a real-time action standard reminder module. The intelligent Internet of Things system includes a physical examination and measurement equipment docking module, an Internet of Things identification module, and a wearable smart device docking module. The big data management system includes a 2D digital visualization display module, a resource storage module, a sharing module, a statistical analysis module, and an intelligent physical measurement equipment platform management module. The smart operation management system includes a user management module, a safety design module, a backup module, and an emergency handling module for unexpected events. The C-end user service system includes a school end, a parent end, and a government end.

[0004] However, in the above patent applications, it is not possible to generate sports guidance information for students to help them better carry out sports training. Therefore, the present invention provides a campus sports digital management method, platform and storage medium. Summary of the invention

[0005] The present invention analyzes the sports data of students through a server module to obtain analysis results, and teachers view the analysis results on the teacher module, formulate sports homework for students, and teachers also enter the test scores of students' sports items into the teacher module, and parents enter the completion information of students' sports homework into the parent module, so that the server module generates sports guidance information for specific students according to the completion information and test scores of all students except the specific students. The main purpose of the present invention is to generate sports guidance information in a targeted manner to help students better carry out sports project training.

[0006] In order to achieve the above-mentioned purpose of the invention, the present invention provides the following campus sports digital management method, which mainly includes the following steps:

[0007] S1, the intelligent sports module collects the student's sports data, uploads the student's sports data to the server module, and the server module analyzes and processes the student's sports data to obtain analysis results including the student's heart rate compliance rate, consumption compliance rate, and exercise frequency compliance rate;

[0008] S2, the server module transmits the analysis result to the teacher module, the teacher checks the analysis result through the teacher module, formulates sports homework for the student, the teacher module sends the sports homework to the parent module, and the teacher enters the test score of the student's sports project into the teacher module, and the teacher module sends the test score to the parent module and the server module;

[0009] S3. Parents check the sports homework and test scores through the parent module, and enter the student's completion information of the sports homework into the parent module. The parent module uploads the completion information to the server module, and the server module generates sports guidance information for the specific student based on the completion information and test scores of all students except the specific student, and sends the sports guidance information to the teacher module and the parent module corresponding to the specific student.

[0010] As a preferred technical solution of the present invention, the completion information includes the time point, duration, frequency, and total number of times that the student performs physical exercise.

[0011] As a preferred technical solution of the present invention, the server module generates exercise guidance information for a specific student, including the following steps:

[0012] S31, the server module sets the total number of score categories based on the test scores of all students, determines the test score range corresponding to each score category, and regards each score category as a leaf node, and uses the completion information of all students to construct a classification tree model;

[0013] S32, the server module searches for a specific route that matches the completion information of a specific student in the classification tree model, the specific route starting from the tree root node and passing through a plurality of tree nodes to reach a target leaf node, and determines that the test score of the specific student is within the test score range corresponding to the score category corresponding to the target leaf node;

[0014] S33, the server module determines different students whose corresponding score categories are better than the score category corresponding to the target leaf node among all the students, performs grouping processing on the specific student and the different students according to the completion information, and the server module determines other students belonging to the same group as the specific student, selects a final score category from the score categories corresponding to the other students, and makes some students corresponding to the final score category constitute a reference group;

[0015] S34. The server module searches for a reference route starting from a tree root node and passing through a number of tree nodes to a leaf node corresponding to a final score category based on a classification tree model, and the server module obtains difference information between the reference route and the specific route, and uses the difference information to generate exercise guidance information for a specific student.

[0016] As a preferred technical solution of the present invention, other students belonging to the same group as a specific student correspond to different score categories, and the server module selects the final score category from the different score categories corresponding to other students, including taking the score category corresponding to the largest number of students as the final score category, and taking the score category corresponding to the leaf node corresponding to other students that is closest to the target leaf node in the classification tree model as the final score category.

[0017] As a preferred technical solution of the present invention, after a preset time of generating sports guidance information for a specific student, the server module determines whether the specific student's sports training situation has undergone a positive change based on the specific student's new completion information, and if no positive change has occurred, increases the total number of scoring categories.

[0018] As a preferred technical solution of the present invention, the server module performs grouping processing on specific students and different students according to the completion information, including the following steps:

[0019] S331, the server module represents the completion information of a specific student and the completion information of different students as vector data respectively, divides each vector data into any group based on the total number of preset groups, and the server module calculates the average vector data of each group according to the grouping result;

[0020] S332, the server module obtains characteristic values ​​of different average vector data and characteristic values ​​of all vector data, and for each vector data, the server module compares the characteristic value of the vector data with the characteristic values ​​of different average vector data to determine a number of pending groups to which the vector data belongs;

[0021] S333. For each vector data, the server module respectively calculates the average vector data corresponding to several pending groups to which the vector data belongs and the degree of similarity between the vector data, determines whether the vector data belongs to the pending group corresponding to the largest degree of similarity, and determines whether the execution has exceeded the preset number of times. If not, the server module calculates the average vector data of each group according to the grouping result and jumps to S332. If yes, terminates all steps.

[0022] As a preferred technical solution of the present invention, the server module compares the characteristic values ​​of the vector data with the characteristic values ​​of different average vector data to determine the several pending groups to which the vector data belongs, including: classifying the characteristic values ​​of the vector data and the characteristic values ​​of different average vector data, searching for the characteristic values ​​of several average vector data belonging to the same category as the characteristic values ​​of the vector data, and treating the groups corresponding to the several average vector data as the several pending groups to which the vector data belongs.

[0023] As a preferred technical solution of the present invention, when the server module compares the characteristic values ​​of the vector data with the characteristic values ​​of different average vector data to determine several pending groups to which the vector data belongs, if there is no characteristic value of the average vector data belonging to the same category as the characteristic value of the vector data, the server module searches for the characteristic value of the average vector data that is most similar to the characteristic value of the vector data, and regards the group corresponding to the average vector data as the pending group to which the vector data belongs.

[0024] The present invention also provides a campus sports digital management platform, including the following modules:

[0025] Intelligent sports module, used to collect students' sports data and upload the students' sports data to the server module;

[0026] The teacher module is used to enable the teacher to view the analysis results of the students including the heart rate reaching the target rate, the consumption reaching the target rate, and the exercise frequency reaching the target rate, formulate exercise homework for the students, send the exercise homework to the parent module, and store the test scores of the students' sports items, and send the test scores to the parent module and the server module;

[0027] A parent module, used to enable parents to view sports homework and test scores, and to store students' completion information on sports homework, and upload the completion information to the server module;

[0028] The server module is used to analyze and process the students' sports data to obtain analysis results about the students, and to generate sports guidance information for specific students based on the completion information and test scores of all students except the specific students, and send the sports guidance information to the teacher module and the parent module corresponding to the specific student.

[0029] The present invention also provides a storage medium, wherein the storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute any one of the above methods.

[0030] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0031] In the present invention, firstly, the intelligent motion module collects the motion data of the student, uploads the motion data of the student to the server module, the server module analyzes and processes the motion data of the student, obtains the analysis result about the student, and secondly, the server module transmits the analysis result to the teacher module, the teacher checks the analysis result through the teacher module, formulates the motion homework for the student, the teacher module sends the motion homework to the parent module, and the teacher enters the test score of the student's sports project into the teacher module at the same time, the teacher module sends the test score to the parent module and the server module, and finally the parent checks the motion homework and the test score through the parent module, enters the completion information of the student to the sports homework into the parent module, the parent module uploads the completion information to the server module, and the server module generates the motion guidance information for the specific student according to the completion information and the test score of all students except the specific student, and sends the motion guidance information to the teacher module and the parent module corresponding to the specific student. The present invention can not only generate personalized motion guidance information for the specific student, thereby helping the specific student to obtain a better exercise effect, but also can accurately predict the test score of the sports project of the specific student. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flow chart of the steps of the campus sports digital management method of the present invention;

[0033] Figure 2 This is a structural diagram of the campus sports digital management platform of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0036] The present invention provides Figure 1 The campus sports digital management method shown is mainly implemented by executing the following steps:

[0037] S1. The intelligent sports module collects the student's sports data, uploads the student's sports data to the server module, and the server module analyzes and processes the student's sports data to obtain analysis results including the student's heart rate compliance rate, consumption compliance rate, and exercise frequency compliance rate;

[0038] S2, the server module transmits the analysis results to the teacher module, the teacher checks the analysis results through the teacher module, formulates sports homework for the students, and the teacher module sends the sports homework to the parent module. At the same time, the teacher enters the test scores of the students' sports items into the teacher module, and the teacher module sends the test scores to the parent module and the server module;

[0039] S3. Parents check the sports homework and test scores through the parent module, and enter the student's completion information of the sports homework into the parent module. The parent module uploads the completion information to the server module, and the server module generates sports guidance information for the specific student based on the completion information and test scores of all students except the specific student, and sends the sports guidance information to the teacher module and the parent module corresponding to the specific student.

[0040] Specifically, in step S1, the intelligent sports module collects the student's sports data and uploads the student's sports data to the server module, wherein the intelligent sports module can be an intelligent skipping rope, which can collect and send the student's skipping rope data. The server module analyzes and processes the student's sports data to obtain analysis results about the student, and the analysis results include analysis indicators such as heart rate compliance rate, consumption compliance rate, and exercise frequency compliance rate. In step S2, the server module transmits the analysis results to the teacher module, and the teacher views the analysis results through the teacher module, so that the teacher can formulate sports homework for the students in a targeted manner based on the analysis results. The teacher module sends the sports homework to the parent module. In addition, the teacher also enters the test scores of the students' sports items into the teacher module for easy viewing at any time. It should be noted that if the student has not yet taken the test of the sports item, the teacher cannot enter his test score. The teacher module sends the test score to the parent. Module and server module, in step S3, parents check test scores through parent module, can timely understand students' learning effect on sports projects, parents can also check sports homework through parent module, conveniently supervise students to complete sports homework, parents enter students' completion information of sports homework into parent module, completion information will be described below, parent module uploads completion information to server module, above method content is all carried out for each student, on this basis, server module generates sports guidance information for specific students according to completion information and test scores of all students except specific students, specific students here can be any student, sports guidance information is sent to teacher module and specific student corresponding parent module, server module can also only send sports guidance information to teacher module, so that teacher can modify sports guidance information, teacher module sends modified sports guidance information to parent module again. By step S1 to step S3, not only can the digital management level of campus sports be improved, but also sports guidance information can be generated for students in a targeted manner, which can help students better carry out sports project learning and harvest better learning results.

[0041] Furthermore, the completion information includes the time point, duration, frequency, and total number of times that the student performs physical exercise.

[0042] Specifically, the completion information is introduced. The completion information includes data information such as the time point, time length, time frequency, and total number of times when students perform physical exercises. For example, the time point when students perform physical exercises may be in the morning or afternoon of a day, the time length is the average time it takes for students to perform physical exercises each time, and the time frequency is the average how often students perform physical exercises. For example, physical exercises can be performed once a day, and the total number of times is the total number of times students have performed physical exercises from the beginning of learning sports to the current time. The completion information not only includes the above-mentioned data information, but may also include other data information according to the actual application scenario.

[0043] Furthermore, the server module generates sports guidance information for specific students, including the following steps:

[0044] S31, the server module sets the total number of score categories based on the test scores of all students, determines the test score range corresponding to each score category, and regards each score category as a leaf node, and constructs a classification tree model using the completion information of all students;

[0045] S32, the server module searches for a specific route that matches the completion information of a specific student in the classification tree model, the specific route starting from the tree root node and passing through a plurality of tree nodes to reach a target leaf node, and determines that the test score of the specific student is within the test score range corresponding to the score category corresponding to the target leaf node;

[0046] S33, the server module determines different students whose corresponding score categories are better than the score category corresponding to the target leaf node among all the students, and performs grouping processing on the specific student and the different students according to the completion information, and the server module determines other students belonging to the same group as the specific student, and selects a final score category from the score categories corresponding to the other students, so that some students corresponding to the final score category constitute a reference group;

[0047] S34. The server module searches for a reference route starting from the root node and passing through several tree nodes to reach a leaf node corresponding to the final score category based on the classification tree model, and the server module obtains difference information between the reference route and the specific route, and uses the difference information to generate sports guidance information for specific students.

[0048] Specifically, in step S31, the server module sets the total number of score categories according to the test scores of all students. Here, all students refer to all students except specific students. The specific method can be to first set a total number threshold, and then set a total number less than the total number threshold in combination with the detailed situation of the test scores of all students. The more the total number, the more detailed the sports guidance information finally generated may be. The test score range corresponding to each score category is also determined. For example, if the total number is 4, then the test score range corresponding to score category 1 is greater than or equal to 0 to less than 25, the test score range corresponding to score category 2 is greater than or equal to 25 to less than 50, the test score range corresponding to score category 3 is greater than or equal to 50 to less than 75, and the test score range corresponding to score category 4 is greater than or equal to 75 to less than or equal to 100. Then, each score category is regarded as a leaf node, and a classification tree model is constructed using the completion information of all students. Specifically, the classification tree model can be constructed using existing technology, which will not be described in detail.

[0049] Here, the constructed classification tree model is explained by way of example. The structure of the classification tree model can be a binary tree. The classification tree model is a four-layer structure, that is, the depth of the binary tree is 4. The first layer structure of the classification tree model includes a root node, the second layer structure of the classification tree model includes a left tree node A and a right tree node B, the third layer structure of the classification tree model includes a left leaf node 1 and a right leaf node 2 under the left tree node A, a left leaf node 3 and a right tree node C under the right tree node B, and the fourth layer structure of the classification tree model includes a leaf node 4 under the right tree node C, wherein the root node corresponds to a data information contained in the completion information, for example, the root node corresponds to the average time taken by the students to perform sports exercises each time, if the time exceeds half an hour, the characterization value corresponding to the branch connecting the root node and the left tree node A is 0, if the time does not exceed half an hour, the characterization value corresponding to the branch connecting the root node and the right tree node B is 1, and so on, each tree node also corresponds to a data information contained in the completion information, in addition, leaf nodes 1 to 4 correspond to score categories 1 to 4 respectively;

[0050] In step S32, the server module searches for a specific route that matches the completion information of a specific student in the classification tree model, that is, based on the root node and the tree nodes in the classification tree model, determines the corresponding data information in the completion information of the student, and determines the specific route according to the content of the corresponding data information. The specific route starts from the root node and passes through several tree nodes to reach the target leaf node. The test score range corresponding to the score category corresponding to the target leaf node is the test score of the specific student. In this embodiment, it is considered that the teacher has not entered the test scores of the sports items of the specific student into the teacher module. In step S33, the server module determines different students whose corresponding score categories are better than the score categories corresponding to the target leaf nodes among all the students. For ease of understanding, for example, score category 2 is better than score category 1, because the test score range corresponding to score category 1 is greater than or equal to 0 to less than 25, and the test score range corresponding to score category 2 is greater than or equal to 25 to less than 50. Then, the specific student and different students are grouped according to the completion information. The grouping process will be described below. The server module determines other students who belong to the same group as the specific student. A final score category is selected from different score categories corresponding to other students. The selection process will also be described below, so that some students corresponding to the final score category constitute a reference group, that is, the test scores of some students are within the test score range corresponding to the final score category. In step S34, the server module searches for a reference route starting from the root node and passing through several tree nodes to reach the leaf node corresponding to the final score category in the classification tree model, and obtains the difference information between the reference route and the specific route. The difference information refers to the difference between all the characterization values ​​corresponding to all branches on the reference route and all the characterization values ​​corresponding to all branches on the specific route. For example, all the characterization values ​​corresponding to all branches on the reference route are "0, 1" in sequence, and all the characterization values ​​corresponding to all branches on the specific route are "1, 0" in sequence, then the difference information is "0, 1", because "0" corresponds to the root node, which means that the average time spent by students on physical exercise is more than half an hour each time, and "1" corresponds to a tree node, which means that students exercise once a day on average, so the difference information can be conveniently used to generate sports guidance information for specific students. The above method can not only estimate the test score of a specific student based on his / her completion information, but also automatically generate exercise guidance information for the specific student.

[0051] Furthermore, other students belonging to the same group as the specific student correspond to different score categories, and the server module selects the final score category from the different score categories corresponding to other students, including taking the score category corresponding to the largest number of students as the final score category, and taking the score category corresponding to the leaf node corresponding to other students that is closest to the target leaf node in the classification tree model as the final score category.

[0052] Specifically, other students belonging to the same group as the specific student correspond to different scoring categories, for example, the scoring categories corresponding to other students include scoring category 3 and scoring category 4, and the server module needs to select the final scoring category from the different scoring categories corresponding to other students. The selection process can be to use the scoring category with the largest number of corresponding students as the final scoring category, such as the number of students corresponding to scoring category 3 is 100, and the number of students corresponding to scoring category 4 is 150, then scoring category 4 is selected as the final scoring category, and the selection process can also be to use the scoring category corresponding to the leaf node corresponding to other students that is closest to the target leaf node in the classification tree model as the final scoring category, such as from the target leaf node to the leaf node corresponding to scoring category 3, it needs to pass through 3 nodes, including the root node and the tree node, from the target leaf node to the leaf node corresponding to scoring category 4, it needs to pass through 4 nodes, also including the root node and the tree node, then scoring category 3 is selected as the final scoring category. Through the above method, a final scoring category can be selected for generating sports guidance information, which can limit the number of sports guidance information, avoid generating multiple sports guidance information, and make it difficult for students to select from multiple sports guidance information.

[0053] Furthermore, after a preset time of generating sports guidance information for a specific student, the server module determines whether the specific student's sports training situation has undergone a positive change based on the specific student's new completion information. If no positive change has occurred, the total number of scoring categories is increased.

[0054] Specifically, when generating the sports guidance information for a specific student, the total number of the above-mentioned scoring categories is not fixed. After the preset time of generating the sports guidance information for the specific student, that is, after the student receives the sports guidance information and performs the sports training for the preset time, according to the new completion information sent by the parent module, it is judged whether the sports training situation of the specific student has changed for the better. If the sports guidance information suggests that the average time required for the student to perform sports training is more than half an hour each time, and the corresponding data information contained in the new completion information is indeed that the average time required for the student to perform sports training is more than half an hour each time, then it is judged that the sports training situation of the specific student has changed for the better. Otherwise, it is judged that the sports training situation of the specific student has not changed for the better. In the case of no good change, it is explained that the effect of the generated sports guidance information is not good, and the total number of scoring categories can be increased. Since the number of tree nodes in the classification tree model needs to be increased while increasing the total number of scoring categories, the tree nodes correspond to the data information contained in the completion information, so that the sports guidance information may become more detailed. Through the above method, in the case of the poor effect of the generated sports guidance information, more detailed sports guidance information can be generated.

[0055] Furthermore, the server module performs grouping processing on specific students and different students according to the completion information, including the following steps:

[0056] S331, the server module represents the completion information of a specific student and the completion information of different students as vector data respectively, divides each vector data into any group based on the total number of preset groups, and the server module calculates the average vector data of each group according to the grouping result;

[0057] S332, the server module obtains characteristic values ​​of different average vector data and characteristic values ​​of all vector data, and for each vector data, the server module compares the characteristic value of the vector data with the characteristic values ​​of different average vector data to determine a number of pending groups to which the vector data belongs;

[0058] S333. For each vector data, the server module respectively calculates the average vector data corresponding to several pending groups to which the vector data belongs and the degree of similarity between the vector data, determines whether the vector data belongs to the pending group corresponding to the largest degree of similarity, and determines whether the execution has exceeded the preset number of times. If not, the server module calculates the average vector data of each group according to the grouping result and jumps to S332. If yes, terminates all steps.

[0059] Specifically, in step S331, the server module represents the completion information of a specific student and the completion information of different students as vector data respectively. The completion information here specifically refers to the data information contained in the completion information that does not participate in building the classification tree model. One data information corresponds to a one-dimensional value in the vector data. For ease of understanding, for example, a data information is a time point when a student performs physical exercise. When the student performs physical exercise in the morning, the corresponding value in the vector data is set to 1. When the student performs physical exercise in the afternoon, the corresponding value in the vector data is set to 0. Then, based on the total number of preset groups, the total number of groups can be set in the actual application scenario, and each vector data is divided into any group. The average vector data of each group is calculated according to the grouping result. In step S332, the server module obtains the characteristic values ​​of different average vector data and the characteristic values ​​of all vector data. The method for obtaining the characteristic values ​​can be the existing The local sensitive hash algorithm in the technology, so that when the vector data is similar, the generated characteristic value is also similar. For each vector data, the server module compares the characteristic value of the vector data with the characteristic value of different average vector data, and determines the several pending groups to which the vector data belongs. The specific method will be described below. In step S333, for each vector data, the server module respectively calculates the average vector data corresponding to the several pending groups to which the vector data belongs and the same degree value of the vector data. The same degree value can be obtained by calculating the distance between the average vector data and the vector data. The smaller the distance, the larger the same degree value. It is determined that the vector data belongs to the pending group corresponding to the largest same degree value. Then the server module determines whether the above method content has been executed more than the preset number of times. If not, the average vector data of each group is recalculated according to the grouping result, and the step S332 is jumped to continue execution. If yes, all steps are terminated, and the grouping result of each vector data is determined at the same time. Through the above method, the test scores of other students belonging to the same group as a specific student are higher than the test scores of the specific student, and the exercise situation is similar to that of the specific student.

[0060] Furthermore, the server module compares the characteristic values ​​of the vector data with the characteristic values ​​of different average vector data to determine the several pending groups to which the vector data belongs, including: classifying the characteristic values ​​of the vector data and the characteristic values ​​of different average vector data, searching for characteristic values ​​of several average vector data belonging to the same category as the characteristic values ​​of the vector data, and treating the groups corresponding to the several average vector data as the several pending groups to which the vector data belongs.

[0061] Specifically, here is introduced a process in which a server module compares the characteristic values ​​of vector data with the characteristic values ​​of different average vector data to determine several pending groups to which the vector data belongs. First, the characteristic values ​​of the vector data and the characteristic values ​​of different average vector data are classified. The classification process can use the classification algorithm in the prior art. Secondly, the characteristic values ​​of several average vector data belonging to the same category as the characteristic values ​​of the vector data are found, and the groups corresponding to these several average vector data are regarded as several pending groups to which the vector data belongs.

[0062] Furthermore, when the server module compares the characteristic values ​​of the vector data with the characteristic values ​​of different average vector data to determine several pending groups to which the vector data belongs, if there is no characteristic value of the average vector data belonging to the same category as the characteristic value of the vector data, the server module searches for the characteristic value of the average vector data that is most similar to the characteristic value of the vector data, and regards the group corresponding to the average vector data as the pending group to which the vector data belongs.

[0063] Specifically, when the server module compares the characteristic values ​​of the vector data with the characteristic values ​​of different average vector data to determine several pending groups to which the vector data belongs, if there is no characteristic value of the average vector data that belongs to the same category as the characteristic value of the vector data, the server module can search for the characteristic value of the average vector data that is most similar to the characteristic value of the vector data. The most similar can be understood as the smallest difference between the characteristic value of the vector data, and the group corresponding to the found average vector data is regarded as the pending group to which the vector data belongs.

[0064] According to another aspect of the embodiment of the present invention, Figure 2 As shown, a campus sports digital management platform is also provided, including an intelligent sports module, a teacher module, a parent module, and a server module, which is used to implement the campus sports digital management method described above. The functions of each module are as follows:

[0065] Intelligent sports module, used to collect students' sports data and upload the students' sports data to the server module;

[0066] The teacher module is used to enable the teacher to view the analysis results of the students including the heart rate reaching the target rate, the consumption reaching the target rate, and the exercise frequency reaching the target rate, formulate exercise homework for the students, send the exercise homework to the parent module, and store the test scores of the students' sports items, and send the test scores to the parent module and the server module;

[0067] A parent module, used to enable parents to view sports homework and test scores, and to store students' completion information on sports homework, and upload the completion information to the server module;

[0068] The server module is used to analyze and process the students' sports data to obtain analysis results about the students, and to generate sports guidance information for specific students based on the completion information and test scores of all students except the specific students, and send the sports guidance information to the teacher module and the parent module corresponding to the specific student.

[0069] In summary, in the present invention, firstly, the intelligent motion module collects the motion data of the student, uploads the motion data of the student to the server module, the server module analyzes and processes the motion data of the student, obtains the analysis result about the student, and secondly, the server module transmits the analysis result to the teacher module, the teacher checks the analysis result through the teacher module, formulates the motion homework for the student, the teacher module sends the motion homework to the parent module, and the teacher enters the test score of the student's sports project into the teacher module at the same time, the teacher module sends the test score to the parent module and the server module, and finally the parent checks the motion homework and the test score through the parent module, enters the completion information of the student to the sports homework into the parent module, the parent module uploads the completion information to the server module, and the server module generates the motion guidance information for the specific student according to the completion information and the test score of all students except the specific student, and sends the motion guidance information to the teacher module and the parent module corresponding to the specific student. The present invention can not only generate personalized motion guidance information for the specific student, thereby helping the specific student to obtain a better exercise effect, but also accurately predict the test score of the specific student's sports project.

[0070] According to another aspect of an embodiment of the present invention, a storage medium is provided, wherein the storage medium stores program instructions, wherein when the program instructions are executed, a device where the storage medium is located is controlled to execute any one of the above methods.

[0071] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0072] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0073] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0075] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A campus sports digital management method, applied to a campus sports digital management platform, including an intelligent sports module, a teacher module, a parent module, and a server module, characterized in that: The method comprises the following steps: S1, the intelligent sports module collects the student's sports data, uploads the student's sports data to the server module, and the server module analyzes and processes the student's sports data to obtain analysis results including the student's heart rate compliance rate, consumption compliance rate, and exercise frequency compliance rate; S2, the server module transmits the analysis result to the teacher module, the teacher checks the analysis result through the teacher module, formulates sports homework for the student, the teacher module sends the sports homework to the parent module, and the teacher enters the test score of the student's sports project into the teacher module, and the teacher module sends the test score to the parent module and the server module; S3, the parent checks the sports homework and test scores through the parent module, enters the student's completion information of the sports homework into the parent module, the parent module uploads the completion information to the server module, and the server module generates sports guidance information for the specific student based on the completion information and test scores of all students except the specific student, and sends the sports guidance information to the teacher module and the parent module corresponding to the specific student; Completion information includes the time, duration, frequency, and total number of times students performed physical exercise; The server module generates sports guidance information for a specific student, including the following steps: S31, the server module sets the total number of score categories based on the test scores of all students, determines the test score range corresponding to each score category, and regards each score category as a leaf node, and uses the completion information of all students to construct a classification tree model; S32, the server module searches for a specific route that matches the completion information of a specific student in the classification tree model, the specific route starting from the tree root node and passing through a plurality of tree nodes to reach a target leaf node, and determines that the test score of the specific student is within the test score range corresponding to the score category corresponding to the target leaf node; S33, the server module determines different students whose corresponding score categories are better than the score category corresponding to the target leaf node among all the students, performs grouping processing on the specific student and the different students according to the completion information, and the server module determines other students belonging to the same group as the specific student, selects a final score category from the score categories corresponding to the other students, and makes some students corresponding to the final score category constitute a reference group; S34. The server module searches for a reference route starting from a tree root node and passing through a number of tree nodes to a leaf node corresponding to a final score category based on a classification tree model, and the server module obtains difference information between the reference route and the specific route, and uses the difference information to generate exercise guidance information for a specific student.

2. The method according to claim 1, characterized in that Other students belonging to the same group as the specific student correspond to different score categories, and the server module selects the final score category from the different score categories corresponding to other students, including taking the score category corresponding to the largest number of students as the final score category, and taking the score category corresponding to the leaf node corresponding to other students that is closest to the target leaf node in the classification tree model as the final score category.

3. The method according to claim 2, characterized in that After a preset time of generating sports guidance information for a specific student, the server module determines whether the specific student's sports training situation has undergone a positive change based on the specific student's new completion information. If no positive change has occurred, the total number of score categories is increased.

4. The method according to claim 3, characterized in that The server module performs grouping processing on specific students and different students according to the completion information, including the following steps: S331, the server module represents the completion information of a specific student and the completion information of different students as vector data respectively, divides each vector data into any group based on the total number of preset groups, and the server module calculates the average vector data of each group according to the grouping result; S332, the server module obtains characteristic values ​​of different average vector data and characteristic values ​​of all vector data, and for each vector data, the server module compares the characteristic value of the vector data with the characteristic values ​​of different average vector data to determine a number of pending groups to which the vector data belongs; S333. For each vector data, the server module respectively calculates the average vector data corresponding to several pending groups to which the vector data belongs and the degree of similarity between the vector data, determines whether the vector data belongs to the pending group corresponding to the largest degree of similarity, and determines whether the execution has exceeded the preset number of times. If not, the server module calculates the average vector data of each group according to the grouping result and jumps to S332. If yes, terminates all steps.

5. The method according to claim 4, characterized in that The server module compares the characteristic values ​​of the vector data with the characteristic values ​​of different average vector data to determine the several pending groups to which the vector data belongs, including: classifying the characteristic values ​​of the vector data and the characteristic values ​​of different average vector data, searching for characteristic values ​​of several average vector data belonging to the same category as the characteristic values ​​of the vector data, and treating the groups corresponding to the several average vector data as the several pending groups to which the vector data belongs.

6. The method according to claim 5, characterized in that When the server module compares the characteristic values ​​of the vector data with the characteristic values ​​of different average vector data to determine several pending groups to which the vector data belongs, if there is no characteristic value of the average vector data belonging to the same category as the characteristic value of the vector data, the server module searches for the characteristic value of the average vector data that is most similar to the characteristic value of the vector data, and regards the group corresponding to the average vector data as the pending group to which the vector data belongs.

7. A campus sports digital management platform, used to implement the method according to any one of claims 1 to 6, characterized in that: Includes the following modules: Intelligent sports module, used to collect students' sports data and upload the students' sports data to the server module; The teacher module is used to enable the teacher to view the analysis results of the students including the heart rate reaching the target rate, the consumption reaching the target rate, and the exercise frequency reaching the target rate, formulate exercise homework for the students, send the exercise homework to the parent module, and store the test scores of the students' sports items, and send the test scores to the parent module and the server module; A parent module, used to enable parents to view sports homework and test scores, and to store students' completion information on sports homework, and upload the completion information to the server module; The server module is used to analyze and process the students' sports data to obtain analysis results about the students, and to generate sports guidance information for specific students based on the completion information and test scores of all students except the specific students, and send the sports guidance information to the teacher module and the parent module corresponding to the specific student.

8. A storage medium, characterized in that: The storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

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