CN114612066A - Sports teaching management system based on big data - Google Patents
Sports teaching management system based on big data Download PDFInfo
- Publication number
- CN114612066A CN114612066A CN202210229420.1A CN202210229420A CN114612066A CN 114612066 A CN114612066 A CN 114612066A CN 202210229420 A CN202210229420 A CN 202210229420A CN 114612066 A CN114612066 A CN 114612066A
- Authority
- CN
- China
- Prior art keywords
- user
- teaching
- instructor
- management system
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 230000033001 locomotion Effects 0.000 claims abstract description 45
- 238000012797 qualification Methods 0.000 claims abstract description 23
- 238000011156 evaluation Methods 0.000 claims description 21
- 238000000034 method Methods 0.000 claims description 20
- 238000012937 correction Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 230000004913 activation Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 3
- 150000001875 compounds Chemical class 0.000 claims description 3
- 230000007423 decrease Effects 0.000 claims description 2
- 238000002372 labelling Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 4
- 230000009471 action Effects 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 210000003414 extremity Anatomy 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 2
- 230000033764 rhythmic process Effects 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 210000001015 abdomen Anatomy 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000000386 athletic effect Effects 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 210000000038 chest Anatomy 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011157 data evaluation Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 230000004886 head movement Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000004118 muscle contraction Effects 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
- 210000000707 wrist Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Entrepreneurship & Innovation (AREA)
- General Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Physical Education & Sports Medicine (AREA)
- Epidemiology (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a physical education management system based on big data, which identifies the identity information of a user through a user module, and identifies the intensity, duration and motion items of the user during motion after the user starts to move; furthermore, the accuracy degree of the motion of the user is identified, so that the demand index of the user for the physical education is calculated; furthermore, the management system sorts a plurality of instructors according to the teaching qualification, and preferentially arranges the instructors with higher teaching qualification to perform motion teaching guidance on the user according to the user sorting sequence after the demand index of the user reaches a specified threshold value. The management system sequences and matches users and instructors in the sports field, improves the management efficiency of personnel, and ensures the satisfaction degree of the users to achieve the physical exercise effect.
Description
Technical Field
The invention relates to the technical field of management systems. In particular to a physical education teaching management system based on big data.
Background
As a method for directly improving the personal health and strengthening the physique in sports, the attention of the masses to physical exercise is gradually increased. It is seen that in urban construction, the number of built or built fitness places is gradually increased, and sufficient fields and management conditions are provided for people to continuously perform physical exercises. However, the current group, which is largely implemented freely and lacks scientific knowledge about sports activities, has many wrong or inefficient exercise patterns, and if teaching coaching by professional trainers can be configured at appropriate times, the exercise effect can be obviously improved.
With the application development of the big data technology, the big data technology is used for recording and analyzing the appearance information in the physical exercise, so that a large amount of manpower can be saved for recording and calculating; the final purpose is to use the big data as the reference of statistical analysis and give the relevant departments the overall investment and setting of physical exercise and teaching, so as to carry out more scientific and reasonable guidance strategies, and make the physical exercise more general and more productive.
According to the related published technical scheme, the technical scheme with the publication number of CN113011760(A) provides an education system evaluation method based on TOPSIS, entropy weight method and factor analysis, and the evaluation methods of various education systems are simplified and subjected to data evaluation through the statistical method, so that an optimized education system is found; the technical solution with publication number AU2021103523(a4) proposes an autonomously developed application edify, by which a plurality of independent variables in a teaching process are recorded and how the independent variables affect the final result of the teaching process is studied through a plurality of machine learning algorithms; the technical solution of publication No. CN113570484(a) proposes an analysis system for evaluating learning quality by calculating the concentration degree of a student in a manner of monitoring the fixation point and head movement of the student in the classroom in online teaching. Most of the teaching management systems are based on conventional teaching, but the teaching management systems subdivided into the field of physical teaching are not mentioned.
Disclosure of Invention
The invention aims to provide a physical education teaching management system based on big data, which identifies the identity information of a user through a user module, and identifies the intensity, duration and sports items of the user during sports after the user starts to exercise; furthermore, the accuracy degree of the motion of the user is identified, so that the demand index of the user for the physical education is calculated; furthermore, the management system sorts a plurality of instructors according to the teaching qualification, and preferentially arranges the instructors with higher teaching qualification to perform motion teaching guidance on the user according to the user sorting sequence after the demand index of the user reaches a specified threshold value. The management system effectively utilizes technical means to sequence and match the users and the instructors in the sports field, improves the management efficiency of personnel, and ensures the satisfaction degree of the users to achieve the physical exercise effect.
The invention adopts the following technical scheme:
a big data based physical education management system, the management system comprising: a recording unit; the recording unit comprises the following modules:
the user module is used for acquiring basic body information of a user, including height, weight and face appearance; and for collecting movement data of the user, comprising at least one of: sports items, amount of exercise, duration of exercise;
the teacher module is used for recording teaching qualification data of a teacher, wherein the teaching qualification data comprises a teaching quality index and a user evaluation index;
the management system further comprises:
the processing unit is used for cleaning and processing the data acquired by the recording unit to obtain an effective data set;
the analysis unit is used for matching the user in the current activation state with the instructor according to the data obtained by the processing unit;
a storage unit configured to store data generated by the above units, a user state, and an instructor state using a database;
wherein, the recording unit adopts various sensors to continuously acquire data to the user in the motion state, and calculates the demand coefficient U of the user by using the acquired dataiThe method comprises the following steps:
step S1: judging whether the user enters a motion state or not, and setting the user entering the motion state as an activation state;
step S2: acquiring motion data of a user in an activated state, wherein the motion data of the user is acquired by using an image sensor, and the motion data of the user is compared with standard motion data to obtain a difference index k;
step S3: calculating the demand coefficient of the user, and acquiring the I groups of data in total by acquiring the exercise data of the user in the activated state every 1 minute, wherein I is 1, 2 … … I, and the following formula is used:
in the above formula, UiIs the demand coefficient of the user, e1、e2、e3Is a preset weight parameter; t isiFor the duration of the user' S exercise, SiIs the current exercise intensity index, P, of the useriAs an index of difficulty of exercise, SiAnd PiRelated to the user's current athletic activity; w is the total hours from the current time after the user receives teaching last time, beta is a teaching time correction coefficient, and the teaching team sets the correction coefficient after discussing and researching the user;
step S4: calculating the exercise intensity index S of the last 5 timesiStandard deviation sigma(s) and difficulty of movement index PiThe standard deviation σ (p) of (a), i.e.:
in the formula 2, the first and second groups of the compound,
the mean value of the exercise intensity index in the last 5 recordings;
in the formula 3, the first and second groups,
the average value of the exercise difficulty index in the last 5 records;
when the demand coefficient UiExceeding a safety threshold UmaxAnd both σ(s) and σ (p) are simultaneously less than the standard deviation threshold σminIf so, setting the user to be in an early warning state;
the recording unit further records and analyzes the teaching process of the instructor, and at least determines that the instructor enters a teaching state through the dialogue between the instructor and the learner; and after entering the teaching state, calculating whether the difference index k of the student is improved or not by the following formula 4, thereby evaluating the teaching quality index Ed of the teacher:
in the above formula, M is an evaluation time node calculated every 1 minute after the instructor starts teaching, and the numerical value of the upper limit M is determined according to the motion difficulty PiDetermining; l is a teaching quality correction weight parameter, and correction is made according to the internal rating of a teacher; judging whether the student difference index k gradually decreases along with time through a formula 4, and accumulating the teaching quality index Ed of the teacher;
the instructor module collects subjective scores of instructors for users and calculates the user evaluation index C;
the storage module establishes an independent user profile based on each user; the user profile comprises data information directly collected by the recording unit and analyzed data information output by the analyzing unit;
each user is assigned the user number x by the management system; each instructor is assigned an instructor number y by the management system;
the management system gives the users in the early warning state Ui-x]A label, wherein x is the user number of the user, anContinuous execution demand coefficient UiAnd at the demand coefficient UiAfter updating, the [ U ] is continuously updatedi-x]A label;
the management system calculates a qualification index Q of the instructor according to the teaching quality index of the instructor and the user evaluation index C, namely:
Q＝Ed+Csumequation 5;
wherein Ed is the teaching quality index, CsumThe sum of the user rating indices C for all users to that instructor;
the management system dynamically acquires the qualification indexes of all the instructors and sorts a plurality of instructors from high to low according to the qualification indexes;
the management system also comprises the following steps of matching the user with the instructor for teaching:
step P1: counting the users with the current state as the early warning state, and according to the demand coefficient UiSequencing the users entering the early warning state in a descending manner;
step P2: counting the instructors which are not in the teaching state at present, and sequencing the idle instructors in a descending manner according to the qualification index Q;
step P3: sequentially matching the instructor at the highest order to the user currently at the highest order, and labeling [ U ] of the selected useri-x]Sent to the selected instructor and generates [ Ui-x-y]The tags are given to both the user and the instructor.
The beneficial effects obtained by the invention are as follows:
1. the management system of the invention continuously collects data of users who do sports through a plurality of collecting sensors, and obtains a judgment result of fine description of the users through processing, counting and analyzing a large amount of data, thereby making a more accurate management strategy;
2. the management system can collect the movement action of each user performing movement by configuring the user module, and calculate the required degree of the user needing to receive the movement action guidance from two aspects of time and action, thereby intelligently recording and managing the user;
3. the management system of the invention can make full use of the working time and the working load of the existing teaching personnel by dynamically matching the teaching combination of the student and the instructor, thereby improving the effective utilization rate of the instructor team;
4. the management system adopts modular design and cooperation of all units, and can flexibly optimize and change through software and hardware in the later period, thereby saving a large amount of later maintenance and upgrading cost.
Drawings
The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate corresponding parts throughout the different views.
FIG. 1 is a schematic diagram of the principles of the present invention;
FIG. 2 is a schematic diagram of user limb recognition by image sensor and machine learning according to the present invention;
FIG. 3 is a schematic diagram of the motion data calculation by establishing a three-dimensional recognition of the user's motion gesture according to the present invention;
FIG. 4 is a diagram illustrating steps performed to calculate a demand factor of a user according to the present invention.
Reference numerals in the drawings indicate: 101-a recording unit; 102-a processing unit; 103-an analysis unit; 104-a storage unit; 111-a user module; 112-instructor module.
Detailed Description
In order to make the technical solution and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the embodiments thereof; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Other systems, methods, and/or features of the present embodiments will become apparent to those skilled in the art upon review of the following detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. Additional features of the disclosed embodiments are described in, and will be apparent from, the detailed description that follows.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it is to be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not intended to indicate or imply that the device or assembly referred to must have a specific orientation.
The first embodiment is as follows:
as shown in fig. 1, a big data-based physical education management system, the management system comprising:
a recording unit 101, the recording unit 101 comprising the following two modules:
the user module 111 is used for acquiring basic body information of a user, including height, weight and face appearance; and for collecting movement data of the user, comprising at least one of: sports items, amount of exercise, duration of exercise;
the instructor module 112 is used for recording teaching qualification data of an instructor, wherein the teaching qualification data comprises a teaching quality index and a user evaluation index;
the management system further comprises:
the processing unit 102 is configured to perform data cleaning and processing on the data acquired by the recording unit 101 to obtain an effective data set;
the analysis unit 103 matches the user currently in the activated state with the instructor according to the data obtained by the processing unit;
a storage unit 104 configured to store data generated in the above units, a user status, and an instructor status using a database;
wherein, the recording unit 101 comprises a plurality of sensors for continuously collecting data to users in motion state, and calculating the demand coefficient U of the users by using the collected dataiThe method comprises the following steps:
step S1: judging whether the user enters a motion state or not, and setting the user entering the motion state as an activation state;
step S2: acquiring motion data of a user in an activated state, wherein the motion data of the user is acquired by using an image sensor, and the motion data of the user is compared with standard motion data to obtain a difference index k;
step S3: calculating the demand coefficient of the user, and acquiring the I groups of data in total by acquiring the exercise data of the user in the activated state every 1 minute, wherein I is 1, 2 … … I, and the following formula is used:
in the above formula, UiIs the demand coefficient of the user, e1、e2、e3Is a preset weight parameter; t isiFor the duration of the user' S exercise, SiIs the current exercise intensity index, P, of the useriThe index is a sports difficulty index and is related to the current sports item of the user; w is the total hours from the current time after the user receives teaching last time, beta is a teaching time correction coefficient, and the teaching team sets the correction coefficient after discussing and researching the user;
in the formula 1, the difference between the overall exercise performance condition of the user and standard exercise data is mainly considered, so that the user with larger difference can preferentially obtain teaching arrangement; furthermore, the exercise intensity and the exercise difficulty of the user are considered, so that the user with higher exercise intensity and exercise difficulty can preferentially accept teaching arrangement; furthermore, the distance between the last teaching of the user and the current time is considered, so that the chances of obtaining teaching arrangement by the user are relatively equal;
step S4: computingExercise intensity index S of last 5 timesiStandard deviation sigma(s) and difficulty of movement index PiThe standard deviation σ (p) of (a), i.e.:
in the formula 2, the first and second groups of the compound,
the mean value of the exercise intensity index in the last 5 recordings;
in the formula 3, the first and second groups,
the average value of the exercise difficulty index in the last 5 records;
when the demand coefficient UiExceeding a safety threshold UmaxAnd both σ(s) and σ (p) are simultaneously less than the standard deviation threshold σminSetting the user to be in an early warning state;
wherein, the recording unit 101 further records and analyzes the teaching process of the instructor, and at least determines that the instructor enters a teaching state through the dialogue between the instructor and the learner; and after entering the teaching state, calculating whether the difference index k of the student is improved or not by the following formula 4, thereby evaluating the teaching quality index Ed of the teacher:
in the above formula, M is an evaluation time node calculated every 1 minute after the instructor starts teaching, and the numerical value of the upper limit M is determined according to the motion difficulty PiDetermining; l is a teaching quality correction weight parameter, and correction is made according to the internal rating of the instructor; judging whether the student difference index k is over time or not according to the formula 4Gradually decreasing, thereby accumulating the teaching quality index Ed of the instructor;
further, the instructor module 112 collects subjective scores of instructors for users, and calculates the user evaluation index C;
the storage module establishes an independent user profile based on each user; the user profile includes recording data information directly collected by the recording unit 101, and further includes analyzed data information output by the analyzing unit 103; each user is assigned the user number x by the management system; each instructor is assigned an instructor number y by the management system;
the management system gives the users in the early warning state Ui-x]A label, wherein x is the user number of the user, and the requirement coefficient U is continuously executediAnd at the demand coefficient UiAfter updating, the [ U ] is continuously updatedi-x]A label;
the management system calculates a qualification index Q of the instructor according to the teaching quality index and the user evaluation index of the instructor, namely:
Q＝Ed+Csum(ii) a Equation 5;
wherein Ed is the teaching quality index, CsumThe sum of the user rating indices for that instructor for all users;
the management system dynamically acquires the qualification indexes of all instructors and sorts a plurality of instructors from high to low according to the qualification indexes;
the management system also comprises the following steps of matching the user with the instructor for teaching:
step P1: counting the users with the current state as the early warning state, and according to the demand coefficient UiSequencing the users entering the early warning state in a descending manner;
step P2: counting the instructors which are not in the teaching state at present, and sequencing the idle instructors in a descending manner according to the qualification index Q;
step P3: the instructor in the highest order is matched in turn to give the currentThe user in the highest order and the label [ U ] of the selected useri-x]Sent to the selected instructor and generates [ Ui-x-y]The tags are given to both the user and the instructor.
Example two:
this embodiment should be understood to include at least all of the features of any of the foregoing embodiments and further modifications thereon;
for the users entering the sports field, such as a community sports park, a residential community dwelling, a gymnasium, etc., in order to improve the user experience of the sports field and the work efficiency of the instructor, further improvement is made in the embodiment;
wherein, the user module 111 includes a measuring device disposed at the entrance of the sports venue for measuring the height and weight of the user entering the sports venue; the method comprises the steps that a magnetic induction type card is used for identifying user identity information and further calling user basic information; the method comprises the steps of adopting a face recognition device, such as a depth camera, an infrared camera, a holographic camera and other equipment to recognize user identity information; and, by identifying the user identity, further updating and retrieving data information within the user profile of the user;
further, as shown in fig. 2 and fig. 3, an image sensor is used to capture an image of the body of the user in an activated state, and by recognizing multiple parts of the body, the image is used as an anchor point for three-dimensional processing of the image; preferably including identifying a plurality of joint locations of the body, distal-most extension locations of the extremities, chest, abdomen locations, head, neck locations, etc.; further, physical information such as the motion track, the acceleration, the displacement and the like of the limb is identified; further, a contact device or a non-contact device can be adopted to collect body function information of the user, wherein the body function information comprises heart rate, blood pressure, blood oxygen content, muscle contraction rate and the like; wherein, the adopted contact device comprises a body-building electronic wrist strap and a measuring sensor on the body-building apparatus; the non-contact device comprises a remote sensing biological sign identifier;
by collecting the action data and the physical function information of the user, the exercise intensity and the exercise rhythm information of the user can be further calculated; comparing with standard motion data, namely calculating the difference index k; in addition, the instructor can better implement action teaching by browsing the comparison data, and help the user to improve the movement key points such as action, rhythm, intensity, movement duration and the like.
Example three:
this embodiment should be understood to include at least all of the features of any of the foregoing embodiments and further modifications thereon;
based on different subjective feelings of each user to each instructor, generating a user evaluation index C of the user to the instructor, wherein the user evaluation index C comprises a plurality of subjective factors; therefore, by adding the evaluation weight based on each user to the user evaluation index C, the present embodiment makes it easier for the user to be arranged to obtain a chance for a preferred instructor to give teaching;
in one embodiment, the user number x is included1、x2、x3Three users of (1), and instructor number y1、y2、y3The user evaluation index C given to three instructors by three users respectively is shown in the following table:
the user evaluation index C is divided into full scores according to the score of 10 to score the instructor, and the higher the score is, the higher the preference degree of the instructor of the user is represented; wherein, if the user has not accepted the teaching of a certain instructor, the blank of the score is replaced by the average value of the evaluation scores of other users for the instructor, as shown in the above table, user x3To instructor y1If the evaluation score is vacant, the average score is 6.5;
further, the total score of the assessment is counted for all instructors, and the following table is provided:
scoring of the instructor by the user, denoted as C (x, y), e.g. user x3To instructor y3The score of (A) can be recorded as C (x)3,y3)＝7；
Equation 5 is optimized such that equation 5 is biased based on the weight of each user:
Q(x,y)＝Ed+Csum·[ε+C(x,y)]equation 6;
in equation 6, the qualification index Q of the instructor will have different values based on each user x, and will be influenced by the user preference parameter epsilon and the score C (x, y) of each user on different instructors, thereby changing the priority of the arrangement of each instructor y for user x.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. That is, the methods, systems, and devices discussed above are examples. Various configurations may omit, substitute, or add various procedures or components as appropriate. For example, in alternative configurations, the methods may be performed in an order different than described, and/or various components may be added, omitted, and/or combined. Moreover, features described with respect to certain configurations may be combined in various other configurations, as different aspects and elements of the configurations may be combined in a similar manner. Further, elements therein may be updated as technology evolves, i.e., many elements are examples and do not limit the scope of the disclosure or claims.
Specific details are given in the description to provide a thorough understanding of the exemplary configurations including implementations. However, configurations may be practiced without these specific details, for example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the configurations. This description provides example configurations only, and does not limit the scope, applicability, or configuration of the claims. Rather, the foregoing description of the configurations will provide those skilled in the art with an enabling description for implementing the described techniques. Various changes may be made in the function and arrangement of elements without departing from the spirit or scope of the disclosure.
In conclusion, it is intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that these examples are illustrative only and are not intended to limit the scope of the invention. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (9)
1. A big data based physical education management system, the management system comprising: the recording unit comprises the following modules:
the user module is used for acquiring basic body information of a user, including height, weight and face appearance; and for collecting movement data of the user, comprising at least one of: sports items, amount of exercise, duration of exercise;
the teacher module is used for recording teaching qualification data of a teacher, wherein the teaching qualification data comprises a teaching quality index and a user evaluation index;
the management system further comprises:
the processing unit is used for cleaning and processing the data acquired by the recording unit to obtain an effective data set;
the analysis unit is used for matching the user in the current activation state with the instructor according to the data obtained by the processing unit;
a storage unit configured to store data generated by the above units, a user state, and an instructor state using a database;
wherein the record sheetThe element comprises the steps of adopting various sensors to continuously collect data to a user in a motion state, and calculating a demand coefficient U of the user by using the collected dataiThe method comprises the following steps:
step S1: judging whether the user enters a motion state or not, and setting the user entering the motion state as an activation state;
step S2: acquiring motion data of a user in an activated state, wherein the motion data of the user is acquired by using an image sensor, and the motion data of the user is compared with standard motion data to obtain a difference index k;
step S3: calculating the demand coefficient of the user, and acquiring the I groups of data in total by acquiring the exercise data of the user in the activated state every 1 minute, wherein I is 1, 2 … … I, and the following formula is used:
in the above formula, UiIs the demand coefficient of the user, e1、e2、e3Is a preset weight parameter; t isiFor the duration of the user' S exercise, SiIs the current exercise intensity index, P, of the useriAs an index of difficulty of exercise, SiAnd PiRelated to the user's current sporting event; w is the total hours from the current time after the user receives teaching last time, beta is a teaching time correction coefficient, and the teaching team sets the correction coefficient after discussing and researching the user;
step S4: calculating the exercise intensity index S of the last 5 timesiStandard deviation sigma(s) and difficulty of movement index PiThe standard deviation σ (p) of (a), i.e.:
in the formula 2, the first and second groups of the compound,
the mean value of the exercise intensity index in the last 5 recordings;
in the formula 3, the first and second groups,
the average value of the exercise difficulty index in the last 5 records;
when the demand coefficient UiExceeding a safety threshold UmaxAnd both σ(s) and σ (p) are simultaneously less than the standard deviation threshold σminThen the user is set to the early warning state.
2. The big data based physical education management system of claim 1 wherein the recording unit further includes recording and analyzing the instructor's teaching process, including at least determining that the instructor enters the teaching state through the instructor's dialogue with the learner; and after entering the teaching state, calculating whether the difference index k of the student is improved or not by the following formula 4, thereby evaluating the teaching quality index Ed of the teacher:
in the above formula, M is an evaluation time node calculated every 1 minute after the instructor starts teaching, and the numerical value of the upper limit M is determined according to the motion difficulty PiDetermining; l is a teaching quality correction weight parameter, and correction is made according to the internal rating of the instructor; and judging whether the student difference index k gradually decreases along with the time through a formula 4, thereby accumulating the teaching quality index Ed of the instructor.
3. A big data based sports teaching management system as claimed in claim 2, wherein said instructor module includes means for collecting subjective scores of instructors from users and calculating said user rating index C.
4. A big data based sports teaching management system according to claim 3, wherein said storage module establishes an independent user profile on a per user basis; the user profile includes data information recorded directly collected by the recording unit and analyzed data information output by the analyzing unit.
5. A big data based sports teaching management system according to claim 4, wherein each user is assigned said user number x by said management system; each instructor is assigned an instructor number y by the management system.
6. The big-data-based sports teaching management system according to claim 5, wherein said management system assigns [ U ] to users in early warning statusi-x]A label, wherein x is the user number of the user, and the requirement coefficient U is continuously executediAnd at the demand coefficient UiAfter updating, the [ U ] is continuously updatedi-x]And (4) a label.
7. The big data based sports teaching management system according to claim 6, wherein said management system calculates qualification index Q of instructor according to instructor's said teaching quality index and said user evaluation index C, namely:
Q＝Ed+Csumequation 5;
wherein Ed is the teaching quality index, CsumThe sum of the user rating indices C for that instructor for all users.
8. A big data based sports teaching management system according to claim 7, wherein said management system dynamically obtains said qualification indexes of all instructors and ranks multiple instructors from high to low according to said qualification indexes.
9. A big data based physical education management system as claimed in claim 8 wherein the management system further includes the steps of matching the user with the instructor for teaching:
step P1: counting the users with the current state as the early warning state, and according to the demand coefficient UiSequencing the users entering the early warning state in a descending manner;
step P2: counting the instructors which are not in the teaching state at present, and sequencing the idle instructors in a descending manner according to the qualification index Q;
step P3: sequentially matching the instructor at the highest order to give the user at the highest order, and labeling the selected user [ U [ ]i-x]Sent to the selected instructor and generates [ Ui-x-y]The tags are given to both the user and the instructor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210229420.1A CN114612066A (en) | 2022-03-09 | 2022-03-09 | Sports teaching management system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210229420.1A CN114612066A (en) | 2022-03-09 | 2022-03-09 | Sports teaching management system based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114612066A true CN114612066A (en) | 2022-06-10 |
Family
ID=81860975
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210229420.1A Withdrawn CN114612066A (en) | 2022-03-09 | 2022-03-09 | Sports teaching management system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114612066A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350905A (en) * | 2023-10-27 | 2024-01-05 | 广东海洋大学 | Sports teaching management system based on big data |
-
2022
- 2022-03-09 CN CN202210229420.1A patent/CN114612066A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350905A (en) * | 2023-10-27 | 2024-01-05 | 广东海洋大学 | Sports teaching management system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108090688B (en) | Intelligent fitness management system | |
| CN108090742A (en) | Sport and body-building project management system | |
| DE102020129804A1 (en) | INTELLIGENT FITNESS STUDIO | |
| US11413501B2 (en) | Method and system of planning fitness courses | |
| CN105963956A (en) | Intelligent fitness system suitable for all populations | |
| US20230149774A1 (en) | Handle Motion Counting Method and Terminal | |
| CN114612066A (en) | Sports teaching management system based on big data | |
| CN110970114A (en) | Traditional Chinese medicine health guidance system | |
| CN112270970A (en) | Intelligent motion management system and execution method | |
| Scheer et al. | Judging bias induced by viewing contrived videotapes: A function of selected psychological variables | |
| CN106178466A (en) | A kind of body-building expenditure analysis method and system | |
| CN113974612A (en) | Automatic assessment method and system for upper limb movement function of stroke patient | |
| CN107609191B (en) | Incentive type multi-user intelligent fitness management system and using method thereof | |
| KR20180026196A (en) | Customizing Fitness Care and Management System and Method Thereof | |
| Nagovitsyn et al. | Forecasting the competitive performance of young athletes based on artificial intelligence technology | |
| CN115985462A (en) | Rehabilitation and intelligence-developing training system for children cerebral palsy | |
| Toto | A Systematic Review on Digital Technologies on Sport Science: Didactic of Sport | |
| CN114209309A (en) | Movement behavior analysis method based on visual technology | |
| CN113517052A (en) | Multi-perception man-machine interaction system and method in commercial fitness scene | |
| CN117035249B (en) | Stadium investigation task allocation management system | |
| CN115018271B (en) | Intelligent student course selection recommendation management system based on smart campus construction | |
| CN115798676B (en) | Interactive experience analysis management method and system based on VR technology | |
| CN109621335B (en) | Sports suggestion system based on big data | |
| CN116308921B (en) | Motion data analysis method, system, device and storage medium | |
| CN110503302B (en) | Cultural aged care activity effect evaluation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220610 |