CN113485554A - Intelligent guidance method and device for behavior habits of students in smart campus construction and computer storage medium - Google Patents
Intelligent guidance method and device for behavior habits of students in smart campus construction and computer storage medium Download PDFInfo
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Abstract
The invention discloses an intelligent guiding method, equipment and a computer storage medium for building student behavior habits in a smart campus, wherein wearable classroom behavior guiding terminals are worn on students in classroom teaching, wearable receiving terminals are worn on teachers, so that classroom learning behaviors of the students are identified and bad classroom learning behaviors are judged according to the wearable classroom behavior guiding terminals, and then the students with the bad classroom learning behaviors are intelligently guided in a targeted manner, so that the intelligent guiding of the classroom learning behavior habits of the students is realized, the wearable classroom behavior guiding terminals are used for one-to-one classroom learning behavior habit guiding of the students, all the students in a classroom can be guided, the defects of low guiding efficiency and small guiding coverage area caused by manual guiding by the teachers at present are greatly overcome, the guide efficiency is improved, the omission of the guide range is avoided, and the intelligent guide device has the characteristics of high intelligent degree and strong practicability.
Description
Technical Field
The invention belongs to the technical field of student behavior habit guidance, and particularly relates to an intelligent guidance method, equipment and a computer storage medium for building student behavior habits in an intelligent campus.
Background
People can not have good behavior habits naturally, and the good behavior habits are gradually formed under the good guidance of others. For students, good learning behavior habits directly relate to the improvement of comprehensive qualities of the students and influence the life-long development of the students in the future, wherein the learning behavior habits comprise classroom learning behavior habits, class learning behavior habits, self-learning behavior habits and the like. Among the numerous learning behavior habits, the classroom learning behavior habit is a vital learning behavior habit, and as classroom teaching is the most important learning link in the whole learning life of the student, the behavior habit developed by the student in classroom teaching directly influences the listening effect of the student. If the poor classroom learning behavior is not discriminated according to the classroom learning behavior habits of the students and then the poor classroom learning behavior is guided, the classroom learning effect of the students is increasingly poor after the students spend time, and the examination scores of the students are influenced.
However, at present, guidance of the classroom behavior habit of the student is generally manually operated by a teacher, specifically, the teacher observes the classroom learning behavior of the student in the teaching process, identifies the student with bad classroom learning behavior, and then guides the student. However, since the number of students in a classroom is large, the manual guidance ability of a teacher is limited, the classroom learning behavior of each student cannot be observed in the teaching process, and the subsequent tracking of the bad classroom learning behavior corresponding to the student to be guided cannot be performed, which leads to low guidance efficiency, small guidance coverage range and difficulty in continuous guidance, and further leads to poor guidance effect.
Disclosure of Invention
In order to solve the technical problems, the invention is realized by the following technical scheme:
in a first aspect, the invention provides an intelligent guidance method for behavior habits of students in smart campus construction, which comprises the following steps:
the wearable classroom behavior guidance terminal is worn, namely the wearable classroom behavior guidance terminal is arranged on a seat corresponding to each classroom in a campus respectively, a wearable receiving terminal is arranged at the position of a platform, when a student enters the classroom and sits on the corresponding seat, the wearable classroom behavior guidance terminal on each seat is worn on the body of the student correspondingly, the wearable receiving terminal is worn on the body of a teacher, wherein each wearable classroom behavior guidance terminal corresponds to a seat number respectively, and each wearable classroom behavior guidance terminal is numbered;
step two, capturing the class images of the students, wherein when the wearable classroom behavior guide terminal is worn successfully, a camera in the wearable classroom behavior guide terminal starts to work and is used for capturing the class images corresponding to the students according to the set capturing time periods to obtain the class images corresponding to the capturing time periods of the students;
step three, determining the class-taking state, namely determining the class-taking state of the class-taking image corresponding to each capturing time period of each student, if the class-taking state is the class-listening state, executing the step four and the step five, and if the class-taking state is the working state, executing the step six and the step seven;
step four, class-attending behavior identification, namely identifying class-attending sitting posture behavior names and class-attending action names of class-attending images corresponding to each capturing time period of students respectively;
judging whether the class-attending sitting posture behavior name and the class-attending action behavior name corresponding to the class-attending image of each capturing time period of each student are of a bad class-attending sitting posture behavior type and a bad class-attending action behavior type respectively, if so, recording the capturing time period as a bad capturing time period, and carrying out intelligent guidance processing on the student;
sixth, homework behavior recognition, namely recognizing the name of the homework sitting posture behavior and the name of the pen holding behavior of the images of the students in class corresponding to the capturing time periods respectively;
and seventhly, judging poor homework behaviors and intelligently guiding, namely judging whether the homework sitting posture behavior name and the pen holding behavior name corresponding to the images taken at class of each capturing time period of each student are a poor homework sitting posture behavior type and a poor pen holding behavior type respectively, if so, marking the capturing time period as a poor capturing time period, and intelligently guiding the students.
According to a preferred embodiment of the first aspect of the present invention, the wearable classroom behavior guidance terminal includes a camera, a vibration motor and a display screen, and the wearable receiving terminal includes a vibration motor and a display screen.
According to a preferred embodiment of the first aspect of the present invention, in the third step, the class image corresponding to each capturing time period of each student is determined as the class state, and the specific determination method is to focus the class image corresponding to each capturing time period of each student on the eyes of the student, so as to obtain the viewing target corresponding to the eyes of the student, if the viewing target corresponding to the eyes of a student is a teaching board, the class state corresponding to the student is the class listening state, and if the viewing target corresponding to the eyes of a student is a book, the class state corresponding to the student is the homework state.
According to a preferred embodiment of the first aspect of the present invention, in the fourth step, class-attending sitting posture name and class-attending action name recognition is performed on the class-attending images corresponding to each capturing time period of each student, and the specific recognition method performs the following steps:
s1, focusing the lesson images corresponding to the capturing time periods of the students on the backs and the heads of the students respectively, and obtaining the distances from the backs of the students to the backrests of the seats, the rotation angles of the backs of the students and the rotation angles of the heads of the students as the characteristic parameters of the sitting posture of the students;
s2, comparing the class-attending sitting posture behavior characteristic parameters with class-attending sitting posture behavior characteristic parameters corresponding to various preset class-attending sitting posture behavior names, and screening out class-attending sitting posture behavior names corresponding to class-attending images of each capturing time period of each student;
s3, focusing the lesson images corresponding to each capturing time period of each student on the hands and the heads of the students, and extracting the hand action characteristics and the head action characteristics of the students;
and S4, comparing the extracted hand action characteristics and head action characteristics of the students with the hand action characteristics and head action characteristics corresponding to the preset various lesson-taking action names, and screening out lesson-taking action names corresponding to the lesson-taking images of the students in each capturing time period.
According to a preferred embodiment of the first aspect of the present invention, in the fifth step, the lesson-listening sitting posture name and the lesson-listening behavior name corresponding to the images taken during each capturing time period of each student are respectively determined whether the images are of the bad lesson-listening sitting posture type and the bad lesson-listening behavior type, and the specific determination process is as follows:
a1, matching the class-attending sitting posture name corresponding to the class-attending image of each capturing time period of each student with various class-attending sitting posture names corresponding to the bad class-attending sitting posture types in the bad behavior database, if the class-attending sitting posture name corresponding to the class-attending image of a certain capturing time period of a certain student is successfully matched, indicating that the class-attending sitting posture corresponding to the capturing time period of the student is the bad class-attending sitting posture;
a2, matching the names of the action of listening to lessons corresponding to the images of the lessons in each capturing time period of the students with the names of the action of listening to lessons corresponding to the types of the action of listening to lessons in the bad action database, if the matching of the names of the action of listening to lessons corresponding to the images of the lessons in a certain capturing time period of a certain student is successful, indicating that the action of listening to lessons corresponding to the capturing time period of the student is the bad action of listening to lessons.
According to a preferred embodiment of the first aspect of the present invention, in the sixth step, the assignment sitting posture name and the pen holding posture name recognition are performed on the lesson images corresponding to the capturing time periods of the students, respectively, and the specific recognition method performs the following steps:
b1, focusing the images corresponding to each capturing time period of each student on the eyes and the back of the student, and obtaining the distance between the eyes of the student and the book and the rotation angle of the back of the student as the characteristic parameters of the homework sitting posture behaviors;
b2, comparing the work sitting posture behavior characteristic parameters with preset work sitting posture behavior characteristic parameters corresponding to various work sitting posture behavior names, and screening out work sitting posture behavior names corresponding to the images of the students in each capturing time period;
b3, focusing the lesson images corresponding to each capturing time period of each student on the hands of the students, and further extracting holding and holding action parameters corresponding to the holding and holding pen of the students;
b4, comparing the holding action parameters corresponding to the holding pen of the student with the holding action parameters corresponding to the preset pen holding action names, and screening out the pen holding action names corresponding to the images of the students in each capturing time period.
According to a preferred embodiment of the first aspect of the present invention, in the seventh step, whether the homework sitting posture behavior name and the pen holding behavior name corresponding to each capturing time period of each student are respectively determined as a poor homework sitting posture behavior type and a poor pen holding behavior type, a specific determination process is as follows:
c1, matching the homework sitting posture behavior name corresponding to each capturing time period of each student with various homework sitting posture behavior names corresponding to the bad homework sitting posture behavior types in the bad behavior database, if the homework sitting posture behavior name corresponding to a certain capturing time period of a certain student is successfully matched, indicating that the homework sitting posture behavior corresponding to the capturing time period of the student is the bad homework sitting posture behavior;
c2, matching the pen holding action name corresponding to each capturing time period of each student with various pen holding action names corresponding to the bad pen holding action types in the bad action database, if the matching of the pen holding action name corresponding to a certain capturing time period of a certain student is successful, indicating that the pen holding action corresponding to the capturing time period of the student is the bad pen holding action.
According to a preferred embodiment of the first aspect of the present invention, the specific operation procedure of the intelligent guidance processing is as follows:
d1, acquiring the names of students with bad sitting posture or bad exercise sitting posture or bad pen holding behavior, recording the names as bad classroom learning behavior students, and acquiring the seat numbers corresponding to the bad classroom learning behavior students;
d2: acquiring the numbers of the wearable classroom behavior guidance terminals corresponding to the students with bad classroom learning behaviors according to the corresponding relation between the wearable classroom behavior guidance terminals and the seat numbers, further starting a vibration motor in the corresponding wearable class behavior guidance terminal in the bad capturing time period to carry out vibration prompt on the students of the bad class learning behaviors, and the names of the bad classroom learning behaviors corresponding to the students of the bad classroom learning behaviors are displayed on a display screen of the wearable classroom behavior guidance terminal, and simultaneously links to the Internet to search the harmful text content or the harmful video image corresponding to the bad classroom learning behavior name, further displaying the standard behavior action image on the display screen of the wearable classroom behavior guidance terminal, so as to simultaneously obtain the standard behavior action image corresponding to the bad classroom learning behavior name and display the standard behavior action image on the display screen of the wearable classroom behavior guidance terminal;
d3, monitoring the bad classroom learning behaviors corresponding to the students in the bad classroom learning behaviors in the predefined guide time length, judging whether the bad classroom learning behaviors still exist, and if the bad classroom learning behaviors still exist, increasing the vibration frequency of a vibration motor in the wearable classroom behavior guide terminal;
and D4, monitoring the bad classroom learning behavior corresponding to the student of the bad classroom learning behavior again within the predefined guiding time, judging whether the bad classroom learning behavior still exists, if so, controlling a vibration motor in the wearable receiving terminal to perform vibration prompt on a teacher, feeding back the name, the seat number and the name of the bad classroom learning behavior corresponding to the student of the bad classroom learning behavior to a display screen of the wearable receiving terminal, and performing targeted manual guiding by the teacher.
In a second aspect, the present invention provides an apparatus, comprising a processor, and a memory and a network interface connected to the processor; the network interface is connected with a nonvolatile memory in the server; the processor calls a computer program from the nonvolatile memory through the network interface during running, and runs the computer program through the memory so as to execute the intelligent guidance method for behavior habits of students in intelligent campus construction.
In a third aspect, the invention provides a computer storage medium, wherein a computer program is burned in the computer storage medium, and when the computer program runs in a memory of a server, the intelligent guidance method for behavior habits of students in intelligent campus building is realized.
Based on any one of the above aspects, the invention has the following beneficial effects:
(1) the invention realizes intelligent guidance of the learning behavior habits of the students by wearing the wearable classroom behavior guidance terminal on each student in classroom teaching and wearing the wearable receiving terminal on a teacher so as to identify the classroom learning behaviors of each student according to the wearable classroom behavior guidance terminal and judge the bad classroom learning behaviors of the identified classroom learning behaviors, and further performs targeted intelligent guidance on the students with the bad classroom learning behaviors as a judgment result, and performs one-to-one classroom learning behavior habit guidance on the students through the wearable classroom behavior guidance terminal, so that all students in a classroom can be guided, the defects of low guidance efficiency and small guidance coverage area caused by manual guidance of the teacher at present are greatly overcome, the guidance efficiency is improved, and the omission of the guidance range is avoided, the intelligent teaching device has the characteristics of high intelligent degree and strong practicability, on one hand, the level of guidance of the classroom learning behaviors of students is improved, on the other hand, the teaching burden of teachers is also lightened, and the teachers can be put into teaching by mind of the whole body.
(2) According to the invention, in the process of identifying the classroom learning behaviors of each student according to the wearable classroom behavior guide terminal, the class images of the students are captured, and the class state of the captured class images is judged, so that the classroom learning behaviors in the corresponding class states are identified according to different class states, and the classroom learning behavior identification is more specific and has more practical operability.
(3) In the invention, in the process of guiding students with bad classroom learning behaviors, the vibration motor is firstly used for carrying out vibration prompt on the students, then in the predefined guiding time, if the students still have the bad classroom learning behaviors, the vibration frequency of the vibration motor is increased, and then in the predefined guiding time, if the students still have the bad classroom learning behaviors, a teacher is prompted to carry out manual guidance, so that the guidance is carried out step by step, the follow-up guidance tracking on the students with the bad classroom learning behaviors is realized, the defect of difficult guidance and persistence caused by the manual guidance of the teacher at present is overcome, and the guidance effect is strengthened.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in a first aspect, the present invention provides an intelligent guidance method for behavior habits of students in a smart campus, including the following steps:
the wearable classroom behavior guidance terminal is worn, wherein the wearable classroom behavior guidance terminal is respectively arranged on seats corresponding to classrooms in a campus and comprises a camera, a vibration motor and a display screen, the camera is used for capturing class images of students, the vibration motor is used for carrying out vibration prompt on the students, the display screen is used for displaying text or video image content, a wearable receiving terminal is arranged at the position of a platform and comprises the vibration motor and the display screen, the vibration motor is used for carrying out vibration prompt on teachers, the display screen is used for displaying names, seat numbers and names of bad classroom learning behaviors corresponding to the students, when the students sit in the classrooms on the corresponding seats, the wearable classroom behavior guidance terminal on each seat is correspondingly worn on the students, the method comprises the steps that wearable receiving terminals are worn on a teacher, wherein each wearable classroom behavior guidance terminal corresponds to a seat number and is numbered;
in the embodiment, the wearable classroom behavior guide terminal and the wearable receiving terminal are fixed in a classroom, and are worn after students and teachers enter the classroom, so that the situation that the students or the teachers forget to wear the wearable classroom behavior guide terminal and the wearable receiving terminal directly is avoided;
step two, capturing the class images of the students, wherein when the wearable classroom behavior guide terminal is worn successfully, a camera in the wearable classroom behavior guide terminal starts to work and is used for capturing the class images corresponding to the students according to the set capturing time periods to obtain the class images corresponding to the capturing time periods of the students;
step three, class-taking state determination, namely determining class-taking states of class-taking images corresponding to all capturing time periods of students, wherein the specific determination method comprises the steps of focusing the class-taking images corresponding to all capturing time periods of the students on eyes of the students to further obtain watching targets corresponding to eyes of the students, if the watching targets corresponding to eyes of a certain student are teaching boards, wherein the teaching boards comprise blackboards, whiteboards, teaching screens and the like, the class-taking states corresponding to the students are class-listening states, if the watching targets corresponding to the eyes of the certain student are books, the class-taking states corresponding to the students are homework states, if the class-taking states are class-listening states, the step four and the step five are executed, and if the class-taking states are homework states, the step six and the step seven are executed;
in the embodiment, the class learning behaviors of the students are recognized by the wearable class behavior guide terminal, the class images of the students are captured, and the class states of the captured class images are judged, so that the class learning behaviors in the corresponding class states are recognized according to different class states, and the class learning behavior recognition is more specific and practical and operable;
step four, lesson-listening behavior recognition, namely recognizing lesson-listening sitting posture behaviors and lesson-listening behavior names of lesson-listening images corresponding to each capturing time period of students respectively, wherein the specific recognition method comprises the following steps:
s1, focusing the lesson images corresponding to the capturing time periods of the students on the backs and the heads of the students respectively, and obtaining the distances from the backs of the students to the backrests of the seats, the rotation angles of the backs of the students and the rotation angles of the heads of the students as the characteristic parameters of the sitting posture of the students;
s2, comparing the class-attending sitting posture behavior characteristic parameters with class-attending sitting posture behavior characteristic parameters corresponding to various preset class-attending sitting posture behavior names, and screening out class-attending sitting posture behavior names corresponding to class-attending images of each capturing time period of each student;
s3, focusing the lesson images corresponding to each capturing time period of each student on the hands and the heads of the students, and extracting the hand action characteristics and the head action characteristics of the students;
s4, comparing the extracted hand action characteristics and head action characteristics of the students with hand action characteristics and head action characteristics corresponding to various preset lesson-listening action names, and screening lesson-listening action names corresponding to lesson-taking images of each capturing time period of the students;
and step five, judging bad lecture listening behaviors and intelligently guiding, namely judging whether the lecture listening sitting posture behavior name and the lecture listening action behavior name corresponding to the lesson image in each capturing time period of each student are of a bad lecture listening sitting posture behavior type and a bad lecture listening action behavior type respectively, wherein the specific judgment process is as follows:
a1, matching the class-listening sitting posture behavior name corresponding to the class-taking image of each capturing time period of each student with various class-listening sitting posture behavior names corresponding to the bad class-listening sitting posture behavior types in the bad behavior database, wherein the various class-listening sitting posture behavior names corresponding to the bad class-listening sitting posture behavior types comprise inclined sitting, lying down sitting, leaning on sitting, askew sitting and the like, if the class-listening sitting posture behavior name corresponding to the class-taking image of a certain capturing time period of a certain student is consistent with any class-listening sitting posture name corresponding to the bad class-listening sitting posture behavior types, the matching is successful, and the class-listening behavior corresponding to the capturing time period of the student is the bad class-listening sitting behavior;
a2, matching the names of the lecture listening actions corresponding to the images of the lessons in each capturing time period of the students with the names of the lecture listening actions corresponding to the bad lecture listening action types in the bad action database, wherein the names of the lecture listening actions corresponding to the bad lecture listening action types comprise pen turning, mobile phone playing, ear jointing and the like;
if yes, recording the capturing time period as a bad capturing time period, and carrying out intelligent guidance processing on the student;
and sixthly, job behavior recognition, namely recognizing the name of the job sitting posture behavior and the name of the pen holding behavior of the images of the students in the class corresponding to the capturing time periods respectively, wherein the specific recognition method comprises the following steps:
b1, focusing the images corresponding to each capturing time period of each student on the eyes and the back of the student, and obtaining the distance between the eyes of the student and the book and the rotation angle of the back of the student as the characteristic parameters of the homework sitting posture behaviors;
b2, comparing the work sitting posture behavior characteristic parameters with preset work sitting posture behavior characteristic parameters corresponding to various work sitting posture behavior names, and screening out work sitting posture behavior names corresponding to the images of the students in each capturing time period;
b3, focusing the lesson images corresponding to each capturing time period of each student on the hands of the students, and further extracting holding and holding action parameters corresponding to the holding and holding pen of the students, wherein the holding and holding action parameters comprise the height of the holding pen, the number of the fingers holding the holding pen, the name of the fingers and the distance between each finger holding the pen and the pen;
b4, comparing the holding and holding action parameters corresponding to the holding pen of the student with the holding and holding action parameters corresponding to the preset pen holding action names, and screening out the pen holding action names corresponding to the images of the students in each capturing time period;
and seventhly, judging the bad homework behaviors and intelligently guiding, namely judging whether the homework sitting posture behavior name and the pen holding behavior name corresponding to the images taken in class in each capturing time period of each student are the bad homework sitting posture behavior type and the bad pen holding behavior type respectively, wherein the specific judgment process is as follows:
c1, matching the work sitting posture behavior names corresponding to the capturing time periods of the students with various work sitting posture behavior names corresponding to the bad work sitting posture behavior types in the bad behavior database, wherein the work sitting posture behavior names corresponding to the bad work sitting posture behavior types comprise head distortion, shoulder inclination, back bending and the like, if the work sitting posture behavior name corresponding to a certain capturing time period of a certain student is consistent with any work sitting posture behavior name corresponding to the bad work sitting posture behavior type, the matching is successful, and the work sitting posture behavior corresponding to the capturing time period of the student is the bad work sitting behavior;
c2, matching the pen holding action name corresponding to each capturing time period of each student with various pen holding action names corresponding to the bad pen holding action types in the bad action database, wherein the various pen holding action names corresponding to the bad pen holding action types comprise improper pen holding height, improper pen holding finger, improper pen holding distance and the like, if the pen holding action name corresponding to a certain capturing time period of a certain student is consistent with any one of the pen holding action names corresponding to the bad pen holding action types, the matching is successful, and the pen holding action corresponding to the capturing time period of the student is the bad pen holding action;
if yes, recording the capturing time period as a bad capturing time period, and carrying out intelligent guiding processing on the student.
The embodiment of the invention can identify the classroom learning behavior of each student and judge the bad classroom learning behavior according to the wearable classroom behavior guide terminal by wearing the wearable classroom behavior guide terminal for each student in classroom teaching, and further can perform targeted intelligent guide for the students with the bad classroom learning behavior, and the wearable classroom behavior guide terminal can perform classroom learning behavior habit guide one to one for the students, so that all students in a classroom can be guided, the defects of low guide efficiency and small guide coverage area caused by manual guide by the teacher at present are greatly overcome, the guide efficiency is improved, the guide range omission is avoided, the teaching desk has the characteristics of high intelligent degree and strong practicability, on one hand, the guide level of the classroom learning behavior of the students is improved, on the other hand, the teaching burden of the teacher is also lightened, so that the teacher can be put into teaching by mind.
The specific operation process of the intelligent guidance processing is as follows:
d1, acquiring the names of students with bad sitting posture or bad work sitting posture or bad pen holding behavior, recording the names as bad classroom learning behavior students, referring the bad sitting posture or bad pen holding behavior as bad classroom learning behavior, and acquiring the seat numbers corresponding to the bad classroom learning behavior students;
d2: acquiring the numbers of the wearable classroom behavior guidance terminals corresponding to the students with bad classroom learning behaviors according to the corresponding relation between the wearable classroom behavior guidance terminals and the seat numbers, further starting a vibration motor in the corresponding wearable class behavior guidance terminal in the bad capturing time period to carry out vibration prompt on the students of the bad class learning behaviors, and the names of the bad classroom learning behaviors corresponding to the students of the bad classroom learning behaviors are displayed on a display screen of the wearable classroom behavior guidance terminal, and simultaneously links to the Internet to search the harmful text content or the harmful video image corresponding to the bad classroom learning behavior name, further displaying the standard behavior action image on the display screen of the wearable classroom behavior guidance terminal, so as to simultaneously obtain the standard behavior action image corresponding to the bad classroom learning behavior name and display the standard behavior action image on the display screen of the wearable classroom behavior guidance terminal;
in the embodiment, the vibration motor is adopted to carry out vibration prompt on the students, and the voice prompt of the students by the voice prompt is avoided, so that the voice information sent by the voice prompt is prevented from interfering with normal learning of other students;
in the embodiment, in the process of guiding the bad classroom learning behaviors corresponding to the bad classroom learning behaviors, harmful text contents or harmful video images corresponding to the bad classroom learning behavior names and standard behavior action images corresponding to the bad classroom learning behavior names are displayed on a display screen of a wearable classroom behavior guide terminal of the student, so that the student can intuitively know the damage corresponding to the bad classroom learning behaviors conveniently, the intuition and visualization of the guidance are embodied, and the guidance effect is further improved;
d3, monitoring the bad classroom learning behaviors corresponding to the students in the bad classroom learning behaviors in the predefined guide time length, judging whether the bad classroom learning behaviors still exist, and if the bad classroom learning behaviors still exist, increasing the vibration frequency of a vibration motor in the wearable classroom behavior guide terminal;
and D4, monitoring the bad classroom learning behavior corresponding to the student of the bad classroom learning behavior again within the predefined guiding time, judging whether the bad classroom learning behavior still exists, if so, controlling a vibration motor in the wearable receiving terminal to perform vibration prompt on a teacher, feeding back the name, the seat number and the name of the bad classroom learning behavior corresponding to the student of the bad classroom learning behavior to a display screen of the wearable receiving terminal, and performing targeted manual guiding by the teacher.
In the embodiment, in the process of guiding students with bad classroom learning behaviors, the vibration motor is firstly utilized to carry out vibration prompt on the students, then in the predefined guiding time, if the students still have the bad classroom learning behaviors, the vibration frequency of the vibration motor is increased, and then in the predefined guiding time, if the students still have the bad classroom learning behaviors, a teacher is prompted to carry out manual guidance, so that the students are guided step by step, the follow-up guidance tracking of the students with the bad classroom learning behaviors is realized, the defect that the guidance is difficult to continue due to the fact that the students are guided manually at present is overcome, and the guidance effect is strengthened.
In a second aspect, the present invention provides an apparatus, comprising a processor, and a memory and a network interface connected to the processor; the network interface is connected with a nonvolatile memory in the server; the processor calls a computer program from the nonvolatile memory through the network interface during running, and runs the computer program through the memory so as to execute the intelligent guidance method for behavior habits of students in intelligent campus construction.
In a third aspect, the invention provides a computer storage medium, wherein a computer program is burned in the computer storage medium, and when the computer program runs in a memory of a server, the intelligent guidance method for behavior habits of students in intelligent campus building is realized.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (10)
1. An intelligent guidance method for behavior habits of students in smart campus construction is characterized by comprising the following steps:
the wearable classroom behavior guidance terminal is worn, namely the wearable classroom behavior guidance terminal is arranged on a seat corresponding to each classroom in a campus respectively, a wearable receiving terminal is arranged at the position of a platform, when a student enters the classroom and sits on the corresponding seat, the wearable classroom behavior guidance terminal on each seat is worn on the body of the student correspondingly, the wearable receiving terminal is worn on the body of a teacher, wherein each wearable classroom behavior guidance terminal corresponds to a seat number respectively, and each wearable classroom behavior guidance terminal is numbered;
step two, capturing the class images of the students, wherein when the wearable classroom behavior guide terminal is worn successfully, a camera in the wearable classroom behavior guide terminal starts to work and is used for capturing the class images corresponding to the students according to the set capturing time periods to obtain the class images corresponding to the capturing time periods of the students;
step three, determining the class-taking state, namely determining the class-taking state of the class-taking image corresponding to each capturing time period of each student, if the class-taking state is the class-listening state, executing the step four and the step five, and if the class-taking state is the working state, executing the step six and the step seven;
step four, class-attending behavior identification, namely identifying class-attending sitting posture behavior names and class-attending action names of class-attending images corresponding to each capturing time period of students respectively;
judging whether the class-attending sitting posture behavior name and the class-attending action behavior name corresponding to the class-attending image of each capturing time period of each student are of a bad class-attending sitting posture behavior type and a bad class-attending action behavior type respectively, if so, recording the capturing time period as a bad capturing time period, and carrying out intelligent guidance processing on the student;
sixth, homework behavior recognition, namely recognizing the name of the homework sitting posture behavior and the name of the pen holding behavior of the images of the students in class corresponding to the capturing time periods respectively;
and seventhly, judging poor homework behaviors and intelligently guiding, namely judging whether the homework sitting posture behavior name and the pen holding behavior name corresponding to the images taken at class of each capturing time period of each student are a poor homework sitting posture behavior type and a poor pen holding behavior type respectively, if so, marking the capturing time period as a poor capturing time period, and intelligently guiding the students.
2. The intelligent guidance method for behavior habits of students building on smart campus of claim 1, wherein: wearable classroom action guide terminal includes camera, vibrating motor and display screen, wearable receiving terminal includes vibrating motor and display screen.
3. The intelligent guidance method for behavior habits of students building on smart campus of claim 1, wherein: in the third step, the class-taking state of the class-taking image corresponding to each capturing time period of each student is determined, the specific determination method is that the class-taking image corresponding to each capturing time period of each student is focused on the eyes of the student, so as to obtain the watching target corresponding to the eyes of the student, if the watching target corresponding to the eyes of a certain student is a teaching board, the class-taking state corresponding to the student is a class-listening state, and if the watching target corresponding to the eyes of a certain student is a book, the class-taking state corresponding to the student is a homework state.
4. The intelligent guidance method for behavior habits of students building on smart campus of claim 1, wherein: in the fourth step, class-attending sitting posture behavior name and class-attending action name recognition is respectively carried out on the class-attending images corresponding to each capturing time period of each student, and the specific recognition method comprises the following steps:
s1, focusing the lesson images corresponding to the capturing time periods of the students on the backs and the heads of the students respectively, and obtaining the distances from the backs of the students to the backrests of the seats, the rotation angles of the backs of the students and the rotation angles of the heads of the students as the characteristic parameters of the sitting posture of the students;
s2, comparing the class-attending sitting posture behavior characteristic parameters with class-attending sitting posture behavior characteristic parameters corresponding to various preset class-attending sitting posture behavior names, and screening out class-attending sitting posture behavior names corresponding to class-attending images of each capturing time period of each student;
s3, focusing the lesson images corresponding to each capturing time period of each student on the hands and the heads of the students, and extracting the hand action characteristics and the head action characteristics of the students;
and S4, comparing the extracted hand action characteristics and head action characteristics of the students with the hand action characteristics and head action characteristics corresponding to the preset various lesson-taking action names, and screening out lesson-taking action names corresponding to the lesson-taking images of the students in each capturing time period.
5. The intelligent guidance method for behavior habits of students building on smart campus of claim 1, wherein: in the fifth step, whether the class-attending sitting posture behavior name and the class-attending action behavior name corresponding to the class-attending image of each capturing time period of each student are a bad class-attending sitting posture behavior type and a bad class-attending action behavior type is judged respectively, and the specific judgment process is as follows:
a1, matching the class-attending sitting posture name corresponding to the class-attending image of each capturing time period of each student with various class-attending sitting posture names corresponding to the bad class-attending sitting posture types in the bad behavior database, if the class-attending sitting posture name corresponding to the class-attending image of a certain capturing time period of a certain student is successfully matched, indicating that the class-attending sitting posture corresponding to the capturing time period of the student is the bad class-attending sitting posture;
a2, matching the names of the action of listening to lessons corresponding to the images of the lessons in each capturing time period of the students with the names of the action of listening to lessons corresponding to the types of the action of listening to lessons in the bad action database, if the matching of the names of the action of listening to lessons corresponding to the images of the lessons in a certain capturing time period of a certain student is successful, indicating that the action of listening to lessons corresponding to the capturing time period of the student is the bad action of listening to lessons.
6. The intelligent guidance method for behavior habits of students building on smart campus of claim 1, wherein: in the sixth step, the class images corresponding to each capturing time period of each student are respectively identified by homework sitting posture behavior names and pen holding behavior names, and the specific identification method comprises the following steps:
b1, focusing the images corresponding to each capturing time period of each student on the eyes and the back of the student, and obtaining the distance between the eyes of the student and the book and the rotation angle of the back of the student as the characteristic parameters of the homework sitting posture behaviors;
b2, comparing the work sitting posture behavior characteristic parameters with preset work sitting posture behavior characteristic parameters corresponding to various work sitting posture behavior names, and screening out work sitting posture behavior names corresponding to the images of the students in each capturing time period;
b3, focusing the lesson images corresponding to each capturing time period of each student on the hands of the students, and further extracting holding and holding action parameters corresponding to the holding and holding pen of the students;
b4, comparing the holding action parameters corresponding to the holding pen of the student with the holding action parameters corresponding to the preset pen holding action names, and screening out the pen holding action names corresponding to the images of the students in each capturing time period.
7. The intelligent guidance method for behavior habits of students building on smart campus of claim 1, wherein: and seventhly, respectively judging whether the homework sitting posture behavior name and the pen holding behavior name corresponding to each capturing time period of each student are the bad homework sitting posture behavior type and the bad pen holding behavior type, wherein the specific judgment process is as follows:
c1, matching the homework sitting posture behavior name corresponding to each capturing time period of each student with various homework sitting posture behavior names corresponding to the bad homework sitting posture behavior types in the bad behavior database, if the homework sitting posture behavior name corresponding to a certain capturing time period of a certain student is successfully matched, indicating that the homework sitting posture behavior corresponding to the capturing time period of the student is the bad homework sitting posture behavior;
c2, matching the pen holding action name corresponding to each capturing time period of each student with various pen holding action names corresponding to the bad pen holding action types in the bad action database, if the matching of the pen holding action name corresponding to a certain capturing time period of a certain student is successful, indicating that the pen holding action corresponding to the capturing time period of the student is the bad pen holding action.
8. The intelligent guidance method for behavior habits of students building on smart campus of claim 1, wherein: the specific operation process of the intelligent guidance processing is as follows:
d1, acquiring the names of students with bad sitting posture or bad exercise sitting posture or bad pen holding behavior, recording the names as bad classroom learning behavior students, and acquiring the seat numbers corresponding to the bad classroom learning behavior students;
d2: acquiring the numbers of the wearable classroom behavior guidance terminals corresponding to the students with bad classroom learning behaviors according to the corresponding relation between the wearable classroom behavior guidance terminals and the seat numbers, further starting a vibration motor in the corresponding wearable class behavior guidance terminal in the bad capturing time period to carry out vibration prompt on the students of the bad class learning behaviors, and the names of the bad classroom learning behaviors corresponding to the students of the bad classroom learning behaviors are displayed on a display screen of the wearable classroom behavior guidance terminal, and simultaneously links to the Internet to search the harmful text content or the harmful video image corresponding to the bad classroom learning behavior name, further displaying the standard behavior action image on the display screen of the wearable classroom behavior guidance terminal, so as to simultaneously obtain the standard behavior action image corresponding to the bad classroom learning behavior name and display the standard behavior action image on the display screen of the wearable classroom behavior guidance terminal;
d3, monitoring the bad classroom learning behaviors corresponding to the students in the bad classroom learning behaviors in the predefined guide time length, judging whether the bad classroom learning behaviors still exist, and if the bad classroom learning behaviors still exist, increasing the vibration frequency of a vibration motor in the wearable classroom behavior guide terminal;
and D4, monitoring the bad classroom learning behavior corresponding to the student of the bad classroom learning behavior again within the predefined guiding time, judging whether the bad classroom learning behavior still exists, if so, controlling a vibration motor in the wearable receiving terminal to perform vibration prompt on a teacher, feeding back the name, the seat number and the name of the bad classroom learning behavior corresponding to the student of the bad classroom learning behavior to a display screen of the wearable receiving terminal, and performing targeted manual guiding by the teacher.
9. An apparatus, characterized by: the system comprises a processor, a memory and a network interface, wherein the memory and the network interface are connected with the processor; the network interface is connected with a nonvolatile memory in the server; the processor, when running, retrieves a computer program from the non-volatile memory via the network interface and runs the computer program via the memory to perform the method of any of claims 1-8.
10. A computer storage medium, characterized in that: the computer storage medium is burned with a computer program, which when run in the memory of the server implements the method of any of the above claims 1-8.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090181353A1 (en) * | 2008-01-14 | 2009-07-16 | Verizon Data Services Inc. | Interactive learning |
| CN204331993U (en) * | 2014-05-09 | 2015-05-13 | 郑州信息科技职业学院 | A kind of pupil's safety and the comprehensive backup system of study |
| EP2902993A1 (en) * | 2014-01-29 | 2015-08-05 | Provadis Partner für Bildung und Beratung GmbH | Wireless teaching system |
| CN107154202A (en) * | 2017-07-12 | 2017-09-12 | 昆明理工大学 | Smart classroom system |
| US20170352285A1 (en) * | 2016-06-03 | 2017-12-07 | Mats A. Selen | System and method of monitoring behavior and engagement using a mobile device |
| US20180285997A1 (en) * | 2017-04-03 | 2018-10-04 | International Business Machines Corporation | Cognitive education advisor |
| CN109284737A (en) * | 2018-10-22 | 2019-01-29 | 广东精标科技股份有限公司 | A kind of students ' behavior analysis and identifying system for wisdom classroom |
| CN109523441A (en) * | 2018-12-20 | 2019-03-26 | 合肥凌极西雅电子科技有限公司 | A kind of Teaching Management Method and system based on video identification |
| CN110210369A (en) * | 2019-05-28 | 2019-09-06 | 华中科技大学 | A kind of attention of student measuring system based on moved seat |
| CN110246383A (en) * | 2019-07-18 | 2019-09-17 | 许昌学院 | A kind of multimedia education system and its application method |
| US20200193859A1 (en) * | 2018-12-17 | 2020-06-18 | Carnegie Mellon University | Tool, system and method for mixed-reality awareness educational environments |
| CN211878778U (en) * | 2020-03-31 | 2020-11-06 | 温州大学 | Real-time interactive chemical experiment teaching system based on virtual reality technology |
-
2021
- 2021-07-06 CN CN202110761320.9A patent/CN113485554A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090181353A1 (en) * | 2008-01-14 | 2009-07-16 | Verizon Data Services Inc. | Interactive learning |
| EP2902993A1 (en) * | 2014-01-29 | 2015-08-05 | Provadis Partner für Bildung und Beratung GmbH | Wireless teaching system |
| CN204331993U (en) * | 2014-05-09 | 2015-05-13 | 郑州信息科技职业学院 | A kind of pupil's safety and the comprehensive backup system of study |
| US20170352285A1 (en) * | 2016-06-03 | 2017-12-07 | Mats A. Selen | System and method of monitoring behavior and engagement using a mobile device |
| US20180285997A1 (en) * | 2017-04-03 | 2018-10-04 | International Business Machines Corporation | Cognitive education advisor |
| CN107154202A (en) * | 2017-07-12 | 2017-09-12 | 昆明理工大学 | Smart classroom system |
| CN109284737A (en) * | 2018-10-22 | 2019-01-29 | 广东精标科技股份有限公司 | A kind of students ' behavior analysis and identifying system for wisdom classroom |
| US20200193859A1 (en) * | 2018-12-17 | 2020-06-18 | Carnegie Mellon University | Tool, system and method for mixed-reality awareness educational environments |
| CN109523441A (en) * | 2018-12-20 | 2019-03-26 | 合肥凌极西雅电子科技有限公司 | A kind of Teaching Management Method and system based on video identification |
| CN110210369A (en) * | 2019-05-28 | 2019-09-06 | 华中科技大学 | A kind of attention of student measuring system based on moved seat |
| CN110246383A (en) * | 2019-07-18 | 2019-09-17 | 许昌学院 | A kind of multimedia education system and its application method |
| CN211878778U (en) * | 2020-03-31 | 2020-11-06 | 温州大学 | Real-time interactive chemical experiment teaching system based on virtual reality technology |
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