CN112258090A - Online education management system based on Internet of things - Google Patents
Online education management system based on Internet of things Download PDFInfo
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Abstract
The invention discloses an online education management system based on the Internet of things, which relates to the technical field of online education management and solves the technical problem that teachers cannot be effectively and reasonably allocated to classes in the prior art, so that the matching is improper, the learning efficiency is reduced, class data is analyzed through an allocation unit, teachers are reasonably allocated to the classes, analysis coefficients of the classes are obtained, the classes and teachers are divided, teachers are reasonably matched for the classes, the learning efficiency of students is improved, the teaching quality of teachers is enhanced, student information is analyzed through a course recommendation unit, audio course recommendation is carried out on the students, audio courses are reasonably recommended for the students, the eyes of the students are effectively protected, the vision problems of the students are reduced, the operation data of a cloud management platform is analyzed through a network protection unit, and the cloud management platform is detected, the cloud management platform is protected by the network, and the phenomenon that the cloud management platform cannot be used due to network faults in the using process is prevented.
Description
Technical Field
The invention relates to the technical field of online education management, in particular to an online education management system based on the Internet of things.
Background
With the rapid development of information technology, especially from the internet to the mobile internet, cross-space life, work and learning modes are created, and the mode of acquiring knowledge is fundamentally changed. The teaching and learning can be free from the limitation of time, space and place conditions, and the knowledge acquisition channel is flexible and diversified; based on the characteristics and advantages of online education, online schools are accepted by more and more people, various emerging online schools and related websites are more and more emerging, and obviously, the online schools represent the main trend that the online schools gradually enter the lives of the public and become learning. Many people have therefore begun to choose online education, particularly white-collar clans and college students, where users of online education are scaled up.
But in prior art, can not be effectual to class rational distribution teacher, prevent to match improper and lead to the study efficiency to reduce, can not carry out reasonable protection to student's eyesight simultaneously.
Disclosure of Invention
The invention aims to provide an online education management system based on the Internet of things, which is characterized in that a distribution unit is used for analyzing class data so as to reasonably distribute teachers for classes, reasonably match teachers for classes, improve the learning efficiency of students, enhance the teaching quality of teachers, and analyze student information through a course recommendation unit so as to recommend audio courses to students, reasonably recommend audio courses to students, effectively protect eyes of students and reduce the vision problems of students; the operation data of the cloud management platform are analyzed through the network protection unit, so that the cloud management platform is detected, network protection is performed on the cloud management platform, the situation that the cloud management platform cannot be used due to network faults in the using process is prevented, and the working efficiency is reduced.
The purpose of the invention can be realized by the following technical scheme:
an online education management system based on the Internet of things comprises a cloud management platform, a course recommendation unit, a distribution unit, a teacher management unit, a network protection unit, a registration login unit and a database;
the assignment unit is used for analyzing class data to reasonably assign teachers to the classes, the class data are the difference value of the highest score and the lowest score of the classes, the difference value of the average score of the classes and the average score of the grades, and the proportion of boys and girls in the classes, the classes are marked as o, o is 1, 2, and the class is a positive integer, and the specific analysis and assignment process is as follows:
step S1: acquiring the difference value between the highest grade and the lowest grade and the difference value between the average grade and the average grade, and respectively marking the difference value between the highest grade and the lowest grade and the difference value between the average grade and the average grade as Bo and Co;
step S2: by the formula
Obtaining an analysis coefficient Xo of a class; wherein s1 and s2 are both preset proportionality coefficients, s1 is greater than s2 is greater than 0, and beta is an error correction factor and takes the value of 2.302561;
step S3: comparing the analysis coefficient Xo for the class with L1, L2, L1 and L2 are both analysis coefficient thresholds, and L1 > L2:
if the analysis coefficient Xo of the class is larger than L1, judging that the comprehensive performance of the class is excellent, and marking the class as a class with a higher level;
if the analysis coefficient Xo of the class is less than or equal to L2 and less than or equal to L1, judging that the comprehensive score of the class is medium, and marking the class as a consolidated class;
if the analysis coefficient Xo of the class is less than L2, judging that the comprehensive score of the class is general, and marking the class as a reinforcement class;
step S4: acquiring an advanced teacher in a database, collecting lesson preparation notes of the advanced teacher, then randomly selecting k students to comment the lesson preparation notes, classifying the lesson preparation notes into three classes of difficulty, medium and simple according to the evaluation number of the students, respectively marking the corresponding teachers as a difficult teacher, a medium teacher and a simple teacher according to the classes, wherein k is a number threshold;
step S5: the class pulling-up, the class fixing and the class repairing are correspondingly matched with a difficult teacher, a medium teacher and a simple teacher, the class serial number and the corresponding teacher name job number are sent to the cloud management platform, the cloud management platform sends the class serial number to the mobile phone terminal of the corresponding teacher, the teacher sends a receiving signal to the cloud management platform through the mobile phone terminal, and the cloud management platform sends the class serial number and the corresponding teacher name job number to the database for storage after receiving.
Further, the registration login unit is used for a teacher and a manager to submit teacher information and manager information for registration through a mobile phone terminal, and send the successfully registered teacher information and manager information to the database for storage, wherein the teacher information is a mobile phone number for authenticating the name, the religion time, the age, the job number, the religion subject and the real name of the manager, and the manager information is a mobile phone number for authenticating the name, the age, the time of entry and the real name of the manager.
Further, teacher's management unit is used for analyzing teacher's data, and carry out the rank division to the teacher, teacher's data is teacher's course data, student's data and comment data, the course data is the course quantity of teacher a week and the course quantity sum of reservation, student's data is the teacher's student's total number and the total number of graduates, comment data is the ratio of the head of a family to the teacher's quantity of commenting with good comment and the total number that the teacher received, mark the teacher as i, i is 1, 2, the.
Step one, acquiring the sum of the course number of the teacher in one week and the reserved course number, and summing the course number of the teacher in one week and the reserved course number Ki;
step two, acquiring the sum of the total number of students and the total number of graduates of the teacher, and marking the sum of the total number of the students and the total number of the graduates of the teacher as Xi;
step three, acquiring the ratio of the number of the parents commenting the teacher to the total number of the comments received by the teacher, and marking the ratio of the number of the parents commenting the teacher to the total number of the comments received by the teacher as Bi;
step four, passing through a formula
Acquiring a teacher analysis coefficient Si, wherein c1, c2 and c3 are all preset proportional coefficients, and c1 is larger than c2 and c3 is larger than 0;
step five, comparing the teacher analysis coefficient Si with an analysis coefficient threshold value:
if the teacher analysis coefficient Si is larger than or equal to the analysis coefficient threshold value, marking the teacher corresponding to the teacher analysis coefficient as a high-class teacher, and sending the high-class teacher and the job number and the name of the corresponding teacher to the cloud management platform;
and if the teacher analysis coefficient Si is less than the analysis coefficient threshold value, marking the teacher corresponding to the teacher analysis coefficient as a primary teacher, and sending the work number and the name of the primary teacher and the corresponding teacher to the cloud management platform.
Further, the course recommending unit is used for analyzing student information, so that audio course recommendation is performed on students, the student information is duration data, frequency data and family data, the duration data is the average duration of the students in online class watching, the frequency data is the frequency of the students in class within a week, the family data is the number of people wearing glasses in relatives of the students, the audio course is a course converted into audio in the online class, the students are marked as u, u is 1, 2, the.
Step SS 1: acquiring the average time length of the students watching the classroom online, the class-taking frequency of the students within one week and the number of people wearing glasses in relatives of the students, and correspondingly marking the average time length of the students watching the classroom online, the class-taking frequency of the students within one week and the number of people wearing glasses in the relatives of the students as Su, Pu and Ru;
step SS 2: by the formula
Acquiring a recommendation coefficient Au of a student, wherein both b1 and b2 are preset proportionality coefficients;
step SS 3: comparing the recommendation coefficient Au of the student with a recommendation coefficient threshold:
if the recommendation coefficient Au of the student is larger than or equal to the recommendation coefficient threshold value, generating a recommendation signal and sending the recommendation signal to the cloud management platform, recommending an audio course to the student after the cloud management platform receives the recommendation model, selecting the course section by the student through the mobile phone terminal, sending the selected course section to the cloud management platform, and converting the course section into the audio course;
and if the recommendation coefficient Au of the student is less than the recommendation coefficient threshold value, generating a setting signal and sending the setting signal to the cloud management platform, setting interval rest time for the student after the cloud management platform receives the setting signal, and playing the music of the eye exercises in the interval rest time.
Further, the network protection unit is configured to analyze operation data of the cloud management platform, so as to detect the cloud management platform, where the operation data is a maximum number of users who support the online all-day cloud management platform, the number of times of disconnection of the all-day online classroom network, and the number of times of delay of the all-day network, and a specific analysis and detection process is as follows:
step L1: acquiring the maximum number of people who support the online of a user, the offline times of the all-day online classroom network and the times of the delay of the all-day network by the all-day cloud management platform, and respectively marking the maximum number of people who support the online of the user, the offline times of the all-day online classroom network and the times of the delay of the all-day network as ZD, DX and CS;
step L2: by the formula
Acquiring an operation coefficient XX of the cloud management platform, wherein v1, v2 and v3 are all preset proportionality coefficients, v1 is greater than v2 is greater than v3 is greater than 0, and alpha is an error correction factor and is 1.3698563;
step L3: comparing the operation coefficient XX of the cloud management platform with an operation coefficient threshold value:
if the operation coefficient XX of the cloud management platform is larger than or equal to the operation coefficient threshold value, judging that the cloud management platform operates normally, generating a normal signal and sending the normal signal to a mobile phone terminal of a manager;
if the operation coefficient XX of the cloud management platform is smaller than the operation coefficient threshold value, judging that the cloud management platform is abnormal in operation, generating an abnormal signal and sending the abnormal signal to a mobile phone terminal of a manager, setting protection time and predicted completion time by the cloud management platform, and sending the protection time and the predicted completion time to the mobile phone terminal of the manager.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, the distribution unit is used for analyzing class data so as to reasonably distribute teachers for classes, obtain the difference value between the highest score and the lowest score of the classes and the difference value between the average score of the classes and the average score of the grades, and obtain the analysis coefficient Xo of the classes through a formula; comparing the analysis coefficient Xo of the class with L1 and L2, if the analysis coefficient Xo of the class is larger than L1, judging that the comprehensive performance of the class is excellent, and marking the class as a class with a higher level; if the analysis coefficient Xo of the class is less than or equal to L2 and less than or equal to L1, judging that the comprehensive score of the class is medium, and marking the class as a consolidated class; if the analysis coefficient Xo of the class is less than L2, judging that the comprehensive score of the class is general, and marking the class as a reinforcement class; acquiring high-class teachers in a database, collecting lesson preparation notes of the high-class teachers, then randomly selecting k students to comment the lesson preparation notes, classifying the lesson preparation notes into three classes of difficulty, medium and simple according to the evaluation number of the students, and respectively marking corresponding teachers as difficult teachers, medium teachers and simple teachers according to the classes; correspondingly matching the pull-up class, the consolidation class and the reinforcement class with a difficult teacher, a medium teacher and a simple teacher; the classes are reasonably matched with teachers, so that the learning efficiency of students is improved, and the teaching quality of the teachers is enhanced;
2. according to the method, the information of a student is analyzed through a course recommending unit, so that audio course recommendation is performed on the student, the average time length of the student watching a classroom online, the frequency of the student getting class within a week and the number of people wearing glasses in relatives of the student are obtained, the recommending coefficient Au of the student is obtained through a formula, if the recommending coefficient Au of the student is larger than or equal to the recommending coefficient threshold value, a recommending signal is generated and sent to a cloud management platform, the cloud management platform recommends an audio course to the student after receiving a recommending model, the student selects course chapters through a mobile phone terminal and sends the selected course chapters to the cloud management platform, and the course chapters are converted into audio courses; if the recommendation coefficient Au of the student is less than the recommendation coefficient threshold value, generating a setting signal and sending the setting signal to the cloud management platform, setting interval rest time for the student after the cloud management platform receives the setting signal, and playing music of the eye exercises within the interval rest time; the audio course is reasonably recommended for the students, so that the eyes of the students are effectively protected, and the vision problems of the students are reduced;
3. in the invention, the operation data of the cloud management platform is analyzed through a network protection unit, so that the cloud management platform is detected, the maximum number of online users supported by the all-day cloud management platform, the number of offline times of the all-day online classroom network and the number of times of delay of the all-day network are obtained, the operation coefficient XX of the cloud management platform is obtained through a formula, if the operation coefficient XX of the cloud management platform is less than the operation coefficient threshold value, the cloud management platform is judged to be abnormal in operation, an abnormal signal is generated and sent to a mobile phone terminal of a manager, the cloud management platform sets protection time and predicted completion time, and the protection time and the predicted completion time are sent to the mobile phone terminal of the manager; the cloud management platform is protected by the network, the phenomenon that the cloud management platform cannot be used due to network faults in the using process is prevented, and the working efficiency is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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, an online education management system based on the internet of things includes a cloud management platform, a course recommendation unit, a distribution unit, a teacher management unit, a network protection unit, a registration unit and a database;
the registration login unit is used for a teacher and a manager to submit teacher information and manager information through a mobile phone terminal for registration, and sends the successfully registered teacher information and manager information to a database for storage, wherein the teacher information is the name, the teaching time, the age, the employee number, the teaching subject and the mobile phone number of the identity real name authentication, and the manager information is the name, the age, the enrollment time and the mobile phone number of the identity real name authentication;
teacher management unit is used for analyzing teacher's data, and carry out rank division to the teacher, teacher's data is teacher's course data, student's data and comment data, the course data is the course quantity sum of teacher a week and the course quantity of reservation, student's data is the sum of teacher's student's total quantity and graduate's total quantity, comment data be the ratio of the total number of commenting that the head of a family received to the teacher, mark the teacher as i, i is 1, 2, a.
Step one, acquiring the sum of the course number of the teacher in one week and the reserved course number, and summing the course number of the teacher in one week and the reserved course number Ki;
step two, acquiring the sum of the total number of students and the total number of graduates of the teacher, and marking the sum of the total number of the students and the total number of the graduates of the teacher as Xi;
step three, acquiring the ratio of the number of the parents commenting the teacher to the total number of the comments received by the teacher, and marking the ratio of the number of the parents commenting the teacher to the total number of the comments received by the teacher as Bi;
step four, passing through a formula
Acquiring a teacher analysis coefficient Si, wherein c1, c2 and c3 are all preset proportional coefficients, and c1 is larger than c2 and c3 is larger than 0;
step five, comparing the teacher analysis coefficient Si with an analysis coefficient threshold value:
if the teacher analysis coefficient Si is larger than or equal to the analysis coefficient threshold value, marking the teacher corresponding to the teacher analysis coefficient as a high-class teacher, and sending the high-class teacher and the job number and the name of the corresponding teacher to the cloud management platform;
if the teacher analysis coefficient Si is smaller than the analysis coefficient threshold value, the teacher corresponding to the teacher analysis coefficient is marked as a primary teacher, and the work number and the name of the primary teacher and the work number and the name of the corresponding teacher are sent to the cloud management platform;
the assignment unit is used for analyzing class data to reasonably assign teachers to the classes, the class data is the difference value of the highest score and the lowest score of the classes, the difference value of the average score and the average score of the grades and the male-female ratio inside the classes, the classes are marked as o, o is 1, 2, and m is a positive integer, and the specific analysis and assignment process is as follows:
step S1: acquiring the difference value between the highest grade and the lowest grade and the difference value between the average grade and the average grade, and respectively marking the difference value between the highest grade and the lowest grade and the difference value between the average grade and the average grade as Bo and Co;
step S2: by the formula
Obtaining an analysis coefficient Xo of a class; wherein s1 and s2 are both preset proportionality coefficients, s1 is greater than s2 is greater than 0, and beta is an error correction factor and takes the value of 2.302561;
step S3: comparing the analysis coefficient Xo for the class with L1, L2, L1 and L2 are both analysis coefficient thresholds, and L1 > L2:
if the analysis coefficient Xo of the class is larger than L1, judging that the comprehensive performance of the class is excellent, and marking the class as a class with a higher level;
if the analysis coefficient Xo of the class is less than or equal to L2 and less than or equal to L1, judging that the comprehensive score of the class is medium, and marking the class as a consolidated class;
if the analysis coefficient Xo of the class is less than L2, judging that the comprehensive score of the class is general, and marking the class as a reinforcement class;
step S4: acquiring an advanced teacher in a database, collecting lesson preparation notes of the advanced teacher, then randomly selecting k students to comment the lesson preparation notes, classifying the lesson preparation notes into three classes of difficulty, medium and simple according to the evaluation number of the students, respectively marking the corresponding teachers as a difficult teacher, a medium teacher and a simple teacher according to the classes, wherein k is a number threshold;
step S5: the class pulling-up, class consolidating and class supplementing are correspondingly matched with a difficult teacher, a medium teacher and a simple teacher, the class serial number and the corresponding teacher name job number are sent to the cloud management platform, the cloud management platform sends the class serial number to a mobile phone terminal of the corresponding teacher, the teacher sends a receiving signal to the cloud management platform through the mobile phone terminal, and the cloud management platform sends the class serial number and the corresponding teacher name job number to a database for storage after receiving the class serial number and the corresponding teacher name job number;
the course recommending unit is used for analyzing student information so as to recommend audio courses to students, the student information is duration data, frequency data and family data, the duration data is the average duration of the students in online class watching, the frequency data is the frequency of the students in class within a week, the family data is the number of people wearing glasses in relatives of the students, the audio courses are courses which are converted into audio in online classes, the students are marked as u, u is 1, 2, the.
Step SS 1: acquiring the average time length of the students watching the classroom online, the class-taking frequency of the students within one week and the number of people wearing glasses in relatives of the students, and correspondingly marking the average time length of the students watching the classroom online, the class-taking frequency of the students within one week and the number of people wearing glasses in the relatives of the students as Su, Pu and Ru;
step SS 2: by the formula
Acquiring a recommendation coefficient Au of a student, wherein both b1 and b2 are preset proportionality coefficients;
step SS 3: comparing the recommendation coefficient Au of the student with a recommendation coefficient threshold:
if the recommendation coefficient Au of the student is larger than or equal to the recommendation coefficient threshold value, generating a recommendation signal and sending the recommendation signal to the cloud management platform, recommending an audio course to the student after the cloud management platform receives the recommendation model, selecting the course section by the student through the mobile phone terminal, sending the selected course section to the cloud management platform, and converting the course section into the audio course;
if the recommendation coefficient Au of the student is less than the recommendation coefficient threshold value, generating a setting signal and sending the setting signal to the cloud management platform, setting interval rest time for the student after the cloud management platform receives the setting signal, and playing music of the eye exercises within the interval rest time;
the network protection unit is used for analyzing operation data of the cloud management platform so as to detect the cloud management platform, the operation data are the maximum number of users who support the online all-day cloud management platform, the offline times of all-day online classroom networks and the times of all-day network delay, and the specific analysis and detection process is as follows:
step L1: acquiring the maximum number of people who support the online of a user, the offline times of the all-day online classroom network and the times of the delay of the all-day network by the all-day cloud management platform, and respectively marking the maximum number of people who support the online of the user, the offline times of the all-day online classroom network and the times of the delay of the all-day network as ZD, DX and CS;
step L2: by the formula
Acquiring an operation coefficient XX of the cloud management platform, wherein v1, v2 and v3 are all preset proportionality coefficients, v1 is greater than v2 is greater than v3 is greater than 0, and alpha is an error correction factor and is 1.3698563;
step L3: comparing the operation coefficient XX of the cloud management platform with an operation coefficient threshold value:
if the operation coefficient XX of the cloud management platform is larger than or equal to the operation coefficient threshold value, judging that the cloud management platform operates normally, generating a normal signal and sending the normal signal to a mobile phone terminal of a manager;
if the operation coefficient XX of the cloud management platform is smaller than the operation coefficient threshold value, judging that the cloud management platform is abnormal in operation, generating an abnormal signal and sending the abnormal signal to a mobile phone terminal of a manager, setting protection time and predicted completion time by the cloud management platform, and sending the protection time and the predicted completion time to the mobile phone terminal of the manager.
The working principle of the invention is as follows:
when the online education management system based on the Internet of things works, class data are analyzed through a distribution unit, teachers are reasonably distributed to classes, the difference value between the highest score and the lowest score of the classes and the difference value between the average score of the classes and the average score of the grades are obtained, and the analysis coefficient Xo of the classes is obtained through a formula; comparing the analysis coefficient Xo of the class with L1 and L2, if the analysis coefficient Xo of the class is larger than L1, judging that the comprehensive performance of the class is excellent, and marking the class as a class with a higher level; if the analysis coefficient Xo of the class is less than or equal to L2 and less than or equal to L1, judging that the comprehensive score of the class is medium, and marking the class as a consolidated class; if the analysis coefficient Xo of the class is less than L2, judging that the comprehensive score of the class is general, and marking the class as a reinforcement class; acquiring high-class teachers in a database, collecting lesson preparation notes of the high-class teachers, then randomly selecting k students to comment the lesson preparation notes, classifying the lesson preparation notes into three classes of difficulty, medium and simple according to the evaluation number of the students, and respectively marking corresponding teachers as difficult teachers, medium teachers and simple teachers according to the classes; the class pulling-up, the class fixing and the class repairing are correspondingly matched with a difficult teacher, a medium teacher and a simple teacher, the class serial number and the corresponding teacher name job number are sent to the cloud management platform, the cloud management platform sends the class serial number to the mobile phone terminal of the corresponding teacher, the teacher sends a receiving signal to the cloud management platform through the mobile phone terminal, and the cloud management platform sends the class serial number and the corresponding teacher name job number to the database for storage after receiving.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
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 (5)
1. An online education management system based on the Internet of things is characterized by comprising a cloud management platform, a course recommendation unit, a distribution unit, a teacher management unit, a network protection unit, a registration login unit and a database;
the assignment unit is used for analyzing class data to reasonably assign teachers to the classes, the class data are the difference value of the highest score and the lowest score of the classes, the difference value of the average score of the classes and the average score of the grades, and the proportion of boys and girls in the classes, the classes are marked as o, o is 1, 2, and the class is a positive integer, and the specific analysis and assignment process is as follows:
step S1: acquiring the difference value between the highest grade and the lowest grade and the difference value between the average grade and the average grade, and respectively marking the difference value between the highest grade and the lowest grade and the difference value between the average grade and the average grade as Bo and Co;
step S2: by the formula
Obtaining an analysis coefficient Xo of a class; wherein s1 and s2 are both preset proportionality coefficients, s1 is greater than s2 is greater than 0, and beta is an error correction factor and takes the value of 2.302561;
step S3: comparing the analysis coefficient Xo for the class with L1, L2, L1 and L2 are both analysis coefficient thresholds, and L1 > L2:
if the analysis coefficient Xo of the class is larger than L1, judging that the comprehensive performance of the class is excellent, and marking the class as a class with a higher level;
if the analysis coefficient Xo of the class is less than or equal to L2 and less than or equal to L1, judging that the comprehensive score of the class is medium, and marking the class as a consolidated class;
if the analysis coefficient Xo of the class is less than L2, judging that the comprehensive score of the class is general, and marking the class as a reinforcement class;
step S4: acquiring an advanced teacher in a database, collecting lesson preparation notes of the advanced teacher, then randomly selecting k students to comment the lesson preparation notes, classifying the lesson preparation notes into three classes of difficulty, medium and simple according to the evaluation number of the students, respectively marking the corresponding teachers as a difficult teacher, a medium teacher and a simple teacher according to the classes, wherein k is a number threshold;
step S5: the class pulling-up, the class fixing and the class repairing are correspondingly matched with a difficult teacher, a medium teacher and a simple teacher, the class serial number and the corresponding teacher name job number are sent to the cloud management platform, the cloud management platform sends the class serial number to the mobile phone terminal of the corresponding teacher, the teacher sends a receiving signal to the cloud management platform through the mobile phone terminal, and the cloud management platform sends the class serial number and the corresponding teacher name job number to the database for storage after receiving.
2. The internet of things-based online education management system according to claim 1, wherein the registration login unit is used for teachers and managers to submit teacher information and manager information for registration through mobile phone terminals, and send the successfully registered teacher information and manager information to the database for storage, the teacher information is the name of the teacher, the time of the teacher in charge, the age, the number of the employee, the mobile phone number for authenticating the department of the teacher in charge and the real name of the manager in charge, and the manager information is the name, the age, the time of the employee in charge and the mobile phone number for authenticating the real name of the manager in charge.
3. The system for managing online education based on the internet of things as claimed in claim 1, wherein the teacher management unit is used for analyzing teacher data and performing level division on teachers, the teacher data are teacher course data, student data and comment data, the course data are sum of the course number of the teacher for one week and the reserved course number, the student data are sum of the total number of students in school and the total number of graduates of the teacher, the comment data are the ratio of the number of the parents commenting on the teacher to the total number of comments the teacher receives, and the teacher is marked as i, i 1, 2, a.
Step one, acquiring the sum of the course number of the teacher in one week and the reserved course number, and summing the course number of the teacher in one week and the reserved course number Ki;
step two, acquiring the sum of the total number of students and the total number of graduates of the teacher, and marking the sum of the total number of the students and the total number of the graduates of the teacher as Xi;
step three, acquiring the ratio of the number of the parents commenting the teacher to the total number of the comments received by the teacher, and marking the ratio of the number of the parents commenting the teacher to the total number of the comments received by the teacher as Bi;
step four, passing through a formula
Acquiring a teacher analysis coefficient Si, wherein c1, c2 and c3 are all preset proportional coefficients, and c1 is larger than c2 and c3 is larger than 0;
step five, comparing the teacher analysis coefficient Si with an analysis coefficient threshold value:
if the teacher analysis coefficient Si is larger than or equal to the analysis coefficient threshold value, marking the teacher corresponding to the teacher analysis coefficient as a high-class teacher, and sending the high-class teacher and the job number and the name of the corresponding teacher to the cloud management platform;
and if the teacher analysis coefficient Si is less than the analysis coefficient threshold value, marking the teacher corresponding to the teacher analysis coefficient as a primary teacher, and sending the work number and the name of the primary teacher and the corresponding teacher to the cloud management platform.
4. The system of claim 1, wherein the course recommendation unit is configured to analyze student information to recommend audio courses to students, the student information is duration data, frequency data and family data, the duration data is an average duration of online class watching of students, the frequency data is a frequency of online class watching of students within one week, the family data is a number of people wearing glasses in relatives of students, the audio course is a course converted from the online class to audio, and the students are marked as u, u 1, 2,......, m1, and m1 are positive integers, and the specific analysis and recommendation process is as follows:
step SS 1: acquiring the average time length of the students watching the classroom online, the class-taking frequency of the students within one week and the number of people wearing glasses in relatives of the students, and correspondingly marking the average time length of the students watching the classroom online, the class-taking frequency of the students within one week and the number of people wearing glasses in the relatives of the students as Su, Pu and Ru;
step SS 2: by the formula
Acquiring a recommendation coefficient Au of a student, wherein both b1 and b2 are preset proportionality coefficients;
step SS 3: comparing the recommendation coefficient Au of the student with a recommendation coefficient threshold:
if the recommendation coefficient Au of the student is larger than or equal to the recommendation coefficient threshold value, generating a recommendation signal and sending the recommendation signal to the cloud management platform, recommending an audio course to the student after the cloud management platform receives the recommendation model, selecting the course section by the student through the mobile phone terminal, sending the selected course section to the cloud management platform, and converting the course section into the audio course;
and if the recommendation coefficient Au of the student is less than the recommendation coefficient threshold value, generating a setting signal and sending the setting signal to the cloud management platform, setting interval rest time for the student after the cloud management platform receives the setting signal, and playing the music of the eye exercises in the interval rest time.
5. The internet of things-based online education management system according to claim 1, wherein the network protection unit is configured to analyze operation data of the cloud management platform so as to detect the cloud management platform, the operation data includes a maximum number of users who are supported by the all-day cloud management platform to be online, the number of times of offline of the all-day online classroom network, and the number of times of delay of the all-day network, and the specific analysis and detection process includes:
step L1: acquiring the maximum number of people who support the online of a user, the offline times of the all-day online classroom network and the times of the delay of the all-day network by the all-day cloud management platform, and respectively marking the maximum number of people who support the online of the user, the offline times of the all-day online classroom network and the times of the delay of the all-day network as ZD, DX and CS;
step L2: by the formula
Acquiring an operation coefficient XX of the cloud management platform, wherein v1, v2 and v3 are all preset proportionality coefficients, v1 is greater than v2 is greater than v3 is greater than 0, and alpha is an error correction factor and is 1.3698563;
step L3: comparing the operation coefficient XX of the cloud management platform with an operation coefficient threshold value:
if the operation coefficient XX of the cloud management platform is larger than or equal to the operation coefficient threshold value, judging that the cloud management platform operates normally, generating a normal signal and sending the normal signal to a mobile phone terminal of a manager;
if the operation coefficient XX of the cloud management platform is smaller than the operation coefficient threshold value, judging that the cloud management platform is abnormal in operation, generating an abnormal signal and sending the abnormal signal to a mobile phone terminal of a manager, setting protection time and predicted completion time by the cloud management platform, and sending the protection time and the predicted completion time to the mobile phone terminal of the manager.
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