CN112163119A - Big data online education platform course optimization method and system - Google Patents
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Abstract
The embodiment of the invention provides a big data online education platform course optimization method and a big data online education platform course optimization system, wherein the method comprises the following steps: randomly obtaining a teacher from a teacher big database as a target recommendation teacher, and sending teacher information of the target recommendation teacher to students; if the operation that the student audits the course video is monitored, controlling a camera to be opened and shooting an audition video of the student in the process of audition of the course video; when the operation of listening the course video in a trial mode is monitored, conducting adaptive scoring on a teacher corresponding to the course video according to the listening video, and sending an adaptive scoring result to the student; obtaining the sum of the matching index of the target recommendation teacher and the deviation value to obtain an adjusted matching index; and transmitting the teacher information in the teacher big database corresponding to the adjusted matching index to the student. So can match suitable teacher for the student, improve student's experience effect.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a big data online education platform course optimization method and system.
Background
With the rapid development of science and technology, the form of online class becomes more and more popular. Both students and teachers teach and learn through the form of a network. In the prior art, an online education platform of mobile phone application software is usually used for directly configuring teachers for students. Students directly go to the internet lesson. However, it is well known that the interest is the best teacher, and that teaching is not good if the teacher assigned to the student is not appropriate for the student.
In the prior art, a teacher is pushed to a student, but the prior push is generally based on the user rating of the teacher or the membership grade of the teacher. In fact, the styles that different students fit differ, such as for the same teacher, who was last on a web class scored the teacher high, but perhaps for the student teacher who is present, the teacher does not fit because each individual likes a different style.
Therefore, if teachers can be recommended economically, economically and accurately, the learning effect of students can be improved and optimized.
Disclosure of Invention
The invention aims to provide a method and a system for optimizing teaching information of a big data online education platform, which are used for solving the problems in the prior art.
In a first aspect, an embodiment of the present invention provides a big data-based online education platform course optimization method, including:
randomly obtaining a teacher from a teacher big database as a target recommendation teacher, and sending teacher information of the target recommendation teacher to students; the teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video;
if the operation that the student audits the course video is monitored, controlling a camera to be opened and shooting an audition video of the student in the process of audition of the course video;
when the operation of listening the course video in a trial mode is monitored, conducting adaptive scoring on a teacher corresponding to the course video according to the listening video, and sending an adaptive scoring result to the student;
if the fact that the students choose to learn or collect the teacher information is monitored, the teacher information is stored in a teacher library of the students;
if the operation that the students refuse to audition the course video is monitored, the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones and the personality information of the students are obtained, and deviation values between the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones and the personality information of the students and the teaching subjects, the teaching levels, the teaching rhythms, the teaching timbres, the teaching tones and the teaching styles of the target recommendation teacher are calculated;
obtaining the sum of the matching index of the target recommendation teacher and the deviation value to obtain an adjusted matching index;
and transmitting the teacher information in the teacher big database corresponding to the adjusted matching index to the student.
Optionally, the video includes multiple frames of face images, and the adaptive scoring is performed on the teacher corresponding to the course video according to the audition video, including:
identifying face information of students in each frame of face image of the video, wherein the plurality of frames of face images correspond to a plurality of pieces of face information;
aiming at each frame of face image, obtaining the preference value of the student to the teacher according to the face information, wherein the preference value represents the reaction condition of the student to the course teaching of the teacher;
and taking the sum of the preference values corresponding to all the face images as a result of the adaptability scoring of the students to the teacher.
Optionally, the face information includes a face information graph, and the face information graph is an image obtained by combining face features and a face contour; the obtaining of the preference value of the student to the teacher according to the face information includes:
obtaining a deformation information image of the face based on the face information image and the standard face information image; the standard face information image is obtained based on a standard face image, and the standard face image is shot in advance and stored in a large database;
taking the pixel values of the deformation information graph as preference values of the students to the teacher; and aiming at the pixel values in the deformation information image, the pixel values of the mouth corner feature points are the change distances between the positions of the mouth corners in the face information image and the positions of the mouth corners in the standard face information.
Optionally, calculating a deviation value between the learning subject, the learning level, the adaptation rhythm, the preference tone, and the personality information of the student and the teaching subject, the teaching level, the teaching rhythm, the teaching tone, and the teaching style of the target recommendation teacher, specifically:
weighting and summing the difference values of the learning subjects, the learning level, the adaptive rhythm, the preferred tone and the character information with the teaching subjects, the teaching level, the teaching rhythm, the teaching tone, the teaching style to obtain a first deviation index; the specific calculation mode is shown as formula (1):
where r1 denotes a first deviation index, akDenotes the kth weighting factor, akThe values of k being 1, 2, 3, 4, 5, 6 are 0.5, 0.2, 0.1, 0.09, 0.06, 0.05, S, respectivelykK is 1, 2, 3, 4, 5, 6 respectively representing values of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, and personality information, wherein the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones, and the personality information are respectively represented by one digital level;
taking standard deviations of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, character information, teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones and teaching styles as second deviation indexes;
and taking the quotient of the first deviation index and the second deviation index as a deviation value.
In a second aspect, an embodiment of the present invention provides a big data-based online education platform course optimization system, including:
the acquisition module randomly acquires a teacher from a teacher big database as a target recommendation teacher and sends teacher information of the target recommendation teacher to students; the teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video;
the shooting module is used for controlling a camera to be opened and shooting the audition video of the student in the audition process of the course video if the audition operation of the student on the course video is monitored;
the grading module is used for conducting adaptive grading on teachers corresponding to the curriculum videos according to the audition videos and sending adaptive grading results to the students when the operation of audition of the curriculum videos is finished is monitored;
the storage module is used for storing the teacher information into a teacher library of the students if the condition that the students select to learn or collect the teacher information is monitored;
the adjusting module is used for obtaining the learning subjects, learning levels, adaptive rhythms, preferential timbres, preferential tones and personality information of the students if the operation that the students refuse to audition the course videos is monitored, and calculating deviation values between the learning subjects, the learning levels, the adaptive rhythms, the preferential timbres, the preferential tones and the personality information of the students and the teaching subjects, the teaching levels, the teaching rhythms, the teaching timbres, the teaching tones and the teaching styles of the target recommendation teacher; obtaining the sum of the matching index of the target recommendation teacher and the deviation value to obtain an adjusted matching index; and transmitting the teacher information in the teacher big database corresponding to the adjusted matching index to the student.
Optionally, the video includes multiple frames of face images, and the adaptive scoring is performed on the teacher corresponding to the course video according to the audition video, including:
identifying face information of students in each frame of face image of the video, wherein the plurality of frames of face images correspond to a plurality of pieces of face information;
aiming at each frame of face image, obtaining the preference value of the student to the teacher according to the face information, wherein the preference value represents the reaction condition of the student to the course teaching of the teacher;
and taking the sum of the preference values corresponding to all the face images as a result of the adaptability scoring of the students to the teacher.
Optionally, the face information includes a face information graph, and the face information graph is an image obtained by combining face features and a face contour; the obtaining of the preference value of the student to the teacher according to the face information includes:
obtaining a deformation information image of the face based on the face information image and the standard face information image; the standard face information image is obtained based on a standard face image, and the standard face image is shot in advance and stored in a large database;
taking the pixel values of the deformation information graph as preference values of the students to the teacher; and aiming at the pixel values in the deformation information image, the pixel values of the mouth corner feature points are the change distances between the positions of the mouth corners in the face information image and the positions of the mouth corners in the standard face information.
Optionally, calculating a deviation value between the learning subject, the learning level, the adaptation rhythm, the preference tone, and the personality information of the student and the teaching subject, the teaching level, the teaching rhythm, the teaching tone, and the teaching style of the target recommendation teacher, specifically:
weighting and summing the difference values of the learning subjects, the learning level, the adaptive rhythm, the preferred tone and the character information with the teaching subjects, the teaching level, the teaching rhythm, the teaching tone, the teaching style to obtain a first deviation index; the specific calculation mode is shown as formula (1):
where r1 denotes a first deviation index, akDenotes the kth weighting factor, akThe values of k being 1, 2, 3, 4, 5, 6 are 0.5, 0.2, 0.1, 0.09, 0.06, 0.05, S, respectivelykK is 1, 2, 3, 4, 5, 6 respectively representing values of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, and personality information, wherein the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones, and the personality information are respectively represented by one digital level;
taking standard deviations of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, character information, teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones and teaching styles as second deviation indexes;
and taking the quotient of the first deviation index and the second deviation index as a deviation value.
In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps of any one of the methods described above.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the methods described above when executing the program.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a method and a system for optimizing teaching information (online lesson) of a big data online education platform, wherein the method comprises the following steps: randomly obtaining a teacher from a teacher big database as a target recommendation teacher, and sending teacher information of the target recommendation teacher to students; the teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video; if the operation that the student audits the course video is monitored, controlling a camera to be opened and shooting an audition video of the student in the process of audition of the course video; when the operation of listening the course video in a trial mode is monitored, conducting adaptive scoring on a teacher corresponding to the course video according to the listening video, and sending an adaptive scoring result to the student; if the fact that the students choose to learn or collect the teacher information is monitored, the teacher information is stored in a teacher library of the students; if the operation that the students refuse to audition the course video is monitored, the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones and the personality information of the students are obtained, and deviation values between the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones and the personality information of the students and the teaching subjects, the teaching levels, the teaching rhythms, the teaching timbres, the teaching tones and the teaching styles of the target recommendation teacher are calculated; obtaining the sum of the matching index of the target recommendation teacher and the deviation value to obtain an adjusted matching index; and transmitting the teacher information in the teacher big database corresponding to the adjusted matching index to the student. So can match suitable teacher for the student, improve student's experience effect.
Drawings
Fig. 1 is a flowchart of a course optimization method based on a big data online education platform according to an embodiment of the present invention.
Fig. 2 is a schematic block structure diagram of a course optimization system based on a big data online education platform according to an embodiment of the present invention.
Fig. 3 is a schematic block structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
Examples
The embodiment of the invention provides a course optimization method based on a big data online education platform, as shown in figure 1, the method comprises the following steps:
s101: and randomly obtaining a teacher from a teacher big database as a target recommendation teacher, and sending teacher information of the target recommendation teacher to students. The teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video.
S102: and if the operation that the student audits the course video is monitored, controlling the camera to be opened and shooting the audition video of the student in the course of audition of the course video.
S103: and when the operation of finishing audition of the course video is monitored, adaptively scoring the teacher corresponding to the course video according to the audition video, and sending the result of the adaptively scoring to the student.
S104: and if the condition that the students select to study or collect the teacher information is monitored, storing the teacher information into a teacher library of the students.
S105: if the operation that the students refuse audition on the course videos is monitored, learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones and personality information of the students are obtained, and deviation values between the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones and the personality information of the students and teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones and teaching styles of the target recommendation teacher are calculated.
S106: and obtaining the sum of the matching index of the target recommended teacher and the deviation value to obtain an adjusted matching index, and sending teacher information in a teacher big database corresponding to the adjusted matching index to students.
Through adopting above scheme, so can match suitable teacher for the student, improve student's experience effect.
As an optional embodiment, the online education platform course optimization method based on big data further includes:
and acquiring the imported music data which the students like to listen to daily through the music data import interface.
The music data comprises music songs, and the music songs which the students like to listen to can be obtained from the application programs which the students like to listen to songs in daily life.
Learning plan data selected by the student is obtained, wherein the learning plan data comprises learning subjects and learning levels. The study subjects comprise courses of English, mathematics, computers and the like, and the study levels comprise levels of first grade, second grade, first grade, second grade and the like which represent difficulty of students in learning courses.
And matching the music data which the student likes to listen to daily with the standard music in the big database to obtain the target standard music.
The target standard music is standard music matched with the music data which the student likes to listen to in daily life in the big database.
And obtaining rhythm, tone and character information corresponding to the target standard music from a large database.
And matching the learning subjects, the learning level, the rhythm, the tone and the character information with the teaching subjects, the teaching levels, the teaching rhythm, the teaching tone and the teaching style of the teachers in the teacher database to obtain the matching index of the teachers.
The matching index represents the matching degree of the student and the teacher.
And taking the teacher corresponding to the maximum value of the matching index as a target recommendation teacher, and sending teacher information of the target recommendation teacher to students.
The teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video.
By adopting the scheme, the favorite teachers which the students should be interested in are obtained through the songs which the students enjoy listening daily, and the subjects, the class levels, the rhythm, the tone and the style of teaching of the teachers are favored by the students, so that the learning effect of the students can be improved. The student no longer needs to listen to many courses to select suitable teachers, and the online education platform can intelligently match teachers that the student probably likes for the student, and is efficient, scientific and reliable.
Optionally, the music data that the student likes to listen to in daily life includes a plurality of songs that the student likes to listen to in daily life. The music data that the student likes to listen to in daily life is matched with the standard music in the big database to obtain the target standard music, and the specific method may be as follows:
obtaining oscillograms of a plurality of songs that students like to listen to everyday;
fitting the oscillograms of the multiple curves to obtain a fitted oscillogram;
obtaining a first difference value of a plurality of peaks of the fitted oscillogram and a plurality of peaks of the oscillogram of the standard music; obtaining a second difference value of the plurality of wave troughs of the fitting oscillogram and the plurality of wave troughs of the oscillogram of the standard music;
obtaining a matching factor between the fitting oscillogram and the standard music based on the first difference and the second difference; a plurality of standard music in the large database are provided, and a plurality of matching factors are correspondingly provided;
and obtaining the standard music with the maximum matching factor as the target standard music.
The plurality of wave crests of the fitting oscillogram correspond to the plurality of wave crests of the oscillogram of the standard music one by one; the obtaining of the first difference value between the plurality of peaks of the fitted oscillogram and the plurality of peaks of the oscillogram of the standard music is specifically:
obtaining the time difference and the amplitude difference between the wave crest of the fitting oscillogram and the wave crest of the oscillogram of the standard music in a one-to-one correspondence manner, and obtaining the quotient of the amplitude difference and the time difference of all the wave crests; squaring the sum of all the quotients to obtain the first difference value; the first difference is specifically calculated as shown in equation (1):
where d1 denotes the first difference, n denotes the number of peaks of the fitted wave pattern, tpiRepresenting the time point of the ith peak in the fitted oscillogram; TPiThe point in time, peak, at which the ith peak is located in the waveform diagram representing standard musiciIndicating the position of the ith Peak in the fitted wave pattern, PeakiShowing the position of the ith peak in the oscillogram of the standard music and fitting the position peak of the ith peak in the oscillogramiPeak position of ith Peak in oscillogram of standard musiciAnd (7) corresponding.
By adopting the scheme, the change rate of the peak positions in the two oscillograms (the fitted oscillogram and the oscillogram of the standard music) is calculated, then the standard deviation of the change rate is used as the first difference value for obtaining the plurality of peaks of the fitted oscillogram and the plurality of peaks of the oscillogram of the standard music, but the position change difference value of the peaks is not directly used as the first difference value, so that the first difference value can accurately represent the change difference of the peaks of the two oscillograms, and the matching degree of the two oscillograms can be accurately represented.
And the wave troughs of the fitting waveform map correspond to the wave troughs of the waveform map of the standard music one by one. Obtaining the time difference and the amplitude difference between the wave trough of the fitting oscillogram and the wave trough of the oscillogram of the standard music in a one-to-one correspondence manner, and obtaining the quotient of the amplitude difference and the time difference of all wave crests; squaring the sum of all the quotients to obtain the second difference value; the second difference is calculated in a specific manner as shown in equation (2):
where d2 denotes the second difference, m denotes the number of valleys of the fitted waveform map, ttjRepresenting the time point of the jth wave trough in the fitted oscillogram; TTjRepresents the time point, trough, of the jth wave trough in the waveform diagram of standard musicjDenotes the position of the jth Trough, in the fitted waveform mapjShowing the position of the jth wave trough in the oscillogram of the standard music and the position of the jth wave trough in the fitting oscillogramjThe position Trough of the jth wave Trough in the waveform diagram of the standard musicjAnd (7) corresponding.
By adopting the scheme, the change rate of the positions of the wave troughs in the two oscillograms (the fitted oscillogram and the oscillogram of the standard music) is calculated, then the standard deviation of the change rate is used as the second difference value for obtaining the plurality of wave troughs of the fitted oscillogram and the plurality of wave troughs of the oscillogram of the standard music, instead of directly using the position change difference value of the wave troughs as the second difference value, and thus, the second difference value can accurately represent the change difference of the wave troughs of the two oscillograms, and the matching degree of the two oscillograms can be accurately represented.
In this way, obtaining a matching factor between the fitted oscillogram and the standard music based on the first difference and the second difference can accurately represent the degree of similarity (matching degree) between the two oscillograms.
The obtaining of the matching factor between the fitted oscillogram and the standard music based on the first difference and the second difference specifically includes:
obtaining an absolute value of a third difference between the first difference and the second difference;
taking the reciprocal of an exponent of the absolute value of the third difference based on e as the matching factor; the specific calculation manner of the matching factor is shown in formula (3):
p＝e-|d1-d2| (3)
wherein p represents a matching factor and e is a natural number base.
The matching accuracy of the two oscillograms can be improved by subtracting the matching degree of the wave trough (second difference) from the matching degree of the wave crest (first difference). The smaller the matching factor, the higher the degree of matching between the two waveforms.
In an embodiment of the present invention, after taking a teacher corresponding to a maximum value of a matching index as a target recommendation teacher and sending teacher information of the target recommendation teacher to a student, the method further includes:
and if the operation that the student audits the course video is monitored, controlling the camera to be opened and shooting the audition video of the student in the course of audition of the course video. And when the operation of finishing audition of the course video is monitored, adaptively scoring the teacher corresponding to the course video according to the audition video, and sending the result of the adaptively scoring to the student. And then, if the condition that the students select to study or collect the teacher information is monitored, the teacher information is stored in a teacher library of the students. At this time, it means that the students are liked to the teacher's teaching style, and the students can be collected in the teacher library for later learning and the teachers from the teacher.
If the operation that the students refuse to listen to the course video is monitored, the fact that the students do not like the target recommended teacher is indicated, and then the teacher more suitable for the students needs to be recommended to the students. At the moment, learning subjects, learning levels, adaptive rhythms, preferential timbres, preferential tones and personality information of the students are obtained, deviation values among the learning subjects, the learning levels, the adaptive rhythms, the preferential timbres, the preferential tones and the personality information of the students and teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones and teaching styles of the target recommendation teacher are calculated, the sum of matching indexes of the target recommendation teacher and the deviation values is obtained, an adjustment matching index is obtained, and teacher information in a teacher big database corresponding to the adjustment matching index is sent to the students.
So can match suitable teacher for the student, improve student's experience effect.
Wherein, including multiframe face image in the video, according to the audition video is right the teacher that the course video corresponds carries out adaptability and marks, include: identifying face information of students in each frame of face image of the video, wherein the plurality of frames of face images correspond to a plurality of pieces of face information;
aiming at each frame of face image, obtaining the preference value of the student to the teacher according to the face information, wherein the preference value represents the reaction condition of the student to the course teaching of the teacher;
and taking the sum of the preference values corresponding to all the face images as a result of the adaptability scoring of the students to the teacher.
It should be noted that the face information includes a face information graph, and the face information graph is an image obtained by combining face features and a face contour; the obtaining of the preference value of the student to the teacher according to the face information includes:
obtaining a deformation information image of the face based on the face information image and the standard face information image; the standard face information image is obtained based on a standard face image, and the standard face image shoots students in advance and is stored in a big database. And taking the pixel value of the deformation information image as the preference value of the student to the teacher, and aiming at the pixel value in the deformation information image, taking the pixel value of the mouth corner characteristic point as the change distance between the position of the mouth corner in the face information image and the position of the mouth corner in the standard face information.
As a further step, calculating the deviation value between the learning subjects, the learning level, the adaptive rhythm, the preferred tone and the character information of the students and the teaching subjects, the teaching level, the teaching rhythm, the teaching tone and the teaching style of the target recommendation teacher, specifically:
weighting and summing the difference values of the learning subjects, the learning level, the adaptive rhythm, the preferred tone and the character information with the teaching subjects, the teaching level, the teaching rhythm, the teaching tone, the teaching style to obtain a first deviation index; the first deviation index is calculated specifically as shown in equation (4):
where r1 denotes a first deviation index, akDenotes the kth weighting factor, akThe values of k being 1, 2, 3, 4, 5, 6 are 0.5, 0.2, 0.1, 0.09, 0.06, 0.05, S, respectivelykAnd k is 1, 2, 3, 4, 5 and 6 respectively represent values of a learning subject, a learning level, an adaptive rhythm, a preferred tone and character information, and the learning subject, the learning level, the adaptive rhythm, the preferred tone and the character information are respectively represented by a digital level. For example, a learning subject is 1, which means the learning subject is a Chinese language, and a learning subject is 2, which means the learning subject is a math; the learning level of 1 represents that the course of 1 grade is learned, and the learning level of 2 represents the course of two grades; the adaptation rhythm is 1, the adaptation speed is 1 class teaching speed, the adaptation rhythm is 2, the adaptation speed is 2 class teaching speed, the preference timbre is 1, the preference timbre is low-pitched, and the preference timbre is 2, the preference timbre is sharp; a preferred tone of 1 indicates a preferred light tone, and a preferred tone of 2 indicates a preferred high toneThe tone, the character information of 1 indicates preference for the lively style, and the character information of 2 indicates preference for the serious style.
TekAnd k is 1, 2, 3, 4, 5 and 6 respectively representing the values of the teaching subjects, the teaching levels, the teaching rhythm, the teaching tone and the teaching style, and the values of the teaching subjects, the teaching levels, the teaching rhythm, the teaching tone and the teaching style are respectively represented by digital levels. For example, a subject 1 indicates that the subject is a Chinese language, and a subject 2 indicates that the subject is a math; class level 1 indicates that the professor is a class level 1, and class level 2 indicates a class level two; the teaching rhythm is 1, which represents the teaching speed with 1 level, the teaching rhythm is 2, which represents the teaching speed with 2 level, the teaching tone is 1, which represents the deep tone, and the teaching tone is 2, which represents the sharp tone; a lecture tone of 1 indicates a light tone, a lecture tone of 2 indicates a high tone, a lecture style of 1 indicates a lively style, and a lecture style of 2 indicates a serious style.
Taking standard deviations of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, character information, teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones and teaching styles as second deviation indexes; as shown in equation (5):
wherein r2 represents a second deviation index;
taking the quotient of the first deviation index and the second deviation index as a deviation value, specifically:
where pc denotes the deviation value.
Optionally, the recognizing, in each frame of face image of the video, face information of the student includes:
and obtaining a front pixel point of a previous frame image of the current frame image and a rear pixel point corresponding to the position of the front pixel point in a subsequent frame image. If the current frame image is the first frame image, the residual region is obtained based on the current frame image and a next frame image to the current frame image.
And obtaining first difference value information between the front pixel point and the rear pixel point through absolute difference value sum operation based on the front block and the rear block corresponding to the front pixel point. The front block is a block in the previous frame of image and the rear block is an area in the next frame of image. The front area block comprises a plurality of pixel points. When the previous block corresponding to the pixel point is a rectangular block with the previous pixel point as the center and the size is set, for example, the current block is a rectangular block of a block of 2 × 2. If the position of the previous pixel point is at the edge of the current frame image, the previous block comprises a plurality of pixel points which are adjacent to the current pixel point and are obtained by taking the current pixel point as the center, and a determined block formed by the pixel points is the current block.
Aiming at each pixel point in the previous block, obtaining the difference value between the value of each pixel point and the value of the pixel point corresponding to the position of each pixel point in the later block; and carrying out summation operation on the absolute values of the differences to obtain first difference information, wherein a plurality of front pixel points correspond to a plurality of first difference information, and the plurality of first difference information form a residual block according to the corresponding relation with the front pixel points. Each pixel point in each previous block corresponds to a difference, and specifically, the absolute value of each difference is summed. In order to obtain a rear block corresponding to the position of the front block in the previous frame of image, a rear block corresponding to the position of the front block is obtained in the next frame of image, and each pixel point in the current block corresponds to each pixel point in the rear block in a one-to-one position. The position correspondence refers to position one-to-one correspondence, for example, the position of the front pixel point is the same as the position of the rear pixel point, which is specifically embodied that the value of the position of the front pixel point is the same as the value of the position of the rear pixel point, for example, if the value of the position of the front pixel point is (1, 2) and the value of the position of the rear pixel point is (1, 2), the front pixel point corresponds to the position of the rear pixel point. Thus, the sizes of the front and rear blocks are consistent. Specifically, the first difference information is obtained by the following formula (6).
Wherein a (i, j) represents the value of the pixel point (i, j) in the front block corresponding to the front pixel point (m, n), b (i, j) represents the value of the pixel point corresponding to the position of the pixel point (i, j) in the rear block, k represents the number of the pixel points of the front block in the horizontal axis direction, and s1(m, n) represents the first difference information. The method comprises the steps of obtaining the absolute value of an obtained difference value by adopting the value of each pixel point in a front block corresponding to a front pixel point, subtracting the value of each pixel point corresponding to each pixel point of the front block in a rear block corresponding to the front pixel point, summing the absolute values corresponding to the pixel points, obtaining first difference value information, and enabling the obtained residual block to accurately represent the difference of a front frame image relative to a rear frame image.
And obtaining the sum of the pixel value of the pixel point (i, j) in the residual region and the pixel value of the pixel point (i, j + k) in the current frame image, wherein i, j is a positive integer, and k is an integer greater than or equal to 0. Then, if the sum is greater than 255, the pixel value of the pixel point (i, j) of the fused current frame image is a first difference value, and the first difference value is a difference value between 255 and a remainder of a quotient of the sum and 255. For example, the sum of the pixel value of the pixel point (i, j) in the residual region and the pixel value of the pixel point (i, j + k) in the current frame image is Y, Y >255, and the remainder of Y/255 is X, then the first difference value is equal to 255-X. If the sum is not more than 255, the pixel value of the pixel point (i, j) of the fused current frame image is the sum. Namely, the pixel value of the pixel point (i, j) of the fused current frame image is Y.
And carrying out high-pass filtering on the fused current frame image to obtain a high-frequency current frame image. Carrying out low-pass filtering on the fused current frame image to obtain a low-frequency current frame image;
if the pixel value of the pixel point (i, j) in the high-frequency current frame image is equal to the pixel value of the pixel point (i, j) in the low-frequency current frame image, and the pixel value of the pixel point is a first value, the pixel value of the pixel point (i, j) of the composite current frame image is assigned to be a second value; if the pixel value of the pixel point (i, j) in the high-frequency current frame image is the same as that of the pixel point (i, j) in the low-frequency current frame image, and the pixel value of the pixel point is not a first value, the pixel point (i, j) of the composite current frame image is expanded, so that the pixel point (i, j) comprises a fusion channel; and assigning the fusion channel so as to enable the value of the fusion channel to be a second value, wherein the second value is different from the pixel value of the pixel point and the first value. By adopting the scheme, the obtained composite current frame image comprises the pixel information in the high-frequency current frame image and the pixel information in the low-frequency current frame image, the characteristics of the composite current frame image are enhanced, and the accuracy of the detected target is improved.
Obtaining the distance between the fused current frame image and the composite current frame image;
if the distance is smaller than the target value, a face region to be detected is obtained based on the current frame target region and the composite target region, wherein the face region comprises face information, specifically face position, contour and color information.
By adopting the scheme, the residual error region is obtained based on the previous frame image and the next frame image of the current frame image in the video, the residual error region and the current frame image are fused to obtain the fused current frame image, and the characteristic information of the current frame image is enhanced. The high-pass filtering is carried out on the fused current frame image to obtain a high-frequency current frame image, the high-frequency current frame image reserves the high-frequency characteristic information of the current frame image, the low-pass filtering is carried out on the fused current frame image to obtain a low-frequency current frame image, the low-frequency current frame image reserves the low-frequency characteristic information of the current frame image, the high-frequency current frame image and the low-frequency frame image are fused to obtain a composite current frame image, the characteristic information in the composite current frame image is enhanced, and meanwhile the fidelity of the characteristic information is improved. Performing target detection on the current frame image based on the first convolution neural network to obtain a current frame target area; and performing target detection on the composite current frame image based on the second convolutional neural network to obtain a composite target region, further improving the probability of the target in the target region, and improving the accuracy of target detection. The method comprises the steps of obtaining the distance between a current frame target area and a composite target area, obtaining a target to be detected based on the current frame target area and the composite target area if the distance is smaller than a target value, combining a target detection result of a traditional neural network and a target detection result of a second neural network with high accuracy, improving the accuracy of target detection and improving the precision of the target to be detected.
The embodiment of the present application further provides an execution subject for executing the above steps, and the execution subject may be the online education platform course optimization system 200 based on big data in fig. 2. Referring to fig. 2, the system includes:
the import module 210 is used for obtaining and importing music data which students like to listen to everyday through a music data import interface;
a first obtaining module 220, configured to obtain learning plan data selected by a student, where the learning plan data includes a learning subject and a learning level;
the first matching module 230 is configured to match the music data that the student likes to listen to daily with standard music in a big database to obtain target standard music, where the target standard music is standard music in the big database that matches the music data that the student likes to listen to daily;
a second obtaining module 240, configured to obtain, from a big database, rhythm, timbre, tone, and character information corresponding to the target standard music;
a second matching module 250, configured to match the learning subjects, the learning level, the rhythm, tone, and character information with a teaching subject, a teaching level, a teaching rhythm, a teaching tone, and a teaching style of a teacher in a teacher database, to obtain a matching index of the teacher, where the matching index represents a matching degree between the student and the teacher;
the recommending module 260 is used for taking the teacher corresponding to the maximum value of the matching index as a target recommending teacher and sending teacher information of the target recommending teacher to students; the teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video.
Optionally, the system further includes:
the acquisition module randomly acquires a teacher from a teacher big database as a target recommendation teacher and sends teacher information of the target recommendation teacher to students; the teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video;
the shooting module is used for controlling a camera to be opened and shooting the audition video of the student in the audition process of the course video if the audition operation of the student on the course video is monitored;
the grading module is used for conducting adaptive grading on teachers corresponding to the curriculum videos according to the audition videos and sending adaptive grading results to the students when the operation of audition of the curriculum videos is finished is monitored;
the storage module is used for storing the teacher information into a teacher library of the students if the condition that the students select to learn or collect the teacher information is monitored;
the adjusting module is used for obtaining the learning subjects, learning levels, adaptive rhythms, preferential timbres, preferential tones and personality information of the students if the operation that the students refuse to audition the course videos is monitored, and calculating deviation values between the learning subjects, the learning levels, the adaptive rhythms, the preferential timbres, the preferential tones and the personality information of the students and the teaching subjects, the teaching levels, the teaching rhythms, the teaching timbres, the teaching tones and the teaching styles of the target recommendation teacher; obtaining the sum of the matching index of the target recommendation teacher and the deviation value to obtain an adjusted matching index; and transmitting the teacher information in the teacher big database corresponding to the adjusted matching index to the student.
So can match suitable teacher for the student, improve student's experience effect.
With regard to the system in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
An electronic device is further provided in the embodiments of the present application, as shown in fig. 3, the electronic device at least includes a data interface 501 and a processor 502. The processor 502 performs data interaction with the memory system 600 through the data interface 501, and the specific processor 502 performs data interaction with a memory block in the memory system 600 through the data interface 501.
Optionally, as shown in fig. 3, the electronic device further includes a storage system 600. Similarly, the processor 502 interacts with the memory blocks in the memory system 600 through the data interface 501.
Optionally, the electronic device further comprises a memory 504, a computer program stored on the memory 504 and executable on the processor 502, the processor 502 implementing the steps of any one of the above-described big data based online education platform course optimization methods when executing the program.
The storage system 600 may be the memory 504, or may be different from the memory 504, or the storage system 600 may be a partial storage partition of the memory 504, or the memory 504 may be a certain storage block in the storage system 600. The teacher and resource matching method and system for storing the big data online education platform relate to data, such as standard music data, music data which students like to listen to, teacher information and the like.
Where in fig. 3 a bus architecture (represented by bus 500) is shown, bus 500 may include any number of interconnected buses and bridges, and bus 500 links together various circuits including one or more processors, represented by processor 502, and memory, represented by memory 504. The bus 500 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The processor 502 is responsible for managing the bus 500 and general processing, and the memory 504 may be used for storing data used by the processor 502 in performing operations.
Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the above-mentioned big data based online education platform course optimization methods. The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Moreover, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in an apparatus according to embodiments of the present application. The present application may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A course optimization method based on a big data online education platform is characterized by comprising the following steps:
randomly obtaining a teacher from a teacher big database as a target recommendation teacher, and sending teacher information of the target recommendation teacher to students; the teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video;
if the operation that the student audits the course video is monitored, controlling a camera to be opened and shooting an audition video of the student in the process of audition of the course video;
when the operation of listening the course video in a trial mode is monitored, conducting adaptive scoring on a teacher corresponding to the course video according to the listening video, and sending an adaptive scoring result to the student;
if the fact that the students choose to learn or collect the teacher information is monitored, the teacher information is stored in a teacher library of the students;
if the operation that the students refuse to audition the course video is monitored, the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones and the personality information of the students are obtained, and deviation values between the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones and the personality information of the students and the teaching subjects, the teaching levels, the teaching rhythms, the teaching timbres, the teaching tones and the teaching styles of the target recommendation teacher are calculated;
obtaining the sum of the matching index of the target recommendation teacher and the deviation value to obtain an adjusted matching index;
and transmitting the teacher information in the teacher big database corresponding to the adjusted matching index to the student.
2. The method of claim 1, wherein the video comprises a plurality of frames of face images, and the adaptively scoring the teacher corresponding to the lesson video according to the audition video comprises:
identifying face information of students in each frame of face image of the video, wherein the plurality of frames of face images correspond to a plurality of pieces of face information;
aiming at each frame of face image, obtaining the preference value of the student to the teacher according to the face information, wherein the preference value represents the reaction condition of the student to the course teaching of the teacher;
and taking the sum of the preference values corresponding to all the face images as a result of the adaptability scoring of the students to the teacher.
3. The method of claim 2, wherein the face information comprises a face information map, and the face information map is an image obtained by combining face features and face contours; the obtaining of the preference value of the student to the teacher according to the face information includes:
obtaining a deformation information image of the face based on the face information image and the standard face information image; the standard face information image is obtained based on a standard face image, and the standard face image is shot in advance and stored in a large database;
taking the pixel values of the deformation information graph as preference values of the students to the teacher; and aiming at the pixel values in the deformation information image, the pixel values of the mouth corner feature points are the change distances between the positions of the mouth corners in the face information image and the positions of the mouth corners in the standard face information.
4. The method of claim 3, wherein the calculating of the deviation value between the student's learning subject, learning level, adaptation rhythm, preference tone, and character information and the target recommendation teacher's teaching subject, teaching level, teaching rhythm, teaching tone, teaching style is specifically:
weighting and summing the difference values of the learning subjects, the learning level, the adaptive rhythm, the preferred tone and the character information with the teaching subjects, the teaching level, the teaching rhythm, the teaching tone, the teaching style to obtain a first deviation index; the specific calculation mode is shown as formula (1):
where r1 denotes a first deviation index, akDenotes the kth weighting factor, akThe values of k being 1, 2, 3, 4, 5, 6 are 0.5, 0.2, 0.1, 0.09, 0.06, 0.05, S, respectivelykK is 1, 2, 3, 4, 5, 6 respectively representing values of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, and personality information, wherein the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones, and the personality information are respectively represented by one digital level;
taking standard deviations of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, character information, teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones and teaching styles as second deviation indexes;
and taking the quotient of the first deviation index and the second deviation index as a deviation value.
5. A big data based online education platform course optimization system is characterized by comprising:
the acquisition module randomly acquires a teacher from a teacher big database as a target recommendation teacher and sends teacher information of the target recommendation teacher to students; the teacher information comprises a teacher name, a teaching subject, a teaching level and a teaching-trying course video;
the shooting module is used for controlling a camera to be opened and shooting the audition video of the student in the audition process of the course video if the audition operation of the student on the course video is monitored;
the grading module is used for conducting adaptive grading on teachers corresponding to the curriculum videos according to the audition videos and sending adaptive grading results to the students when the operation of audition of the curriculum videos is finished is monitored;
the storage module is used for storing the teacher information into a teacher library of the students if the condition that the students select to learn or collect the teacher information is monitored;
the adjusting module is used for obtaining the learning subjects, learning levels, adaptive rhythms, preferential timbres, preferential tones and personality information of the students if the operation that the students refuse to audition the course videos is monitored, and calculating deviation values between the learning subjects, the learning levels, the adaptive rhythms, the preferential timbres, the preferential tones and the personality information of the students and the teaching subjects, the teaching levels, the teaching rhythms, the teaching timbres, the teaching tones and the teaching styles of the target recommendation teacher; obtaining the sum of the matching index of the target recommendation teacher and the deviation value to obtain an adjusted matching index; and transmitting the teacher information in the teacher big database corresponding to the adjusted matching index to the student.
6. The system of claim 5, wherein the video comprises a plurality of frames of face images, and the adaptive scoring of the teacher corresponding to the lesson video according to the audition video comprises:
identifying face information of students in each frame of face image of the video, wherein the plurality of frames of face images correspond to a plurality of pieces of face information;
aiming at each frame of face image, obtaining the preference value of the student to the teacher according to the face information, wherein the preference value represents the reaction condition of the student to the course teaching of the teacher;
and taking the sum of the preference values corresponding to all the face images as a result of the adaptability scoring of the students to the teacher.
7. The system of claim 6, wherein the face information comprises a face information map, and the face information map is an image obtained by combining face features and face contours; the obtaining of the preference value of the student to the teacher according to the face information includes:
obtaining a deformation information image of the face based on the face information image and the standard face information image; the standard face information image is obtained based on a standard face image, and the standard face image is shot in advance and stored in a large database;
taking the pixel values of the deformation information graph as preference values of the students to the teacher; and aiming at the pixel values in the deformation information image, the pixel values of the mouth corner feature points are the change distances between the positions of the mouth corners in the face information image and the positions of the mouth corners in the standard face information.
8. The system of claim 7, wherein the calculating of the deviation value between the learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, and character information of the students and the teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones, and teaching styles of the target recommendation teacher is specifically:
weighting and summing the difference values of the learning subjects, the learning level, the adaptive rhythm, the preferred tone and the character information with the teaching subjects, the teaching level, the teaching rhythm, the teaching tone, the teaching style to obtain a first deviation index; the specific calculation mode is shown as formula (1):
where r1 denotes a first deviation index, akDenotes the kth weighting factor, akK is 1, 2, 3, 4, 5, 6, 0.5,0.2、0.1、0.09、0.06、0.05，SkK is 1, 2, 3, 4, 5, 6 respectively representing values of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, and personality information, wherein the learning subjects, the learning levels, the adaptive rhythms, the preferred timbres, the preferred tones, and the personality information are respectively represented by one digital level;
taking standard deviations of learning subjects, learning levels, adaptive rhythms, preferred timbres, preferred tones, character information, teaching subjects, teaching levels, teaching rhythms, teaching timbres, teaching tones and teaching styles as second deviation indexes;
and taking the quotient of the first deviation index and the second deviation index as a deviation value.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 4 when executing the program.
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| CN113743645B (en) * | 2021-07-16 | 2024-02-02 | 广东财经大学 | Online education course recommendation method based on path factor fusion |
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