CN116069971A - Educational video data pushing system based on big data - Google Patents

Educational video data pushing system based on big data Download PDF

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CN116069971A
CN116069971A CN202211519918.8A CN202211519918A CN116069971A CN 116069971 A CN116069971 A CN 116069971A CN 202211519918 A CN202211519918 A CN 202211519918A CN 116069971 A CN116069971 A CN 116069971A
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CN116069971B (en
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陈家峰
秦曙光
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Readboy Education Technology Co Ltd
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Abstract

The invention relates to an educational video data pushing system based on big data, in particular to the technical field of video pushing, which comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring basic information of a user, and resolution ratios and pixel values of a plurality of educational videos in the big data; the storage module is connected with the acquisition module and used for storing the acquired basic information of the user, the resolutions of a plurality of education videos and pixel values; the analysis module is connected with the acquisition module and the storage module and used for analyzing and comparing the basic information of the user, the resolutions of a plurality of education videos and pixel values; the recognition module is connected with the analysis module and used for carrying out character recognition and summarization on the basic information of the analyzed user and key frames of a plurality of education videos; and the adjusting module is connected with the analyzing module and the identifying module and used for adjusting the analyzed and identified data, so that the video pushing accuracy and the user watching experience are improved.

Description

Educational video data pushing system based on big data
Technical Field
The invention relates to the technical field of video pushing, in particular to an educational video data pushing system based on big data.
Background
The high-speed development and wide application of the information technology have great influence on life and study of people, more and more education videos appear on the Internet, and the rich audio, image and other information provide convenience for the study of people, so that the method attracts attention of a plurality of learners.
Chinese patent publication No.: CN114443898B discloses a video big data pushing method for intelligent education of internet, firstly screening out the non-ideal intelligent education video in the network database in advance, and then extracting a unique core key frame image from the rest intelligent education video as the core key frame image to be detected; extracting corresponding unique core key frame images from intelligent education videos of different loving degrees of a target learner, taking the unique core key frame images as sample core key frame images, and finally training a sample by utilizing a differential weight SVM model to obtain a target key frame image retrieval decision model, so that the decision model is utilized to judge the target video to be pushed to the learner; therefore, the video big data pushing method for the internet intelligent education has the problem that the watching experience of the user is affected due to inaccurate video pushing content.
Disclosure of Invention
Therefore, the invention provides an educational video data pushing system based on big data, which is used for solving the problem that the watching experience of a user is influenced due to inaccurate video pushing content in the prior art.
To achieve the above object, the present invention provides a big data based educational video data pushing system, comprising:
the acquisition module is used for acquiring basic information of a user, the resolution and pixel values of a plurality of education videos in big data;
the storage module is connected with the acquisition module and used for storing the acquired basic information of the user, the resolutions of a plurality of education videos and pixel values;
the analysis module is respectively connected with the acquisition module and the storage module and used for analyzing and comparing the basic information of the user, the resolutions of a plurality of education videos and pixel values;
the recognition module is connected with the analysis module and used for carrying out character recognition and summarization on the basic information of the analyzed user and the key frames of a plurality of education videos;
the adjusting module is respectively connected with the analyzing module and the identifying module and is used for adjusting the analyzed and identified data;
when the acquisition module acquires pixel values of a plurality of education videos, the first operation unit extracts pixel values of the same position of all frames in a single education video, calculates pixel average values of the same position of all frames, calculates pixel difference values of the pixel average values and pixel values of an ith frame according to the pixel average values, and the data comparison unit selects the minimum value of the pixel difference values as a key frame of the single education video;
when the identification module completes character identification on a plurality of key frames of the education video, the first operation unit calculates the similarity between the key frames and the data characteristic values retrieved by the user, and the data comparison unit is used for judging whether to push the education video according to the comparison result of the similarity and the preset similarity.
Further, the analysis module includes a first operation unit, where the first operation unit is configured to calculate a requirement parameter Y of the user according to the search data and the search frequency of the obtained basic information when the obtaining of the basic information of the user is completed, and set y=mxy, where M is a data feature value searched by the user, and Y is a number of times the user searches for the data feature value in a time of entering the system.
Further, the analysis module comprises a data comparison unit, when the acquisition module acquires the education video in the big data, the data comparison unit compares the resolution P of the education video with the preset resolution P1 and judges whether the definition of the education video reaches the standard according to the comparison result,
if P is less than P1, the data comparison unit judges that the definition of the education video does not reach the standard;
and if P is more than or equal to P1, the data comparison unit judges that the definition of the education video reaches the standard.
Further, the adjustment module comprises a second operation unit and a data adjustment unit, when the data comparison unit judges that the definition of the education video does not reach the standard, the second operation unit calculates a resolution difference delta P between the resolution P of the education video and a preset resolution P1, and sets delta P=P1-P, and compares the resolution difference with the preset resolution difference, the data adjustment unit determines corresponding interpolation according to the comparison result to process the definition of the education video,
wherein the data adjustment unit is provided with a first preset resolution difference value delta P1, a second preset resolution difference value delta P, a first interpolation C1, a second interpolation C2 and a third interpolation C3, wherein delta P1 < [ delta ] P2, C1 < C2 < C3,
if delta P is less than or equal to delta P1, the data adjustment unit determines to select a first interpolation C1 to process the definition of the education video;
if delta P1 is less than delta P2, the data adjustment unit determines to select a second interpolation C2 to process the definition of the educational video;
if DeltaP > DeltaP2, the data adjustment unit determines to select a third interpolation C3 to process the definition of the educational video.
Further, when the acquisition module finishes acquiring the pixel values of the educational videos, the first operation unit extracts the pixel values of the same position of all frames in a single educational video, and calculates the average value of the pixels of the same position of all frames
Figure BDA0003971100800000031
Setting up
Figure BDA0003971100800000032
Wherein G1 is the pixel value of the first frame, G2 is the pixel value of the second frame, G3 is the pixel value of the third frame, gn is the pixel value of the nth frame.
Further, when the first operation unit completes calculating the pixel average value of the same position of all frames, the first operation unit calculates the pixel average value
Figure BDA0003971100800000033
Pixel difference Δai from pixel value of the i-th frame, setting +.>
Figure BDA0003971100800000034
The data comparison unit selects a minimum value of Δai as a key frame of a single educational video, where i=1, 2, 3.
Further, when the recognition module performs character recognition on a plurality of key frames of the education video, the first operation unit calculates the similarity D between the key frames and the data characteristic values retrieved by the user, compares the similarity D with a preset similarity D1, the data comparison unit judges whether to push the education video according to the comparison result,
if D is smaller than D1, the data comparison unit judges that the education video is not pushed;
and if D is more than or equal to D1, the data comparison unit judges that the education video is pushed.
Further, when the data comparison unit determines that pushing is not performed on the educational video, the second operation unit calculates a similarity difference Δd between the similarity D and a preset similarity D1, sets Δd=d1-D, compares the similarity difference with the preset similarity difference, determines a corresponding compensation value according to the comparison result, compensates the number of key frames of the selected individual educational video,
wherein the data adjustment unit is provided with a first preset similarity difference DeltaD 1, a second preset similarity difference DeltaD 2, a first compensation value Z1, a second compensation value Z2 and a third compensation value Z3, wherein DeltaD 1 < DeltaD2, Z1 < Z2 < Z3,
if delta D is less than or equal to delta D1, the data adjustment unit determines to select a first compensation value Z1 to compensate the number of key frames of the single selected educational video;
if delta D1 is less than delta D2, the data comparison unit determines to select a second compensation value Z2 to compensate the number of key frames of the single selected educational video;
and if the delta D is not less than delta D2, the data comparison unit determines to select a third compensation value Z3 to compensate the number of key frames of the selected single education video.
Further, the acquisition module acquires the watching duration T of the educational video in the basic information of the user, the first operation unit compares the watching duration with the preset watching duration, the data comparison unit determines the pushing frequency of the user according to the comparison result,
wherein the data comparison unit is provided with a first preset watching duration B1, a second preset watching duration B2, a first pushing frequency V1, a second pushing frequency V2 and a third pushing frequency V3, wherein B1 is smaller than B2, V1 is smaller than V2 and smaller than V3,
if T is less than or equal to B1, the data comparison unit determines that the pushing frequency to the user is V1;
if B1 is more than T and less than or equal to B2, the data comparison unit determines that the pushing frequency to the user is V2;
if T is more than B2, the data comparison unit determines that the pushing frequency to the user is V3.
Further, when the data comparison unit determines that the pushing frequency is completed, the first operation unit compares the requirement parameter Y of the user with the preset requirement parameter Y1, the data comparison unit determines whether to adjust the pushing frequency of the user according to the comparison result,
if Y is less than Y1, the data comparison unit judges that the pushing frequency of the user is not adjusted;
if Y is more than or equal to Y1, the data comparison unit judges that the pushing frequency of the user is adjusted;
when the data comparison unit judges that the pushing frequency of the user is adjusted, the second operation unit calculates a parameter difference delta Y between a user demand parameter Y and a preset demand parameter Y1, and sets delta Y=Y-Y1, the data adjustment unit is used for adjusting the pushing frequency of the user according to the parameter difference, the adjusted pushing frequency is set as V4, V4=Kj×vm is set, wherein m is 1,2 or 3, j is 1,2 or 3, and Kj is an adjustment coefficient of the pushing frequency.
Compared with the prior art, the method has the beneficial effects that the user search data and the search frequency are analyzed through the data characteristic values searched by the user and the times of searching the data characteristic values by the user in the time of entering the system, so that the demand parameters of the user are calculated, and the demand parameters are used as the characteristic parameters for evaluating the demand of the user on the education video type, and the accuracy of pushing the video is further improved.
Further, according to the method, through analysis of the resolutions of the education videos in the big data, whether the definitions of the education videos reach the standard is further evaluated, and for the education videos with the definitions not reaching the standard, the resolutions of the videos are adjusted through adjustment of the interpolation numbers, so that the definitions of the videos reach a preset value, and therefore the watching experience of users is further improved.
Further, the method and the device for extracting the key frames of the single education video are used for extracting the key frames of the single education video by calculating the average value of the pixel values of the same position of all frames in the single education video and selecting the pixel value closest to the average value as the key frame of the single education video, and the extracted key frames are used as video indexes of the education video, so that users can be accurately pushed when searching, and the accuracy of pushing contents of the video is further improved.
Further, when the extraction of the key frames is completed, performing text recognition on the key frames, analyzing the similarity between text information extracted from the key frames and the characteristic values of the search data of the user, and pushing the educational video when the similarity can reach the preset similarity, so that the accuracy of pushing content of the video is further improved;
in particular, when the similarity does not reach the preset similarity, the number of key frames of the single education video is compensated by selecting a corresponding compensation value according to the calculation of the difference between the similarity and the preset similarity, and the index key content in the single video is further increased by increasing the number of the key frames extracted from the single video, so that the similarity with the retrieval data characteristics of the user is improved, the accurate pushing of the education video is further realized, and the accuracy of pushing the content of the video is further improved.
Further, the method and the device for searching the educational video by the user have the advantages that the watching time of the user on the searched educational video is obtained, the pushing frequency of the user is further determined, the longer the watching time is, the more interested the user is in the video, the more accurate the video recommendation is, and therefore the watching experience of the user is further improved.
Further, the method and the device further adjust the pushing frequency by calculating the parameter difference between the demand parameter of the user and the preset demand parameter, and further improve the accuracy of pushing the educational video by adjusting the pushing frequency, so that the watching experience of the user is further improved.
Drawings
FIG. 1 is a logic block diagram of a big data based educational video data pushing system according to the present invention;
FIG. 2 is a logic block diagram of an analysis module of the big data based educational video data pushing system of the present invention;
fig. 3 is a logic block diagram of an adjustment module of the big data based educational video data pushing system according to the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1-3, fig. 1 is a logic block diagram of an educational video data pushing system based on big data according to the present invention; FIG. 2 is a logic block diagram of an analysis module of the big data based educational video data pushing system of the present invention; fig. 3 is a logic block diagram of an adjustment module of the big data based educational video data pushing system according to the present invention.
An educational video data pushing system based on big data, comprising:
the acquisition module is used for acquiring basic information of a user, the resolution and pixel values of a plurality of education videos in big data;
the storage module is connected with the acquisition module and used for storing the acquired basic information of the user, the resolutions of a plurality of education videos and pixel values;
the analysis module is respectively connected with the acquisition module and the storage module and used for analyzing and comparing the basic information of the user, the resolutions of a plurality of education videos and pixel values;
the recognition module is connected with the analysis module and used for carrying out character recognition and summarization on the basic information of the analyzed user and the key frames of a plurality of education videos;
and the adjusting module is respectively connected with the analyzing module and the identifying module and is used for adjusting the data after analysis and identification.
In the embodiment of the invention, the basic information of the user comprises, but is not limited to, search data, search duration, search frequency, and viewing duration, viewing times, praise times and comment frequency of the video of the user.
Specifically, the analysis module includes a first operation unit, where the acquisition module is configured to calculate a requirement parameter Y of the user according to the search data and the search frequency of the acquired basic information when the basic information of the user is acquired, and set y=mxy, where M is a data feature value searched by the user, and Y is a number of times the user searches for the data feature value in a time period when the user enters the system.
In the embodiment of the invention, the data characteristic values searched by the user comprise but are not limited to financial accounting study, child care study, flower art study, tea art study, musical instrument study and American sound study.
Specifically, the analysis module comprises a data comparison unit, when the acquisition module acquires the education video in the big data, the data comparison unit compares the resolution P of the education video with the preset resolution P1 and judges whether the definition of the education video meets the standard according to the comparison result,
if P is less than P1, the data comparison unit judges that the definition of the education video does not reach the standard;
and if P is more than or equal to P1, the data comparison unit judges that the definition of the education video reaches the standard.
Specifically, the adjustment module comprises a second operation unit and a data adjustment unit, when the data comparison unit judges that the definition of the education video does not reach the standard, the second operation unit calculates a resolution difference delta P between the resolution P of the education video and a preset resolution P1, and sets delta P=P1-P, and compares the resolution difference with the preset resolution difference, the data adjustment unit determines corresponding interpolation according to the comparison result to process the definition of the education video,
wherein the data adjustment unit is provided with a first preset resolution difference value delta P1, a second preset resolution difference value delta P, a first interpolation C1, a second interpolation C2 and a third interpolation C3, wherein delta P1 < [ delta ] P2, C1 < C2 < C3,
if delta P is less than or equal to delta P1, the data adjustment unit determines to select a first interpolation C1 to process the definition of the education video;
if delta P1 is less than delta P2, the data adjustment unit determines to select a second interpolation C2 to process the definition of the educational video;
if DeltaP > DeltaP2, the data adjustment unit determines to select a third interpolation C3 to process the definition of the educational video.
SpecificallyWhen the acquisition module finishes acquiring the pixel values of a plurality of educational videos, the first operation unit extracts the pixel values of the same position of all frames in a single educational video and calculates the average value of the pixels of the same position of all frames
Figure BDA0003971100800000081
Setting up
Figure BDA0003971100800000082
Wherein G1 is the pixel value of the first frame, G2 is the pixel value of the second frame, G3 is the pixel value of the third frame, gn is the pixel value of the nth frame.
Specifically, when the first operation unit completes calculating the pixel average value of the same position of all frames, the first operation unit calculates the pixel average value
Figure BDA0003971100800000083
Pixel difference Δai from pixel value of the i-th frame, setting +.>
Figure BDA0003971100800000084
The data comparison unit selects a minimum value of Δai as a key frame of a single educational video, where i=1, 2, 3.
In the embodiment of the invention, a pixel frame averaging method is adopted to extract the key frames of the single education video, and the extracted key frames of the single education video are used as the video index of the education video.
Specifically, when the recognition module performs character recognition on a plurality of key frames of the education video, the first operation unit calculates the similarity D between the key frames and the data characteristic values retrieved by the user, compares the similarity D with the preset similarity D1, the data comparison unit judges whether to push the education video according to the comparison result,
if D is smaller than D1, the data comparison unit judges that the education video is not pushed;
and if D is more than or equal to D1, the data comparison unit judges that the education video is pushed.
In particular, when the data comparison unit determines that pushing is not performed on the educational video, the second operation unit calculates a similarity difference Δd between the similarity D and a preset similarity D1, sets Δd=d1-D, compares the similarity difference with the preset similarity difference, determines a corresponding compensation value according to the comparison result, compensates the number of key frames of the selected individual educational video,
wherein the data adjustment unit is provided with a first preset similarity difference DeltaD 1, a second preset similarity difference DeltaD 2, a first compensation value Z1, a second compensation value Z2 and a third compensation value Z3, wherein DeltaD 1 < DeltaD2, Z1 < Z2 < Z3,
if delta D is less than or equal to delta D1, the data adjustment unit determines to select a first compensation value Z1 to compensate the number of key frames of the single selected educational video;
if delta D1 is less than delta D2, the data comparison unit determines to select a second compensation value Z2 to compensate the number of key frames of the single selected educational video;
and if the delta D is not less than delta D2, the data comparison unit determines to select a third compensation value Z3 to compensate the number of key frames of the selected single education video.
In particular, the acquisition module acquires the watching duration T of the educational video in the basic information of the user, the first operation unit compares the watching duration with the preset watching duration, the data comparison unit determines the pushing frequency of the user according to the comparison result,
wherein the data comparison unit is provided with a first preset watching duration B1, a second preset watching duration B2, a first pushing frequency V1, a second pushing frequency V2 and a third pushing frequency V3, wherein B1 is smaller than B2, V1 is smaller than V2 and smaller than V3,
if T is less than or equal to B1, the data comparison unit determines that the pushing frequency to the user is V1;
if B1 is more than T and less than or equal to B2, the data comparison unit determines that the pushing frequency to the user is V2;
if T is more than B2, the data comparison unit determines that the pushing frequency to the user is V3.
Specifically, when the data comparison unit determines that the pushing frequency is completed, the first operation unit compares the demand parameter Y of the user with the preset demand parameter Y1, the data comparison unit determines whether to adjust the pushing frequency of the user according to the comparison result,
if Y is less than Y1, the data comparison unit judges that the pushing frequency of the user is not adjusted;
and if Y is more than or equal to Y1, the data comparison unit judges that the pushing frequency of the user is adjusted.
Specifically, when the data comparison unit determines to adjust the push frequency of the user, the second operation unit calculates a parameter difference Δy between the user's demand parameter Y and a preset demand parameter Y1, sets Δy=y-Y1, compares the parameter difference with the preset parameter difference, the data adjustment unit selects a corresponding adjustment coefficient according to the comparison result to adjust the push frequency of the user,
wherein the data adjustment unit is provided with a first preset parameter difference delta Y1, a second preset parameter difference delta Y2, a first adjustment coefficient K1, a second adjustment coefficient K2 and a third adjustment coefficient K3, wherein delta P1 < [ delta ] P2,1 < K2 < K3 < 1.2,
if delta Y is less than or equal to delta Y1, the data adjusting unit selects a first adjusting coefficient K1 to adjust the pushing frequency of the user;
if delta Y1 is less than delta Y2, the data adjusting unit selects a second adjusting coefficient K2 to adjust the pushing frequency of the user;
if delta Y > -delta Y2, the data adjustment unit selects a third adjustment coefficient K3 to adjust the push frequency of the user;
when the data adjustment unit determines that the j-th adjustment coefficient is selected to adjust the pushing frequency of the user, the adjusted pushing frequency is set to be V4, and v4=kj×vm is set, wherein m is 1,2 or 3, j is 1,2 or 3, and Kj is the adjustment coefficient of the pushing frequency.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An educational video data pushing system based on big data, comprising:
the acquisition module is used for acquiring basic information of a user, the resolution and pixel values of a plurality of education videos in big data;
the storage module is connected with the acquisition module and used for storing the acquired basic information of the user, the resolutions of a plurality of education videos and pixel values;
the analysis module is respectively connected with the acquisition module and the storage module and used for analyzing and comparing the basic information of the user, the resolutions of a plurality of education videos and pixel values;
the recognition module is connected with the analysis module and used for carrying out character recognition and summarization on the basic information of the analyzed user and the key frames of a plurality of education videos;
the adjusting module is respectively connected with the analyzing module and the identifying module and is used for adjusting the analyzed and identified data;
when the acquisition module acquires pixel values of a plurality of education videos, the first operation unit extracts pixel values of the same position of all frames in a single education video, calculates pixel average values of the same position of all frames, calculates pixel difference values of the pixel average values and pixel values of an ith frame according to the pixel average values, and the data comparison unit selects the minimum value of the pixel difference values as a key frame of the single education video;
when the identification module completes character identification on a plurality of key frames of the education video, the first operation unit calculates the similarity between the key frames and the data characteristic values retrieved by the user, and the data comparison unit is used for judging whether to push the education video according to the comparison result of the similarity and the preset similarity.
2. The system according to claim 1, wherein the analysis module includes a first operation unit, and the acquisition module is configured to calculate a user demand parameter Y according to the retrieved data and the frequency of retrieval of the acquired basic information when the basic information of the user is acquired, where M is a data feature value retrieved by the user, and Y is a number of times the user retrieves the data feature value in a time period of entering the system.
3. The system for pushing educational video data based on big data according to claim 1, wherein the analyzing module comprises a data comparing unit, when the obtaining module obtains educational video in big data, the data comparing unit compares the resolution P of the educational video with a preset resolution P1, and determines whether the definition of the educational video meets the standard according to the comparison result,
if P is less than P1, the data comparison unit judges that the definition of the education video does not reach the standard;
and if P is more than or equal to P1, the data comparison unit judges that the definition of the education video reaches the standard.
4. The system according to claim 1, wherein the adjustment module includes a second operation unit and a data adjustment unit, the data adjustment unit calculates a resolution difference Δp between a resolution P of the educational video and a preset resolution P1 when it is determined that the sharpness of the educational video does not reach the standard, sets Δp=p1-P, and compares the resolution difference with the preset resolution difference, the data adjustment unit determines a corresponding interpolation to process the sharpness of the educational video based on the comparison result,
wherein the data adjustment unit is provided with a first preset resolution difference value delta P1, a second preset resolution difference value delta P, a first interpolation C1, a second interpolation C2 and a third interpolation C3, wherein delta P1 < [ delta ] P2, C1 < C2 < C3,
if delta P is less than or equal to delta P1, the data adjustment unit determines to select a first interpolation C1 to process the definition of the education video;
if delta P1 is less than delta P2, the data adjustment unit determines to select a second interpolation C2 to process the definition of the educational video;
if DeltaP > DeltaP2, the data adjustment unit determines to select a third interpolation C3 to process the definition of the educational video.
5. The large data based educational video data pushing system according to claim 1, wherein when the acquisition module finishes acquiring the pixel values of several educational videos, the first operation unit extracts the pixel values of the same position of all frames in a single educational video, and calculates the average value of the pixels of the same position of all frames
Figure FDA0003971100790000021
Setting up
Figure FDA0003971100790000022
Wherein G1 is the pixel value of the first frame, G2 is the pixel value of the second frame, G3 is the pixel value of the third frame, gn is the pixel value of the nth frame.
6. The big data based educational video data pushing system according to claim 1, wherein said first operation unit calculates said pixel average value when calculating the pixel average value of the same position of all frames is completed
Figure FDA0003971100790000023
Pixel difference Δai from pixel value of the i-th frame, setting +.>
Figure FDA0003971100790000024
The data comparison unit selects a minimum value of Δai as a key frame of a single educational video, where i=1, 2, 3.
7. The system for pushing educational video data based on big data according to claim 1, wherein the recognition module calculates the similarity D between the key frame and the data feature value retrieved by the user and compares the similarity D with the preset similarity D1 when the character recognition is completed on the key frames of the educational video, the data comparison unit determines whether to push the educational video according to the comparison result,
if D is smaller than D1, the data comparison unit judges that the education video is not pushed;
and if D is more than or equal to D1, the data comparison unit judges that the education video is pushed.
8. The system for pushing educational video data based on big data according to claim 1, wherein when the data comparing unit determines that pushing of the educational video is not performed, the second computing unit calculates a similarity difference Δd between the similarity D and a preset similarity D1, sets Δd=d1-D, compares the similarity difference with the preset similarity difference, the data adjusting unit determines a corresponding compensation value according to the comparison result to compensate the number of key frames of the selected individual educational video,
wherein the data adjustment unit is provided with a first preset similarity difference DeltaD 1, a second preset similarity difference DeltaD 2, a first compensation value Z1, a second compensation value Z2 and a third compensation value Z3, wherein DeltaD 1 < DeltaD2, Z1 < Z2 < Z3,
if delta D is less than or equal to delta D1, the data adjustment unit determines to select a first compensation value Z1 to compensate the number of key frames of the single selected educational video;
if delta D1 is less than delta D2, the data comparison unit determines to select a second compensation value Z2 to compensate the number of key frames of the single selected educational video;
and if the delta D is not less than delta D2, the data comparison unit determines to select a third compensation value Z3 to compensate the number of key frames of the selected single education video.
9. The system for pushing educational video data based on big data according to claim 1, wherein the acquisition module acquires a viewing time period T of the educational video in the basic information of the user, the first operation unit compares the viewing time period T with a preset viewing time period, the data comparison unit determines the pushing frequency to the user according to the comparison result,
wherein the data comparison unit is provided with a first preset watching duration B1, a second preset watching duration B2, a first pushing frequency V1, a second pushing frequency V2 and a third pushing frequency V3, wherein B1 is smaller than B2, V1 is smaller than V2 and smaller than V3,
if T is less than or equal to B1, the data comparison unit determines that the pushing frequency to the user is V1;
if B1 is more than T and less than or equal to B2, the data comparison unit determines that the pushing frequency to the user is V2;
if T is more than B2, the data comparison unit determines that the pushing frequency to the user is V3.
10. The system for pushing educational video data based on big data according to claim 1, wherein the data comparing unit compares the user's demand parameter Y with the preset demand parameter Y1 when determining that the pushing frequency is completed, the data comparing unit determines whether to adjust the pushing frequency of the user according to the comparison result,
if Y is less than Y1, the data comparison unit judges that the pushing frequency of the user is not adjusted;
if Y is more than or equal to Y1, the data comparison unit judges that the pushing frequency of the user is adjusted;
when the data comparison unit judges that the pushing frequency of the user is adjusted, the second operation unit calculates a parameter difference delta Y between a user demand parameter Y and a preset demand parameter Y1, and sets delta Y=Y-Y1, the data adjustment unit is used for adjusting the pushing frequency of the user according to the parameter difference, the adjusted pushing frequency is set as V4, V4=Kj×vm is set, wherein m is 1,2 or 3, j is 1,2 or 3, and Kj is an adjustment coefficient of the pushing frequency.
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