English auxiliary teaching intelligent video slice detection platform

Abstract

The invention discloses an intelligent video slice detection platform for English aided teaching, and relates to the field of video detection. Comprising an original teaching video intelligent slice processing module, a segmented slice video abnormity judgment analysis correction module, a single slice video key teaching content matching analysis module, an adjacent slice content multi-block combination connection judgment module, a multi-path video playing feedback module and a keyword data annotation matching push module. According to the method, coherence checking and correction are carried out on the basis of video mean value segmentation, matching is carried out on each slice video and synchronous English teaching materials, key slice videos are extracted, meanwhile, multi-block splicing analysis is carried out on slice videos adjacent to the key slice videos, and carefully selected key teaching slice videos are formed. And analyzing the playing amount of each slice video, and carrying out precise keyword pushing on the key teaching slice video of which the playing amount is higher than a set threshold value, thereby satisfying intelligent slicing of the English teaching video.

Description

Technical Field

[0001] The present invention relates to the field of video detection, and in particular to an intelligent video slice detection platform for English-assisted teaching. Background Art

[0002] Teaching videos refer to all videos used in classroom teaching activities. They can be used for demonstration and explanation to students, or for teachers to watch for learning analysis. In a narrow sense, teaching videos are videos that record the knowledge and skills that teachers want to impart to students, so that more teachers can use them for teaching and research analysis in the future.

[0003] Video courses are a form of teaching that uses video, that is, the dissemination of knowledge through video media. In many ways, it is usually based on multimedia video, that is, this learning medium can provide users with both visual and auditory content. In a broad sense, learning through guided video materials can be called video courses.

[0004] Video slicing, also known as video segmentation or video editing, is a technology that cuts raw video content into multiple independent segments or "slices". This technology plays a vital role in today's digital media era, especially in the fields of video editing, streaming services, advertising promotion, and online education. During the video editing process, editors often need to cut, splice, and reorganize raw materials to create video works that meet the requirements. Video slicing technology can help editors quickly locate the required footage and improve editing efficiency; at the same time, streaming services need to provide a large amount of video content to users for online viewing. Through video slicing technology, video content can be cut into multiple small segments to achieve fast loading and smooth playback, thereby improving user experience;

[0005] At present, the means of slicing video images are relatively limited. Video slicing is mainly based on manual slicing or average slicing. Manual slicing is more precise, but the manpower cost is high and requires a lot of browsing. Average slicing is based on average slicing of video images, and it is impossible to highlight the key content in the video. The accuracy of video slicing is too low. This application aims to perform continuity inspection and correction based on the average slicing of the video, match each slice video with the synchronized English teaching materials, extract the key slice video, and perform multi-block splicing analysis on the slice videos adjacent to the key slice video to form a selected key teaching slice video, analyze the playback volume of each slice video, and perform precise keyword push for the key teaching slice videos whose playback volume is higher than the set threshold to meet the intelligent slicing of English teaching videos. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent video slice detection platform for English-assisted teaching to solve the problems in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent video slice detection platform for English-assisted teaching, the platform includes an original teaching video intelligent slice processing module, a segmented slice video abnormality judgment analysis and correction module, a single slice video key teaching content matching analysis module, an adjacent slice content multi-block combination connection judgment module, a multi-path video playback feedback module and a keyword data annotation matching push module;

[0009] The intelligent slicing processing module of the original teaching video is used to perform mean slicing according to the number of frames of the teaching video, and to screen and adjust the continuity of the sliced ​​video; the abnormal judgment, analysis and correction module of the segmented sliced ​​video is used to check and correct the voice and subtitles of the sliced ​​video in segments; the key teaching content matching and analysis module of the single sliced ​​video is used to analyze and match the content of the sliced ​​video with the corresponding teaching material, and mark the matching sliced ​​video according to the key teaching content within the teaching material; the multi-block combination connection judgment module of the adjacent slice content screens the key teaching information in multiple blocks of the adjacent video slices according to the marked slice video, and performs splicing analysis according to the consistency of the teaching focus; the multi-path video playback feedback module is used to monitor and summarize the playback volume of different video slices, so as to mark and feedback the video slices that are played as the key; the keyword data annotation matching push module is used to extract keywords for the video slices that are played as the key according to the teaching content within the matched textbook, so as to push them in a targeted manner through the matched keywords.

[0010] Further setting: the original teaching video intelligent slicing processing module includes a video frame mean slicing segmentation submodule and a slice video coherence determination and analysis submodule, wherein the video frame mean slicing segmentation submodule is used to obtain the number of frames of the original teaching video, perform mean segmentation according to the total number of video frames, and summarize each mean-sliced ​​video slice; the slice video coherence determination and analysis submodule is used to perform coherence retrieval on the internal content of the sliced ​​video; when the sentences in the sliced ​​video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete them, and the video slices after the retrieval and completion are re-summarized.

[0011] Further setting: the segmented video abnormality judgment analysis and correction module includes a video voice pronunciation proofreading judgment submodule and a voice corresponding subtitle calibration judgment submodule. The video voice pronunciation proofreading judgment submodule includes a screening unit, a comparison unit and a correction unit. The video voice pronunciation proofreading judgment submodule is used to input each summarized video slice into the screening unit and the comparison unit. The screening unit is used to screen whether there is an error or non-standard pronunciation in the English voice in each frame of video content, and mark the ones with obvious errors. The comparison unit is used to compare the English voice in each frame of video content with the teaching voice in the textbook, identify the video slices whose internal voice is inconsistent with the teaching voice in the textbook, and mark the marked content to send the marked content to the correction unit. The correction unit performs intelligent big data modification and correction on the internal voice of the video according to the teaching voice of the textbook corresponding to the marked video slice, and at the same time, the internal voice of the video slice that is seriously inconsistent with the corresponding textbook voice is marked twice. When the video slice marked twice is larger than the set threshold, it is determined that the original teaching video does not meet the playback standard, and feedback is deleted;

[0012] The speech-to-subtitle calibration judgment submodule is used to repeatedly detect the subtitles corresponding to the speech pronunciation in each frame of the calibrated video slice, and to judge and calibrate the erroneous subtitles.

[0013] Further setting: the single slice video key teaching content matching analysis module includes a video-textbook synchronization matching rate analysis submodule and a textbook key content synchronization video slice marking submodule. The video-textbook synchronization matching rate analysis submodule is used to match the textbook teaching content corresponding to each slice video with the original teaching video, analyze the textbook matching rate of each slice video, and filter the slice videos with a matching rate greater than the set threshold. The textbook key content synchronization video slice marking submodule marks the slice videos with a high matching rate with the key teaching content according to the corresponding internal key teaching content of the textbook, and selects the key teaching slice videos.

[0014] Further setting: the video teaching material synchronization matching rate analysis submodule obtains the key words of the teaching content in the corresponding teaching material and matches the keywords with the internal content of the sliced ​​video, obtains the matching rate of each key word with the subtitle words in the sliced ​​video, wherein the key words are summarized and the number of key words is set as C gAt the same time, through big data analysis of the strongly related words involved in each key word, the strongly related words are graded and set as first-level strongly related words, second-level strongly related words, and third-level strongly related words. Among them, the first-level strongly related words are set as the extended knowledge point words marked in the teaching content of the corresponding textbook, the second-level strongly related words are set as the key words defined in the textbook similar to the corresponding textbook, and the third-level strongly related words are set as the related words analyzed by big data based on the key words of the teaching content in the corresponding textbook. The relevance of the first-level strongly related words is set to u1%, the relevance of the second-level strongly related words is set to u2%, and the relevance of the third-level strongly related words is set to u3%.

[0015] Set the total vocabulary of subtitles in each slice video as G′, match the total vocabulary with key words and different levels of strongly related words, select the key words and strongly related words that match the vocabulary of the slice video, and set the number of key words in the vocabulary of the slice video as C g ′, set the number of first-level strongly associated words to C k1 ′, the number of secondary strongly associated words is C k2 ′, the number of third-level strongly associated words is C k3 ′, calculate the matching rate U between the subtitles in each video slice and the key words in the textbook according to the formula:

[0016]

[0017] Calculate the matching rate of each video slice, sort the matching rates of each video slice in descending order, and send them to the textbook key content synchronization video slice marking submodule for processing.

[0018] Further setting: the adjacent slice content multi-block combination connection judgment module includes a video slice multi-block key screening submodule and an adjacent video slice multi-block splicing analysis submodule. The video slice multi-block key screening submodule is used to perform multi-block secondary slicing on the slice video at the adjacent position of the key teaching slice video, and send the secondary slice video to the video teaching material synchronization matching rate analysis submodule for matching rate screening. When the matching rate of the secondary slice video is greater than the set threshold, it is marked. The adjacent video slice multi-block splicing analysis submodule is used to obtain the marked secondary slice video and determine the secondary slice video block. Whether the position is connected to the key teaching slice video. When the position of the secondary slice video block is connected to the key teaching slice video, the secondary slice video and the key teaching slice video are spliced, and the spliced ​​video is retrieved for secondary coherence. When the sentences inside the slice video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete them. When the position of the secondary slice video block is not connected to the key teaching slice video, the number of secondary slice videos j between the marked secondary slice video block and the key teaching slice video is obtained, and the matching rate is screened, and the matching rate is set to U1, U2, ..., U j , setting the screening matching rate to the threshold of U for the secondary slice video splicing * , set the minimum screening matching rate threshold of each secondary slice video to U * min , according to the formula:

[0019]

[0020] When the secondary slices between the screened marked secondary slice video blocks and the key teaching slice video meet the above formula, it is determined that it meets the multi-block video splicing standard, and the marked secondary slice video blocks, the secondary slice videos between the marked secondary slice video blocks and the key teaching slice video, and the key teaching slice video are spliced, and secondary coherence retrieval is performed at the same time. When the sentences within the slice video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete them, and the slice videos after splicing are updated and summarized.

[0021] Further setting: the multi-path video playback feedback module includes a video slice playback volume data summary submodule and a popular playback video slice marking submodule. The video slice playback volume data summary submodule is used to count the playback volume data of the re-spliced ​​and summarized slice videos one by one, and the popular playback video slice marking submodule is used to sort the playback data of each slice video, and to mark the slice videos whose playback data is higher than the set threshold in real time.

[0022] Further setting: the keyword data annotation matching and push module includes a real-time tagging video keyword retrieval and annotation sub-module and a slice video matching and push sub-module. The real-time tagging video keyword retrieval and annotation sub-module obtains the slice video whose marked playback data is higher than the set threshold, and extracts keywords from the content inside the slice video according to the matched teaching materials. At the same time, the slice video matching and push sub-module is used to match and overlap the slice video whose marked playback data is higher than the set threshold with the re-spliced ​​and summarized slice video, and screen out the key teaching slice videos that meet the playback data higher than the set threshold and are spliced ​​and summarized at the same time, and push the slice video on the playback network according to the extracted keywords.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention aims to perform continuity check and correction based on video mean segmentation, match each slice video with a synchronized English teaching material, extract key slice videos, and simultaneously perform multi-block splicing analysis on slice videos adjacent to the key slice videos to form selected key teaching slice videos, analyze the playback volume of each slice video, and perform precise keyword push for key teaching slice videos whose playback volume is higher than a set threshold, so as to meet the intelligent slicing of English teaching videos. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0025] Figure 1 This is a schematic diagram of the specific structure of an intelligent video slice detection platform for English-assisted teaching according to the present invention;

[0026] Figure 2 This is a partial structural diagram of an intelligent video slice detection platform for English-assisted teaching according to the present invention;

[0027] Figure 3 The present invention is a flow chart of an intelligent video slice detection platform for English-assisted teaching. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figures 1 to 3In an embodiment of the present invention, an intelligent video slicing detection platform for English-assisted teaching is provided. The platform includes an original teaching video intelligent slicing processing module, a segmented slicing video abnormality judgment analysis and correction module, a single slicing video key teaching content matching analysis module, an adjacent slicing content multi-block combination connection judgment module, a multi-path video playback feedback module and a keyword data annotation matching push module, wherein the original teaching video intelligent slicing processing module, the segmented slicing video abnormality judgment analysis and correction module, the single slicing video key teaching content matching analysis module, the adjacent slicing content multi-block combination connection judgment module, the multi-path video playback feedback module and the keyword data annotation matching push module are electrically connected one by one;

[0030] The original teaching video intelligent slicing processing module is used to perform mean slicing according to the number of frames of the teaching video, and to perform continuity screening and adjustment on the sliced ​​video;

[0031] It should also be specifically explained that the original teaching video intelligent slicing processing module includes a video frame mean slicing segmentation submodule and a slice video continuity judgment analysis submodule, wherein the video frame mean slicing segmentation submodule is used to obtain the number of frames of the original teaching video, perform mean segmentation according to the total number of video frames, and summarize each mean-sliced ​​video slice; the slice video continuity judgment analysis submodule is used to perform continuity retrieval on the internal content of the sliced ​​video; when the sentences in the sliced ​​video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete them, and the retrieved and completed video slices are re-summarized.

[0032] The segmented slice video anomaly judgment analysis and correction module is used to perform segmented inspection and correction one by one according to the voice and subtitles of the sliced ​​video;

[0033] It is also necessary to explain in detail that the segmented slice video abnormality judgment analysis and correction module includes a video voice pronunciation proofreading judgment submodule and a voice corresponding subtitle calibration judgment submodule;

[0034] according to Figure 2 As shown, the video speech pronunciation proofreading judgment submodule includes a screening unit, a comparison unit and a correction unit, and the video speech pronunciation proofreading judgment submodule is used to input each of the summarized video slices into the screening unit and the comparison unit;

[0035] The screening unit is used to screen whether there are errors or non-standard pronunciations in the English voice in each frame of video content, and mark those with obvious errors;

[0036] The comparison unit is used to compare the English voice in each frame of video content with the teaching voice in the teaching material, identify the video slices where the voice in the video is inconsistent with the teaching voice in the teaching material, mark them, and send the marked content to the correction unit;

[0037] The correction unit performs intelligent big data modification and correction of the internal voice of the video according to the teaching material voice corresponding to the marked video slice, and at the same time performs secondary marking on the internal voice of the video slice that is seriously inconsistent with the corresponding teaching material voice. When the secondary marked video slice is larger than the set threshold, it is determined that the original teaching video does not meet the playback standard and feedback is given for deletion;

[0038] The speech-to-subtitle calibration judgment submodule is used to repeatedly detect the subtitles corresponding to the speech pronunciation in each frame of the calibrated video slice, and to judge and calibrate the erroneous subtitles.

[0039] The single slice video key teaching content matching analysis module is used to analyze and match the content of the slice video with the corresponding teaching material, and mark the matching slice video according to the key teaching content within the teaching material;

[0040] It is also necessary to explain in detail that the single slice video key teaching content matching analysis module includes a video teaching material synchronization matching rate analysis submodule and a teaching material key content synchronization video slice marking submodule. The video teaching material synchronization matching rate analysis submodule is used to match the teaching material teaching content corresponding to each slice video with the original teaching video, analyze the teaching material matching rate of each slice video, and screen the slice videos whose matching rate is greater than the set threshold;

[0041] It is also necessary to further specify that the video teaching material synchronization matching rate analysis submodule obtains the key words of the teaching content in the corresponding teaching material and matches the internal content of the sliced ​​video with keywords, and obtains the matching rate of each key word with the subtitle words in the sliced ​​video. Among them, the key words are summarized and the number of key words is set as C. g At the same time, through big data analysis of the strongly related words involved in each key word, the strongly related words are graded and set as first-level strongly related words, second-level strongly related words, and third-level strongly related words. Among them, the first-level strongly related words are set as the extended knowledge point words marked in the teaching content of the corresponding textbook, the second-level strongly related words are set as the key words defined in the textbook similar to the corresponding textbook, and the third-level strongly related words are set as the related words analyzed by big data based on the key words of the teaching content in the corresponding textbook. The relevance of the first-level strongly related words is set to u1%, the relevance of the second-level strongly related words is set to u2%, and the relevance of the third-level strongly related words is set to u3%.

[0042] Set the total vocabulary of subtitles in each slice video as G′, match the total vocabulary with key words and different levels of strongly related words, select the key words and strongly related words that match the vocabulary of the slice video, and set the number of key words in the vocabulary of the slice video as C g ′, set the number of first-level strongly associated words to Ck1 ′, the number of secondary strongly associated words is C k2 ′, the number of third-level strongly associated words is C k3 ′, calculate the matching rate U between the subtitles in each video slice and the key words in the textbook according to the formula:

[0043]

[0044] Calculate the matching rate of each slice video, sort the matching rates of each slice video in descending order, and send them to the textbook key content synchronization video slice marking submodule for processing. The textbook key content synchronization video slice marking submodule marks the slice videos with high matching rate with the key teaching content according to the key teaching content within the corresponding textbook, and selects the key teaching slice videos.

[0045] The adjacent slice content multi-block combination connection determination module screens the key teaching information multi-blocks in the adjacent video slices according to the marked slice videos, and performs splicing analysis based on the consistency of the teaching focus;

[0046] It is also necessary to explain in detail that the adjacent slice content multi-block combination connection determination module includes a video slice multi-block key screening submodule and an adjacent video slice multi-block splicing analysis submodule. The video slice multi-block key screening submodule is used to perform multi-block secondary slicing on the slice video at the adjacent position of the key teaching slice video, and send the secondary slice video to the video teaching material synchronization matching rate analysis submodule for matching rate screening. When the matching rate of the secondary slice video is greater than the set threshold, it is marked. The adjacent video slice multi-block splicing analysis submodule is used to obtain the marked secondary slice video and determine whether the secondary slice video area Whether the block position is connected to the key teaching slice video. When the block position of the secondary slice video is connected to the key teaching slice video, the secondary slice video and the key teaching slice video are spliced, and the spliced ​​video is retrieved for secondary coherence. When the sentences inside the slice video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete them. When the block position of the secondary slice video is not connected to the key teaching slice video, the number of secondary slice videos j between the marked secondary slice video block and the key teaching slice video is obtained, and the matching rate is screened, and the matching rate is set to U1, U2, ..., U j , setting the screening matching rate to the threshold of U for the secondary slice video splicing * , set the minimum screening matching rate threshold of each secondary slice video to U * min , according to the formula:

[0047]

[0048] When the secondary slices between the screened marked secondary slice video blocks and the key teaching slice video meet the above formula, it is determined that it meets the multi-block video splicing standard, and the marked secondary slice video blocks, the secondary slice videos between the marked secondary slice video blocks and the key teaching slice video, and the key teaching slice video are spliced, and secondary coherence retrieval is performed at the same time. When the sentences within the slice video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete them, and the slice videos after splicing are updated and summarized.

[0049] The multi-path video playback feedback module is used to monitor and summarize the playback volume of different video slices, so as to mark and feedback the video slices that are played most;

[0050] It is also necessary to explain in detail that the multi-path video playback feedback module includes a video slice playback volume data summary submodule and a popular playback video slice marking submodule. The video slice playback volume data summary submodule is used to count the playback volume data of the re-spliced ​​and summarized slice videos one by one, and the popular playback video slice marking submodule is used to sort the playback data of each slice video and mark the slice videos whose playback data is higher than the set threshold in real time.

[0051] The keyword data annotation matching push module is used to extract keywords from the key video slices according to the matching internal teaching content of the textbook, so as to push them in a targeted manner through the matching keywords.

[0052] It is also necessary to explain in detail that the keyword data annotation matching and push module includes a real-time tagging video keyword retrieval and annotation sub-module and a slice video matching and push sub-module. The real-time tagging video keyword retrieval and annotation sub-module obtains the slice video whose marked playback data is higher than the set threshold, and extracts keywords from the content inside the slice video according to the matched teaching materials. At the same time, the slice video matching and push sub-module is used to match and overlap the slice video whose marked playback data is higher than the set threshold with the re-spliced ​​and summarized slice video, and screen out the key teaching slice videos that meet the playback data higher than the set threshold and are spliced ​​and summarized at the same time, and push the slice video on the playback network according to the extracted keywords.

[0053] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent video slice detection platform for English-assisted teaching, characterized by: The platform includes an original teaching video intelligent slicing processing module, a segmented slicing video abnormality judgment analysis and correction module, a single slicing video key teaching content matching analysis module, an adjacent slicing content multi-block combination connection judgment module, a multi-path video playback feedback module and a keyword data annotation matching push module; The original teaching video intelligent slicing processing module is used to perform mean slicing according to the number of frames of the teaching video, and at the same time perform continuity screening and adjustment on the sliced ​​video; the segmented slice video abnormality judgment analysis and correction module is used to perform segmented inspection and correction one by one according to the voice and subtitles of the slice video; the single slice video key teaching content matching analysis module is used to analyze and match the content of the slice video with the corresponding teaching material, and mark the matched slice video according to the key teaching content within the teaching material; the adjacent slice content multi-block combination connection judgment module screens the key teaching information multi-blocks within the adjacent video slices according to the marked slice video, and performs splicing analysis according to the consistency of the teaching focus; the multi-path video playback feedback module is used to monitor and summarize the playback volume of different video slices, so as to mark and feedback the video slices that are played as the focus; the keyword data annotation matching push module is used to extract keywords for the video slices that are played as the focus according to the teaching content inside the matched teaching material, so as to perform targeted push through the matched keywords.

2. The intelligent video slice detection platform for English-assisted teaching according to claim 1 is characterized in that The original teaching video intelligent slicing processing module includes a video frame mean slicing segmentation submodule and a slice video coherence determination and analysis submodule, wherein the video frame mean slicing segmentation submodule is used to obtain the number of frames of the original teaching video, perform mean segmentation according to the total number of video frames, and summarize each mean-sliced ​​video slice; the slice video coherence determination and analysis submodule is used to perform coherence retrieval on the internal content of the sliced ​​video. When the sentences in the sliced ​​video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained for completion, and the video slices after retrieval and completion are re-summarized.

3. The intelligent video slice detection platform for English-assisted teaching according to claim 1 is characterized in that : The segmented slice video abnormality judgment analysis and correction module includes a video voice pronunciation proofreading judgment submodule and a voice corresponding subtitle calibration judgment submodule. The video voice pronunciation proofreading judgment submodule includes a screening unit, a comparison unit and a correction unit. The video voice pronunciation proofreading judgment submodule is used to input each summarized video slice into the screening unit and the comparison unit. The screening unit is used to screen whether there is an error or non-standard pronunciation in the English voice in each frame of video content, and mark the ones with obvious errors. The comparison unit is used to compare the English voice in each frame of video content with the teaching voice in the textbook, identify the video slices whose internal voice is inconsistent with the teaching voice in the textbook, and mark the marked content to send the marked content to the correction unit. The correction unit performs intelligent big data modification and correction on the internal voice of the video according to the teaching voice of the textbook corresponding to the marked video slice, and at the same time, the internal voice of the video slice is seriously inconsistent with the corresponding teaching voice. When the video slice marked twice is larger than the set threshold, it is determined that the original teaching video does not meet the playback standard, and feedback deletion is performed; The speech-to-caption calibration judgment submodule is used to repeatedly detect the captions corresponding to the speech pronunciation in each frame of the calibrated video slice, and to judge and calibrate the wrong captions.

4. The intelligent video slice detection platform for English-assisted teaching according to claim 1 is characterized in that :The single slice video key teaching content matching analysis module includes a video teaching material synchronization matching rate analysis submodule and a teaching material key content synchronization video slice marking submodule. The video teaching material synchronization matching rate analysis submodule is used to match the teaching material teaching content corresponding to each slice video with the original teaching video, analyze the teaching material matching rate of each slice video, and screen the slice videos with a matching rate greater than a set threshold. The teaching material key content synchronization video slice marking submodule marks the slice videos with a high matching rate with the key teaching content according to the corresponding teaching material internal key teaching content, and selects the key teaching slice videos.

5. The intelligent video slice detection platform for English-assisted teaching according to claim 4 is characterized in that The video teaching material synchronization matching rate analysis submodule obtains the key words of the teaching content in the corresponding teaching material and matches the internal content of the sliced ​​video with keywords, obtains the matching rate of each key word with the subtitle words in the sliced ​​video, wherein the key words are summarized and the number of key words is set as C g At the same time, through big data analysis of the strongly related words involved in each key word, the strongly related words are graded and set as first-level strongly related words, second-level strongly related words, and third-level strongly related words. Among them, the first-level strongly related words are set as the extended knowledge point words marked in the teaching content of the corresponding textbook, the second-level strongly related words are set as the key words defined in the textbook similar to the corresponding textbook, and the third-level strongly related words are set as the related words analyzed by big data based on the key words of the teaching content in the corresponding textbook. The relevance of the first-level strongly related words is set to u1%, the relevance of the second-level strongly related words is set to u2%, and the relevance of the third-level strongly related words is set to u3%. Set the total vocabulary of subtitles in each slice video as G', match the total vocabulary with key words and different levels of strongly related words, filter out the key words and strongly related words that match the vocabulary of the slice video, and set the number of key words in the vocabulary of the slice video as C g ', set the number of first-level strongly associated words to C k1 ', the number of secondary strongly associated words is C k2 ', the number of third-level strongly associated words is C k3 ', calculate the matching rate U of each slice video internal subtitle and the textbook internal key vocabulary according to the formula: Calculate the matching rate of each video slice, sort the matching rates of each video slice in descending order, and send them to the textbook key content synchronization video slice marking submodule for processing.

6. The intelligent video slice detection platform for English-assisted teaching according to claim 1 is characterized in that The adjacent slice content multi-block combination connection determination module includes a video slice multi-block key screening submodule and an adjacent video slice multi-block splicing analysis submodule. The video slice multi-block key screening submodule is used to perform multi-block secondary slicing on the slice video at the adjacent position of the key teaching slice video, and send the secondary slice video to the video teaching material synchronization matching rate analysis submodule for matching rate screening. When the matching rate of the secondary slice video is greater than the set threshold, it is marked. The adjacent video slice multi-block splicing analysis submodule is used to obtain the marked secondary slice video and determine the block position of the secondary slice video. Whether it is connected to the key teaching slice video. When the position of the secondary slice video block is connected to the key teaching slice video, the secondary slice video and the key teaching slice video are spliced, and the spliced ​​video is retrieved for secondary coherence. When the sentences inside the slice video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete it. When the position of the secondary slice video block is not connected to the key teaching slice video, the number of secondary slice videos j between the marked secondary slice video block and the key teaching slice video is obtained, and the matching rate is screened, and the matching rate is set to U1, U2, ..., U j , setting the screening matching rate to the threshold of U for the secondary slice video splicing * , set the minimum screening matching rate threshold of each secondary slice video to U * min , according to the formula: When the secondary slices between the screened marked secondary slice video blocks and the key teaching slice video meet the above formula, it is determined that it meets the multi-block video splicing standard, and the marked secondary slice video blocks, the secondary slice videos between the marked secondary slice video blocks and the key teaching slice video, and the key teaching slice video are spliced, and secondary coherence retrieval is performed at the same time. When the sentences within the slice video are incoherent, the video content corresponding to the adjacent coherent sentences is obtained to complete them, and the slice videos after splicing are updated and summarized.

7. The intelligent video slice detection platform for English-assisted teaching according to claim 1 is characterized in that :The multi-path video playback feedback module includes a video slice playback volume data summary submodule and a popular playback video slice marking submodule. The video slice playback volume data summary submodule is used to count the playback volume data of the re-spliced ​​and summarized slice videos one by one, and the popular playback video slice marking submodule is used to sort the playback data of each slice video and mark the slice videos whose playback data is higher than the set threshold in real time.

8. The intelligent video slice detection platform for English-assisted teaching according to claim 1 is characterized in that :The keyword data annotation matching and pushing module includes a real-time tagging video keyword retrieval and annotation submodule and a slice video matching and pushing submodule. The real-time tagging video keyword retrieval and annotation submodule obtains the slice video whose marked playback data is higher than the set threshold, and extracts keywords from the content inside the slice video according to the matched teaching materials. At the same time, the slice video matching and pushing submodule is used to match and overlap the slice video whose marked playback data is higher than the set threshold with the re-spliced ​​and summarized slice video, and screen out the key teaching slice videos that meet the playback data higher than the set threshold and are spliced ​​and summarized at the same time, and push the slice video on the playback network according to the extracted keywords.

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