Campus cloud platform intelligent storage method and system

Abstract

The application discloses a kind of intelligent storage methods of campus cloud platform, including the following steps: S1, the electronic book storage of all subject textbooks is collected to cloud platform;S2, the board writing video of all blackboards in current classroom is obtained by multiple angle setting camera;S3, the board writing video data of blackboard is preprocessed, and multiple video stream data are obtained;S4, the subject textbook in corresponding electronic book of the board writing content contained in multiple video stream data is analyzed;S5, the position of the teacher explained that the board writing content contained in multiple video stream data should be specifically positioned corresponding subject textbook;S6, the clearest video frame picture in multiple video stream is extracted, and it is inserted in the position of subject textbook explanation in electronic book;S7, upload cloud platform and synchronously store.The application obtains clear and complete video recording and electronic book illustration notes for students to review and consolidate after class according to traditional blackboard writing teaching, and stores to cloud platform.

Description

Technical Field

[0001] This invention belongs to the field of data storage technology, specifically, it relates to an intelligent storage method and system for a campus cloud platform. Background Technology

[0002] With the rapid development of information technology, campus informatization has entered the "Internet+" era, and informatization has become a new engine for school development. In recent years, as traditional teaching models have gradually failed to meet students' needs for multimedia informatization methods that facilitate playback and review at any time, online teaching technology has also developed rapidly with the development and popularization of the Internet, especially the mobile Internet. In the process of online teaching, the teaching process can be recorded through classroom recording technology, and excellent teaching resources can be shared on the Internet. Students can access these teaching resources online using their terminals, which can meet their needs for after-class review.

[0003] Chinese invention patent CN201910581326.0 discloses a shared educational method and system for campus classrooms. The method includes the following steps: S1. Recording classroom teaching data using an electronic whiteboard system and uploading the data along with the recording time to a cloud platform; S2. Transmitting classroom notes from a smart writing device to the corresponding student's end in real time, whereby the student uploads the notes along with the writing time to the cloud platform; S3. Segmenting the classroom notes and establishing a correlation between each segment and the corresponding classroom teaching data based on their time relationship; S4. Receiving note access requests from students and returning the corresponding classroom notes and associated classroom teaching data to the students. Uploading student notes and teaching data together to the cloud platform and associating them by time facilitates after-class review and consolidation for students.

[0004] The existing patent has a drawback: while it achieves the simultaneous uploading of student notes and teaching data to a cloud platform and their chronological association for easier after-class review, it overlooks the fact that most schools lack electronic whiteboard systems for recording classroom teaching data, and many teachers still rely on traditional blackboard writing. Therefore, recording classroom teaching data still requires video recording of teachers' blackboard writing to obtain videos suitable for student review. However, current technology lacks the capability to obtain clear and complete video recordings of traditional blackboard writing for student review and storage on a cloud platform. Summary of the Invention

[0005] To address the problem that existing campus classroom shared cloud platform storage technologies lack the technology to obtain clear and complete video recordings for students' after-class review and consolidation based on traditional blackboard writing instruction, and to store them on the cloud platform, this invention provides a campus cloud platform intelligent storage method and system.

[0006] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A campus cloud platform intelligent storage method includes the following steps:

[0008] S1. Collect e-books of all subjects and store them on the cloud platform;

[0009] S2. Acquire video of all blackboard writing in the current classroom using cameras set at multiple angles;

[0010] S3. Preprocess the blackboard writing video data to obtain multi-segment video stream data;

[0011] S4. Analyze the blackboard content contained in the multi-segment video stream data to correspond to the subject textbooks in the e-book;

[0012] S5. Specifically locate the position of the blackboard content contained in the multi-segment video stream data corresponding to the position of the teacher explaining the subject in the textbook (the specific position in the page number);

[0013] S6. Extract the clearest video frame from the multi-segment video stream and insert it into the subject textbook explanation section of the e-book;

[0014] S7. Upload to the cloud platform and synchronize storage.

[0015] Furthermore, in step S1, the method of collecting e-books for all subjects' textbooks involves scanning each textbook individually with a scanner to obtain the e-books, or obtaining them from the textbook's printing platform (library system). Simultaneously, the electronic documents of each exam paper can also be stored on a cloud platform to facilitate the blackboard explanation process of the exam paper, and the blackboard explanation can be inserted for students to access later.

[0016] Furthermore, step S2 involves setting up cameras on the front, sides, and top and bottom of the blackboard to capture video of the writing on the blackboard. This prevents parts of the writing from being easily erased due to the teacher's obstruction during the lecture, and the video obtained from multiple angles compensates for any obstructions during the lecture, thus obtaining a complete video of the entire classroom lecture process without any blind spots.

[0017] Furthermore, the detailed steps of step S3 include:

[0018] S301. Obtain the blackboard writing video data;

[0019] S302. Split the blackboard writing video data of the current lesson into video frames; each frame represents an image.

[0020] S303. Compare the first video frame of the current lesson with the subsequent video frames one by one. Determine the video frame with the content written on the blackboard that is different from the content written on the blackboard in the first video frame. This is the second video frame. Repeat this process to split the entire blackboard video data into multiple video frames.

[0021] S304. Based on the chronological order of each video frame, the data is converted into multi-segment video stream data.

[0022] The entire board of notes written by the teacher is treated as a single video stream segment. This reduces the number of video stream segments and allows for quick determination of the number of notes written in a lesson, facilitating students' easy location of notes later when searching for them.

[0023] Furthermore, the detailed steps of step S4 include:

[0024] S401. Identify all text in the whiteboard of the last video frame of each multi-segment video stream data;

[0025] S402. Retrieve special characters or phrases from the fixed special characters and phrases table in the subject textbook; the fixed special characters and phrases table in the subject textbook stores the special characters and phrases used for each subject, such as the 26 commonly used letters of English, commonly used mathematical formulas, commonly used chemical element symbols in chemistry, commonly used formula symbols in physics, etc.

[0026] S403. Obtain the possible subject textbooks to which the query results belong, and then use a probability algorithm to determine the probability value of the subject textbook to which it belongs. The subject textbook with the highest probability value is the one with the highest probability value. The probability algorithm calculates the probability value by the more identical special characters or phrases found in the query results of the fixed special characters and phrases table of subject textbooks.

[0027] Furthermore, the detailed steps of step S5 include:

[0028] S501. Obtain the last video frame of each segment of multi-segment video stream data as the complete whiteboard video frame image of the multi-segment video stream data through the detailed steps in S3.

[0029] S502. Obtain all text in the entire complete whiteboard video frame image using image recognition methods;

[0030] S503. Randomly combine all the text to generate several word groups, search for the source of the word groups in the subject textbooks of the corresponding e-books, and locate the specific location of the e-books in which the teachers of the subject textbooks explain the subject groups.

[0031] Furthermore, the detailed steps of step S6 include:

[0032] S601. Compare multiple video streams acquired from different perspectives at the same time.

[0033] S602. Capture the high-resolution and unobstructed video frame images of the blackboard writing as the optimal video frame images for that video frame time period.

[0034] S603. Insert it onto the next page after the subject's explanation in the e-book. Alternatively, the text content can be extracted using an image recognition algorithm and inserted into the subject's explanation section in the e-book, making the annotation more accurate and facilitating students who did not take notes to supplement or retrieve their notes after class.

[0035] A campus cloud platform intelligent storage system includes an e-book module for collecting subject textbooks, a whiteboard video acquisition module, a whiteboard video data preprocessing module, a video stream data analysis module, a video stream data corresponding to textbook location module, a whiteboard video frame image insertion e-book module, and a transmission module.

[0036] The e-book module for collecting subject textbooks is used to collect e-books of all subjects and store them on the cloud platform;

[0037] The blackboard video capture module is used to capture blackboard videos of all blackboards in the current classroom using cameras set at multiple angles;

[0038] The blackboard video data preprocessing module is connected to the blackboard video acquisition module and is used to preprocess the blackboard video data to obtain multi-segment video stream data.

[0039] The video stream data analysis module is connected to the whiteboard video data preprocessing module to analyze the whiteboard content contained in the multi-segment video stream data and the corresponding subject textbooks in the e-book.

[0040] The video stream data corresponds to the textbook location module and the video stream data analysis module. It is used to specifically locate the position of the blackboard content contained in the multi-segment video stream data corresponding to the teacher's explanation in the textbook of the corresponding subject.

[0041] The module for inserting video frame images into the e-book is connected to the module for locating the textbook position corresponding to the video stream data. It is used to extract the clearest video frame images from the multi-segment video stream and insert them into the subject textbook explanation position in the e-book.

[0042] The transmission module communicates with the whiteboard video frame image insertion e-book module, and is used to upload the e-book after the subject textbook explanation position inserted into the e-book to the cloud platform for storage.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] By using the intelligent storage method of the campus cloud platform, the lecturer's blackboard writing video is intelligently split into complete blackboard writing video frame images of each page and inserted into the subject textbook's explanation position. This enables the acquisition of clear and complete video recordings and e-book illustrations for students to review and consolidate after class, based on traditional blackboard writing teaching, and storage on the cloud platform. Attached Figure Description

[0045] Figure 1 This is an overall flowchart of a campus cloud platform intelligent storage method according to an embodiment of the present invention;

[0046] Figure 2 This is an overall framework diagram of a campus cloud platform intelligent storage system according to an embodiment of the present invention. Detailed Implementation

[0047] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0048] like Figure 1 As shown, this embodiment provides a smart storage method for a campus cloud platform, including the following steps:

[0049] S1. Collect e-books of all subjects and store them on the cloud platform;

[0050] S2. Acquire video of all blackboard writing in the current classroom using cameras set at multiple angles;

[0051] S3. Preprocess the blackboard writing video data to obtain multi-segment video stream data;

[0052] S4. Analyze the blackboard content contained in the multi-segment video stream data to correspond to the subject textbooks in the e-book;

[0053] S5. Specifically locate the position of the blackboard content contained in the multi-segment video stream data corresponding to the position of the teacher explaining the subject in the textbook (the specific position in the page number);

[0054] S6. Extract the clearest video frame from the multi-segment video stream and insert it into the subject textbook explanation section of the e-book;

[0055] S7. Upload to the cloud platform and synchronize storage.

[0056] In step S1, the e-books of all subject textbooks are collected by scanning each textbook individually with a scanner, or by obtaining the e-books from the subject textbook's printing platform (library system). Simultaneously, the electronic documents of each exam paper can also be stored on a cloud platform to facilitate the whiteboard explanation process of the exam paper, and the whiteboard explanation can be inserted for students to refer to later.

[0057] Step S2 involves setting up cameras on the front, sides, and top and bottom of the blackboard to capture video of the writing on the blackboard. This prevents parts of the writing from being easily erased due to the teacher's obstruction during the lecture. The video from multiple angles compensates for any obstructions during the lecture, thus obtaining a complete video of the entire lecture process without any blind spots.

[0058] The detailed steps of step S3 include:

[0059] S301. Obtain the blackboard writing video data;

[0060] S302. Split the blackboard writing video data of the current lesson into video frames; each frame represents an image.

[0061] S303. Compare the first video frame of the current lesson with the subsequent video frames one by one. Determine the video frame with the content written on the blackboard that is different from the content written on the blackboard in the first video frame. This is the second video frame. Repeat this process to split the entire blackboard video data into multiple video frames.

[0062] S304. Based on the chronological order of each video frame, the data is converted into multi-segment video stream data.

[0063] The entire board of notes written by the teacher is treated as a single video stream segment. This reduces the number of video stream segments and allows for quick determination of the number of notes written in a lesson, facilitating students' easy location of notes later when searching for them.

[0064] The detailed steps of step S4 include:

[0065] S401. Identify all text in the whiteboard of the last video frame of each multi-segment video stream data;

[0066] S402. Retrieve special characters or phrases from the fixed special characters and phrases table in the subject textbook; the fixed special characters and phrases table in the subject textbook stores the special characters and phrases used for each subject, such as the 26 commonly used letters of English, commonly used mathematical formulas, commonly used chemical element symbols in chemistry, commonly used formula symbols in physics, etc.

[0067] S403. Obtain the possible subject textbooks to which the query results belong, and then use a probability algorithm to determine the probability value of the subject textbook to which it belongs. The subject textbook with the highest probability value is the one with the highest probability value. The probability algorithm calculates the probability value by the more identical special characters or phrases found in the query results of the fixed special characters and phrases table of subject textbooks.

[0068] The detailed steps of step S5 include:

[0069] S501. Obtain the last video frame of each segment of multi-segment video stream data as the complete whiteboard video frame image of the multi-segment video stream data through the detailed steps in S3.

[0070] S502. Obtain all text in the entire complete whiteboard video frame image using image recognition methods;

[0071] S503. Randomly combine all the text to generate several word groups, search for the source of the word groups in the subject textbooks of the corresponding e-books, and locate the specific location of the e-books in which the teachers of the subject textbooks explain the subject groups.

[0072] The detailed steps of step S6 include:

[0073] S601. Compare multiple video streams acquired from different perspectives at the same time.

[0074] S602. Capture the high-resolution and unobstructed video frame images of the blackboard writing as the optimal video frame images for that video frame time period.

[0075] S603. Insert it onto the next page after the subject's explanation in the e-book. Alternatively, the text content can be extracted using an image recognition algorithm and inserted into the subject's explanation section in the e-book, making the annotation more accurate and facilitating students who did not take notes to supplement or retrieve their notes after class.

[0076] A campus cloud platform intelligent storage system includes an e-book module for collecting subject textbooks, a whiteboard video acquisition module, a whiteboard video data preprocessing module, a video stream data analysis module, a video stream data corresponding to textbook location module, a whiteboard video frame image insertion e-book module, and a transmission module.

[0077] The e-book module for collecting subject textbooks is used to collect e-books of all subjects and store them on the cloud platform;

[0078] The blackboard video capture module is used to capture blackboard videos of all blackboards in the current classroom using cameras set at multiple angles;

[0079] The blackboard video data preprocessing module is connected to the blackboard video acquisition module and is used to preprocess the blackboard video data to obtain multi-segment video stream data.

[0080] The video stream data analysis module is connected to the whiteboard video data preprocessing module to analyze the whiteboard content contained in the multi-segment video stream data and the corresponding subject textbooks in the e-book.

[0081] The video stream data corresponds to the textbook location module and the video stream data analysis module. It is used to specifically locate the position of the blackboard content contained in the multi-segment video stream data corresponding to the teacher's explanation in the textbook of the corresponding subject.

[0082] The module for inserting video frame images into the e-book is connected to the module for locating the textbook position corresponding to the video stream data. It is used to extract the clearest video frame images from the multi-segment video stream and insert them into the subject textbook explanation position in the e-book.

[0083] The transmission module communicates with the whiteboard video frame image insertion e-book module, and is used to upload the e-book after the subject textbook explanation position inserted into the e-book to the cloud platform for storage.

[0084] Compared with the prior art, the present invention has the following advantages:

[0085] By using the intelligent storage method of the campus cloud platform, the lecturer's blackboard writing video is intelligently split into complete blackboard writing video frame images of each page and inserted into the subject textbook's explanation position. This enables the acquisition of clear and complete video recordings and e-book illustrations for students to review and consolidate after class, based on traditional blackboard writing teaching, and storage on the cloud platform.

[0086] The above provides a detailed description of a campus cloud platform intelligent storage method and system provided by this application. The specific embodiments are described only to aid in understanding the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A campus cloud platform intelligent storage method, characterized in that, Including the following steps: S1. Collect e-books of all subjects and store them on the cloud platform; S2. Acquire video of all blackboard writing in the current classroom using cameras set at multiple angles; S3. Preprocess the blackboard writing video data to obtain multi-segment video stream data; S4. Analyze the blackboard content contained in the multi-segment video stream data to correspond to the subject textbooks in the e-book; S5. Specifically locate the position of the blackboard content contained in the multi-segment video stream data corresponding to the position of the teacher explaining the subject in the textbook; S6. Extract the clearest video frame from the multi-segment video stream and insert it into the subject textbook explanation section of the e-book; S7. Upload to the cloud platform and synchronize storage; In step S1, the method for collecting e-books of all subject textbooks is to scan the textbooks one by one to obtain the e-books, or to obtain the e-books of the subject textbooks through the printing platform. Step S2 involves setting up cameras on the front, sides, and top and bottom of the blackboard to capture video of the writing on the blackboard. The detailed steps of step S3 include: S301. Obtain the blackboard writing video data; S302. Split the blackboard writing video data of the current lesson into video frames; S303. Compare the first video frame of the current lesson with the subsequent video frames one by one. Determine the video frame with the content written on the blackboard that is different from the content written on the blackboard in the first video frame. This is the second video frame. Repeat this process to split the entire blackboard video data into multiple video frames. S304. Based on the chronological order of each video frame, the data is converted into multi-segment video stream data. The detailed steps of step S4 include: S401. Identify all text in the whiteboard of the last video frame of each multi-segment video stream data; S402. Extract special characters or phrases from the fixed special characters and phrases table in the subject textbook; S403. Obtain the possible subject textbooks to which the query results belong, and then use a probability algorithm to determine the probability value of the subject textbook to which it belongs. The subject textbook with the highest probability value is the one with the highest probability value. The detailed steps of step S5 include: S501. Obtain the last video frame of each segment of multi-segment video stream data as the complete whiteboard video frame image of the multi-segment video stream data through the detailed steps in S3. S502. Obtain all text in the entire complete whiteboard video frame image using image recognition methods; S503. Randomly combine all the text to generate several word groups, search for the source of the word groups in the subject textbooks of the corresponding e-books, and locate the specific location of the e-books in which the teachers of the subject textbooks explain the subject groups. 2.The intelligent storage method of a campus cloud platform according to claim 1, wherein, The detailed steps of step S6 include: S601. Compare multiple video streams acquired from different perspectives at the same time. S602. Capture the high-resolution and unobstructed video frame images of the blackboard writing as the optimal video frame images for that video frame time period. S603. Insert it onto the next page after the subject textbook explanation in the e-book.

3. A campus cloud platform intelligent storage system, characterized in that, It includes an e-book module for collecting subject textbooks, a whiteboard video acquisition module, a whiteboard video data preprocessing module, a video stream data analysis module, a video stream data corresponding to textbook location module, a whiteboard video frame image insertion e-book module, and a transmission module; The e-book module for collecting subject textbooks is used to collect e-books of all subjects and store them on the cloud platform; The blackboard video capture module is used to capture blackboard videos of all blackboards in the current classroom using cameras set at multiple angles; The blackboard video data preprocessing module is connected to the blackboard video acquisition module and is used to preprocess the blackboard video data to obtain multi-segment video stream data. The video stream data analysis module is connected to the whiteboard video data preprocessing module to analyze the whiteboard content contained in the multi-segment video stream data and the corresponding subject textbooks in the e-book. The video stream data corresponds to the textbook location module and the video stream data analysis module. It is used to specifically locate the position of the blackboard content contained in the multi-segment video stream data corresponding to the teacher's explanation in the textbook of the corresponding subject. The module for inserting video frame images into the e-book is connected to the module for locating the textbook position corresponding to the video stream data. It is used to extract the clearest video frame images from the multi-segment video stream and insert them into the subject textbook explanation position in the e-book. The transmission module communicates with the whiteboard video frame image insertion e-book module, and is used to upload the e-book after the subject textbook explanation position inserted into the e-book to the cloud platform for storage.

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