Video editing system for artificial intelligence teaching

Abstract

The invention discloses a video editing system for artificial intelligence teaching, which comprises an editing source material determination module, a source material content learning determination module, a video editing mode determination module, a video editing execution module, an editing result evaluation module and a user interaction module, and relates to the technical field of artificial intelligence. According to the video editing system for artificial intelligence teaching, through integration of intelligent screening, classification, deep learning and automatic editing technologies, operations of preliminary screening, content analysis, editing mode determination, special effect addition and the like of a large number of video materials can be automatically completed. Therefore, the workload of manual editing is greatly reduced, and the accuracy and efficiency of video editing are remarkably improved. Meanwhile, the system supports touch operation and voice control, the editing process is further simplified, and the teacher can make high-quality teaching videos more quickly and conveniently.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a video editing system for artificial intelligence teaching. Background Art

[0002] With the rapid development of artificial intelligence technology, its application in the field of education is becoming more and more extensive. As an important teaching method, video teaching is favored because of its intuitiveness and easy understanding. However, the traditional video editing process is cumbersome and complicated, and requires a lot of manpower and time, which is obviously a bottleneck for artificial intelligence teaching that requires a large amount of video materials.

[0003] At present, although there are some video editing software that can realize automatic editing, most of these software have single functions and cannot meet the diverse needs of artificial intelligence teaching for video editing. Therefore, there is an urgent need for a system that can realize efficient and intelligent video editing based on the characteristics of artificial intelligence teaching. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a video editing system for artificial intelligence teaching, which solves the problems raised in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a video editing system for artificial intelligence teaching, including an editing source material determination module, a source material content learning determination module, a video editing mode determination module, a video editing execution module, an editing result evaluation module, and a user interaction module. The editing source material determination module is used to screen, classify and manage a large amount of video materials for subsequent editing and use. The source material content learning determination module is used to conduct in-depth analysis of the screened source materials, extract key information, and evaluate the value and role of the materials in teaching based on the extracted information, as well as to construct a knowledge graph with the association with knowledge points. The video editing The editing mode determination module is used to analyze and determine the appropriate video editing mode and style according to the teaching content, teaching objectives and student characteristics. The video editing execution module is used to perform intelligent editing, splicing and special effect adding operations on the source materials according to the determined editing mode and style to generate a teaching video that meets the teaching requirements. The editing result evaluation module is used to objectively and subjectively evaluate the edited video and collect feedback and suggestions in order to optimize the video. The user interaction module provides interactive methods such as touch operation or voice control, as well as a concise and clear user interface to facilitate users to edit and manage videos and collect user feedback and suggestions.

[0006] Preferably, the output end of the editing source material determination module is electrically connected to the input end of the source material content learning determination module, the output end of the source material content learning determination module is electrically connected to the input end of the video editing mode determination module, the output end of the video editing mode determination module is electrically connected to the input end of the video editing execution module, the output end of the video editing execution module is electrically connected to the input end of the editing result evaluation module, and the output end of the editing result evaluation module is electrically connected to the input end of the user interaction module.

[0007] Preferably, the editing source material determination module includes a material screening unit, a material classification unit, and a material management unit; the source material content learning determination module includes a content analysis unit, a value assessment unit, and a knowledge graph construction unit; the video editing mode determination module includes a demand analysis unit, a mode selection unit, and a style adjustment unit; the video editing execution module includes a clipping unit, a splicing unit, a special effects adding unit, and an output unit; the editing result evaluation module includes an objective evaluation unit, a subjective evaluation unit, and a feedback and optimization unit; and the user interaction module includes an interface design unit, an interaction design unit, and a user feedback unit.

[0008] Preferably, the editing source material determination module is used to determine the source material required for video editing, and to screen out materials that meet teaching needs from a large amount of video materials through pre-set rules or algorithms.

[0009] Preferably, the source material content learning determination module is used to perform content learning on the screened source material to determine its value and role in teaching. This module uses a deep learning algorithm to conduct in-depth analysis of the video content and extract key information to provide a basis for subsequent editing work. The video editing mode determination module determines the mode and style of video editing according to teaching needs. This module selects the appropriate editing mode and style by analyzing factors such as teaching content, teaching objectives and student characteristics to ensure that the edited video meets the teaching requirements.

[0010] Preferably, the video editing execution module edits and synthesizes the source material according to the determined editing mode and style to generate a teaching video that meets the teaching requirements. The module uses automated editing technology to implement operations such as video clipping, splicing, and special effects adding to improve editing efficiency and quality.

[0011] Preferably, the editing result evaluation module is used to evaluate and provide feedback on the edited video to ensure that it meets the teaching requirements and quality standards. The module evaluates the content, form, effect and other aspects of the video through a combination of objective and subjective evaluations, and puts forward opinions and suggestions for improvement.

[0012] Preferably, the user interaction module provides a user-friendly interface and interaction method to facilitate users to edit and manage videos. The module realizes interaction and communication between users and the system through a graphical interface, touch operation, etc., thereby improving the system's usability and user experience.

[0013] Beneficial Effects

[0014] The present invention provides a video editing system for artificial intelligence teaching. Compared with the prior art, it has the following beneficial effects:

[0015] (1) By integrating intelligent screening, classification, deep learning and automated editing technologies, it can automatically complete operations such as preliminary screening, content analysis, editing mode determination and special effects addition of a large amount of video materials. This not only greatly reduces the workload of manual editing, but also significantly improves the accuracy and efficiency of video editing. At the same time, the system supports touch operation and voice control, which further simplifies the editing process, allowing teachers to produce high-quality teaching videos more quickly and conveniently.

[0016] (2) The system of the present invention is designed with a concise and clear user interface and rich interactive modes, including touch operation and voice control, so that users can easily get started and efficiently edit and manage videos. In addition, the system also provides real-time operation prompts and help documents, which makes it convenient for users to obtain the required information at any time, reducing the learning cost. These designs significantly improve the user experience and enable teachers to focus more on the realization of teaching content and goals.

[0017] (3) The video editing system proposed in the present invention supports the selection and adjustment of multiple editing modes and styles, so that teachers can flexibly adjust the video presentation and teaching style according to the teaching content, objectives and characteristics of students. This personalized customization capability not only enriches the diversity and fun of teaching videos, but also helps to stimulate students' learning interest and enthusiasm. At the same time, the system also provides an editing result evaluation and feedback mechanism to help teachers continuously optimize and improve teaching videos and promote teaching innovation and personalized development. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a system block diagram of the present invention;

[0019] Figure 2 A schematic diagram of an editing source material determination module of the present invention;

[0020] Figure 3 A schematic diagram of a source material content learning and determination module of the present invention;

[0021] Figure 4 A schematic diagram of a video editing mode determination module of the present invention;

[0022] Figure 5 A schematic diagram of a video editing execution module of the present invention;

[0023] Figure 6 It is a schematic diagram of the editing result evaluation module of the present invention;

[0024] Figure 7 Schematic diagram of the user interaction module of the present invention.

[0025] In the figure: 01, editing source material determination module; 02, source material content learning determination module; 03, video editing mode determination module; 04, video editing execution module; 05, editing result evaluation module; 06, user interaction module; 011, material screening unit; 012, material classification unit; 013, material management unit; 021, content analysis unit; 022, value evaluation unit; 023, knowledge graph construction unit; 031, demand analysis unit; 032, mode selection unit; 033, style adjustment unit; 041, editing unit; 042, splicing unit; 043, special effects adding unit; 044, output unit; 051, objective evaluation unit; 052, subjective evaluation unit; 053, feedback and optimization unit; 061, interface design unit; 062, interaction design unit; 063, user feedback unit. DETAILED DESCRIPTION

[0026] 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.

[0027] The present invention provides three technical solutions, including the following embodiments:

[0028] Example 1

[0029] See also Figure 1A video editing system for artificial intelligence teaching includes an editing source material determination module 01, a source material content learning determination module 02, a video editing mode determination module 03, a video editing execution module 04, an editing result evaluation module 05, and a user interaction module 06. The editing source material determination module 01 is used to screen, classify and manage a large amount of video materials for subsequent editing. The source material content learning determination module 02 is used to conduct in-depth analysis on the screened source materials, extract key information, and evaluate the value and role of the materials in teaching based on the extracted information, as well as to build a knowledge graph based on the association with knowledge points. The video editing mode determination module 03 is used to analyze and determine the appropriate video editing mode and style according to the teaching content, teaching objectives and student characteristics. The video editing execution module 04 is used to perform intelligent editing, splicing and special effect adding operations on the source materials according to the determined editing mode and style, and generate a teaching video that meets the teaching requirements. The editing result evaluation module 05 is used to objectively and subjectively evaluate the edited video, collect feedback and suggestions in order to optimize the video. The user interaction module 06 provides interactive methods such as touch operation or voice control, as well as a concise and clear user interface to facilitate users to edit and manage videos, and collect user feedback and suggestions.

[0030] The output end of the editing source material determination module 01 is electrically connected to the input end of the source material content learning determination module 02, the output end of the source material content learning determination module 02 is electrically connected to the input end of the video editing mode determination module 03, the output end of the video editing mode determination module 03 is electrically connected to the input end of the video editing execution module 04, the output end of the video editing execution module 04 is electrically connected to the input end of the editing result evaluation module 05, and the output end of the editing result evaluation module 05 is electrically connected to the input end of the user interaction module 06.

[0031] Example 2

[0032] Based on Example 1, see Figure 2-Figure 7As shown, the editing source material determination module 01 includes a material screening unit 011, a material classification unit 012, and a material management unit 013. The material screening unit 011 uses image recognition technology to perform a preliminary analysis on the video content and eliminate materials that do not meet the requirements. At the same time, according to parameters such as video length and resolution, it further screens suitable materials. The material classification unit 012 uses natural language processing technology to analyze the video content, extract keywords, and classify the materials according to the keywords. The material management unit 013 uses distributed storage technology to store the screened and classified materials on multiple servers to ensure data security and reliability. At the same time, a friendly user interface is provided to facilitate users to retrieve and manage materials. The source material content learning determination module 02 includes a content analysis unit 021, a value evaluation unit 022, and a knowledge graph construction unit 023. The video editing mode determination module 03 includes a demand analysis unit 031, a mode selection unit 032, and a style adjustment unit 033. The video editing execution module 04 includes a clipping unit 041, a splicing unit 042, a special effect adding unit 043, and an output unit 044. The editing result evaluation module 05 includes an objective evaluation unit 051, a subjective evaluation unit 052, and a feedback and optimization unit 053. The user interaction module 06 includes an interface design unit 061, an interaction design unit 062, and a user feedback unit 063. The content analysis unit 021 uses a deep learning algorithm to perform frame-level analysis on the video content and extract key information. For example, the target detection algorithm is used to identify objects and people in the video; the action recognition algorithm is used to identify actions in the video, etc. The value evaluation unit 022 evaluates the value of the video material based on the extracted key information and combined with teaching needs. For example, for a certain knowledge point, if the video material contains clear explanations and examples, the material has a higher value. The knowledge graph construction unit 023 associates the evaluated video material with the knowledge point to construct a knowledge graph. The knowledge graph is represented by a graph structure, with nodes representing knowledge points and edges representing the association between knowledge points. Through the knowledge graph, the relationship between each knowledge point can be intuitively understood, providing guidance for subsequent video editing. The demand analysis unit 031 collects opinions and suggestions from students and teachers through questionnaires, interviews, etc., to understand their needs and expectations for video editing. At the same time, the teaching content and teaching objectives are analyzed to clarify the key points and difficulties of editing. The mode selection unit 032 selects a suitable editing mode and style based on the analysis results. For example, for teaching content with a strong theoretical nature, a concise and clear editing style can be selected; for teaching content with a strong practical nature, a vivid and interesting editing style can be selected. The style adjustment unit 033 fine-tunes and optimizes the selected editing mode and style.For example, adjust the brightness, contrast, saturation and other parameters of the video to make it more in line with visual aesthetics; add background music and sound effects to enhance the appeal and attraction of the video. The editing unit 041 uses automated editing technology to intelligently edit the source material according to the preset editing rules. For example, use voice recognition technology to identify the explanation content in the video, and automatically edit the video according to the rhythm and focus of the explanation content. The splicing unit 042 splices the edited materials according to the needs of the teaching content and teaching objectives. When splicing, pay attention to maintaining the continuity and fluency of the video. The special effects adding unit 043 adds appropriate special effects to the video, such as subtitles, animations, filters, etc. For example, use subtitle technology to add text descriptions to the video to help students better understand the teaching content; use animation technology to transform abstract knowledge points into intuitive animation forms to improve students' learning interest. The output unit 044 outputs the edited video in a format and resolution that meets the teaching requirements. For example, the video is output to MP4 format with a resolution of 1080P or higher to ensure the quality and clarity of the video. The objective evaluation unit 051 uses automated evaluation tools to objectively evaluate the content, form, effect, etc. of the video. For example, image recognition technology is used to evaluate the image quality in the video; speech recognition technology is used to evaluate the explanation content in the video. The subjective evaluation unit 052 invites students and teachers to conduct subjective evaluations of the video. When evaluating, questionnaires, interviews, etc. can be used to collect their opinions and suggestions. At the same time, evaluation standards and scoring rules can be set to ensure the objectivity and accuracy of the evaluation. The feedback and optimization unit 053 puts forward improvement opinions and suggestions based on the evaluation results. For example, if the image quality in the video is poor, the image processing algorithm can be optimized; if the explanation content in the video is not clear enough, it can be re-recorded or subtitled. Through continuous optimization and improvement, the quality of videos and teaching effects are improved. The interface design unit 061 designs a simple and clear user interface to facilitate users to edit and manage videos. The interactive design unit 062 designs touch operation, voice control and other interactive methods to improve the system's usability and user experience. The user feedback unit 063 collects user feedback and suggestions, and continuously optimizes and improves the user interface and interactive methods.

[0033] Example 3

[0034] Based on Example 2, see Figure 2-Figure 7 As shown, the editing source material determination module 01 is used to determine the source material required for video editing, and to select the material that meets the teaching needs from a large amount of video materials through pre-set rules or algorithms.

[0035] The source material content learning determination module 02 is used to conduct content learning on the screened source materials to determine their value and role in teaching. This module uses a deep learning algorithm to conduct in-depth analysis of the video content, extract key information, and provide a basis for subsequent editing work. The video editing mode determination module 03 determines the mode and style of video editing according to teaching needs. This module selects the appropriate editing mode and style by analyzing factors such as teaching content, teaching objectives, and student characteristics to ensure that the edited video meets the teaching requirements.

[0036] The video editing execution module 04 edits and synthesizes the source materials according to the determined editing mode and style to generate a teaching video that meets the teaching requirements. This module uses automated editing technology to achieve operations such as video editing, splicing, and special effects addition to improve editing efficiency and quality.

[0037] The editing result evaluation module 05 is used to evaluate and provide feedback on the edited video to ensure that it meets the teaching requirements and quality standards. This module evaluates the content, form, effect and other aspects of the video through a combination of objective and subjective evaluation, and puts forward improvement opinions and suggestions.

[0038] The user interaction module 06 provides a user-friendly interface and interaction mode to facilitate users to edit and manage videos. This module enables interaction and communication between users and the system through a graphical interface, touch operation, etc., thereby improving the system's usability and user experience.

[0039] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0040] The above embodiments of the invention are described in detail, but the contents are only preferred embodiments of the invention and cannot be considered to limit the scope of the invention. All equivalent changes and improvements made within the scope of the invention should still fall within the scope of the invention.

Claims

1. A video editing system for artificial intelligence teaching, characterized by: The system comprises an editing source material determination module (01), a source material content learning determination module (02), a video editing mode determination module (03), a video editing execution module (04), an editing result evaluation module (05), and a user interaction module (06). The editing source material determination module (01) is used to screen, classify, and manage a large amount of video materials for subsequent editing. The source material content learning determination module (02) is used to conduct an in-depth analysis of the screened source materials, extract key information, and evaluate the value and role of the materials in teaching based on the extracted information, as well as to construct a knowledge graph in association with knowledge points. The video editing mode determination module (03) is used to According to the teaching content, teaching objectives and student characteristics, the appropriate video editing mode and style are analyzed and determined. The video editing execution module (04) is used to perform intelligent editing, splicing and special effect adding operations on the source materials according to the determined editing mode and style, so as to generate a teaching video that meets the teaching requirements. The editing result evaluation module (05) is used to objectively and subjectively evaluate the edited video and collect feedback and suggestions in order to optimize the video. The user interaction module (06) provides interactive methods such as touch operation or voice control, as well as a concise and clear user interface, so as to facilitate users to edit and manage videos and collect user feedback and suggestions.

2. A video editing system for artificial intelligence teaching according to claim 1, characterized in that: The output end of the editing source material determination module (01) is electrically connected to the input end of the source material content learning determination module (02), the output end of the source material content learning determination module (02) is electrically connected to the input end of the video editing mode determination module (03), the output end of the video editing mode determination module (03) is electrically connected to the input end of the video editing execution module (04), the output end of the video editing execution module (04) is electrically connected to the input end of the editing result evaluation module (05), and the output end of the editing result evaluation module (05) is electrically connected to the input end of the user interaction module (06).

3. A video editing system for artificial intelligence teaching according to claim 1, characterized in that: The editing source material determination module (01) includes a material screening unit (011), a material classification unit (012), and a material management unit (013); the source material content learning determination module (02) includes a content analysis unit (021), a value evaluation unit (022), and a knowledge graph construction unit (023); the video editing mode determination module (03) includes a demand analysis unit (031), a mode selection unit (032), and a style adjustment unit (033); the video editing execution module (04) includes a clipping unit (041), a splicing unit (042), a special effect adding unit (043), and an output unit (044); the editing result evaluation module (05) includes an objective evaluation unit (051), a subjective evaluation unit (052), and a feedback and optimization unit (053); the user interaction module (06) includes an interface design unit (061), an interaction design unit (062), and a user feedback unit (063).

4. The video editing system for artificial intelligence teaching according to claim 1, characterized in that: The editing source material determination module (01) is used to determine the source material required for video editing, and to select the material that meets the teaching requirements from a large amount of video materials through a preset rule or algorithm.

5. The video editing system for artificial intelligence teaching according to claim 1, characterized in that: The source material content learning determination module (02) is used to conduct content learning on the screened source material to determine its value and role in teaching. This module uses a deep learning algorithm to conduct in-depth analysis of the video content and extract key information to provide a basis for subsequent editing work. The video editing mode determination module (03) determines the mode and style of video editing according to teaching needs. This module selects a suitable editing mode and style by analyzing factors such as teaching content, teaching objectives and student characteristics to ensure that the edited video meets the teaching requirements.

6. A video editing system for artificial intelligence teaching according to claim 1, characterized in that: The video editing execution module (04) edits and synthesizes the source material according to the determined editing mode and style to generate a teaching video that meets the teaching requirements. This module uses automated editing technology to implement operations such as video clipping, splicing, and special effects addition, thereby improving editing efficiency and quality.

7. The video editing system for artificial intelligence teaching according to claim 1, characterized in that: The editing result evaluation module (05) is used to evaluate and provide feedback on the edited video to ensure that it meets the teaching requirements and quality standards. This module evaluates the content, form, effect and other aspects of the video through a combination of objective and subjective evaluation, and puts forward improvement opinions and suggestions.

8. The video editing system for artificial intelligence teaching according to claim 1, characterized in that: The user interaction module (06) provides a user-friendly interface and interaction mode, which facilitates users to edit and manage videos. The module realizes interaction and communication between users and the system through a graphical interface, touch operation and other modes, thereby improving the usability of the system and user experience.

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