Lens splitting adjustment demonstration system based on artificial intelligence

Abstract

The invention discloses a split adjustment demonstration system based on artificial intelligence, particularly relates to the technical field of video split, and comprises a split input semantic analysis module which is used for analyzing a text and a preliminary visual material input by a user and converting the text and the preliminary visual material into structured data for subsequent processing. According to the method, text description is converted into structured data and scene elements are extracted through the sub-lens input semantic analysis module, so that accurate data support is provided for follow-up lens language analysis, composition optimization and scene scheduling, and the lens analysis and optimization module identifies potential problems and provides optimization suggestions by analyzing lens languages and composition, so that the lens analysis and optimization efficiency is improved. The intelligent adjustment video generation module automatically adjusts lens splitting parameters according to optimization suggestions and generates a preliminary demonstration video, video details can be further adjusted through the interactive editing module, it is ensured that the video quality reaches the expectation, and the intelligent level of the system is improved; therefore, the flexibility, the innovativeness and the efficiency of lens splitting adjustment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of video storyboards, and in particular to a storyboard adjustment and demonstration system based on artificial intelligence. Background Art

[0002] As the requirements for video production quality and efficiency in the film, television, advertising, animation and other industries continue to increase, the technical requirements for storyboard adjustment and video creation are gradually increasing. Storyboards are the basic components of film and television works, which determine the narrative method of the story, the expressiveness of the picture and the visual experience of the audience. Traditional storyboard adjustments mostly rely on manual operations. Production staff manually adjust the lens, composition, focus and scene elements according to the script, director's intention and visual needs. The workload is huge and easily affected by subjective factors, resulting in low adjustment efficiency and it is difficult to meet the rapidly changing creative needs. In addition, manual storyboard adjustments usually cannot fully optimize the lens language, composition and scene scheduling, and there may be problems such as inconsistent pictures and poor visual effects, which affect the overall quality of the work.

[0003] Although current video production software and systems have introduced some automated and intelligent functions, most of them are still limited to simple editing or lens switching adjustments. Although image processing technology has made certain progress, the existing technology has not yet achieved comprehensive intelligence and precision adjustment in terms of storyboard language analysis, composition optimization and lens adjustment. The existing lens adjustment system cannot accurately capture the details of each lens and scene, and often cannot automatically adapt to different styles and creative needs, which limits the creative space of creators. Due to the lack of in-depth analysis of lens language and scene elements in the existing system, the optimization suggestions and adjustment operations still require a lot of manual intervention, which affects the efficiency and quality of creation. Therefore, we provide a storyboard adjustment demonstration system based on artificial intelligence. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a storyboard adjustment demonstration system based on artificial intelligence.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An artificial intelligence-based storyboard adjustment demonstration system, comprising a storyboard input semantic parsing module for parsing user input text and preliminary visual materials to convert them into structured data for subsequent processing, a lens analysis optimization module for performing comprehensive lens language and composition analysis on the storyboard, an intelligent adjustment video generation module for automatically applying optimization adjustments and generating demonstration videos, and a feedback iteration system learning module for collecting user feedback and optimizing algorithms through continuous learning;

[0007] The storyboard input semantic parsing module includes an input interface design module for providing a user-friendly interface for inputting storyboard scripts, a natural language parsing module for analyzing and parsing the input text description to convert it into operable structured data, a scene semantic recognition module for identifying and classifying the scene categories in the input description to extract scene elements, and a data management and storage module for storing and managing the input storyboard data and parsing results;

[0008] The lens analysis and optimization module includes a lens language evaluation module for analyzing the lens language according to the input storyboard data to identify potential lens errors, a composition and visual optimization module for providing suggestions on composition improvement, a scene scheduling evaluation module for analyzing the scheduling of characters and props to ensure their reasonable configuration in the scene, and a creative generation and recommendation module for calling the built-in creative library to provide diversified creative suggestions.

[0009] The present invention is further configured as follows: the intelligent adjustment video generation module includes an automated storyboard adjustment module for automatically adjusting storyboard parameters according to the analysis module's suggestion, a user feedback integration module for allowing the user to view the adjustment suggestion and selectively apply or manually fine-tune, a rendering and video generation module for generating a preliminary demonstration video from the adjusted storyboard data, and an interactive editing and optimization module for providing a user interaction interface to allow the user to directly edit the details of the generated video in the system;

[0010] The feedback iteration system learning module includes a video expectation-actual comparison module for comparing the generated video with the user's expectations to identify differences, an adaptive proposal generation module for generating adjustment suggestions based on comparative analysis to narrow the gap between expected and actual effects, a learning feedback algorithm optimization module for automatically adjusting the analysis and generation algorithm based on the user's adjustment feedback, and a system update resource management module for regularly updating the system creative library and analysis model.

[0011] The present invention is further configured as follows: the automatic storyboard adjustment module includes a lens grammar analysis module for detecting and parsing lens language using image processing and grammar analysis technology, a composition optimization calculation module for calculating the best composition based on visual composition principles such as the golden section and the rule of thirds, a dynamic focus adjustment module for automatically adjusting the focus position according to scene changes, a scene element scheduling module for optimizing the position and movement path of characters and props in the scene, a multi-lens collaborative adjustment module for coordinating multiple lenses to ensure overall visual consistency, a user feedback learning module for optimizing the adjustment algorithm using user feedback data, and a quality assessment decision module for evaluating whether the adjusted storyboard meets preset quality standards and deciding whether to accept, further optimize, or feedback to the user for manual adjustment.

[0012] The present invention is further configured as follows: the lens syntax analysis module is responsible for parsing the lens language and scene structure, and providing necessary visual information for the composition optimization calculation module; the composition optimization calculation module provides the dynamic focus adjustment module with a lens composition framework by calculating the optimal composition principle; the dynamic focus adjustment module automatically adjusts the focus position according to scene changes and the movement of characters / objects to ensure the concentration and clarity of the visual effect, and at the same time provides a basis for the focus position for the scene element scheduling module.

[0013] The present invention is further configured as follows: the scene element scheduling module optimizes the position and movement path of the characters and props in the scene according to the focus adjustment result, and provides accurate element scheduling information for the multi-lens collaborative adjustment module; the multi-lens collaborative adjustment module coordinates the switching and visual transition between multiple lenses to ensure the overall visual consistency, and provides the adjusted lens data for the user feedback learning module; the user feedback learning module adjusts the algorithm strategy according to the user feedback to optimize the lens design and adjustment process, and provides the optimized lens data for the quality assessment decision module.

[0014] The present invention is further configured as follows: the input interface design module provides a user-friendly interface for inputting storyboard scripts, provides input data for the natural language parsing module, and ensures that the text description input by the user can be received and parsed by the system; the natural language parsing module analyzes and converts the text input by the user into structured data, and provides the parsed text description for the scene semantic recognition module to help identify and classify scene elements; the scene semantic recognition module extracts scene categories and element information based on the structured data output by the natural language parsing module, and provides necessary data storage and management for the data management storage module; the data management storage module stores and manages the storyboard data and parsing results, and provides structured input data for the subsequent lens analysis and optimization module.

[0015] The present invention is further configured as follows: the lens language evaluation module performs lens language analysis according to the input storyboard data, identifies potential lens errors, and provides lens improvement suggestions for the composition and visual optimization module; after receiving the optimization suggestions from the lens language evaluation module, the composition and visual optimization module improves the composition and provides the improved composition information for the scene scheduling evaluation module; based on the composition optimization result, the scene scheduling evaluation module analyzes the scheduling of characters and props in the scene to ensure their reasonable configuration and provides the scene scheduling evaluation data for the creative generation and recommendation module; the creative generation and recommendation module calls the built-in creative library and generates a variety of creative suggestions according to the information provided by the aforementioned modules, so as to provide more creative directions for subsequent storyboard adjustments.

[0016] The present invention is further configured as follows: the automatic storyboard adjustment module automatically adjusts the storyboard parameters according to the suggestions provided by the lens analysis and optimization module, and transmits the adjusted storyboard data to the user feedback integration module, allowing the user to view the suggestions and selectively apply or fine-tune; the user feedback integration module allows the user to view the automatic adjustment suggestions and perform fine-tune or selectively apply them, and transmits the adjustment data after user feedback to the rendering and video generation module to generate a preliminary demonstration video; the rendering and video generation module converts the adjusted storyboard data into a preliminary demonstration video, and provides a video file for the interactive editing and optimization module, allowing the user to further edit the details of the video; the interactive editing and optimization module provides a user interaction interface, allowing the user to edit and optimize the generated video within the system, and finally adjust the video quality.

[0017] The present invention is further configured as follows: the video expectation-actual comparison module compares the generated video with the user's expectations, identifies the differences, and passes the results to the adaptive proposal generation module; the adaptive proposal generation module generates adjustment suggestions based on the analysis results of the video expectation-actual comparison module to narrow the gap between the expected and actual effects, and passes the adjustment suggestions to the learning feedback algorithm optimization module for adjusting the analysis and generation algorithm; the learning feedback algorithm optimization module continuously optimizes the analysis and generation algorithm based on the user's adjustment feedback, improves the intelligence level of the storyboard adjustment, and provides updated data and models for the system update resource management module; the system update resource management module regularly updates the system creative library and analysis model to provide the latest creative resources and analysis tools for the entire system.

[0018] The beneficial effects of the present invention are:

[0019] 1. The present invention converts text descriptions into structured data and extracts scene elements through the storyboard input semantic parsing module, thereby providing accurate data support for subsequent lens language analysis, composition optimization and scene scheduling. The lens analysis and optimization module identifies potential problems and provides optimization suggestions by analyzing lens language and composition, helping to improve the accuracy and visual effects of the lens. The intelligent adjustment video generation module automatically adjusts the storyboard parameters according to the optimization suggestions and generates a preliminary demonstration video, and the user can further adjust the video details through the interactive editing module to ensure that the video quality meets the expectations. The feedback iteration system learning module continuously optimizes and adjusts the algorithm by analyzing the differences between user feedback and generated videos, improves the intelligence level of the system, and thus enhances the flexibility, innovation and efficiency of storyboard adjustment.

[0020] 2. The present invention parses the lens language and scene structure by using image processing and syntax analysis technology to provide key visual information for subsequent composition optimization. The composition optimization calculation module calculates the optimal composition based on the golden section, the rule of thirds and other principles to ensure that the lens picture is more visually impactful. The dynamic focus adjustment module automatically adjusts the focus according to scene changes or character movements to improve picture clarity and focusing effect. The scene element scheduling module optimizes the layout and movement path of characters and props to make the scene more reasonable and improve the consistency of visual effects. The multi-lens collaborative adjustment module coordinates the switching and visual transition between multiple lenses to ensure the smoothness and unity of the overall visual of the video. Finally, through automated lens optimization and learning adjustment based on user feedback, the system reduces manual intervention, improves the efficiency and accuracy of storyboard adjustment, and provides greater creative space and flexibility for creation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the system modules in the present invention.

[0022] Figure 2 It is a partial system flow diagram of the automatic mirror adjustment module in the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0024] Example 1

[0025] like Figure 1 As shown, an artificial intelligence-based storyboard adjustment demonstration system includes a storyboard input semantic parsing module for parsing user input text and preliminary visual materials to convert them into structured data for subsequent processing, a lens analysis optimization module for performing a comprehensive lens language and composition analysis on the storyboard, an intelligent adjustment video generation module for automatically applying optimization adjustments and generating a demonstration video, and a feedback iteration system learning module for collecting user feedback and optimizing the algorithm through continuous learning;

[0026] The storyboard input semantic parsing module includes an input interface design module for providing a user-friendly interface for inputting storyboard scripts, a natural language parsing module for analyzing and parsing the input text description to convert it into operable structured data, a scene semantic recognition module for identifying and classifying the scene categories in the input description to extract scene elements, and a data management and storage module for storing and managing the input storyboard data and parsing results;

[0027] The lens analysis and optimization module includes a lens language evaluation module for analyzing the lens language according to the input storyboard data to identify potential lens errors, a composition and visual optimization module for providing suggestions on composition improvement, a scene scheduling evaluation module for analyzing the scheduling of characters and props to ensure reasonable configuration in the scene, and a creative generation and recommendation module for calling a built-in creative library to provide diversified creative suggestions;

[0028] The intelligent adjustment video generation module includes an automatic storyboard adjustment module for automatically adjusting storyboard parameters according to the analysis module's suggestion, a user feedback integration module for allowing the user to view the adjustment suggestions and selectively apply or manually fine-tune, a rendering and video generation module for generating a preliminary demonstration video from the adjusted storyboard data, and an interactive editing and optimization module for providing a user interaction interface to allow the user to directly edit the details of the generated video in the system;

[0029] The feedback iteration system learning module includes a video expectation-actual comparison module for comparing the generated video with the user's expectation to identify the difference points, an adaptive proposal generation module for generating adjustment suggestions based on the comparative analysis to narrow the gap between the expected and actual effects, a learning feedback algorithm optimization module for automatically adjusting the analysis and generation algorithm based on the user's adjustment feedback, and a system update resource management module for regularly updating the system creative library and analysis model;

[0030] The input interface design module provides a user-friendly interface for inputting storyboard scripts, provides input data for the natural language parsing module, and ensures that the text description input by the user can be received and parsed by the system; the natural language parsing module analyzes and converts the text input by the user into structured data, and provides the parsed text description for the scene semantic recognition module to help identify and classify scene elements; the scene semantic recognition module extracts scene categories and element information based on the structured data output by the natural language parsing module, and provides necessary data storage and management for the data management storage module; the data management storage module stores and manages the storyboard data and parsing results, and provides structured input data for the subsequent lens analysis and optimization module;

[0031] The lens language evaluation module performs lens language analysis based on the input storyboard data, identifies potential lens errors, and provides lens improvement suggestions for the composition and visual optimization module; after receiving the optimization suggestions from the lens language evaluation module, the composition and visual optimization module improves the composition and provides the improved composition information for the scene scheduling evaluation module; based on the composition optimization results, the scene scheduling evaluation module analyzes the scheduling of characters and props in the scene to ensure their reasonable configuration and provides scene scheduling evaluation data for the creative generation and recommendation module; the creative generation and recommendation module calls the built-in creative library and generates a variety of creative suggestions based on the information provided by the aforementioned modules, providing more creative directions for subsequent storyboard adjustments;

[0032] The automatic storyboard adjustment module automatically adjusts the storyboard parameters according to the suggestions provided by the lens analysis and optimization module, and transmits the adjusted storyboard data to the user feedback integration module, allowing the user to view the suggestions and selectively apply or fine-tune; the user feedback integration module allows the user to view the automatic adjustment suggestions and make fine-tuning or selective application, and transmits the adjustment data after user feedback to the rendering and video generation module to generate a preliminary demonstration video; the rendering and video generation module converts the adjusted storyboard data into a preliminary demonstration video, and provides a video file for the interactive editing and optimization module, allowing the user to further edit the video in detail; the interactive editing and optimization module provides a user interaction interface, allowing the user to edit and optimize the generated video in the system, and finally adjust the video quality;

[0033] The video expectation-actual comparison module compares the generated video with the user's expectations, identifies the differences, and passes the results to the adaptive proposal generation module; the adaptive proposal generation module generates adjustment suggestions based on the analysis results of the video expectation-actual comparison module to narrow the gap between the expected and actual effects, and passes the adjustment suggestions to the learning feedback algorithm optimization module for adjusting the analysis and generation algorithm; the learning feedback algorithm optimization module continuously optimizes the analysis and generation algorithm based on the user's adjustment feedback, improves the intelligence level of the storyboard adjustment, and provides updated data and models for the system update resource management module; the system update resource management module regularly updates the system creative library and analysis model to provide the latest creative resources and analysis tools for the entire system.

[0034] In the above embodiment, the storyboard script input by the user is parsed through the storyboard input semantic parsing module, the text is converted into structured data, and the scene category and element information are extracted; then, the lens analysis and optimization module identifies potential problems and provides optimization suggestions through the analysis of lens language and composition, and further optimizes the composition, scene scheduling and creative direction of the lens; then, the intelligent adjustment video generation module automatically applies these optimization suggestions to generate a preliminary demonstration video, and allows the user to further fine-tune the video details through the interactive editing module. The system also compares the generated video with the user's expectations through the feedback iteration system learning module, automatically generates adjustment suggestions, and uses user feedback to continuously optimize the storyboard adjustment algorithm and creative library, and continuously improve the intelligence level of the system.

[0035] Example 2

[0036] like Figure 1-2 As shown, a storyboard adjustment demonstration system based on artificial intelligence, the automatic storyboard adjustment module includes a lens grammar analysis module for detecting and parsing lens language using image processing and grammar analysis technology, a composition optimization calculation module for calculating the best composition based on visual composition principles such as the golden section and the rule of thirds, a dynamic focus adjustment module for automatically adjusting the focus position according to scene changes, a scene element scheduling module for optimizing the position and movement path of characters and props in the scene, a multi-lens collaborative adjustment module for coordinating multiple lenses to ensure overall visual consistency, a user feedback learning module for optimizing the adjustment algorithm using user feedback data, and a quality assessment decision module for evaluating whether the adjusted storyboard meets the preset quality standards and deciding whether to accept, further optimize or feedback to the user for manual adjustment;

[0037] The lens syntax analysis module is responsible for parsing the lens language and scene structure, and providing the necessary visual information for the composition optimization calculation module; the composition optimization calculation module provides the lens composition framework for the dynamic focus adjustment module by calculating the optimal composition principle; the dynamic focus adjustment module automatically adjusts the focus position according to the scene changes and the movement of the characters / objects to ensure the concentration and clarity of the visual effect, and at the same time provides the basis for the focus position for the scene element scheduling module;

[0038] The scene element scheduling module optimizes the position and movement path of the characters and props in the scene according to the focus adjustment result, and provides accurate element scheduling information for the multi-lens collaborative adjustment module; the multi-lens collaborative adjustment module coordinates the switching and visual transition between multiple lenses to ensure the overall visual consistency, and provides the adjusted lens data for the user feedback learning module; the user feedback learning module adjusts the algorithm strategy according to user feedback to optimize the lens design and adjustment process, and provides the optimized lens data for the quality assessment decision module.

[0039] In the above embodiment, the shot grammar analysis module uses image processing and grammar analysis technology to analyze the shot language and scene structure, and provides necessary visual information for subsequent composition optimization. The composition optimization calculation module calculates the best composition scheme based on visual composition principles such as the golden section and the rule of thirds, and provides a composition framework for the dynamic focus adjustment module to ensure that the focus adjustment is consistent with the scene composition. Figure 1 The dynamic focus adjustment module automatically adjusts the focus position according to scene changes and object movement to ensure the concentration and clarity of the visual effect, and provides focus position information to the scene element scheduling module for optimizing the layout and movement path of characters and props. The scene element scheduling module further optimizes the distribution and path of elements in the scene, and provides accurate scheduling information to the multi-lens collaborative adjustment module to ensure the visual transition and coordination between multiple lenses. The multi-lens collaborative adjustment module is responsible for coordinating the switching of multiple lenses to ensure visual consistency between lenses, and passes the adjusted data to the user feedback learning module, which further optimizes the algorithm strategy through user feedback, and ultimately provides support for video quality assessment and optimization.

[0040] Among them, the quality assessment module uses the SVM machine learning classification algorithm combined with the scoring function to perform quality assessment and divides the video into three categories: qualified, automatically optimized and manually optimized according to the evaluation results. The qualified video can be output automatically, and the automatically optimized video is transmitted to the composition optimization calculation module, dynamic focus adjustment module and multi-lens collaborative scheduling module for optimization according to the content that needs to be optimized.

[0041] The quality assessment module transmits the video that is assessed to need manual optimization to the user feedback learning module, and adjusts the video through the manual adjustment mode.

[0042] Working principle: When the present invention is used, the storyboard script input by the user is first received through the storyboard input semantic parsing module and converted into structured data. The module contains multiple subsystems, including an input interface design module that provides a user-friendly interface; a natural language parsing module that converts text descriptions into structured data; a scene semantic recognition module that extracts and classifies scene elements; and a data management storage module that is used to store storyboard data and parsing results. These steps ensure that the system can understand user input and provide accurate structured information for subsequent lens analysis and optimization. Subsequently, the system passes the parsed data to the lens analysis and optimization module, which analyzes the lens language and composition, identifies potential errors, and provides suggestions for optimization, thereby improving the accuracy of lens design.

[0043] Next, the intelligent adjustment video generation module automatically adjusts the storyboard parameters according to the suggestions of the lens analysis and optimization module, and generates a preliminary demonstration video. During this process, users can view the automatically generated suggestions and make fine adjustments through the user feedback integration module to further optimize the storyboard design. After that, the rendering and video generation module converts the adjusted storyboard data into a preliminary demonstration video and provides it to users for detailed editing and final optimization, ultimately ensuring the quality and effect of the video. At the same time, the feedback iteration system learning module continuously optimizes and adjusts the algorithm by analyzing the comparison between user feedback and generated videos to improve the intelligence level of the system, and provides a variety of creative suggestions through the creative generation and recommendation module to enhance the flexibility and innovation of storyboard adjustment.

[0044] In the specific implementation process of the automatic storyboard adjustment module, the lens grammar analysis module uses image processing and grammar analysis technology to parse the lens language and scene structure, providing the necessary visual information for subsequent composition optimization. Then, the composition optimization calculation module calculates the best composition scheme based on the principles of golden section and rule of thirds, and provides the lens framework for the dynamic focus adjustment module. When the scene changes or the character moves, the dynamic focus adjustment module can automatically adjust the focus position to ensure the clarity of the lens and the concentration of the visual effect. The scene element scheduling module optimizes the position and path of the characters and props according to the focus adjustment results, thereby improving the rationality of the scene layout. Finally, the multi-lens collaborative adjustment module coordinates the switching and visual transition between multiple lenses to ensure the visual consistency between the lenses. Through these functions, the system automatically optimizes the lens design, reduces manual intervention, improves the efficiency and accuracy of storyboard adjustment, and provides users with more creativity and adjustment space.

[0045] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A storyboard adjustment demonstration system based on artificial intelligence, characterized in that: It includes a storyboard input semantic parsing module for parsing user input text and preliminary visual materials and converting them into structured data for subsequent processing, a shot analysis and optimization module for comprehensive shot language and composition analysis of storyboards, an intelligent adjustment video generation module for automatically applying optimization adjustments and generating demonstration videos, and a feedback iteration system learning module for collecting user feedback and optimizing algorithms through continuous learning; The storyboard input semantic parsing module includes an input interface design module for providing a user-friendly interface for inputting storyboard scripts, a natural language parsing module for analyzing and parsing the input text description to convert it into operable structured data, a scene semantic recognition module for identifying and classifying the scene categories in the input description to extract scene elements, and a data management and storage module for storing and managing the input storyboard data and parsing results; The lens analysis and optimization module includes a lens language evaluation module for analyzing the lens language according to the input storyboard data to identify potential lens errors, a composition and visual optimization module for providing suggestions on composition improvement, a scene scheduling evaluation module for analyzing the scheduling of characters and props to ensure their reasonable configuration in the scene, and a creative generation and recommendation module for calling the built-in creative library to provide diversified creative suggestions.

2. The artificial intelligence-based split screen adjustment demonstration system according to claim 1, characterized in that: The intelligent adjustment video generation module includes an automatic storyboard adjustment module for automatically adjusting storyboard parameters according to the analysis module's suggestion, a user feedback integration module for allowing the user to view the adjustment suggestions and selectively apply or manually fine-tune, a rendering and video generation module for generating a preliminary demonstration video from the adjusted storyboard data, and an interactive editing and optimization module for providing a user interaction interface to allow the user to directly edit the details of the generated video in the system; The feedback iteration system learning module includes a video expectation-actual comparison module for comparing the generated video with the user's expectations to identify differences, an adaptive proposal generation module for generating adjustment suggestions based on comparative analysis to narrow the gap between expected and actual effects, a learning feedback algorithm optimization module for automatically adjusting the analysis and generation algorithm based on the user's adjustment feedback, and a system update resource management module for regularly updating the system creative library and analysis model.

3. The artificial intelligence-based split-mirror adjustment demonstration system according to claim 2, characterized in that: The automatic storyboard adjustment module includes a lens grammar analysis module for detecting and parsing lens language using image processing and grammar analysis technology, a composition optimization calculation module for calculating the best composition based on visual composition principles such as the golden section and the rule of thirds, a dynamic focus adjustment module for automatically adjusting the focus position according to scene changes, a scene element scheduling module for optimizing the position and movement path of characters and props in the scene, a multi-lens collaborative adjustment module for coordinating multiple lenses to ensure overall visual consistency, a user feedback learning module for optimizing the adjustment algorithm using user feedback data, and a quality assessment decision module for evaluating whether the adjusted storyboard meets the preset quality standards and deciding whether to accept, further optimize, or feedback to the user for manual adjustment.

4. The artificial intelligence-based storyboard adjustment demonstration system according to claim 3, characterized in that: The lens syntax analysis module is responsible for parsing the lens language and scene structure, and providing the necessary visual information for the composition optimization calculation module; the composition optimization calculation module provides the lens composition framework for the dynamic focus adjustment module by calculating the optimal composition principle; the dynamic focus adjustment module automatically adjusts the focus position according to the scene changes and the movement of the characters / objects to ensure the concentration and clarity of the visual effect, and at the same time provides the basis for the focus position for the scene element scheduling module.

5. The artificial intelligence-based storyboard adjustment demonstration system according to claim 4, characterized in that: The scene element scheduling module optimizes the position and movement path of the characters and props in the scene according to the focus adjustment result, and provides accurate element scheduling information for the multi-lens collaborative adjustment module; the multi-lens collaborative adjustment module coordinates the switching and visual transition between multiple lenses to ensure the overall visual consistency, and provides the adjusted lens data for the user feedback learning module; the user feedback learning module adjusts the algorithm strategy according to user feedback to optimize the lens design and adjustment process, and provides the optimized lens data for the quality assessment decision module.

6. The artificial intelligence-based storyboard adjustment demonstration system according to claim 1, characterized in that: The input interface design module provides a user-friendly interface for inputting storyboard scripts, provides input data for the natural language parsing module, and ensures that the text description input by the user can be received and parsed by the system; The natural language parsing module analyzes and converts the text input by the user into structured data, and provides the scene semantic recognition module with the parsed text description to help identify and classify scene elements; The scene semantic recognition module extracts scene categories and element information based on the structured data output by the natural language parsing module, and provides necessary data storage and management for the data management storage module; the data management storage module stores and manages the storyboard data and parsing results, and provides structured input data for the subsequent lens analysis and optimization module.

7. The artificial intelligence-based storyboard adjustment demonstration system according to claim 1, characterized in that: The lens language evaluation module performs lens language analysis based on the input storyboard data, identifies potential lens errors, and provides lens improvement suggestions to the composition and visual optimization module; after receiving the optimization suggestions from the lens language evaluation module, the composition and visual optimization module improves the composition and provides the improved composition information to the scene scheduling evaluation module; The scene scheduling evaluation module analyzes the scheduling of characters and props in the scene based on the composition optimization results, ensures their reasonable configuration and provides scene scheduling evaluation data to the creative generation and recommendation module; the creative generation and recommendation module calls the built-in creative library and generates a variety of creative suggestions based on the information provided by the aforementioned modules, providing more creative directions for subsequent storyboard adjustments.

8. The artificial intelligence-based storyboard adjustment demonstration system according to claim 2, characterized in that: The automatic storyboard adjustment module automatically adjusts the storyboard parameters according to the suggestions provided by the lens analysis and optimization module, and passes the adjusted storyboard data to the user feedback integration module, allowing the user to view the suggestions and selectively apply or fine-tune; the user feedback integration module allows the user to view the automatic adjustment suggestions and make fine-tuning or selective application, and passes the adjustment data after user feedback to the rendering and video generation module to generate a preliminary demonstration video; the rendering and video generation module converts the adjusted storyboard data into a preliminary demonstration video, and provides a video file for the interactive editing and optimization module, allowing the user to further edit the video in detail; the interactive editing and optimization module provides a user interaction interface, allowing the user to edit and optimize the generated video within the system, and finally adjust the video quality.

9. The artificial intelligence-based storyboard adjustment demonstration system according to claim 2, characterized in that: The video expectation-actual comparison module compares the generated video with the user's expectations, identifies the differences, and passes the results to the adaptive proposal generation module; the adaptive proposal generation module generates adjustment suggestions based on the analysis results of the video expectation-actual comparison module to narrow the gap between the expected and actual effects, and passes the adjustment suggestions to the learning feedback algorithm optimization module for adjusting the analysis and generation algorithm; the learning feedback algorithm optimization module continuously optimizes the analysis and generation algorithm based on the user's adjustment feedback, improves the intelligence level of the storyboard adjustment, and provides updated data and models for the system update resource management module; the system update resource management module regularly updates the system creative library and analysis model to provide the latest creative resources and analysis tools for the entire system.

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