Efficient video script creative generation method and system

Abstract

According to the efficient video script creative generation method and system provided by the invention, the finally generated script is more original and personalized by identifying and retaining innovative features. And the integration of multi-dimensional features comprises content, structure, style, sound effect and frame, so that the coherence and diversity of video scripts are improved. In combination with a natural language processing technology, the creative elements in the reference material can be effectively extracted and fused, the high-quality creative script is generated, and the technical problem that in the prior art, the requirement for the high-quality creative script is increasingly increased is solved.

Description

Technical Field

[0001] The present invention relates to the field of data recognition technology, and in particular to an efficient video script creative generation method and system. Background Art

[0002] In the field of video script creation, with the booming development of the video content market, the demand for high-quality and innovative scripts is growing. Traditional video script creation methods often rely on the creator's personal experience and inspiration, which is inefficient and difficult to ensure the novelty and diversity of creativity. In the creation process, the existing technology lacks a systematic method to screen out valuable reference content from a large amount of material, making it difficult to make full use of the rich video resource library; in terms of lens processing, the classification, analysis and reorganization of the lens are not sophisticated enough, resulting in insufficient coherence and optimization of the script logic; in script similarity analysis, there is no mature and effective model to accurately screen unique script features; in the mining and integration of innovative elements, there is a lack of scientific means to identify and integrate novel elements, which limits the innovation and attractiveness of the script. Therefore, there is an urgent need for an efficient video script creative generation method and system to solve these problems. Summary of the invention

[0003] In view of the above problems, the present invention proposes an efficient video script creative generation method and system to solve the technical problems existing in the above background technology, and the technical solutions adopted are as follows:

[0004] An efficient video script creative generation method, the method comprising:

[0005] S1: The theme of the video to be created is clear, and a search is performed in the video library based on the theme and emotion tags to screen out several videos with similar themes and emotions, and the scripts of the several videos with similar themes and emotions are obtained as the initial script set;

[0006] S2: Classify and label each shot in the initial script set according to its nature, length and angle, analyze the logical relationship of each shot in several scripts, and reorganize each shot in several scripts according to the analysis results to form a new video;

[0007] S3: Obtain the script of the new video, and based on the structure, style and sound effect features of the new video script, calculate the similarity between the new script and the initial script set through the script similarity calculation model, screen out the scripts with low similarity, and use the scripts with low similarity as reference materials for the features of the video script to be created. ;

[0008] S4: Perform natural language processing on the reference material, screen out innovative elements in the reference material, integrate the innovative elements, and generate a script for the video to be created.

[0009] Preferably, the S1 includes:

[0010] S11: clearly defining the theme of the video to be created, and also clarifying the description content of the video to be created, obtaining the main emotional tone of the video to be created according to the description content, and marking the video to be created with an emotional label, wherein the emotional label includes but is not limited to joy, sadness, anger, surprise, and fear;

[0011] S12: Search the video library according to the theme and emotion tags of the video to be created, screen out several videos with similar themes and emotions, and obtain scripts from the screened videos to unify them into an initial script set.

[0012] Preferably, S2 includes:

[0013] S21: labeling each shot according to the nature of the shot, including but not limited to action, dialogue, and close-up;

[0014] S22: Measure the length of each lens and mark the length of each lens;

[0015] S23: Recording the shooting angle of each lens, wherein the shooting angles include front, side, back, top and top, and marking the angle of each lens;

[0016] S24: obtaining a logical relationship between each shot, wherein the logical relationship includes but is not limited to a cause-effect relationship, a time sequence, and a space conversion, and analyzing the logical relationship between multiple shots to construct a logical chain for each shot, and ensuring the integrity of the logical chain;

[0017] S25: According to the result of analyzing the logical relationship of the multiple shots, the shots are reorganized to form a new video.

[0018] Preferably, said S3 includes,

[0019] S31: for the script of the newly generated video, in terms of structural features, analyzing the number and order of shots; in terms of style features, analyzing whether the style of the video is realistic, exaggerated, or humorous; in terms of sound effects, identifying the volume and rhythm; and quantifying all the features into data for analysis;

[0020] S32: Input the new script feature data into a pre-built script similarity calculation model, perform similarity calculation on the new script and each script in the initial script set, and obtain a calculation result. The higher the value corresponding to the calculation result, the higher the similarity, and vice versa.

[0021] S33: Compare the numerical value corresponding to the obtained calculation result with the preset similarity threshold, filter and retain the script features below the similarity threshold, and use the retained script features with low similarity as reference materials for the script features of the video to be created.

[0022] Preferably, the S4 includes:

[0023] S41: converting the reference material into a semantic vector representation through a natural language model, and identifying the emotional tendency in the text by applying a sentiment analysis algorithm, wherein the emotional sentiment includes positive, neutral and negative;

[0024] S42: Extract each word in the reference material and identify innovative elements that are different from conventional content through a novelty scoring mechanism;

[0025] S43: Aggregate the identified innovative elements, analyze their correlation and potential combination possibilities according to sentiment tendency, and fuse the innovative elements according to the analysis results to generate an innovative element combination;

[0026] S44: Generate a script for the video to be created based on the combination of innovative elements, and proofread and optimize the script for the video to be created.

[0027] An efficient video script creative generation system, the system comprising:

[0028] Emotion matching Emotion matching screening system Emotion matching screening system: clarify the theme of the video to be created, search in the video library according to the theme and emotion tags, screen out several videos with similar themes and emotions, and obtain the scripts of the several videos with similar themes and emotions, and clarify them as the initial script set;

[0029] Shot multi-dimensional analysis and reorganization system Shot multi-dimensional analysis and reorganization system: classify and label each shot in the initial script set according to its nature, length and angle, analyze the logical relationship of each shot in several scripts, and reorganize each shot in several scripts according to the analysis results to form a new video;

[0030] Multi-feature script similarity evaluation system: obtain the script of a new video, and based on the structural, style and sound effect features of the new video script, calculate the similarity between the new script and the initial script set through a script similarity calculation model, screen out scripts with low similarity, and use the scripts with low similarity as reference material for the features of the video script to be created.

[0031] Innovative elements fusion Innovative elements fusion script generation system system: natural language processing is performed on the reference material, the innovative elements in the reference material are screened out, the innovative elements are integrated, and the script of the video to be created is generated.

[0032] Preferably, the emotion matching screening system comprises:

[0033] Content emotional tone determination system: clarifies the theme of the video to be created, and at the same time clarifies the description content of the video to be created, obtains the main emotional tone of the video to be created based on the description content, and marks the emotional tone for the video to be created, and the emotional tag includes but is not limited to joy, sadness, anger, surprise and fear;

[0034] Emotional label video retrieval system: Search the video library according to the theme and emotional label of the video to be created, screen out several videos with similar themes and emotions, and obtain scripts from the screened videos to unify them into an initial script set.

[0035] Preferably, the lens multi-dimensional analysis and reconstruction system comprises:

[0036] Shot nature identification and annotation system: annotate each shot according to its nature, including but not limited to action, dialogue, and close-up;

[0037] Shot duration measurement and annotation system: measure the length of each shot and mark the length of each shot;

[0038] Lens angle recording and annotation system: records the shooting angle of each lens, including front, side, back, top and bottom, and annotates the angle of each lens;

[0039] Shot logical relationship analysis system: obtains the logical relationship between each shot, including but not limited to cause-effect relationship, time sequence and space conversion, analyzes the logical relationship of multiple shots, builds the logical chain of each shot, and ensures the integrity of the logical chain;

[0040] Lens reorganization plan formulation and execution system: Based on the results of the logical relationship analysis of multiple lenses, the lenses are reorganized to form a new video.

[0041] Preferably, the multi-feature script similarity evaluation system comprises:

[0042] Video script feature quantitative analysis system: For the newly generated video script, in terms of structural features, the number and order of shots are analyzed; in terms of style features, the style of the video is analyzed to determine whether it is realistic, exaggerated, or humorous; in terms of sound effects, the volume and rhythm are identified; and all the above features are quantified into data for analysis;

[0043] Script similarity calculation execution system: inputs the new script feature data into the pre-built script similarity calculation model, calculates the similarity between the new script and each script in the initial script set, and obtains the calculation result. The higher the value corresponding to the calculation result, the higher the similarity, and vice versa;

[0044] Low-similarity script feature screening system: compare the numerical value corresponding to the calculated result with the preset similarity threshold, screen and retain the script features below the similarity threshold, and use the retained script features with low similarity as reference material for the script features of the video to be created.

[0045] Preferably, the innovative element fusion script generation system comprises:

[0046] Sentiment analysis and extraction system: Through the natural language model, the reference material is converted into a semantic vector representation, and the sentiment tendency in the text is identified by applying the sentiment analysis algorithm. The sentiment includes positive, neutral and negative emotions;

[0047] Vocabulary Novelty Innovation Identification System: Extract every word in the reference material and identify innovative elements that are different from conventional content through a novelty scoring mechanism;

[0048] Emotion-related innovation fusion system: Aggregate the identified innovation elements, analyze their correlation and potential combination possibilities based on emotional tendencies, and fuse the innovation elements based on the analysis results to generate an innovation element combination;

[0049] Script generation and proofreading system: Generate the script of the video to be created based on the combination of innovative elements, and proofread and optimize the script of the video to be created. Beneficial effects of the invention: The invention uses a script similarity calculation model to accurately identify and retain innovative features, so that the final generated script is more original and personalized. The integration of multi-dimensional features covering content, structure, style, sound effects, and narration enhances the coherence and richness of the video script. At the same time, through natural language processing, the solution can effectively obtain and integrate innovative elements in reference materials to generate high-quality creative scripts. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is an efficient video script creative generation method described in the present invention. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0052] One embodiment of the present invention provides an efficient method for generating creative video scripts, characterized in that the method comprises:

[0053] An efficient video script creative generation method, the method comprising:

[0054] S1: The theme of the video to be created is clear, and a search is performed in the video library based on the theme and emotion tags to screen out several videos with similar themes and emotions, and the scripts of the several videos with similar themes and emotions are obtained as the initial script set;

[0055] S2: Classify and label each shot in the initial script set according to its nature, length and angle, analyze the logical relationship of each shot in several scripts, and reorganize each shot in several scripts according to the analysis results to form a new video;

[0056] S3: Obtain the script of the new video, and based on the structure, style and sound effect features of the new video script, calculate the similarity between the new script and the initial script set through the script similarity calculation model, screen out the scripts with low similarity, and use the scripts with low similarity as reference materials for the features of the video script to be created. ;

[0057] S4: Perform natural language processing on the reference material, screen out innovative elements in the reference material, integrate the innovative elements, and generate a script for the video to be created.

[0058] The working principle and effect of the above technical solution are as follows: first, the theme of the video to be created is clarified, and combined with the emotional tags, the material matching the target theme and emotion is identified from a large number of videos through natural language processing technology and emotional analysis tools. By obtaining the scripts of these videos, an initial script set is formed. Each shot in the initial script set is analyzed in detail, and classified and labeled based on factors such as the nature, length, and shooting angle of the shot. Computer vision technology and film language rules are used to understand the logical relationship between shots. By analyzing these logical relationships, these shots are recombined to create a new video sequence, aiming to generate a video based on the original material but with a completely new presentation method. A new video script is obtained from the recombined video, and its characteristics are analyzed, including multi-dimensional features such as content, structure, style, sound effects, and narration. The script similarity calculation model is used to compare the new script with the initial script set to identify the differences between them. Special attention is paid to features with low similarity, because these features often represent innovative and unique content, and they are used as reference materials for the video script to be created. Natural language processing is performed on the retained reference materials to screen out innovative elements. This process includes key phrase acquisition, emotional and semantic analysis, etc. After identifying creative and potential elements, they are integrated to create a comprehensive, coherent and innovative latest video script. The present invention uses a script similarity calculation model to accurately identify and retain innovative features, making the final generated script more original and personalized. The integration of multi-dimensional features including content, structure, style, sound effects and narration enhances the coherence and richness of the video script. At the same time, through natural language processing technology, the solution can effectively obtain and integrate innovative elements in reference materials to generate high-quality creative scripts.

[0059] In one embodiment of the present invention, S1 includes:

[0060] S11: clearly defining the theme of the video to be created, and also clarifying the description content of the video to be created, obtaining the main emotional tone of the video to be created according to the description content, and marking the video to be created with an emotional label, wherein the emotional label includes but is not limited to joy, sadness, anger, surprise, and fear;

[0061] S12: Search the video library according to the theme and emotion tags of the video to be created, select several videos with similar themes and emotions, and obtain scripts from the selected videos to unify them into an initial script set

[0062] The working principle and effect of the above technical solution are as follows: S1 mainly carries out preliminary planning and preparation for video creation to ensure that the theme, content and emotion of the video are in line with expectations. By clarifying the theme and description content of the video, it is easier to grasp the overall direction of the video and the information to be conveyed. After clarifying the theme and emotional tags of the video, these information can be used as keywords to search in the video library. In this way, videos with similar themes and similar emotional expressions to the videos to be created can be found as references. Several videos with the most similar themes and emotions to the videos to be created are selected from the retrieved videos. Then, scripts are obtained from these videos. The scripts or storylines obtained from multiple videos are integrated and unified to form an initial script set. This script set is the framework or foundation of the video to be created. By retrieving and screening similar videos in the video library and obtaining scripts, existing resources can be fully utilized, duplication of work can be avoided, and work efficiency can be improved. Clarifying the emotional main tone of the video and labeling it with emotions can help to better create an atmosphere and arouse the emotional resonance of the audience during the creation process, thereby enhancing the attractiveness and appeal of the video. Obtaining scripts and storylines from multiple similar videos can provide creators with diverse reference materials, helping to create richer and more diverse video works.

[0063] In one embodiment of the present invention, S2 includes:

[0064] S21: labeling each shot according to the nature of the shot, including but not limited to action, dialogue, and close-up;

[0065] S22: Measure the length of each lens and mark the length of each lens;

[0066] S23: Recording the shooting angle of each lens, wherein the shooting angles include front, side, back, top and top, and marking the angle of each lens;

[0067] S24: obtaining a logical relationship between each shot, wherein the logical relationship includes but is not limited to a cause-effect relationship, a time sequence, and a space conversion, and analyzing the logical relationship between multiple shots to construct a logical chain for each shot, and ensuring the integrity of the logical chain;

[0068] S25: According to the result of analyzing the logical relationship of the multiple shots, the shots are reorganized to form a new video.

[0069] The working principle and effect of the above technical solution are as follows: First, each shot in the video is carefully analyzed to clarify its nature, such as action, dialogue, close-up, etc. Then, the shots are marked according to their nature so that they can be quickly identified and used in the subsequent editing and synthesis process. By marking the action, dialogue, and close-up characteristics of the shots, the content and function of the shots can be accurately described. Use professional video editing software or tools to accurately measure the length of each shot. Mark the measurement results so that the duration of each shot can be accurately controlled during editing. This ensures the rhythm and fluency of the video and avoids too long or too short shots affecting the audience's viewing experience. Analyze the shooting angle of each shot to clarify whether it is front, side, back, top or bottom view, etc. Mark the shooting angle so that the appropriate shot can be selected as needed to present different visual effects during editing. In-depth analysis of the logical relationship between each shot, such as cause and effect, time sequence, spatial conversion, etc. Construct the logical chain of each shot based on the logical relationship to ensure the integrity and coherence of the logical chain. According to the results of the logical relationship analysis of multiple shots, a detailed shot reorganization plan is formulated. Use professional video editing software or tools to edit and synthesize the footage according to the reorganization plan to form a new video work.

[0070] In one embodiment of the present invention, S3 includes:

[0071] S31: for the script of the newly generated video, in terms of structural features, analyzing the number of shots; in terms of style features, analyzing whether the style of the video is realistic, exaggerated, or humorous; in terms of sound effect features, identifying the volume and rhythm; and quantifying all the features into data for analysis;

[0072] S32: Input the new script feature data into a pre-built script similarity calculation model, perform similarity calculation on the new script and each script in the initial script set, and obtain a calculation result. The higher the value corresponding to the calculation result, the higher the similarity, and vice versa.

[0073] S33: Compare the numerical value corresponding to the obtained calculation result with the preset similarity threshold, filter and retain the script features below the similarity threshold, and use the retained script features with low similarity as reference materials for the script features of the video to be created.

[0074] And, the similarity formula calculation method is as follows:

[0075] The new script is defined as N, the initial script set is defined as I, and the similarity calculation function of the structural features is obtained by the following formula:

[0076]

[0077] Among them, S(N,I) represents the similarity value of the structural features, S N represents the quantized value of the structural features of the new script N, S I represents the quantized value of the structural features of the initial script set I, S max Represents the maximum value among all script structure feature quantization values;

[0078] Similarly, the similarity value F(N,I) of the style feature and the similarity value A(N,I) of the sound effect feature are obtained, and finally, a similarity calculation formula is obtained, and the similarity calculation formula is as follows:

[0079]

[0080] Among them, Q(N,I) represents the similarity value between the new script and the initial script set;

[0081] Moreover, the closer the similarity value is to 1, the higher the similarity is, and vice versa.

[0082] The working principle and effect of the above technical solution are as follows: in the similarity value calculation formula of structural features, style features and sound effect features, the exponential function is used because it has a smooth transition characteristic and can well simulate the changing trend of similarity under different degrees of difference. The value range of the exponential function is naturally limited to (0,1], which just meets the requirements of the similarity range, that is, the similarity is a value between (0,1], and no additional complex normalization operation is required. The rate of change of the trigonometric function in a specific interval can be used to emphasize the sensitivity to differences in structural features. It can highlight the significant impact on similarity when the feature difference is large, making the similarity calculation more sensitive to large differences, which is a feature that some other functions do not have. In the overall similarity value calculation formula, the geometric mean is used to combine the three local similarities. The characteristic of the geometric mean is that it will not over-amplify or reduce the influence of a certain dimension, so that the contribution of each dimension to the overall similarity is relatively balanced. For example, if a script has a high similarity in structure but a low similarity in style and sound effects, the geometric mean will appropriately reduce the overall similarity to avoid overestimating the overall similarity due to the advantage of only one dimension. The nature of the geometric mean determines that only Only when the similarity of all dimensions is relatively high, the overall similarity will approach 1. If the similarity of one dimension is extremely low, even if the similarity of other dimensions is very high, the overall similarity will be significantly affected and reduced, which is consistent with the actual situation of the script. Because for one script to be highly similar to another, it is necessary to maintain a high degree of consistency in multiple aspects such as structure, style, and sound effects. The method for obtaining the quantitative value of structural features includes: obtaining the number of shots of the new script and the initial script set; the method for obtaining the quantitative value of style features includes: clarifying the list of specific vocabulary related to each language style. For example, for the gorgeous and elegant style, it includes words such as "gorgeous", "magnificent", and "quiet"; for the humorous style, it includes words such as "funny", "amusing", and "funny". Count the frequency E of these specific words in the script lines and descriptive language, calculate the total number of characters in all sentences in the script, and divide it by the total number of sentences to obtain the average sentence length L, the style feature value F of the new script N. N =a×E+b×L, a and b represent the vocabulary frequency weight and sentence length weight respectively, and a+b=1, and, in the initial script set, F I =1; the method for obtaining the quantized value of the sound effect feature includes: traversing the volume values ​​of all sampling points in the new script, adding them up and dividing them by the total number of sampling points to obtain the average volume. Calculate the maximum and minimum values ​​of the volume, and the difference between the two is the volume change amplitude; normalize the average volume and the volume change amplitude, According to the detected beats, the number of beats per minute is calculated. For example, if D beats are detected within a duration T (in minutes), then Then the sound effect feature value = c×volume feature value + d×rhythm feature value, c and d represent the weights of the volume feature value and the rhythm feature value respectively, and c+d=1.

[0083] In one embodiment of the present invention, the S4 includes:

[0084] S41: converting the reference material into a semantic vector representation through a natural language model, and identifying the emotional tendency in the text by applying a sentiment analysis algorithm, wherein the emotional sentiment includes positive, neutral and negative;

[0085] S42: Extract each word in the reference material and identify innovative elements that are different from conventional content through a novelty scoring mechanism;

[0086] S43: Aggregate the identified innovative elements, analyze their correlation and potential combination possibilities according to sentiment tendency, and fuse the innovative elements according to the analysis results to generate an innovative element combination;

[0087] S44: Generate a script for the video to be created based on the combination of innovative elements, and proofread and optimize the script for the video to be created.

[0088] The working principle and effect of the above technical solution are as follows: the reference material is converted into a semantic vector representation through a natural language model. These vectors capture the deep semantic information of the text and provide a detailed content background. The sentiment analysis algorithm is used to identify and quantify the sentiment tendency in the reference material. Identifying whether the text expresses positive, negative or neutral sentiment helps to understand its influence and communication purpose. The word segmentation and analysis of each word in the material are combined with the novelty scoring mechanism to identify the novel and creative elements therein. The identified innovative elements are aggregated, and the correlation between these elements is evaluated by analyzing the sentiment tendency. According to the results of the correlation analysis, these innovative elements are strategically integrated to generate an organic combination of innovative elements. Based on the fused combination of innovative elements, the video script to be created is generated. The generation process uses an automated script editing system to improve efficiency and creative consistency. The generated script is proofread and optimized, and multiple iterations are evaluated through the language model to ensure the coherence of the text, accurate emotional expression, and consistency of style. At the same time, the semantic vector representation can capture the deep meaning of the text, so that the model can understand the content of the reference material more accurately. Sentiment analysis algorithms can identify emotional tendencies in texts, provide emotional tone for script creation, and make content more in line with the emotional needs of the audience. The novelty scoring mechanism can screen out unique vocabulary and expressions to inject freshness and creativity into video scripts. By identifying innovative elements, video scripts can be distinguished from other similar works in terms of content and enhance their appeal. Through sentiment analysis, it is possible to ensure that innovative elements maintain emotional consistency when combined, enhancing the coherence and appeal of the content. The integration of innovative elements can generate new creative points, making the video script richer and more interesting in content. The script generated based on the combination of innovative elements is unique in content and can attract the audience's attention. The proofreading and optimization process can ensure that the grammar, logic, and expression of the script are in the best state, improving the overall quality of the video.

[0089] One embodiment of the present invention is a highly efficient video script creative generation system, characterized in that the system comprises:

[0090] Emotion matching screening system: clarify the theme of the video to be created, search the video library according to the theme of the video to be created and the emotional tag, screen out several videos with similar themes and emotions, and obtain the scripts of the several videos with similar themes and emotions as the initial script set;

[0091] Shot multi-dimensional analysis and reorganization system: classify and label each shot in the initial script set according to its nature, length and angle, analyze the logical relationship of each shot in several scripts, and reorganize each shot in several scripts according to the analysis results to form a new video;

[0092] Script similarity calculation system: obtains the script of the new video, and based on the new video script's features including but not limited to content, structure, style, sound effects, and narration, calculates the similarity between the new script and the initial script set through the script similarity calculation model, retains script features with low similarity, and uses the retained script features with low similarity as reference materials for the script features of the video to be created;

[0093] Script generation system: performs natural language processing on the reference materials, screens out innovative elements in the reference materials, integrates the innovative elements, and generates a script for the video to be created.

[0094] The working principle and effect of the above technical solution are as follows: first, the theme of the video to be created is clarified, and combined with the emotional tags, the material matching the target theme and emotion is identified from a large number of videos through natural language processing technology and emotional analysis tools. By obtaining the scripts of these videos, an initial script set is formed. Each shot in the initial script set is analyzed in detail, and classified and labeled based on factors such as the nature, length, and shooting angle of the shot. Computer vision technology and film language rules are used to understand the logical relationship between shots. By analyzing these logical relationships, these shots are recombined to create a new video sequence, aiming to generate a video based on the original material but with a completely new presentation method. A new video script is obtained from the recombined video, and its characteristics are analyzed, including multi-dimensional features such as content, structure, style, sound effects, and narration. The script similarity calculation model is used to compare the new script with the initial script set to identify the differences between them. Special attention is paid to features with low similarity, because these features often represent innovative and unique content, and they are used as reference materials for the video script to be created. Natural language processing is performed on the retained reference materials to screen out innovative elements. This process includes key phrase acquisition, emotional and semantic analysis, etc. After identifying creative and potential elements, they are integrated to create a comprehensive, coherent and innovative latest video script. The present invention uses a script similarity calculation model to accurately identify and retain innovative features, making the final generated script more original and personalized. The integration of multi-dimensional features including content, structure, style, sound effects and narration enhances the coherence and richness of the video script. At the same time, through natural language processing technology, the solution can effectively obtain and integrate innovative elements in reference materials to generate high-quality creative scripts.

[0095] In one embodiment of the present invention, the emotion matching screening system comprises:

[0096] Content emotional tone determination system: clarifies the theme of the video to be created, and at the same time clarifies the description content of the video to be created, obtains the main emotional tone of the video to be created based on the description content, and marks the emotional tone for the video to be created, and the emotional tag includes but is not limited to joy, sadness, anger, surprise and fear;

[0097] Emotional label video retrieval system: Search the video library according to the theme and emotional label of the video to be created, screen out several videos with similar themes and emotions, and obtain scripts from the screened videos to unify them into an initial script set.

[0098] The working principle and effect of the above technical solution are as follows: S1 mainly carries out preliminary planning and preparation for video creation to ensure that the theme, content and emotion of the video are in line with expectations. By clarifying the theme and description content of the video, it is easier to grasp the overall direction of the video and the information to be conveyed. After clarifying the theme and emotional tags of the video, these information can be used as keywords to search in the video library. In this way, videos with similar themes and similar emotional expressions to the videos to be created can be found as references. Several videos with the most similar themes and emotions to the videos to be created are selected from the retrieved videos. Then, scripts are obtained from these videos. The scripts or storylines obtained from multiple videos are integrated and unified to form an initial script set. This script set is the framework or foundation of the video to be created. By retrieving and screening similar videos in the video library and obtaining scripts, existing resources can be fully utilized, duplication of work can be avoided, and work efficiency can be improved. Clarifying the emotional main tone of the video and labeling it with emotions can help to better create an atmosphere and arouse the emotional resonance of the audience during the creation process, thereby enhancing the attractiveness and appeal of the video. Obtaining scripts and storylines from multiple similar videos can provide creators with diverse reference materials, helping to create richer and more diverse video works.

[0099] In one embodiment of the present invention, the lens multi-dimensional analysis and reconstruction system comprises:

[0100] Shot nature identification and annotation system: annotate each shot according to its nature, including but not limited to action, dialogue, and close-up;

[0101] Shot duration measurement and annotation system: measure the length of each shot and mark the length of each shot;

[0102] Lens angle recording and annotation system: records the shooting angle of each lens, including front, side, back, top and bottom, and annotates the angle of each lens;

[0103] Shot logical relationship analysis system: obtains the logical relationship between each shot, including but not limited to cause-effect relationship, time sequence and space conversion, analyzes the logical relationship of multiple shots, builds the logical chain of each shot, and ensures the integrity of the logical chain;

[0104] Lens reorganization plan formulation and execution system: Based on the results of the logical relationship analysis of multiple lenses, the lenses are reorganized to form a new video.

[0105] The working principle and effect of the above technical solution are as follows: First, each shot in the video is carefully analyzed to clarify its nature, such as action, dialogue, close-up, etc. Then, the shots are marked according to their nature so that they can be quickly identified and used in the subsequent editing and synthesis process. By marking the action, dialogue, and close-up characteristics of the shots, the content and function of the shots can be accurately described. Use professional video editing software or tools to accurately measure the length of each shot. Mark the measurement results so that the duration of each shot can be accurately controlled during editing. This ensures the rhythm and fluency of the video and avoids too long or too short shots affecting the audience's viewing experience. Analyze the shooting angle of each shot to clarify whether it is front, side, back, top or bottom view, etc. Mark the shooting angle so that the appropriate shot can be selected as needed to present different visual effects during editing. In-depth analysis of the logical relationship between each shot, such as cause and effect, time sequence, spatial conversion, etc. Construct the logical chain of each shot based on the logical relationship to ensure the integrity and coherence of the logical chain. According to the results of the logical relationship analysis of multiple shots, a detailed shot reorganization plan is formulated. Use professional video editing software or tools to edit and synthesize the footage according to the reorganization plan to form a new video work.

[0106] In one embodiment of the present invention, the innovative element fusion script generation system comprises:

[0107] Sentiment analysis extraction system: Generates semantic vector representation of reference materials through natural language models, and identifies emotional tendencies in text by applying sentiment analysis algorithms;

[0108] Vocabulary Novelty Innovation Identification System: Obtain each word in the reference material and identify innovative elements that are different from conventional content through a novelty scoring mechanism;

[0109] Emotion-related innovation fusion system: Aggregate the identified innovation elements, analyze their correlation and potential combination possibilities based on emotional tendencies, and fuse the innovation elements based on the analysis results to generate an innovation element combination;

[0110] Script generation and proofreading system: Generate the script of the video to be created based on the combination of innovative elements, and proofread and optimize the script of the video to be created.

[0111] The working principle and effect of the above technical solution are as follows: the reference material is converted into a semantic vector representation through a natural language model. These vectors capture the deep semantic information of the text and provide a detailed content background. The sentiment analysis algorithm is used to identify and quantify the sentiment tendency in the reference material. Identifying whether the text expresses positive, negative or neutral sentiment helps to understand its influence and communication purpose. The word segmentation and analysis of each word in the material are combined with the novelty scoring mechanism to identify the novel and creative elements therein. The identified innovative elements are aggregated, and the correlation between these elements is evaluated by analyzing the sentiment tendency. According to the results of the correlation analysis, these innovative elements are strategically integrated to generate an organic combination of innovative elements. Based on the fused combination of innovative elements, the video script to be created is generated. The generation process uses an automated script editing system to improve efficiency and creative consistency. The generated script is proofread and optimized, and multiple iterations are evaluated through the language model to ensure the coherence of the text, accurate emotional expression, and consistency of style. At the same time, the semantic vector representation can capture the deep meaning of the text, so that the model can understand the content of the reference material more accurately. Sentiment analysis algorithms can identify emotional tendencies in texts, provide emotional tone for script creation, and make content more in line with the emotional needs of the audience. The novelty scoring mechanism can screen out unique vocabulary and expressions to inject freshness and creativity into video scripts. By identifying innovative elements, video scripts can be distinguished from other similar works in terms of content and enhance their appeal. Through sentiment analysis, it is possible to ensure that innovative elements maintain emotional consistency when combined, enhancing the coherence and appeal of the content. The integration of innovative elements can generate new creative points, making the video script richer and more interesting in content. The script generated based on the combination of innovative elements is unique in content and can attract the audience's attention. The proofreading and optimization process can ensure that the grammar, logic, and expression of the script are in the best state, improving the overall quality of the video.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An efficient video script creative generation method, characterized in that: The method comprises: S1: The theme of the video to be created is clear, and a search is performed in the video library based on the theme and emotion tags to screen out several videos with similar themes and emotions, and the scripts of the several videos with similar themes and emotions are obtained as the initial script set; S2: Classify and label each shot in the initial script set according to its nature, length and angle, analyze the logical relationship of each shot in several scripts, and reorganize each shot in several scripts according to the analysis results to form a new video; S3: Obtain the script of the new video, and based on the structure, style and sound effect features of the new video script, calculate the similarity between the new script and the initial script set through the script similarity calculation model, screen out scripts with low similarity, and use the scripts with low similarity as reference materials for the features of the video script to be created; S4: Perform natural language processing on the reference material, screen out innovative elements in the reference material, integrate the innovative elements, and generate a script for the video to be created.

2. According to claim 1, a highly efficient video script creative generation method is characterized in that: S1 includes: S11: clearly defining the theme of the video to be created, and also clarifying the description content of the video to be created, obtaining the main emotional tone of the video to be created according to the description content, and marking the video to be created with an emotional label, wherein the emotional label includes but is not limited to joy, sadness, anger, surprise, and fear; S12: Search the video library according to the theme and emotion tags of the video to be created, screen out several videos with similar themes and emotions, and obtain scripts from the screened videos to unify them into an initial script set.

3. According to claim 1, a highly efficient video script creative generation method is characterized in that: S2 includes: S21: labeling each shot according to the nature of the shot, including but not limited to action, dialogue, and close-up; S22: Measure the length of each lens and mark the length of each lens; S23: Recording the shooting angle of each lens, wherein the shooting angles include front, side, back, top and top, and marking the angle of each lens; S24: obtaining a logical relationship between each shot, wherein the logical relationship includes but is not limited to a cause-effect relationship, a time sequence, and a space conversion, and analyzing the logical relationship between multiple shots to construct a logical chain for each shot, and ensuring the integrity of the logical chain; S25: According to the result of analyzing the logical relationship of the multiple shots, the shots are reorganized to form a new video.

4. According to claim 1, a highly efficient video script creative generation method is characterized in that: The S3 includes, Get S31: For the script of the newly generated video, in terms of structural features, the number and order of shots are analyzed; in terms of style features, the style of the video is analyzed to determine whether it is realistic, exaggerated, or humorous; in terms of sound effects, the volume and rhythm are identified; and all features are quantified into data for analysis; S32: Input the new script feature data into a pre-built script similarity calculation model, perform similarity calculation on the new script and each script in the initial script set, and obtain a calculation result. The higher the value corresponding to the calculation result, the higher the similarity, and vice versa. S33: Compare the numerical value corresponding to the obtained calculation result with the preset similarity threshold, filter and retain the script features below the similarity threshold, and use the retained script features with low similarity as reference materials for the script features of the video to be created.

5. According to claim 1, a highly efficient video script creative generation method is characterized in that: The S4 includes: S41: converting the reference material into a semantic vector representation through a natural language model, and identifying the emotional tendency in the text by applying a sentiment analysis algorithm, wherein the emotional sentiment includes positive, neutral and negative; S42: Extract every word in the reference material and identify innovative elements that are different from conventional content through a novelty scoring mechanism; S43: Aggregate the identified innovative elements, analyze their correlation and potential combination possibilities according to sentiment tendency, and fuse the innovative elements according to the analysis results to generate an innovative element combination; S44: Generate a script for the video to be created based on the combination of innovative elements, and proofread and optimize the script for the video to be created.

6. An efficient video script creative generation system, characterized in that: The system comprises: Emotion matching Emotion matching screening system Emotion matching screening system: clarify the theme of the video to be created, search in the video library according to the theme and emotion tags, screen out several videos with similar themes and emotions, and obtain the scripts of the several videos with similar themes and emotions, and clarify them as the initial script set; Shot multi-dimensional analysis and reorganization system Shot multi-dimensional analysis and reorganization system: classify and label each shot in the initial script set according to its nature, length and angle, analyze the logical relationship of each shot in several scripts, and reorganize each shot in several scripts according to the analysis results to form a new video; Multi-feature script similarity evaluation system: obtain the script of the new video, and based on the structural, style and sound effect features of the new video script, calculate the similarity between the new script and the initial script set through the script similarity calculation model, screen out the scripts with low similarity, and use the scripts with low similarity as reference materials for the features of the video script to be created; Innovative elements fusion Innovative elements fusion script generation system system: natural language processing is performed on the reference material, the innovative elements in the reference material are screened out, the innovative elements are integrated, and the script of the video to be created is generated.

7. According to claim 6, an efficient video script creative generation system is characterized in that: The emotion matching screening system comprises: Content emotional tone determination system: clarifies the theme of the video to be created, and at the same time clarifies the description content of the video to be created, obtains the main emotional tone of the video to be created based on the description content, and marks the emotional tone for the video to be created, and the emotional tag includes but is not limited to joy, sadness, anger, surprise and fear; Emotional label video retrieval system: Search the video library according to the theme and emotional label of the video to be created, screen out several videos with similar themes and emotions, and obtain scripts from the screened videos to unify them into an initial script set.

8. According to claim 6, an efficient video script creative generation system is characterized in that: The lens multi-dimensional analysis and reconstruction system comprises: Shot nature identification and annotation system: annotate each shot according to its nature, including but not limited to action, dialogue, and close-up; Shot duration measurement and annotation system: measure the length of each shot and mark the length of each shot; Lens angle recording and annotation system: records the shooting angle of each lens, including front, side, back, top and bottom, and annotates the angle of each lens; Shot logical relationship analysis system: obtains the logical relationship between each shot, including but not limited to cause-effect relationship, time sequence and space conversion, analyzes the logical relationship of multiple shots, builds the logical chain of each shot, and ensures the integrity of the logical chain; Lens reorganization plan formulation and execution system: Based on the results of the logical relationship analysis of multiple lenses, the lenses are reorganized to form a new video.

9. According to claim 6, an efficient video script creative generation system is characterized in that: The multi-feature script similarity evaluation system comprises: Video script feature quantitative analysis system: For the newly generated video script, in terms of structural features, the number and order of shots are analyzed; in terms of style features, the style of the video is analyzed to determine whether it is realistic, exaggerated, or humorous; in terms of sound effects, the volume and rhythm are identified; and all features are quantified into data for analysis; Script similarity calculation execution system: inputs the new script feature data into the pre-built script similarity calculation model, calculates the similarity between the new script and each script in the initial script set, and obtains the calculation result. The higher the value corresponding to the calculation result, the higher the similarity, and vice versa; Low-similarity script feature screening system: compare the numerical value corresponding to the calculated result with the preset similarity threshold, screen and retain the script features below the similarity threshold, and use the retained script features with low similarity as reference material for the script features of the video to be created.

10. The efficient video script creative generation system according to claim 6, characterized in that: The innovative element fusion script generation system comprises: Sentiment analysis and extraction system: Through the natural language model, the reference material is converted into a semantic vector representation, and the sentiment tendency in the text is identified by applying the sentiment analysis algorithm. The sentiment includes positive, neutral and negative emotions; Vocabulary Novelty Innovation Identification System: Extract every word in the reference material and identify innovative elements that are different from conventional content through a novelty scoring mechanism; Emotion-related innovation fusion system: Aggregate the identified innovation elements, analyze their correlation and potential combination possibilities based on emotional tendencies, and fuse the innovation elements based on the analysis results to generate an innovation element combination; Script generation and proofreading system: Generate the script of the video to be created based on the combination of innovative elements, and proofread and optimize the script of the video to be created.

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