A method and system for intelligently generating dynamic tweets based on visual feature recognition

Abstract

The present invention relates to the field of personalized recommendation technology, and specifically to a method and system for intelligently generating dynamic tweets based on visual feature recognition. The method and system obtain visual content uploaded by users, recognize the visual content through an intelligent discrimination algorithm, obtain a processing flow corresponding to the visual content, and obtain a static data set and a dynamic data set; construct a data analysis and processing model to perform product feature recognition and high-level semantic analysis on the static data set to obtain a first multidimensional feature level; perform dynamic data segmentation, product feature recognition, time series feature extraction and high-level semantic analysis on the dynamic data set to obtain a second multidimensional feature level; analyze the first multidimensional feature level and the second multidimensional feature level to generate a tweet generation parameter data set; generate personalized tweets through a tweet style adaptation model combined with the tweet generation parameter data set and prompt word guidance, and adjust the quality of the personalized tweets through a tweet quality assessment mechanism.

Description

Technical Field

[0001] The present invention relates to the field of personalized recommendation technology, and in particular to a method and system for intelligently generating dynamic tweets based on visual feature recognition. Background Art

[0002] With the recent rise in popularity of social media, tweets have become a crucial tool for product promotion. Through concise text and vivid visual elements, tweets can quickly capture user attention, boost brand awareness, and increase product sales. However, existing tweet generation methods face significant bottlenecks in dynamic scene response, complex feature analysis, and human-machine collaborative control, making them unable to meet the requirements of real-time, personalized, and consistent content.

[0003] In terms of visual feature recognition technology, existing solutions primarily rely on static image analysis and struggle to adapt to real-time changes in dynamic scenarios. For example, in product promotions, users' visual focus changes over time and across scenarios. Existing technologies are unable to capture and respond to these changes in a timely manner, resulting in a mismatch between tweet content and user interests. Furthermore, the complexity of visual features poses challenges to recognition and interpretation. Especially when fusing multimodal data, existing methods often struggle to accurately extract and fuse fine-grained features, impacting the quality and appeal of tweet content.

[0004] The application of dynamic tweet generation technology in product promotion also has shortcomings. Existing solutions often use preset templates or simple rules to generate tweets, lacking the ability to dynamically adjust to real-time data. This makes it difficult to personalize tweet content for different users and scenarios, affecting promotion effectiveness. Furthermore, when processing multimodal data, existing technologies often only focus on shallow semantic matching, such as object name recognition, and lack the ability to parse complex visual elements such as program code indentation and special symbols in a fine-grained manner, limiting the richness and appeal of tweet content.

[0005] In product promotion scenarios, tweets need to closely integrate the product's visual features with its textual description to create coherent and engaging content. However, existing techniques for generating tweets with different styles and characters are susceptible to biases in the training data. This is particularly true when generating long sequences, which can lead to logical jumps or aesthetic mismatches, such as facial deformations and sudden changes in scene style. Users find it difficult to dynamically intervene in the generation process through visual markers, such as background color or graphic logos, resulting in significant deviations from user intent. Existing systems primarily rely on one-way generation and lack closed-loop optimization mechanisms based on visual feedback.

[0006] The application of human-machine collaborative control in tweet generation also faces challenges. Existing solutions often rely on manual intervention, which is inefficient and costly. In dynamic scenarios, manual intervention is difficult to respond to changes in real time, resulting in delayed tweet generation and missed promotion opportunities. Furthermore, existing technologies lack intelligent support for human-machine collaboration, making it impossible to achieve efficient human-machine interaction and collaborative control, limiting the flexibility and real-time nature of tweet generation.

[0007] To this end, a method and system for intelligent generation of dynamic tweets based on visual feature recognition is proposed. Summary of the Invention

[0008] The purpose of the present invention is to provide a method and system for intelligently generating dynamic tweets based on visual feature recognition, which obtains visual content uploaded by users, recognizes the visual content through an intelligent discrimination algorithm, obtains a processing flow corresponding to the visual content, and obtains a static data set and a dynamic data set; constructs a data analysis and processing model to perform product feature recognition and high-level semantic analysis on the static data set to obtain a first multidimensional feature level; performs dynamic data segmentation, product feature recognition, time series feature extraction and high-level semantic analysis on the dynamic data set to obtain a second multidimensional feature level; analyzes the first multidimensional feature level and the second multidimensional feature level to generate a tweet generation parameter data set; generates personalized tweets through a tweet style adaptation model combined with the tweet generation parameter data set and prompt word guidance, and adjusts the quality of personalized tweets through a tweet quality assessment mechanism.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A method for intelligently generating dynamic tweets based on visual feature recognition, comprising:

[0011] Obtain visual content uploaded by a user, identify the visual content using an intelligent discrimination algorithm, obtain a processing flow corresponding to the visual content, and obtain a static data set and a dynamic data set; the visual content includes static content and dynamic content; the processing flow includes static data processing and dynamic data processing;

[0012] Constructing a data analysis and processing model to perform product feature identification and advanced semantic analysis on the static dataset to obtain a first multidimensional feature level; performing dynamic data segmentation, product feature identification, time series feature extraction, and advanced semantic analysis on the dynamic dataset to obtain a second multidimensional feature level; analyzing the first multidimensional feature level and the second multidimensional feature level to generate a tweet generation parameter dataset, including product type, feature keywords, audience characteristics, and shooting method;

[0013] The tweet style adaptation model is combined with the tweet generation parameter dataset and prompt word guidance to generate personalized tweets, and the quality of the personalized tweets is adjusted through the tweet quality evaluation mechanism.

[0014] Preferably, the static data processing obtains a static data set by performing feature extraction, size standardization and color space conversion analysis on the static content; the dynamic data processing obtains a dynamic data set by performing key frame extraction, scene segmentation, motion trajectory analysis and time series correlation calculation analysis on the dynamic content.

[0015] Preferably, the data analysis and processing model includes a multi-dimensional product feature level acquisition layer and a tweet generation parameter generation layer;

[0016] The multidimensional product feature level acquisition layer obtains the first multidimensional feature level and the second multidimensional feature level by analyzing the static data set and the dynamic data set; the tweet generation parameter generation layer generates the tweet generation parameter data set by analyzing the first multidimensional feature level and the second multidimensional feature level.

[0017] Preferably, the specific steps of the first multi-dimensional feature level include: building a configurable AI big model calling interface, the interface supporting dynamic switching to different product recognition big models without modifying the architecture;

[0018] Analyze the static data set based on the configurable AI large model to extract low-level visual feature data, including color distribution features, material features, shape features, and spatial layout features; and construct a set of physical attributes based on the low-level visual feature data, including product size, color, material, style, and structural features.

[0019] Through advanced semantic analysis, advanced semantic features are extracted from static data sets to identify product categories, brand information, functional attributes and design styles; a product keyword system is established, including a physical attribute keyword layer, a functional characteristic keyword layer and an emotional connection keyword layer; through a combination of fuzzy matching and precise matching, the extracted product features are intelligently matched with the preset product information library to confirm the product identity and supplement the product background information; the physical attribute set, advanced semantic features and keyword system are integrated to form the first multidimensional feature level.

[0020] Preferably, the specific steps of the second multidimensional feature level include: performing frame-level feature extraction on the key frame sequence in the dynamic data set to identify the physical features of the product and scene elements in each frame; performing time dimension analysis on the frame sequence through timing feature extraction to calculate the inter-frame change rate and change pattern; retrieving shooting technique features that match the current video feature pattern from the shooting technique feature library to identify the shooting angle, lighting processing, lens movement and transition techniques; extracting dynamic interactive features in the video content, including product usage methods, operating procedures and function display; integrating frame-level features, timing features, shooting technique features and interactive features to form a second multidimensional feature level.

[0021] Preferably, the specific method for obtaining the tweet generation parameter data set is as follows: through a feature fusion algorithm, the first multidimensional feature level and the second multidimensional feature level are weightedly fused; based on the fused feature level, product types and subcategories are determined, and feature keywords are extracted; according to the product type and feature keywords, target audience characteristics are inferred, including demographic characteristics, interest preferences and consumer psychology; shooting techniques are obtained from the shooting technique characteristics of the second multidimensional feature level; through a priority sorting algorithm, the tweet generation parameters are scored and sorted according to the importance of marketing data, and an influence weight value is set for each parameter; a structured tweet generation parameter data set containing product types, feature keywords, audience characteristics and shooting techniques is generated.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The present invention effectively improves the intelligent level of tweet generation by introducing a dual processing mechanism for static and dynamic visual content. Utilizing visual recognition algorithms and multidimensional feature extraction models, in-depth analysis of images and video content uploaded by users is performed. This not only identifies the basic attributes of products, but also extracts semantic hierarchical information from images. Combined with temporal features and shooting technique analysis in videos, a comprehensive product understanding system is constructed. On this basis, through a tweet style adaptation model, the prompt word guidance and market positioning information are integrated to automatically generate personalized tweet text that fits the product characteristics and is attractive, achieving a closed-loop process from content recognition to language output. This significantly improves the relevance, expression quality, and commercial value of tweet content, meeting the needs of personalized marketing communication in different scenarios.

[0024] 2. The present invention introduces a fusion mechanism of the "first multidimensional feature level" and the "second multidimensional feature level" in the tweet generation process, performs multidimensional feature mining on static images and dynamic video content respectively, and outputs a structured tweet generation parameter set through a feature fusion algorithm, thereby significantly enriching the dimensions of tweet content generation. By utilizing dynamic visual information such as frame-level changes, lens language, and interactive behavior in the video, it is possible to identify the interaction mode, performance effect, and situational characteristics of the product in actual use, and then infer the behavioral preferences and consumer psychology of the product's target audience. This multi-source feature fusion strategy allows the system to flexibly adapt to different types of products and diversified communication scenarios. At the same time, combined with the priority sorting mechanism of tweet generation parameters, it improves the pertinence and effectiveness of content dissemination, thereby effectively expanding user reach and conversion rate, and enhancing the marketing effectiveness of tweets.

[0025] 3. This invention introduces a tweet quality assessment mechanism that supports quality assessment and dynamic adjustment of generated personalized tweets, enabling automatic iterative optimization of high-quality content. Based on a multi-dimensional evaluation index system, this mechanism comprehensively scores initially generated tweets and automatically triggers parameter adjustment and content reconstruction for tweets that do not meet quality thresholds. By introducing quality threshold control and a multi-round generation mechanism, this invention continuously optimizes tweet content to meet preset dissemination quality standards, ensuring that the final output content possesses commercial dissemination value. Furthermore, this mechanism facilitates users to fine-tune tweet styles based on marketing scenarios, further enhancing the system's usability and professionalism. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a method for intelligently generating dynamic tweets based on visual feature recognition provided by the present invention;

[0027] Figure 2 A schematic diagram of the structure of a dynamic tweet intelligent generation system based on visual feature recognition provided by the present invention;

[0028] Figure 3 A schematic diagram of the data analysis and processing model structure provided by the present invention;

[0029] Figure 4 A schematic diagram of the intelligent tweet generation process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] The present invention provides a method for intelligently generating dynamic tweets based on visual feature recognition. This method is applied to a system for intelligently generating dynamic tweets based on visual feature recognition. The flowchart of the specific method and system is shown in FIG. Figure 1 and Figure 2 .

[0032] Example 1

[0033] See also Figure 1 The present invention provides a method for intelligently generating dynamic tweets based on visual feature recognition. The technical solution is as follows: obtaining visual content uploaded by a user, identifying the visual content through an intelligent discrimination algorithm, obtaining a processing flow corresponding to the visual content, and obtaining a static data set and a dynamic data set; the visual content includes static content and dynamic content; the processing flow includes static data processing and dynamic data processing;

[0034] Constructing a data analysis and processing model to perform product feature identification and advanced semantic analysis on the static dataset to obtain a first multidimensional feature level; performing dynamic data segmentation, product feature identification, time series feature extraction, and advanced semantic analysis on the dynamic dataset to obtain a second multidimensional feature level; analyzing the first multidimensional feature level and the second multidimensional feature level to generate a tweet generation parameter dataset, including product type, feature keywords, audience characteristics, and shooting method;

[0035] The tweet style adaptation model is combined with the tweet generation parameter dataset and prompt word guidance to generate personalized tweets, and the quality of the personalized tweets is adjusted through the tweet quality evaluation mechanism.

[0036] Specifically, the static data processing obtains a static data set by performing feature extraction, size standardization and color space conversion analysis on the static content; the dynamic data processing obtains a dynamic data set by performing key frame extraction, scene segmentation, motion trajectory analysis and time series correlation calculation analysis on the dynamic content.

[0037] In this embodiment, by performing feature extraction, size standardization, and color space conversion analysis on static content, the consistency and comparability of images in subsequent feature recognition and semantic analysis processes can be effectively improved, and analysis errors caused by image size, composition differences, or color deviations can be reduced, thereby enhancing the robustness and accuracy of static data processing. At the same time, by performing key frame extraction, scene segmentation, motion trajectory analysis, and time series correlation calculation on dynamic content, representative and timely feature information can be extracted from dynamic visual materials such as videos, thereby improving the expressiveness and adaptability of dynamic data in scenarios such as time-series product display and interactive behavior analysis. This synergistic integration of static and dynamic data processing processes not only achieves multi-dimensional and multi-granular extraction of visual content information, but also provides a more comprehensive, rich, and semantically clear product feature foundation for subsequent tweet generation, which is conducive to the intelligent generation of personalized content and the optimization of dissemination effects.

[0038] Furthermore, the data analysis and processing model includes a multi-dimensional product feature level acquisition layer and a tweet generation parameter generation layer, referring to Figure 3 ;

[0039] The multidimensional product feature level acquisition layer obtains the first multidimensional feature level and the second multidimensional feature level by analyzing the static data set and the dynamic data set; the tweet generation parameter generation layer generates the tweet generation parameter data set by analyzing the first multidimensional feature level and the second multidimensional feature level.

[0040] In this embodiment, by establishing a multidimensional product feature hierarchy acquisition layer and a tweet generation parameter generation layer, efficient conversion from raw visual data to structured tweet generation elements is achieved. The multidimensional product feature hierarchy acquisition layer extracts product features at different dimensions and semantic levels from both static and dynamic data, ensuring comprehensive and layered product information and enhancing understanding of product attributes, scenarios, user behaviors, and other aspects. The tweet generation parameter generation layer, based on these multidimensional feature hierarchies, extracts key parameter information required for constructing high-quality tweets, such as product type, key keywords, and audience characteristics, effectively bridging the semantic gap between visual content and verbal expression. This model structure helps enhance the system's semantic parsing capabilities for complex product information and achieves higher content relevance and dissemination accuracy when generating tweets, significantly improving the intelligence and user acceptance of personalized content generation.

[0041] Furthermore, the specific steps of the first multi-dimensional feature level include: building a configurable AI big model calling interface, which supports dynamic switching to different product recognition big models without modifying the architecture;

[0042] Analyze the static data set based on the configurable AI large model to extract low-level visual feature data, including color distribution features, material features, shape features, and spatial layout features; and construct a set of physical attributes based on the low-level visual feature data, including product size, color, material, style, and structural features.

[0043] Through advanced semantic analysis, advanced semantic features are extracted from static data sets to identify product categories, brand information, functional attributes and design styles; a product keyword system is established, including a physical attribute keyword layer, a functional characteristic keyword layer and an emotional connection keyword layer; through a combination of fuzzy matching and precise matching, the extracted product features are intelligently matched with the preset product information library to confirm the product identity and supplement the product background information; the physical attribute set, advanced semantic features and keyword system are integrated to form the first multidimensional feature level.

[0044] In this embodiment, by introducing a configurable AI large model calling interface, it is possible to flexibly switch between different product recognition models without modifying the overall system architecture, thereby enhancing the adaptability to multi-category and multi-style products and improving the flexibility and scalability of static visual content processing. Combining low-level visual feature analysis with high-level semantic extraction, the system can not only accurately identify the physical properties of the product, but also deeply mine its semantic information such as brand, function and design style, significantly improving the comprehensiveness and depth of product feature recognition. Further, by constructing a multi-level product keyword system, the organizational structure and semantic expression ability of product feature information are effectively enhanced, facilitating the subsequent generation of more targeted and attractive tweet content. At the same time, through an intelligent matching mechanism that combines fuzzy matching and exact matching, the product identity can be accurately confirmed and missing information can be supplemented, improving data integrity and recognition accuracy. The final first multidimensional feature level provides a solid data foundation for personalized content generation, helps to achieve high-quality conversion of visual content to language expression, and enhances the intelligence level of the system and user experience.

[0045] Furthermore, the specific steps of the second multidimensional feature level include: performing frame-level feature extraction on the key frame sequence in the dynamic data set to identify the physical features of the product and scene elements in each frame; performing time dimension analysis on the frame sequence through timing feature extraction to calculate the inter-frame change rate and change pattern; retrieving shooting technique features that match the current video feature pattern from the shooting technique feature library to identify shooting angles, lighting processing, lens movement and transition techniques; extracting dynamic interactive features in the video content, including product usage methods, operating procedures and function displays; integrating frame-level features, timing features, shooting technique features and interactive features to form a second multidimensional feature level.

[0046] In this embodiment, by extracting frame-level features from keyframe sequences in a dynamic dataset, the system can comprehensively capture the physical attributes of the product and the scene context within each frame of the video, providing a highly accurate visual foundation for dynamic information analysis. Combined with temporal feature extraction, the system analyzes inter-frame trends and patterns from a temporal perspective, accurately grasping the product's presentation and rhythm at different time points, providing crucial support for understanding the dynamic evolution of video content. Furthermore, the system integrates a feature library of shooting techniques to match and identify shooting techniques, effectively extracting shooting angles, lighting changes, camera movements, and transition styles within the video, thereby enhancing its ability to perceive the content's presentation. By identifying the interaction between the product and the characters or environment in the video, the system further enhances its understanding of product usage scenarios and functional demonstrations, helping to uncover potential user focus points. Finally, integrating multiple sources of features to construct a second multidimensional feature hierarchy not only enriches the dimensionality of product semantic expression but also lays the data and contextual foundation for the subsequent generation of personalized tweets that align with dynamic communication logic, enhancing the system's intelligent processing of dynamic visual content and its tweet generation capabilities.

[0047] Furthermore, the specific method for obtaining the tweet generation parameter data set is as follows: through a feature fusion algorithm, the first multidimensional feature level and the second multidimensional feature level are weightedly fused; based on the fused feature level, the product type and subcategory are determined, and feature keywords are extracted; according to the product type and feature keywords, the target audience characteristics are inferred, including demographic characteristics, interest preferences and consumer psychology; the shooting technique is obtained from the shooting technique characteristics of the second multidimensional feature level; through a priority sorting algorithm, the tweet generation parameters are scored and sorted according to the importance of marketing data, and an influence weight value is set for each parameter; a structured tweet generation parameter data set containing product type, feature keywords, audience characteristics and shooting technique is generated.

[0048] In this embodiment, by weightedly fusing the first and second multidimensional feature levels, the system achieves a complementary integration of static visual features and dynamic display information, thereby comprehensively and accurately portraying the multidimensional attributes of a product. This fused feature level not only improves the accuracy of product type and subcategory determination but also extracts characteristic keywords that better align with user perception and expression needs, providing high-quality semantic support for the personalized construction of tweet content. By further analyzing product types and keyword sets, the system intelligently infers the demographic characteristics, interests, and consumer psychology of the target user group, effectively improving the match between tweet content and audience and its dissemination efficiency. Furthermore, the system incorporates the characteristics of filming techniques in dynamic content to ensure that the generated tweets are more aligned with the video's expression and visual style, enhancing their appeal. By leveraging a priority ranking algorithm and incorporating marketing data to rank and weight various parameters, the resulting tweet generation parameter dataset is not only structured and interpretable, but also demonstrates strategic optimization for practical marketing scenarios, significantly enhancing the commercial value and dissemination efficiency of tweet generation.

[0049] Furthermore, the quality of personalized tweets is adjusted through the tweet quality assessment mechanism, including: evaluation indicators including relevance, attractiveness, readability, brand consistency and marketing data; automatic parameter adjustment of tweets that do not meet the quality threshold and regeneration of optimized versions; multiple rounds of iterative optimization of tweets until the preset quality requirements are met; and the final high-quality personalized tweets are output to the user interface.

[0050] In this embodiment, a tweet quality assessment mechanism is introduced to comprehensively evaluate personalized tweets from multiple dimensions, including relevance to product features and target audiences, content expressiveness and emotional appeal, readability in terms of language structure and information clarity, brand consistency, and marketing data based on historical data and predictive models. This mechanism can effectively identify content that does not meet expected quality and automatically adjust and optimize generation parameters, thereby achieving intelligent reconstruction of personalized tweets. The system supports multiple rounds of automatic iterative optimization processes to continuously improve content quality and ensure that tweets meet user-preset standards in terms of style, content, and strategy. The resulting high-quality tweet content can be directly applied to various social media platforms or e-commerce channels, not only improving user publishing efficiency but also significantly enhancing brand communication effectiveness and user conversion rates.

[0051] The present invention realizes the extraction of rich and accurate product features from images and videos through multi-dimensional collaborative processing of static and dynamic visual content, laying a solid data foundation for the subsequent generation of tweets. The size standardization, color space conversion and advanced semantic analysis of static content effectively improve the consistency and depth of feature recognition; the key frame extraction, timing characteristics and shooting technique analysis of dynamic content fully capture the diversity and interactivity of the product in the time dimension and visual performance. Relying on the configurable large model calling interface and multi-level product keyword system, the system can flexibly adapt to different scenarios and product categories, and efficiently generate structured tweet parameters; through feature fusion and priority sorting algorithms, it accurately matches the target audience and optimizes marketing strategies, further improving the content fit and communication effect. The introduction of multiple rounds of automated quality assessment and iterative optimization mechanisms has achieved comprehensive control of tweets in dimensions such as relevance, attractiveness, readability and brand consistency, significantly improving the intelligence level and user conversion rate of personalized tweets. For a complete schematic diagram, please refer to Figure 4 .

[0052] Example 2

[0053] The present invention provides a method for intelligently generating dynamic tweets based on visual feature recognition, which is applied to a system for intelligently generating dynamic tweets based on visual feature recognition. Figure 2 This paper provides a dynamic tweet intelligent generation system based on visual feature recognition. This system is applicable to product tweet generation scenarios such as e-commerce platforms, social media marketing, and brand promotion. The system includes a data acquisition module, a tweet data analysis and generation module, and a tweet style assessment and adjustment module. The specific functions and workflows of each module are as follows:

[0054] The data acquisition module is used to acquire visual content uploaded by users, identify it using an intelligent recognition algorithm, and obtain the corresponding processing flow for the visual content, thereby generating static and dynamic data sets. In practical applications, the visual content includes static product images and dynamic video content; the processing flow includes both static and dynamic data processing. For example, a user might upload a product photo and a video of a smartwatch in use; the system will automatically identify the content type and select the appropriate processing flow.

[0055] In this embodiment, the data acquisition module first classifies and identifies the visual content uploaded by the user through an intelligent discrimination algorithm constructed by a deep learning model to determine whether it belongs to static content, such as product main images, detail images, scene images, etc., or dynamic content, such as product demonstration videos, usage tutorials, scene application videos, etc. For static content, the system performs preprocessing operations such as feature extraction, size standardization, and color space conversion to form a structured static data set. The feature extraction includes quantifying the color distribution, texture features, edge contours, and object shapes of the static content; the size standardization includes uniformly converting static content of different resolutions into a preset resolution format; the color space conversion includes converting the RGB color space into the HSV color space to enhance the color semantic analysis capability;

[0056] For dynamic content, the system performs operations such as key frame extraction, scene segmentation, motion trajectory analysis, and time series correlation calculation. For example, for product display videos, the system extracts 2-3 key frames per second and makes adaptive adjustments based on the degree of scene change; it analyzes the object's motion trajectory through the optical flow algorithm, records the changes in the product's display angle in the video, and the interactive methods during use; combined with time series correlation calculation, it identifies key segments such as representative function demonstrations and usage effects in the video, and ultimately forms a dynamic data set containing spatiotemporal information. The key frame extraction uses the FFmpeg tool to extract the first frame per second to obtain the video key frame; the scene segmentation divides the video into multiple coherent scene units by detecting significant shot switching points in the video content; the motion trajectory analysis includes calculating the position change and movement speed of key objects between consecutive frames; the time series correlation calculation includes analyzing the change patterns and correlation strength of objects, colors, and composition between frames.

[0057] The tweet data analysis generation module is used to build a data analysis processing model to perform product feature identification and high-level semantic analysis on the static data set to obtain a first multidimensional feature level; perform dynamic data segmentation, product feature identification, time series feature extraction and high-level semantic analysis on the dynamic data set to obtain a second multidimensional feature level; analyze the first multidimensional feature level and the second multidimensional feature level to generate a tweet generation parameter data set, including product type, feature keywords, audience characteristics, and shooting method.

[0058] In this embodiment, the tweet data analysis and generation module consists of two key sublayers: a multi-dimensional product feature hierarchical acquisition layer and a tweet generation parameter generation layer. The multi-dimensional product feature hierarchical acquisition layer first constructs a configurable AI large model invocation interface. This interface supports the flexible invocation of product recognition models in different fields, such as beauty-specific recognition models for cosmetics and electronic product recognition models for electronic devices, thereby improving the accuracy of product recognition in specific fields.

[0059] Taking smartwatches as an example, the system uses an electronic product recognition model to extract low-level visual feature data from a static dataset. This includes color distribution features (such as dial color and strap material color), material features (such as metal, ceramic, plastic, and leather), shape features (such as round or square dials), and spatial layout features (such as button location and sensor distribution). Based on these low-level visual features, the system constructs a set of physical attributes, including watch size, color, material, style (such as sports or business), and structural features (such as waterproof structure and heart rate sensor location).

[0060] Through advanced semantic analysis, the system further extracts high-level semantic features of the product, identifying product categories such as smart sports watches and brand information; functional attributes such as heart rate monitoring, blood oxygen monitoring, and sleep tracking; and design styles such as minimalist and modern. The system also establishes a three-tiered product keyword system: physical attribute keywords, including "lightweight design" and "high-definition display"; functional feature keywords, including "all-weather heart rate monitoring" and "intelligent health management"; and emotional connection keywords, including "technological sense" and "professional sports companion." Using a combination of fuzzy and exact matching, the system intelligently matches the extracted features against a pre-set product information database, confirming the product model and supplementing it with additional background information, such as product release date and market positioning. Ultimately, the system integrates all features into the first multidimensional feature layer.

[0061] For dynamic datasets, the system first performs frame-level feature extraction on keyframe sequences, identifying changes in the product's physical characteristics and scene elements within each frame. For example, it can identify the functional display of a smartwatch in different usage scenarios, such as the display of athletic data during running, waterproof performance during swimming, and monitoring status during sleep. By extracting temporal features, the system analyzes the inter-frame rate of change and change patterns, such as interface switching speed, animation transition effects, and function response time.

[0062] The system also retrieves shooting technique features that match the current video's feature pattern from a pre-established library of shooting technique features. It identifies shooting angles such as close-ups, surround displays, and real-life applications; lighting treatments for natural and artificial light; camera movements such as smoothing, tracking, and surround; and transition techniques such as fades, cuts, and overlays. Furthermore, the system extracts dynamic interactive features from the video, including user interaction with the watch, such as swiping and button controls; complete operational flows, such as the steps for setting an alarm; and functional demonstrations, such as receiving notifications and replying to messages. Ultimately, the system integrates frame-level features, temporal features, shooting technique features, and interactive features to form a second multidimensional feature hierarchy.

[0063] The tweet generation parameter generation layer uses a feature fusion algorithm to perform a weighted fusion of the first and second multidimensional feature levels. The algorithm automatically adjusts the weight ratio of static and dynamic features based on different product categories. For example, fashion products may focus more on static visual effects, while functional products may place more emphasis on dynamic user experience. Based on the fused feature hierarchy, the system accurately determines product types such as "high-end sports smartwatches" and subcategories such as "outdoor sports professional grade" and extracts feature keywords, including appearance descriptors such as "light and thin" and "scratch and wear resistant"; functional descriptors such as "all-weather heart rate monitoring" and "GPS positioning"; and value descriptors such as "professional sports analysis" and "healthy life manager."

[0064] The system also infers target audience characteristics based on product type and key keywords. These include demographics such as those aged 25-45, middle- to high-income, and urban residents; interests such as fitness enthusiasts, outdoor sports participants, and those pursuing a healthy lifestyle; and consumer psychology such as a focus on quality, professional performance, and health management. Based on the second multidimensional feature layer, the system identifies key shooting methods, such as close-up presentations, actual usage scenario demonstrations, and functional operation guides.

[0065] Using a prioritization algorithm, the system assigns importance scores to each tweet generation parameter based on historical marketing data and assigns influence weights. For example, for a sports smartwatch, the system might assign "professional sports features" a high-weighted keyword, while "fashionable appearance" is a secondary-weighted keyword. Ultimately, the system generates a structured tweet generation parameter dataset encompassing product type, key keywords, audience characteristics, and photography techniques, providing data support for personalized tweet generation.

[0066] The tweet style evaluation and adjustment module is used to generate personalized tweets by combining the tweet style adaptation model with the tweet generation parameter data set and prompt word guidance, and adjust the quality of personalized tweets through the tweet quality evaluation mechanism.

[0067] The implementation process and quality adjustment process of personalized tweets include:

[0068] A tweet style adaptation model is built based on the tweet generation parameter dataset to intelligently match the product's visual features with the text expression style; a diverse tweet template library is designed, including tweet skeletons of different lengths, structures, and expressions; the most suitable tweet structure is selected from the template library based on product type and audience characteristics; a precise prompt word template is designed to embed the key information in the tweet generation parameter dataset into the prompt word structure; the AI large model is called to generate a first draft of the tweet to ensure that the tweet content is highly matched with the product's visual features; the generated tweets are automatically quality-checked through a tweet quality assessment mechanism, with assessment indicators including relevance, attractiveness, readability, brand consistency, and marketing effectiveness; parameters of tweets that do not meet the quality threshold are automatically adjusted and an optimized version is regenerated; multiple rounds of tweet iterative optimization are implemented until the preset quality requirements are met; the final high-quality personalized tweets are output to the user interface, and the tweet generation records are simultaneously saved for continuous learning of the system.

[0069] The tweet style adaptation model is based on deep learning technology and is trained by analyzing historical successful tweet samples. It can adaptively adjust the tone, rhythm and rhetoric of tweets to match the product's visual presentation style.

[0070] The prompt word guidance adopts multi-level structured prompt technology to convert product type, key features, target audience and shooting method into an instruction format that can be efficiently processed by the AI large model.

[0071] The tweet quality assessment mechanism includes four dimensions: semantic relevance assessment, emotional tone matching assessment, audience appeal assessment, and brand consistency assessment. Each dimension is quantitatively scored using a pre-trained quality scoring model.

[0072] In this embodiment, the tweet style adaptation model first selects a basic style template from a pre-set tweet style library that matches the product type and target audience. For example, a professional and concise style might be chosen for a high-end smartwatch. Combining this with the tweet generation parameter dataset, the system constructs customized prompts to guide the generation model in focusing on the product's core selling points and the target audience's pain points. For example, for a sports smartwatch, prompts might include guidance such as "highlighting professional fitness monitoring features," "emphasizing long-lasting battery life," and "using professional terminology to establish a sense of authority."

[0073] After the initial version of the tweet is generated, the system conducts a comprehensive quality assessment through the tweet quality assessment mechanism. The evaluation indicators include relevance to product characteristics (calculating the cosine similarity between the tweet and the product document through the BERT model), content attractiveness (the sentiment analysis API outputs positive sentiment values), text readability (automated readability detection tool), brand consistency (LSTM classification model determines the similarity with the brand corpus) and expected marketing data (using the XGBoost regression model).

[0074] If certain metrics fail to meet preset quality thresholds, the system automatically adjusts parameters and regenerates the tweet. For example, if the initial tweet's brand consistency score is low, the system adjusts the brand's presentation; if the readability score is unsatisfactory, the system optimizes the language structure and presentation. The system supports multiple rounds of iterative optimization until all quality metrics meet preset standards. Ultimately, high-quality personalized tweets are output to the user interface for direct consumption or further refinement.

[0075] In order to verify the effectiveness of the present invention, a comparative experiment was conducted, and the results are shown in Table 1;

[0076] Table 1 Comparison of product promotion effects of different tweet generation methods

[0077]

[0078] As shown in Table 1, the proposed method achieves significant improvements across all key metrics compared to traditional template-based methods and static image-based methods. In particular, click-through rate and conversion rate demonstrate that the tweets generated by the proposed method possess greater appeal and marketing data, significantly reducing content creation costs and improving marketing response speed.

[0079] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligently generating dynamic tweets based on visual feature recognition, characterized in that: include: Obtain visual content uploaded by a user, identify the visual content using an intelligent discrimination algorithm, obtain a processing flow corresponding to the visual content, and obtain a static data set and a dynamic data set; the visual content includes static content and dynamic content; the processing flow includes static data processing and dynamic data processing; Constructing a data analysis and processing model to perform product feature recognition and high-level semantic analysis on the static data set to obtain a first multidimensional feature level; Performing dynamic data segmentation, product feature recognition, time series feature extraction, and advanced semantic analysis on the dynamic data set to obtain a second multidimensional feature level; Analyze the first multidimensional feature level and the second multidimensional feature level to generate a tweet generation parameter dataset, including product type, feature keywords, audience characteristics, and shooting method; The tweet style adaptation model combines the tweet generation parameter dataset and prompt word guidance to generate personalized tweets. Based on the product type and audience characteristics, a suitable tweet structure is selected from the template library. A prompt word template is designed to embed key information from the tweet generation parameter dataset into the prompt word structure. The AI large model is used to generate a first draft of the tweet to ensure that the tweet content is highly consistent with the product's visual characteristics. The tweet quality assessment mechanism is used to adjust the quality of the first draft of the tweet to obtain personalized tweets.

2. The method for intelligently generating dynamic tweets based on visual feature recognition according to claim 1, characterized in that: The static data processing obtains a static data set by performing feature extraction, size standardization and color space conversion analysis on the static content; the dynamic data processing obtains a dynamic data set by performing key frame extraction, scene segmentation, motion trajectory analysis and time series correlation calculation analysis on the dynamic content.

3. The method for intelligently generating dynamic tweets based on visual feature recognition according to claim 1, characterized in that: The data analysis and processing model includes a multi-dimensional product feature level acquisition layer and a tweet generation parameter generation layer; The multi-dimensional product feature level acquisition layer obtains a first multi-dimensional feature level and a second multi-dimensional feature level by analyzing the static data set and the dynamic data set; The tweet generation parameter generation layer generates a tweet generation parameter dataset by analyzing the first multidimensional feature level and the second multidimensional feature level.

4. The method for intelligently generating dynamic tweets based on visual feature recognition according to claim 1, characterized in that: The specific steps of the first multi-dimensional feature level include: building a configurable AI big model calling interface, which supports dynamic switching to different product recognition big models without modifying the architecture; Analyze the static data set based on the configurable AI large model to extract low-level visual feature data, including color distribution features, material features, shape features, and spatial layout features; and construct a set of physical attributes based on the low-level visual feature data, including product size, color, material, style, and structural features. Through advanced semantic analysis, high-level semantic features are extracted from static data sets to identify product categories, brand information, functional attributes and design styles; a product keyword system is established, including a physical attribute keyword layer, a functional characteristic keyword layer and an emotional connection keyword layer; through a combination of fuzzy matching and precise matching, the identified product features are intelligently matched with the preset product information library to confirm the product identity and supplement the product background information; the physical attribute set, high-level semantic features and keyword system are integrated to form the first multidimensional feature level.

5. The method for intelligently generating dynamic tweets based on visual feature recognition according to claim 1, characterized in that: The specific steps of the second multidimensional feature level include: performing frame-level feature extraction on the key frame sequence in the dynamic data set to identify the physical features of the product and scene elements in each frame; performing time dimension analysis on the frame sequence through timing feature extraction to calculate the inter-frame change rate and change pattern; retrieving shooting method features that match the current video feature pattern from the shooting technique feature library to identify shooting angles, lighting processing, lens movement and transition techniques; extracting dynamic interactive features in the video content, including product usage methods, operating procedures and function displays; integrating frame-level features, timing features, shooting method features and dynamic interactive features to form the second multidimensional feature level.

6. The method for intelligently generating dynamic tweets based on visual feature recognition according to claim 1, characterized in that: The specific method for obtaining the tweet generation parameter dataset is as follows: using a feature fusion algorithm, weightedly fusing the first multidimensional feature level and the second multidimensional feature level; determining the product type and subcategory based on the fused feature level, and extracting feature keywords; inferring the target audience characteristics, including demographic characteristics, interest preferences, and consumer psychology, based on the product type and feature keywords; obtaining the shooting technique from the shooting technique characteristics of the second multidimensional feature level; using a priority sorting algorithm, scoring and sorting the importance of tweet generation parameters based on marketing data, and setting an influence weight value for each parameter; and generating a structured tweet generation parameter dataset containing product type, feature keywords, audience characteristics, and shooting technique.

7. The method for intelligently generating dynamic tweets based on visual feature recognition according to claim 1, characterized in that: The quality of personalized tweets is adjusted through the tweet quality assessment mechanism, including: evaluation indicators include relevance, attractiveness, readability, brand consistency and marketing data; automatic parameter adjustment of tweets that do not meet the quality threshold and regeneration of optimized versions; multiple rounds of iterative optimization of tweets until the preset quality requirements are met; and the final high-quality personalized tweets are output to the user interface.

8. A dynamic tweet intelligent generation system based on visual feature recognition, characterized by: include: A data acquisition module is used to acquire visual content uploaded by users, identify the visual content through an intelligent discrimination algorithm, obtain the processing flow corresponding to the visual content, and obtain static data sets and dynamic data sets; the visual content includes static content and dynamic content; the processing flow includes static data processing and dynamic data processing; a tweet data analysis and generation module, configured to construct a data analysis and processing model to perform product feature recognition and high-level semantic analysis on the static data set to obtain a first multi-dimensional feature level; Performing dynamic data segmentation, product feature recognition, time series feature extraction, and advanced semantic analysis on the dynamic data set to obtain a second multidimensional feature level; Analyze the first multidimensional feature level and the second multidimensional feature level to generate a tweet generation parameter dataset, including product type, feature keywords, audience characteristics, and shooting method; The tweet style assessment and adjustment module is used to generate personalized tweets using the tweet style adaptation model, combined with the tweet generation parameter dataset and prompt word guidance. Based on product type and audience characteristics, the module selects an appropriate tweet structure from a template library. It also designs prompt word templates and embeds key information from the tweet generation parameter dataset into the prompt word structure. The module then uses the AI model to generate a draft tweet, ensuring that the tweet content closely matches the product's visual characteristics. The tweet quality assessment mechanism is used to adjust the quality of the first draft of the tweet to obtain personalized tweets.

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