Cross-platform content generation and distribution method based on multi-modal AI

Abstract

The invention discloses a cross-platform content generation and distribution method based on a multi-modal AI, and belongs to the technical field of cross-platform content generation and distribution, and the method comprises the steps: carrying out the content analysis and feature extraction of an original material based on a multi-modal AI model, and generating a structured content label and a semantic vector. By combining a vector matching degree formula of target portrait features and platform features, an adaptation strategy is dynamically generated, it is ensured that content not only conforms to platform rules, but also can accurately reach a target group, the conversion rate and user viscosity are finally improved, visual, text and semantic vectors are aligned through a Transform multi-modal fusion model, a cross-modal joint representation vector is generated, and the user experience is improved. And in combination with an adversarial generative network, differentiated variants of the same theme are generated in batches, and a content diversity score mechanism ensures that generated contents are balanced between creativity and compliance.

Description

Technical Field

[0001] This invention belongs to the field of cross-platform content generation and distribution technology, specifically referring to a cross-platform content generation and distribution method based on multimodal AI. Background Technology

[0002] With the accelerated development of global digital promotion, foreign trade enterprises are increasingly relying on social platforms to carry out cross-border brand promotion and customer outreach.

[0003] However, existing cross-platform content generation and distribution methods still have certain shortcomings. Existing technologies rely on static templates or single-dimensional strategies for generation, which makes it difficult to adapt to the characteristics of different social platforms in real time. The use of a single model or fixed template to output content leads to serious homogenization of content on the same topic, which cannot meet the differentiated needs of multiple platforms. Furthermore, the lack of integration with the platform specification library for automatic parsing results in frequent content format discrepancies with platform requirements, necessitating repeated manual adjustments, which is inefficient. Therefore, this paper proposes a cross-platform content generation and distribution method based on multimodal AI. Summary of the Invention

[0004] The purpose of this invention is to provide a cross-platform content generation and distribution method based on multimodal AI to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a cross-platform content generation and distribution method based on multimodal AI, comprising the following steps: S1. Collect the original materials input by the user and preprocess them; S2. Based on a multimodal AI model, perform content analysis and feature extraction on the original materials to generate structured content tags and semantic vectors; S3. Based on the preset foreign trade industry promotion strategy library and target profile, match and generate content generation strategies for different social media platforms; S4. Based on structured content tags, semantic vectors, and content generation strategies, the AI ​​generation engine automatically generates diverse strategy content that meets the format requirements of various platforms. S5. Based on the publishing guidelines of each platform and the user's active time period, adaptively convert the format of diverse strategy content and arrange the publishing sequence. S6. Distribute the edited content to all connected social media platforms and monitor the publishing status. S7. Collect user interaction data from various platforms and channels, and optimize content generation strategies and distribution parameters based on feedback data.

[0006] Preferably, in step S1, the user-uploaded raw materials are collected through a preset data interface. The raw materials are automatically extracted and integrated from the external data source specified by the user. After collection, the raw materials are preprocessed, including format unification, data cleaning to remove irrelevant information, and preliminary classification and metadata labeling of multimedia materials.

[0007] Preferably, in step S2, a multimodal AI model is preset, which analyzes images and video frames through a visual recognition module; processes audio materials through a speech-to-text module to convert them into text, and simultaneously analyzes the emotional tendency, speech rate, and intonation features of the speech; processes the text and the converted text through natural language processing, integrates all features, and generates a unified structured content tagging system and a high-dimensional semantic vector representation. The high-dimensional semantic vector comprehensively represents the visual, auditory, and textual semantic information of the content.

[0008] Preferably, in step S3, a foreign trade industry promotion strategy library is preset, and target profile features are extracted from the acquired structured content tags and semantic vectors. Through platform characteristics Adaptation involves obtaining the adaptability of the target profile across various platforms, adjusting strategy priorities based on real-time market variables, and generating adaptive strategies for user behavior characteristics across different social platforms. This is implemented as follows: , In the formula, This indicates the content strategy matching degree, where A represents target profile characteristics and B represents platform characteristic characteristics. This represents the magnitude of vector A. This represents the magnitude of vector B.

[0009] Preferably, in step S4, structured content tags, semantic vectors, and content strategies are obtained. Visual features, textual features, and semantic vectors are aligned using the Transformer multimodal fusion model to generate a cross-modal joint representation vector, which is implemented as follows: , In the formula, R represents the multimodal joint representation vector, V represents visual features, V represents textual features, and S represents high-dimensional semantics. This indicates the platform preference weight.

[0010] Preferably, in step S4, based on the format specifications output by the platform characteristic adaptation module, and according to a pre-set template library, the content structure requirements of the target platform are matched, and differentiated content variations on the same topic are generated through an adversarial generative network to generate a content diversity score, which is achieved as follows: , In the formula, The content diversity score is represented by m, which represents the number of content variations generated. This represents the representation vector of the k-th content variant.

[0011] Preferably, in step S5, based on a preset platform specification library, the latest release rules of the target platform are automatically parsed, the generated diverse content is dynamically matched with the platform specifications, the format parameters that need to be adjusted are identified, and the format conversion difference is calculated, thus achieving the following: , In the formula, This indicates format conversion error; 'r' represents the aspect ratio of the current content containing the black silk. This indicates the aspect ratio required by the platform.

[0012] Preferably, in step S5, the peak activity periods of the target user group are predicted based on time series data, and a heatmap of activity across time zones is generated. Based on the predicted activity periods, an optimal publication time is assigned to each piece of content, prioritizing the publication of highly relevant content. The time series matching degree is achieved as follows: , In the formula, Indicates the time series matching degree. Indicates the planned release time. Indicates the time point when a user is active. Indicates the diffusion parameter during the active period. This indicates the normalization format error and outputs a platform-compatible release schedule.

[0013] Preferably, in step S6, the arranged content is obtained and synchronously distributed through an integrated channel management gateway. The gateway encapsulates the publishing APIs of each target social platform. During distribution, the system packages the formatted content, metadata, and publishing instructions and sends them to the API of the corresponding platform, while listening for the returned status code in real time. For content that requires review, the system will continuously poll the status until the publishing is completed.

[0014] Preferably, in step S7, user interaction data is collected by listening to network hooks provided by the APIs of various platforms; content generation strategies and distribution parameters are optimized based on feedback data, an evaluation system including core indicators such as interaction rate and conversion rate is established, the correlation between different content features and interaction effects is analyzed, and the content generation strategies and parameters are dynamically adjusted according to the analysis results.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention uses a foreign trade industry promotion strategy library and combines a vector matching formula of target profile features and platform characteristics to dynamically generate adaptation strategies, ensuring that the content not only complies with platform rules but also accurately reaches the target group, ultimately improving conversion rate and user stickiness. 2. This invention uses the Transformer multimodal fusion model to align visual, textual and semantic vectors to generate cross-modal joint representation vectors. Then, combined with an adversarial generative network, it generates differentiated variants of the same topic in batches. The content diversity score mechanism ensures that the generated content achieves a balance between creativity and compliance, avoiding platform review risks caused by excessive innovation. 3. This invention automatically parses rules through the platform's specification library and combines YOLOv5 object detection to intelligently crop the main subject of the image, ensuring that the content meets the platform's requirements. Based on LSTM time series, it predicts user activity periods, generates time zone heatmaps, and dynamically adjusts the release time. This step completely eliminates the hidden dangers of format errors and timing mismatches, allowing the content to reach users in the best state, significantly improving the open rate and completion rate. Attached Figure Description

[0016] Figure 1 This is the operational flow of the cross-platform content generation and distribution method based on multimodal AI of the present invention. Figure 1 ; Figure 2 This is the operational flow of the cross-platform content generation and distribution method based on multimodal AI of the present invention. Figure 2 ; Figure 3 This is the operational flow of the cross-platform content generation and distribution method based on multimodal AI of the present invention. Figure 3 ; Figure 4 This is the operational flow of the cross-platform content generation and distribution method based on multimodal AI of the present invention. Figure 4 . Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0018] Please see Figures 1-4 As shown, the present invention provides a technical solution comprising the following steps: S1. Collect the original materials input by the user and preprocess them; S2. Based on a multimodal AI model, perform content analysis and feature extraction on the original materials to generate structured content tags and semantic vectors; S3. Based on the preset foreign trade industry promotion strategy library and target profile, match and generate content generation strategies for different social media platforms; S4. Based on structured content tags, semantic vectors, and content generation strategies, the AI ​​generation engine automatically generates diverse strategy content that meets the format requirements of various platforms. S5. Based on the publishing guidelines of each platform and the user's active time period, adaptively convert the format of diverse strategy content and arrange the publishing sequence. S6. Distribute the edited content to all connected social media platforms and monitor the publishing status. S7. Collect user interaction data from various platforms and channels, and optimize content generation strategies and distribution parameters based on feedback data.

[0019] In this embodiment, step S1 involves collecting original materials uploaded by the user through a preset data interface. The original materials are automatically extracted and integrated from external data sources specified by the user. These external data sources include publicly available content from enterprise product databases, historical order information databases, enterprise websites, and independent product websites. After collection, the original materials undergo preprocessing operations, including format standardization, data cleaning to remove irrelevant or duplicate information, and preliminary classification and metadata labeling of the multimedia materials.

[0020] In this embodiment, S2 presets a multimodal AI model, analyzes images and video frames through a visual recognition module to extract product entities, usage scenarios, brand logos, and color and composition features; processes audio materials through a speech-to-text module to convert them into text, and simultaneously analyzes the emotional tendency, speech rate, and intonation features of the speech.

[0021] Specifically, natural language processing is used to process the text and the converted text, and all features are integrated to generate a unified structured content tagging system and a high-dimensional semantic vector representation. The high-dimensional semantic vector comprehensively represents the visual, auditory, and textual semantic information of the content.

[0022] In this embodiment, step S3 involves pre-setting a foreign trade industry promotion strategy library and extracting target profile features from the acquired structured content tags and semantic vectors. Through platform characteristics Adaptation involves obtaining the adaptability of the target profile across various platforms, adjusting strategy priorities based on real-time market variables, and generating adaptive strategies for user behavior characteristics across different social platforms. This is implemented as follows: , In the formula, This indicates the content strategy matching degree; a higher value indicates a higher matching degree. A represents target profile features, and B represents platform characteristic features. Let A represent the magnitude of vector A, and let B represent the strength of the target feature profile feature. for , Let B represent the magnitude of vector B, and let B represent the strength of the platform characteristic feature. for To address the cultural differences in the target market, content strategies are localized and adjusted accordingly to ultimately generate a content strategy.

[0023] In this embodiment, step S4 involves acquiring structured content tags, semantic vectors, and content strategies. Using the Transformer's multimodal fusion model, visual features, textual features, and semantic vectors are aligned to generate a cross-modal joint representation vector. This is achieved as follows: , In the formula, R represents the multimodal joint representation vector, V represents visual features, V represents textual features, and S represents high-dimensional semantics. This indicates the platform preference weight.

[0024] In this embodiment, step S4, based on the format specifications output by the platform characteristic adaptation module, matches the content structure requirements of the target platform according to a pre-set template library, and generates differentiated content variations on the same topic through an adversarial generative network to generate a content diversity score, is implemented as follows: , In the formula, The content diversity score is represented by m, which represents the number of content variations generated. This represents the representation vector of the k-th content variant.

[0025] In this embodiment, step S5, based on a preset platform specification library, automatically parses the latest release rules of the target platform, dynamically matches the generated diverse content with the platform specifications, identifies the format parameters that need to be adjusted, and calculates the format conversion difference, thus achieving the following: , In the formula, This indicates format conversion error; 'r' represents the aspect ratio of the current content containing the black silk. This indicates the aspect ratio required by the platform.

[0026] In this embodiment, step S5 predicts the peak activity periods of the target user group based on time series data and generates a heatmap of activity across time zones. Based on the predicted activity periods, an optimal posting time is assigned to each piece of content, prioritizing the posting of highly relevant content. The time series matching is achieved as follows: , In the formula, This represents the time-series matching degree, ranging from [0, 1]. It measures the matching quality between the content's publication time and the user's active time period. The closer the value is to 1, the better the publication time. Indicates the planned release time. Indicates the time point when a user is active. Indicates the diffusion parameter during the active period. This indicates the normalization format error and outputs a platform-compatible release schedule.

[0027] In this embodiment, step S6 involves acquiring the orchestrated content and distributing it synchronously through an integrated channel management gateway. The gateway encapsulates the publishing APIs of each target social platform or third-party interface protocols that have been securely certified. During distribution, the system packages the formatted content, metadata, and publishing instructions and sends them to the API of the corresponding platform. It also listens for the return status codes in real time to confirm whether the content has entered the publishing queue, the review process, or the publishing has failed. For content that requires review, the system will continuously poll the status until the publishing is completed.

[0028] In this embodiment, step S7 involves collecting user interaction data by monitoring network hooks provided by the APIs of various platforms; optimizing content generation strategies and distribution parameters based on feedback data; establishing an evaluation system that includes core indicators such as interaction rate and conversion rate; analyzing the correlation between different content features and interaction effects; and dynamically adjusting content generation strategies and parameters based on the analysis results.

[0029] Working principle: The system automatically captures materials from external data sources such as enterprise product databases, official websites, and order systems through preset data interfaces and integrates them into a unified format. After collection, the system standardizes the materials, including removing duplicate or irrelevant information, unifying file formats, performing preliminary classification of images and videos, and adding metadata annotations. The system analyzes images and videos through a visual recognition module to extract visual features such as product entities, usage scenarios, brand logos, and composition colors. A speech-to-text module processes audio materials, simultaneously analyzing emotional tendencies, speech rate, and tone. A natural language processing module performs semantic analysis on the text, fusing all features to generate a structured tagging system and high-dimensional semantic vectors. Target user profile features are extracted from the structured tags and semantic vectors, and matching degrees are calculated based on platform characteristics. Strategy priorities are adjusted using real-time market variables, dynamically generating strategies adapted to different platforms. Content expression is adjusted according to cultural differences, ultimately outputting a strategy solution that conforms to the target market and platform rules. Based on structured tags, semantic vectors, and strategies, the system aligns visual, textual, and semantic features using a Transformer multimodal fusion model to generate cross-modal joint representation vectors. Combined with a generative adversarial network, differentiated content variations on the same topic are generated in batches. The system then uses internal... A diversity scoring mechanism is used to select the optimal variant, ensuring that the generated content conforms to the platform's format requirements. The system automatically parses the latest release specifications of each platform, dynamically adjusts content format parameters, and uses an LSTM time series model to predict user activity periods combined with time zone heatmaps to allocate the optimal release time. The system prioritizes the release of highly relevant content and outputs a platform-compatible release schedule. By integrating a channel management gateway, the system encapsulates the APIs of each platform, packages the formatted content, metadata, and release instructions, and sends them to the target platform. During distribution, the system monitors API return status codes in real time to confirm whether the content has entered the release queue or failed. For content requiring review, the system continuously polls the status until release is complete and records the entire distribution process using blockchain evidence. The system collects user interaction data in real time through network hooks monitoring the APIs of each platform, establishing an evaluation system that includes core indicators. By analyzing the correlation between content characteristics and interaction effects, the system dynamically adjusts content generation strategies and distribution parameters.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0031] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for cross-platform content generation and distribution based on multi-modal AI, characterized in that, The method comprises the following steps: S1, collecting user input raw materials and preprocessing; S2, based on the multi-modal AI model, the content analysis and feature extraction of the raw materials are carried out, and the structured content label and semantic vector are generated; S3, according to the preset foreign trade industry promotion strategy library and target image, the content generation strategy for different social platforms is matched and generated; S4, based on the structured content label, semantic vector and content generation strategy, the diversified strategy content conforming to the format requirements of each platform is automatically generated through the AI generation engine; S5, according to the release specification and user active period of each platform, the diversified strategy content is adaptively converted and released in time sequence; S6, the arranged content is synchronized to each accessed social platform channel, and the release state is monitored; S7, the user interaction data of each platform channel is collected, and the content generation strategy and distribution parameters are optimized based on the feedback data.

2. The multi-modal AI based cross-platform content generation and distribution method according to claim 1, characterized in that: Said S1, through the preset data interface, the raw materials uploaded by the user are collected, and the content elements of the raw materials are automatically grabbed and integrated from the external data source specified by the user; After collection, the raw materials are pretreated, including format unification, data cleaning to remove irrelevant information, and preliminary classification and metadata labeling of multimedia materials.

3. The multi-modal AI based cross-platform content generation and distribution method according to claim 1, characterized in that: Said S2, the preset multi-modal AI model is used to analyze images and video frames through a visual recognition module; audio materials are processed through a speech-to-text module to convert them into text, and the emotional tendency, speech speed and tone characteristics of the speech are analyzed synchronously; Through natural language processing, all features are fused to generate a unified structured content label system and high-dimensional semantic vector representation, which comprehensively represents the visual, auditory and text semantic information of the content.

4. The multi-modal AI based cross-platform content generation and distribution method of claim 1, wherein: The S3 extracts target image features from the obtained structured content tags and semantic vectors according to a preset foreign trade industry promotion strategy library , through platform characteristic features adaptation, obtain the adaptation degree of each platform to the target image, adjust the strategy priority in combination with real-time market variables, generate an adapted strategy for the user behavior characteristics of different social platforms, and achieve ， In the formula, represents the content strategy matching degree, A represents the target image feature, B represents the platform characteristic feature, represents the length of the vector A, is , represents the length of the vector B.

5. The multi-modal AI based cross-platform content generation and distribution method according to claim 1, characterized in that: Said S4, the structured content label, semantic vector and content strategy are obtained, the visual features, text features and semantic vectors are aligned through the multi-modal fusion model of Transformer to generate cross-modal joint representation vectors, which are realized as: ， In the formula, R represents a multimodal joint representation vector, V represents a visual feature, V represents a text feature, S represents a high-dimensional semantic, represents a platform preference weight.

6. The multi-modal AI-based cross-platform content generation and distribution method of claim 5, wherein: Said S4, based on the format specification output by the platform characteristic adaptation module, the content structure requirements of the target platform are matched according to the preset template library, the differential content variants of the same topic are generated through the generative adversarial network, the content diversity score is generated, and the realization is as follows: ， In the formula, denotes the content diversity score, m denotes the number of generated content variants, denotes the representation vector of the kth content variant.

7. The multi-modal AI based cross-platform content generation and distribution method of claim 1, wherein: Said S5, based on the preset platform specification library, the latest release rules of the target platform are automatically parsed, the generated diversified content is dynamically matched with the platform specification, the format parameters that need to be adjusted are identified, the format conversion difference is calculated, and the realization is as follows: ， In the formula, represents the format conversion error, r represents the aspect ratio of the current content of the black silk, represents the aspect ratio required by the platform. 8.The multi-modal AI based cross-platform content generation and distribution method of claim 7, wherein: Said S5, based on time series prediction, the active peak period of the target user group is predicted, and the active degree heat map of the time zone is generated, the best release time is allocated for each content according to the active period prediction, the high matching degree content is preferentially released, and the time sequence matching degree is realized as: ， In the formula, represents the timing matching degree, represents the planned release time, represents the user active time point, represents the active period diffusion parameter, represents the normalized format error, and outputs a platform-compatible release time schedule. 9.The multi-modal AI based cross-platform content generation and distribution method of claim 1, wherein: The S6 obtains the arranged content, synchronously distributes through the integrated channel management gateway, and the gateway encapsulates the publishing API of each target social platform; when distributing, the system packs and sends the formatted content, metadata and publishing instructions to the API of the corresponding platform, and listens to the return status code in real time; for the content that needs to be audited, the system will continuously poll the status until the publishing is completed.

10. The multi-modal AI based cross-platform content generation and distribution method of claim 1, wherein: The S7 collects user interaction data through the network hook provided by the API of each platform; optimizes the content generation strategy and distribution parameters based on the feedback data, establishes an evaluation including the core indicators of interaction rate and conversion rate, analyzes the correlation between different content characteristics and interaction effects, and dynamically adjusts the content generation strategy and parameters according to the analysis results.

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