Intelligent marketing content generation and optimization system

Abstract

The invention relates to an intelligent marketing content generation and optimization system, and belongs to the technical field of artificial intelligence and digital marketing. According to the system, through cooperative operation of the data acquisition module, the intelligent text generation module, the multi-modal synthesis module, the risk management and control module and the dynamic optimization module, whole-process closed-loop management of marketing content is realized. The data acquisition module captures unstructured data from a multi-source platform based on a user authorization protocol, semantic cleaning and structured processing are performed through a large model interface, and the problems of data dispersion and fragmentation are solved. The intelligent text generation module receives user parameters through an interactive interface, matches a preset prompt word bank, calls a large model interface to generate multi-version copywriting in batches, and supports concurrent processing of single-choice or multi-choice product lists. The multi-modal synthesis module extracts keywords according to copywriting semantics, matches pre-annotated visual materials and generates multilingual speech paraphrases, so as to ensure the consistency of graphic and text information. The risk management and control module intercepts sensitive content in real time based on an industry rule base, and guarantees output compliance. And the dynamic optimization module integrates the user behavior data to generate a delivery strategy, updates a prompt word bank and a generation model through a closed-loop feedback mechanism, and realizes dynamic adaptation of contents and market demands. Compared with a traditional method, the system has the advantages that the content generation efficiency, the cross-modal matching precision and the compliance control level are remarkably improved, and the system is suitable for the global digital marketing requirements of multiple industry scenes such as e-commerce, industrial equipment and cross-border trade.

Description

Technical Field

[0001] The present invention relates to an intelligent marketing content generation and optimization system, which is particularly suitable for a closed-loop digital content production process based on multimodal data processing and dynamic knowledge evolution.

[0002] The system integrates data collection, intelligent creation, multimodal synthesis and risk management modules to achieve full-process automated management from data cleaning, content generation to strategy optimization, solving marketing effectiveness problems caused by data silos, low manual efficiency and insufficient cross-modal matching accuracy in traditional technologies. Background Art

[0003] Current digital marketing technologies face numerous challenges in practical application. Internal marketing data is stored in disparate systems. For example, product specifications and customer FAQs lack effective linkage, leading to significant information fragmentation. When generating customer responses based on product specifications, the fragmented data makes accurate retrieval difficult, severely impacting marketing content quality and decision-making efficiency. Traditional methods rely on manual effort, requiring significant time to craft copy and produce videos. In a fast-paced market, they struggle to meet the demands for high-volume content across multiple platforms and genres. For example, adaptable content cannot be quickly generated for new product launches or promotional events, resulting in output lagging behind market trends. Furthermore, there is a lack of automated semantic alignment between video assets and copy, leading to frequent inconsistencies in core messaging. For example, the copy may emphasize a product's environmentally friendly features, but the video material may not highlight these elements, reducing the content's appeal. Marketing strategy optimization relies on periodic manual intervention and lacks the ability to dynamically adjust based on real-time interactive data. For example, strategy updates lag behind changes in user behavior data, leading to a disconnect between content and demand and low asset utilization.

[0004] To address the above issues, the present invention provides a closed-loop digital marketing content generation and optimization system that achieves integrated management of data integration, efficient generation, precise matching, and dynamic optimization through multi-module collaboration. Specifically, the system includes the following technical solutions and steps:

[0005] Data collection and structured processing

[0006] With user authorization, the system crawls data from multiple sources, including company websites, e-commerce platforms, and social media platforms, using compliance protocols. This crawling process adheres to platform-specific rules (such as the Robots protocol) and employs a dynamic proxy rotation mechanism to circumvent anti-crawling restrictions. The data cleansing module uses a large-scale model interface to deeply analyze unstructured information. Based on pre-set rules (such as regular expression matching and keyword filtering), it removes duplicate and ambiguous content, extracts basic product information (such as name, selling points, and specifications), and converts it into a unified JSON format. The cleaned data is stored in a distributed database categorized by company profile, product introduction, and FAQ, ensuring data currency and scalability. Users can import PDFs, PowerPoint presentations, and other files. After the system uses an OCR interface to recognize the text, it performs a secondary cleansing step using pre-set prompts (such as "Extract core parameters" and "Retain marketing keywords"). For example, this system accurately extracts technical specifications from product manuals and removes redundant descriptions. Compared to traditional methods, this system leverages the semantic understanding capabilities of large-scale models (such as the BERT model) to perform contextual analysis, retaining key information and building high-quality structured datasets.

[0007] Intelligent text generation and concurrent processing

[0008] The intelligent text creation module receives user-defined copy length, style preferences, and multilingual parameters through an interactive interface, automatically matching pre-set prompt word libraries to generate multiple versions of marketing copy. The prompt word library is grouped by product category and functional tag (e.g., "Home Appliances - Energy Saving Selling Points" and "Industrial Equipment - Technical Parameters"). Using semantic vectorization technology (generating prompt word vectors based on the Sentence-BERT model), a cosine similarity calculation is performed with the user parameters to select prompt words with a matching score above a threshold (e.g., 0.85). Users can select a single or multiple product lists. The system independently packages tasks for each product and submits them in batches to the large model interface using asynchronous thread pool technology. For example, if a user selects 100 industrial equipment products and specifies "Professional Terminology + Chinese and English Bilingual," the system automatically associates prompt words for technical documentation and generates bilingual copy that complies with industry standards, reducing task completion time by 90% compared to single-threaded processing. The generated results are bound to the product information and displayed in a paginated list. Search by product name, online editing, and batch export to CSV / TXT formats are supported.

[0009] Multimodal content synthesis and compliance control

[0010] The video synthesis module extracts keywords (such as "environmentally friendly" and "durable") based on the semantics of the text, and matches visual materials through pre-labeled tags (such as the "green factory video clip" in the material library is labeled "environmentally friendly") to ensure that the core information of the image and text is consistent. The speech synthesis function calls the large model interface to generate multilingual narration and supports adjustment of intonation parameters (such as emphasizing logical stress in Chinese and adapting to regional accents in English). The customer question and answer system extracts new knowledge points (such as "users inquire about product compatibility") through real-time conversation log analysis, updates them to the FAQ library after manual review, and automatically associates them with product parameters. The system has a built-in risk management layer, builds a sensitive word library based on industry public rules (such as the banned vocabulary list of the Advertising Law), and monitors the generated content in real time. For example, if absolute terms such as "best performance" appear in the copy, the system automatically replaces them with "excellent performance" and records the violation. After the user enables this function, the compliance interception accuracy rate exceeds 99%.

[0011] Dynamic optimization and closed-loop feedback

[0012] The operation management module integrates multi-platform user behavior data (such as click-through rate and dwell time), displays the conversion funnel through a visual dashboard, and analyzes the contribution of key elements based on SHAP values ​​(such as "the impact of product close-up duration on conversion"). The system uses conversion rate fluctuations as a trigger condition to automatically start the model fine-tuning process: for example, when the click-through rate of a certain copy drops by 10% for three consecutive days, the system calls historical high conversion data to retrain the prompt word matching model and update the vector weights. The optimized prompt phrases are automatically pushed to the creation interface for users to call, forming a closed loop of "generation → delivery → analysis → iteration". The API cluster is connected to the social media platform through the OAuth protocol to achieve synchronous content distribution and effect monitoring across platforms.

[0013] Innovative Description

[0014] Closed-loop data flow management: Dynamically optimize the prompt vocabulary and generation model through a real-time feedback mechanism to solve the problem of delayed strategy updates;

[0015] Cross-modal precise matching: Based on semantic vectorization and pre-labeled tags, content alignment between copywriting, materials, and voice is achieved, with a matching error rate of less than 5%;

[0016] Adaptive compliance control: Combining the rule engine with large-scale model real-time detection, it enables interception and automatic correction of sensitive content;

[0017] Distributed concurrent architecture: supports batch processing of thousands of products, with a task throughput of 1,000 tasks per minute, 20 times higher than traditional systems.

[0018] Through the above-mentioned technical solutions, this system significantly improves the efficiency of generating and delivering multimodal marketing content while ensuring data compliance and content security. It is suitable for global scenarios such as cross-border e-commerce, industrial equipment, and education and training. DETAILED DESCRIPTION

[0019] The implementation process of this system is based on modular design, and the data flow drives the coordinated operation of each functional unit to achieve the automatic generation and optimization of marketing content. The following are the detailed implementation steps, and the flowchart ( Figure 1 、 Figure 2 ) Explain the logical relationship between each stage.

[0020] Data collection and structured processing (corresponding to Figure 1 )

[0021] After the system is started, the data collection module accesses the company's official website, e-commerce platform and social media interface according to the user authorization agreement (in compliance with GDPR and other data protection regulations), and crawls multi-source data through compliant crawler technology ( Figure 1 The crawling process uses a dynamic proxy rotation mechanism to circumvent the platform's anti-crawling restrictions. The acquired unstructured data (such as product descriptions and user reviews) is transmitted to the cleaning unit, and the large model interface is called for semantic analysis. The preset regular expressions and keyword library are combined to filter out redundant information ( Figure 1 In the "Large Model Cleaning" → "Regular Filtering / Keyword Extraction"), extract the core fields (such as product name, selling points, technical parameters). The cleaned data is formatted through a standardized JSON conversion tool (such as the Python json module) and stored in a distribution according to company introduction, product introduction and FAQ.

[0022] Databases such as MongoDB Figure 1 PDF, PPT, and other files uploaded by users are recognized as text by the OCR interface, cleaned twice with preset prompt words, and then synchronized into the database to ensure the integrity and consistency of the data source.

[0023] Intelligent text generation and task scheduling (corresponding to Figure 2 )

[0024] Users set the target platform, copy length, style preferences and multi-language parameters through the interactive interface ( Figure 2 The intelligent text generation module receives the parameters and starts the prompt word matching process. The preset prompt word library is grouped by product category and function label, and prompt word vectors are generated based on the Sentence-BERT model. The system calculates the cosine similarity between the user input parameters and the prompt word vectors, and selects the prompt word combination with the highest matching degree ( Figure 2After the user selects one or more products, the task scheduling unit uses asynchronous task queue technology (such as Python Celery) to batch call the large model interface to generate copywriting ( Figure 2 The generated results are automatically bound to the product information and displayed as a paged list on the interactive interface. They support searching by product name, online editing, and batch export.

[0025] Multimodal content synthesis and compliance control (corresponding to Figure 2 )

[0026] After the copywriting is generated, the multimodal synthesis process is automatically triggered ( Figure 2 The system extracts semantic keywords (such as technical features and scene descriptions) from the text and matches the corresponding clips in the visual material library based on pre-labeled tags to ensure that the video content is consistent with the core information of the text. The speech synthesis unit calls the large model interface to generate multilingual narration, adjusts the intensity of emotional expression according to the tone parameters set by the user, and adapts to the needs of audiences in different regions. The customer question and answer system monitors the conversation logs in real time, extracts new knowledge points (such as inquiries about product functions that are not included) through contextual analysis, and pushes them to the manual review interface. After the review is passed, it is updated to the FAQ library and associated with the product parameters to form a dynamic knowledge expansion mechanism.

[0027] Risk control and sensitive word interception (corresponding to Figure 2 )

[0028] The system has a built-in sensitive word library, which builds illegal words and replacement rules based on the banned words list of the Advertising Law and industry standards. The risk control module monitors the generated content in real time ( Figure 2 In the "Compliance Check" → "Is it compliant?"), when sensitive words (such as absolute terms, prohibited propaganda) are detected, they are automatically replaced with compliant expressions and the violation is recorded. After the user enables this function, the interception results are fed back to the editing interface in real time, supporting manual review and rule fine-tuning ( Figure 2 "Auto Correction" → "Cross-Platform Publishing") to ensure the legality and security of content output.

[0029] Dynamic optimization and closed-loop feedback (corresponding to Figure 2 )

[0030] The operation management module integrates multi-platform user behavior data (such as click-through rate, conversion path, and dwell time) and displays key indicator trends and conversion funnels through visual dashboards. Figure 2 The system uses conversion rate fluctuations as a trigger, automatically calls historical high conversion data to retrain the prompt word matching model, and optimizes vector weights and matching logic ( Figure 2The updated prompt word library is synchronized to the generation module in real time, forming a closed loop of "generation-delivery-analysis-iteration" ( Figure 2 The cross-platform distribution unit connects to social media via a RESTful API interface, enabling one-click simultaneous publishing of content and monitoring of its effectiveness.

[0031] System deployment and expansion

[0032] The system is deployed using a distributed microservices architecture, with each module operating independently and communicating via message queues (such as RabbitMQ). This allows for horizontal scalability to handle high-concurrency tasks. The data storage layer utilizes the MongoDB distributed database and object storage service, ensuring efficient reading and writing of massive amounts of data and disaster recovery backup. Users can configure multi-role accounts and set data access and function operation permissions through the permissions management interface to meet enterprise-level collaboration needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 : Data collection and preprocessing process, covering key steps such as data source access, cleaning, and storage.

[0034] Figure 2 : Content generation and optimization process, including parameter setting, prompt word matching, multimodal synthesis, compliance detection and closed-loop feedback.

[0035] Through the above implementation methods and flowchart-related descriptions, this system realizes the full-process automated management from data collection, content generation, multimodal synthesis to strategy optimization, significantly improving the production efficiency and delivery accuracy of marketing content, while ensuring data compliance and content security.

Claims

1. An intelligent marketing content generation and optimization system, characterized by It includes a data acquisition module, an intelligent text generation module, a multimodal synthesis module, a risk management module, and a dynamic optimization module. The data acquisition module captures unstructured data from multiple source platforms based on user authorization agreements, performs semantic cleaning and format standardization through preset rules and a large model interface, and generates a structured marketing data set. The intelligent text generation module receives user parameters through an interactive interface, matches the preset prompt word library, and calls the large model interface to batch generate multiple versions of marketing copy. The multimodal synthesis module extracts keywords based on the copy semantics, matches pre-annotated visual materials, and generates multilingual voice narration to ensure consistency of the core information of the image and text. The risk management module detects and intercepts sensitive content in real time based on the industry rule library to ensure output compliance. The dynamic optimization module integrates user behavior data to generate delivery strategy recommendations, and updates the prompt vocabulary and generation model through a closed-loop feedback mechanism; each module forms a closed-loop management through data flow interaction, and dynamically optimizes output content based on user interaction and conversion rate as trigger conditions. The system according to claim 1, characterized in that The data acquisition module captures data through web crawler technology. The capture process follows the platform's public rules and adopts a dynamic agent rotation mechanism. It calls the large model interface to perform contextual association analysis on unstructured text, removes redundant content through regular expressions and keyword filtering, and stores the cleaned data in a structured format according to company introductions, product introductions, and question and answer categories. The system according to claim 1, characterized in that The intelligent text generation module has a built-in prompt word library, which is grouped by product category and function label and generates prompt word vectors through semantic vectorization technology. The similarity between user parameters and prompt word vectors is calculated to screen suitable prompt words. The large model interface is called in batches through asynchronous thread pool technology to generate copy and bind product information output. The system according to claim 1, characterized in that The multimodal synthesis module associates pre-labeled material clips with copy keywords, calls the large model interface to generate narration in the appropriate language and tone, and extracts new knowledge points through dialogue log analysis and updates them to the question and answer library. The system according to claim 1, characterized in that The dynamic optimization module displays the conversion funnel and user behavior path through a visual dashboard, calls historical high conversion data to retrain the model based on conversion rate fluctuations, and realizes content synchronization and effect monitoring through a cross-platform distribution interface.

Images