Preamble

Collaborative Innovation of AIGC Technology in Converged Media Content Creation
Bai Lu (Baotou Converged Media Center, Baotou, Inner Mongolia 014030, China)

Abstract

[Objective] This study explores the innovative application of AI-Generated Content (AIGC) technology within converged media preparation systems to construct a highly efficient and intelligent model for integrated communication. [Methods] Based on collaborative innovation theory, the paper analyzes current application scenarios and implementation paths of AIGC in converged media systems, proposing an innovative framework of "Content Production + Intelligent Processing + Distribution and Communication." [Results] An intelligent preparation system was established, encompassing intelligent collection, multimodal generation, and cross-platform distribution, thereby achieving resource sharing and collaborative creation. [Conclusion] The deep integration of AIGC technology with converged media preparation systems enhances communication effectiveness and advances the transformation and development of traditional media.

Keywords

AI Intelligent Generation; Converged Media Preparation; Multimodal Processing; Collaborative Innovation; Intelligent Distribution

With the rapid development of artificial intelligence, AIGC (AI Generated Content) has demonstrated immense potential in the field of content production. Traditional converged media preparation systems face challenges such as high labor costs and low efficiency, creating an urgent need for new technologies like AIGC to enhance intelligence levels. This paper primarily discusses the innovative application of AIGC technology in converged media preparation systems, providing insights for building a new type of intelligent communication system. In the context of the omni-media era, the innovative application of AIGC is not only related to the reform of media production methods but also serves as a vital engine for promoting the deep integration and development of media, carrying significant practical importance.

1.1 Core AIGC Technical Modules

AIGC technology in converged media preparation systems primarily consists of several core modules. First, multimodal content generation technology based on deep learning enables the intelligent creation of various content forms, including text, images, audio, and video. Second, content understanding and processing technology based on large-scale pre-trained models allows for the intelligent classification, labeling, and quality assessment of raw materials. Third, intelligent auditing technology utilizes computer vision and natural language processing to identify sensitive information and filter non-compliant content. Finally, personalized recommendation technology leverages user profiles and behavioral data to achieve precise content delivery. These modules coordinate to form a complete intelligent content production chain.

1.2 Converged Media Content Production Chain

The converged media content production chain is composed of four key stages: planning, collection, editing, and distribution. In the planning stage, the system assists in developing topic proposals by analyzing trending topics and user needs. During the collection stage, intelligent crawlers and automated tools are used to gather multi-source heterogeneous data. In the editing stage, AIGC technology is applied to intelligently process and integrate materials, generating content tailored to the communication characteristics of various platforms. Finally, in the distribution stage, intelligent recommendation algorithms enable precise multi-channel delivery. By optimizing and reshaping traditional production processes through AIGC, content production efficiency is significantly improved.

1.3 Human-Machine Collaborative Creation Mode

The human-machine collaborative creation mode represents the deep integration of AIGC technology with traditional media production. In this model, AIGC technology primarily handles foundational tasks such as material processing, content generation, and quality inspection, while human editors focus on high-level work including content planning, creative design, and in-depth processing. By establishing standardized collaboration workflows and intelligent task allocation mechanisms, the system achieves a complementary advantage between human intelligence and AI. Furthermore, continuous data accumulation and model optimization enhance the creative capabilities of the AI system, forming a virtuous cycle of collaborative evolution. This mode ensures both the efficiency and scale of content production while maintaining the quality and value of the work.

2.1 Content Planning and Intelligent Collection

The intelligence-oriented content planning and collection process involves three levels. First, for big-data-based topic planning, the system intelligently generates topic suggestions and content plans by analyzing hot events, user interests, and communication effects. Second, for intelligent data collection, automated gathering and integration of multi-source data are achieved through crawler technology, IoT devices, and user contributions. Third, for intelligent preprocessing, the system automatically classifies, tags, and evaluates the quality of raw data to provide a high-quality material library for subsequent production.

Additionally, the system establishes an intelligent copyright management mechanism to automatically identify and process material copyright information, ensuring compliance. A comprehensive data governance system is also constructed to ensure the accuracy and usability of collected data through quality assessment, cleaning, and standardization. Furthermore, the system uses deep learning models to evaluate content value across multiple dimensions—such as media attributes, timeliness, and audience demand—to prioritize collection and ensure that critical content is processed promptly. A multi-source verification mechanism is introduced during collection to guarantee the authenticity and accuracy of information [FIGURE:1].

2.2 Multimodal Material Generation

Multimodal material generation is the core application of AIGC technology. Based on deep learning models, the system achieves the intelligent generation of text, images, audio, and video. In text generation, large-scale pre-trained language models automatically generate news drafts, headlines, and abstracts based on prompts. In image generation, diffusion models and other technologies convert text descriptions into high-quality images. For audio and video, neural rendering technology enables virtual anchor broadcasts, video editing, and scene reconstruction. The system also supports style transfer and intelligent editing of multimodal content to meet diverse creative needs. To ensure quality, multiple evaluation mechanisms are introduced, including relevance assessment, style consistency checks, and quality scoring, while supporting manual intervention to ensure content meets communication requirements. Based on transfer learning, the system can quickly adapt to new generation needs through few-shot learning and domain adaptation.

2.3 Cross-Media Resource Integration

Cross-media resource integration aims to break down barriers between different media forms to achieve efficient integration and reuse. By establishing a unified media resource center, the system centrally manages various materials such as text, images, audio, and video. Semantic analysis technology is used to build a multi-dimensional resource tagging system, supporting intelligent retrieval and recommendation. Cross-modal content understanding technology enables intelligent correlation and conversion between different media formats. The system also provides visual orchestration tools, allowing editors to conveniently combine and arrange multimedia content, thereby improving production efficiency.

To further enhance resource utilization, the system analyzes usage frequency and communication effects to guide the optimization and updating of resources. An intelligent recommendation mechanism proactively suggests relevant materials based on editors' history and preferences. Furthermore, resources related to hot events are intelligently aggregated into multimedia packages for rapid invocation and secondary creation. The system also supports the intelligent processing and distribution of real-time materials.

2.4 Intelligent Distribution and Communication

The intelligent distribution and communication stage utilizes a data-driven approach to achieve precise delivery and effect optimization. Based on user profiles and behavioral data, the system constructs intelligent distribution strategies to push personalized content to different users. By establishing communication effect prediction models, the system evaluates the potential of content to optimize release timing and channel selection.

The system supports real-time monitoring of communication paths and user feedback, continuously optimizing distribution strategies through machine learning algorithms. Simultaneously, a unified publishing platform for the entire media matrix is established, realizing an intelligent "collect once, generate multiple, and distribute across multiple terminals" pattern. An intelligent public opinion monitoring mechanism tracks communication effects in real-time to identify and respond to potential risks, ensuring controllability. The system also introduces an intelligent "time wheel" to automatically optimize publishing times based on user activity patterns across platforms. For important content, differentiated distribution strategies are supported across multiple versions and channels, with an influence evaluation system measuring breadth, depth, and interactivity to guide future optimization.

3.1 Cloud Computing and Distributed Architecture

The technical support system for collaborative innovation adopts a distributed architecture based on cloud computing to build a high-performance, scalable platform. The core system utilizes containerized deployment, where functional modules such as content processing, model training, and data storage are decoupled into microservices to achieve elastic scheduling and load balancing. Distributed computing clusters provide sufficient power for AIGC models, while object storage technology builds a unified media resource pool for cross-regional data sharing. Standardized APIs support flexible integration with third-party systems, fostering an open ecosystem.

The system uses Docker for rapid deployment and Kubernetes for orchestration, enhancing maintainability. Data storage employs a hybrid architecture of distributed file systems and relational databases to ensure both performance and consistency. Service mesh technology is introduced for intelligent routing and failover between microservices. Additionally, edge computing units are deployed at network nodes to accelerate response times. The network architecture utilizes multi-active data centers to provide high disaster recovery capabilities and service availability.

3.2 Intelligent Algorithms and Model Training

Intelligent algorithms serve as the core engine of the system, primarily involving deep learning models in natural language processing, computer vision, and speech recognition. The system uses a "pre-training + fine-tuning" approach, first training base models on large-scale public datasets and then optimizing them for the media domain using professional content. In practice, the system selects appropriate models for different scenarios and achieves lightweight deployment through model distillation and quantization.

A closed-loop optimization process is established to iteratively update models based on production data. AutoML technology is introduced to lower the barrier to algorithm development through automated feature engineering. To improve training efficiency, a distributed framework supports multi-GPU parallel computing. A robust version management mechanism allows for rapid rollbacks and A/B testing. For security, adversarial training enhances model robustness, while federated learning supports collaborative training across institutions while protecting data privacy. A complete evaluation system assesses performance based on accuracy, real-time capability, and resource consumption.

3.3 Data Middle-End and Business Middle-End

The data middle-end serves as the infrastructure for managing structured and unstructured data, ensuring quality through standardized collection, cleaning, and labeling. A unified data service layer provides support for upper-level applications. The business middle-end, based on microservice architecture, encapsulates core business capabilities into components, supporting flexible orchestration and scenario reuse. This architecture effectively solves the problems of data silos and fragmented capabilities, particularly during breaking news or major events.

In terms of data governance, a metadata management system provides unified cataloging and classification. Real-time computing engines support the analysis of massive data streams, while strict data grading and desensitization mechanisms protect sensitive information. Knowledge graph technology is used to build a media knowledge base for intelligent discovery. The platform also provides visualization components to allow business personnel to perform self-service data analysis, meeting the immediate data needs of scenarios such as live broadcasts.

3.4 Security Protection and Quality Monitoring

The system establishes a comprehensive security framework, including identity authentication, access control, and data encryption. In the production stage, a multi-level auditing mechanism performs quality control and compliance checks on AIGC-generated content. Real-time monitoring tracks resource usage, response times, and task execution. Robust log auditing and anomaly alerts ensure stable operation, while disaster recovery plans guarantee business continuity.

During sensitive periods, the system automatically elevates security levels and strengthens content auditing to ensure correct political orientation and positive value alignment. Full-link tracing technology enables end-to-end monitoring of business requests. An intelligent O&M (Operations and Maintenance) platform facilitates automatic fault discovery and resolution. For content security, deep learning-based recognition technology intelligently intercepts prohibited content. AI-based anomaly detection identifies potential threats in real-time, and a multi-model collaborative auditing mechanism improves accuracy and efficiency in quality control.

4.1 Organizational Structure Optimization

The introduction of AIGC technology necessitates the optimization of traditional converged media organizational structures. First, a specialized intelligent content production team should be established to handle AIGC application and optimization. Second, technical support and O&M teams must be strengthened. Third, the editing team structure should be optimized to cultivate composite talents who understand both journalism and technology. Flat management can break down departmental barriers through flexible project-based mechanisms.

Reasonable incentive mechanisms should be established to encourage technical and business innovation. In terms of talent cultivation, systematic AIGC training should be conducted to improve the digital literacy of all staff. Specialized data analysis teams and innovation laboratories can be set up for cutting-edge research. By establishing cross-departmental agile teams, organizational response speed is improved. Finally, technical innovation achievements should be integrated into performance evaluation systems to mobilize innovation enthusiasm [FIGURE:2].

4.2 Production Process Re-engineering

Traditional production workflows must be redesigned to suit AIGC characteristics. In the collection stage, intelligent topic selection systems use big data to identify trends. In the production stage, links between text, video, and live broadcasting are integrated to achieve "collect once, generate multiple, and distribute across multiple terminals." In the auditing stage, a human-machine collaborative system improves efficiency. In the distribution stage, precise pushing is achieved based on user profiles.

The process design adopts agile development concepts, transforming linear workflows into parallel collaborative modes. Low-code platforms allow business staff to build customized workflows. A content creation knowledge base preserves excellent templates and experience. For quality control, A/B testing mechanisms continuously optimize content products, while multi-version management facilitates rapid iteration.

4.3 Resource Allocation Mechanism

An intelligent resource allocation platform provides unified management of computing, storage, and human resources. Computing resources utilize cloud-native architecture for elastic scaling, while storage resources use a hierarchical system to optimize cost-effectiveness. For human resources, intelligent scheduling and task allocation systems improve efficiency.

During major news events, the system can automatically adjust resource configurations to ensure core business continuity. Intelligent prediction technology forecasts resource demand trends based on historical data, enabling proactive scheduling. Resource pooling improves utilization rates, and refined cost accounting optimizes the input-output ratio. The system also supports cross-regional resource coordination and provides early warning mechanisms for resource bottlenecks.

4.4 Evaluation and Feedback System

A multi-dimensional evaluation and feedback mechanism assesses AIGC application effects across content quality, production efficiency, user feedback, and communication impact. A dynamic feedback optimization mechanism ensures continuous improvement of algorithms and workflows based on these results.

User feedback is collected through surveys and interviews, while system performance is tracked through monitoring and fault analysis. An intelligent evaluation platform supports multi-dimensional data visualization, using machine learning to automatically calculate indicators. An intelligent user feedback system collects opinions in real-time, and a tracking mechanism ensures that issues are effectively rectified. The system also supports the automatic generation of evaluation reports to provide data support for management decisions.

The deep integration of AIGC technology with converged media preparation systems is an inevitable trend. By building an intelligent collaborative innovation system, media organizations can significantly enhance production efficiency and communication effects, driving the transformation toward intelligent and digital operations. Future efforts should focus on refining technical solutions, mastering content quality, and ensuring the unity of technical empowerment and value dissemination. In the journey of deepening media integration, AIGC will continue to serve as a driver for profound changes in media production and communication patterns.

References

(Note: Citations [1] through [20] are preserved as per the original text, covering topics from AI in county-level media to digital publishing and network security.)

About the Author: Bai Lu, Senior Engineer, Deputy Director of the Digital Intelligence Development Department at Baotou Converged Media Center. Research interests: Cloud computing, mobile terminals, software engineering, system architecture, network security, media technology, and system operations.