Preamble

The construction of radio and television (R&TV) converged media is a vital measure for promoting the deep integration of traditional and emerging media. In a converged media environment, R&TV institutions must simultaneously process diverse data types, including traditional programs, new media content, and user interaction data, which places higher demands on data processing systems. Specifically, in the wireless data collection and transmission stages, it is essential to design efficient and reliable wireless connection devices to ensure the real-time accuracy of data acquisition. Cloud computing, with its distributed and elastically scalable characteristics, combined with specialized wireless connection devices, provides new avenues for solving the challenges of converged media data processing. Constructing an efficient and reliable data processing architecture based on cloud computing and wireless connection technologies has become a critical issue in the development of R&TV converged media.

1. Analysis of R&TV Converged Media Data Processing Systems

R&TV converged media data is characterized by its multi-source heterogeneity, massive volume, and high timeliness, covering radio, television, social media, and user behavior data. Traditional data processing architectures face storage bottlenecks, limited throughput, and expansion difficulties, making it difficult to meet the needs of rapid business development. With the maturation of technologies such as distributed computing, containerization, and edge computing, the resource elasticity and on-demand allocation features of cloud computing platforms provide the technical support necessary to build a next-generation converged media data processing system.

2.1 Architecture Design Principles

The system architecture follows four core principles: high availability, scalability, high performance, and security. High availability is ensured through distributed cluster deployment and multi-replica data backup to guarantee stable 24/7 operation. Scalability relies on a microservices architecture to achieve both horizontal and vertical system expansion. High performance is achieved through distributed computing for parallel data processing and load balancing. Security is maintained via a multi-layered protection system, including data access control and transmission encryption. The system adopts a layered design where each layer achieves loose coupling through standard interfaces, integrating cloud-native features such as elastic scaling and fault isolation.

2.2 Functional Module Division

The system is divided into four main functional layers: the data collection layer, distributed storage layer, distributed computing layer, and microservices layer [FIGURE:1]. The data collection layer is responsible for the unified access of multi-source heterogeneous data, including wireless signal, social media, user behavior, and video stream collection modules. The distributed storage layer employs a hybrid architecture, selecting appropriate storage solutions for different data types: object storage for large-scale multimedia resources like video and images; distributed database clusters for structured data; and distributed file systems for semi-structured data.

The distributed computing layer builds a unified platform integrating stream processing engines, batch processing frameworks, real-time analysis engines, and data mining modules to achieve both real-time and offline analysis. The microservices layer provides standardized interface services for applications, including core modules for content distribution, data analysis, user profiling, and intelligent recommendation. Comprehensive monitoring and early warning mechanisms are designed between layers, with performance probes and log analysis systems to visualize system status and ensure coordinated operation.

Design of Cloud Computing-Based Data Processing Architecture for R&TV Converged Media
(Pingshan County Converged Media Center, Linyi, Shandong 273300, China)

Abstract:
[Objective] Addressing the characteristics of R&TV converged media data—such as multi-source heterogeneity, high real-time requirements, and massive scale—which traditional architectures struggle to handle, this paper designs a data processing architecture based on cloud computing. [Method] By utilizing distributed storage and computing technologies, a multi-layered architecture comprising collection, storage, computing, and service layers was constructed. Business decoupling was achieved through microservices, system elasticity was enhanced using container technology, and stream computing engines were introduced for real-time data. [Results] System testing indicates that this architecture improves data processing efficiency, throughput, and response times by over 50% compared to traditional architectures, while reducing node expansion time by 80%. [Conclusion] The cloud-based architecture offers significant practical value and provides effective technical support for the deep integration of R&TV media.

Keywords: Cloud computing; R&TV converged media; Data processing; Wireless connection devices; Distributed architecture; Microservices

2.3 Wireless Data Collection and Transmission Design

The wireless data collection and transmission module serves as the data entry point for the entire system, utilizing a distributed deployment strategy to ensure comprehensive and reliable signal acquisition. Hardware nodes utilize high-performance Digital Signal Processors (DSP) and Field Programmable Gate Arrays (FPGA) for real-time sampling and preprocessing. At the transmission level, dedicated data channels are established using mutual authentication mechanisms to ensure security. To improve efficiency, lossless data compression algorithms are introduced to significantly reduce bandwidth consumption. Furthermore, a robust data caching mechanism ensures zero data loss during network fluctuations. Collection nodes feature intelligent scheduling to automatically adjust sampling parameters based on signal quality, adapting to complex environments. The module also integrates a signal quality monitoring system and deploys lightweight AI algorithms at the edge for preliminary data cleaning and classification, improving downstream processing efficiency.

2.4 Data Flow Process

Data flow follows strict processing procedures and specifications. Raw data from the collection layer undergoes preprocessing and format conversion before being transmitted to the distributed storage layer via message queues. The storage layer allocates data to appropriate systems based on type and access characteristics, establishing a unified metadata index. The distributed computing layer coordinates resources through a scheduler for real-time or batch analysis. Results are stored in a distributed cache for rapid retrieval by the microservices layer. Throughout this process, a unified data exchange format ensures consistency and traceability. A task scheduling center coordinates data flow across layers, providing visual monitoring and quality control mechanisms to guarantee accuracy and timeliness. To optimize efficiency, the system incorporates intelligent routing and data prefetching based on business access patterns.

3.1 Hardware Structure of the Wireless Connection Device

The wireless connection device adopts a modular design philosophy [FIGURE:2], consisting of an antenna system, RF front-end, signal processing unit, AD conversion unit, main controller, power management system, and network interfaces. The antenna system uses high-gain directional arrays with beamforming technology to enhance reception quality. The RF front-end integrates low-noise and power amplifiers for signal control. The signal processing unit, based on a hybrid DSP and FPGA architecture, handles preprocessing and complex algorithms. The main controller runs a real-time operating system (RTOS) to manage all modules. The power management system provides multi-channel regulated output with protective functions, while network interfaces support Gigabit Ethernet and fiber access for high-speed transmission. The hardware design utilizes multi-layer PCB layouts and shielding to minimize internal interference.

3.2 Signal Collection and Processing Module

This module handles the reception, conditioning, and processing of wireless signals. It employs wideband direct sampling with multi-stage amplification and filtering for adaptive signal conditioning. Digital down-conversion (DDC) utilizes digital intermediate frequency (IF) technology to reduce analog circuit complexity. The FPGA implements signal processing algorithms and multi-channel parallel processing, while the DSP performs spectrum analysis and demodulation using a pipelined architecture. The module is equipped with intelligent threshold detection algorithms to automatically identify signal mutations and trigger data capture, ensuring real-time performance through high-speed caching and serial interfaces.

3.3 Data Transmission and Synchronization Mechanism

A layered design strategy is used to build a reliable transmission network. The transmission layer utilizes an improved TCP/IP stack for reliable delivery and flow control. The system integrates the IEEE 1588 Precision Time Protocol (PTP) to achieve synchronization accuracy better than 100ns. Data transmission supports multiplexing for simultaneous control signaling and business data. To enhance efficiency, an adaptive frame length control algorithm dynamically adjusts frame sizes based on channel quality. Security is maintained via AES-256 encryption. The system also implements a sliding-window flow control mechanism to prevent congestion and uses bidirectional timestamping for precise latency recording. A multi-level cache structure with pre-reading mechanisms reduces access latency, while a network monitoring unit supports automatic failover to backup links.

3.4 Anti-interference Design

The anti-interference design integrates several advanced technologies to create a multi-dimensional protection system. The RF front-end uses high-linearity amplifiers to increase dynamic range and suppress strong interference. Signal processing employs adaptive filtering algorithms to inhibit narrowband and pulse interference. Digital pre-distortion (DPD) technology is integrated to monitor and compensate for non-linear distortion in real-time. Spatial filtering is achieved through adaptive beamforming, adjusting the antenna array pattern to suppress interference sources. Time-domain processing introduces wavelet transforms to detect transient interference, while frequency-domain processing uses multi-resolution spectrum analysis for precise identification. Additionally, an interference source database combined with machine learning algorithms enables intelligent identification and classification. Electromagnetic compatibility (EMC) is enhanced through multi-layer shielding and specialized grounding.

4.1 Distributed Storage Solution

The distributed storage system uses a multi-level hybrid architecture to provide a unified platform for heterogeneous data. The underlying architecture is divided into three core components: object storage, a distributed file system, and a time-series database. Object storage utilizes the Ceph system, ensuring reliability through data sharding and multi-replica mechanisms for PB-level media resources. The distributed file system is built on HDFS with a master-slave architecture, where NameNodes manage metadata and DataNodes handle storage for large-scale unstructured data. For structured business data, a distributed NewSQL database cluster is deployed using a sharding model for horizontal scaling. A distributed cache layer using Redis clusters with master-slave replication and sentinel mechanisms significantly improves hot data access efficiency. The system provides a unified access interface and global indexing, implementing a tiered storage strategy that caches hot data in memory, stores warm data on SSDs, and migrates cold data to object storage.

4.2 Real-time Computing Framework

The real-time computing framework is built on Apache Flink for stream processing and Apache Spark for unified batch-stream computing. The distributed architecture uses a JobManager for scheduling and TaskManagers for execution. Apache Kafka serves as the data buffering layer, utilizing topic partitioning for parallel processing. The framework supports windowing, state management, and event-time processing, with consistency guaranteed by a CheckPoint mechanism. Resource scheduling is managed via Kubernetes for elastic scaling and self-healing. Task scheduling is based on a DAG workflow engine to handle complex dependencies. The framework integrates machine learning libraries for intelligent analysis and provides a SQL-based query interface to lower development barriers. An optimizer automatically refines tasks through operator reordering and resource allocation strategies. As shown in [FIGURE:2], the framework establishes a complete data processing chain for real-time analysis and decision support.

4.3 Microservices Architecture Design

The microservices architecture employs Domain-Driven Design (DDD) to partition functions into core services such as content management, user analysis, and recommendation engines. The service governance layer uses the Spring Cloud framework for registration, discovery, and configuration management. The service gateway, built on Kong, provides routing, load balancing, and circuit breaking. Inter-service communication utilizes the gRPC protocol with Protocol Buffers for efficient serialization. Distributed transactions are managed using the Saga pattern to ensure data consistency, while Hystrix provides fault tolerance through circuit breaking and thread pool isolation. Configuration is managed dynamically via Apollo, and authentication is based on OAuth2.0 and JWT. Development follows a contract-first principle with Swagger for automated API documentation. DevOps practices, including CI/CD pipelines, are used to enhance delivery efficiency.

4.4 System Monitoring and O&M

The monitoring and operations (O&M) platform provides comprehensive real-time oversight. It uses a Prometheus time-series database for metrics and Grafana for visualization. Application Performance Monitoring (APM) is handled by SkyWalking for distributed tracing and bottleneck identification. Log management follows the ELK (Elasticsearch, Logstash, Kibana) architecture for centralized collection and complex querying. Resource monitoring utilizes cAdvisor for container metrics and Node Exporter for host status. The alerting system, built on AlertManager, supports multi-channel notifications. Automation is achieved through Ansible for configuration and Jenkins for continuous delivery. Security O&M incorporates honeypot technology and intrusion detection systems (IDS) for real-time threat protection. A unified portal integrates these functions to improve overall operational efficiency.

5.1 Test Environment and Scheme

The test environment consists of a distributed cluster of 30 high-performance server nodes, each equipped with Intel Xeon Gold 6248R processors, 256GB DDR4 RAM, and NVMe SSDs. The network uses 10GbE interconnects with SDN technology for intelligent traffic scheduling. The test dataset includes 300TB of video material, 5 million user behavior records, and 1 million social media records to simulate real-world R&TV scenarios. The testing strategy covers unit, integration, performance, and stress testing. A distributed pressure testing platform built on JMeter simulated 50,000 concurrent users over a 168-hour duration. Typical business scenarios, such as video transcoding and real-time stream processing, were tested using APM tools and the ELK platform. Fault injection was used to evaluate fault tolerance, while core metrics like CPU, memory, and I/O throughput were collected automatically.

5.2 Performance Metric Evaluation

Evaluation focused on throughput, response time, and resource utilization. In terms of throughput, the system achieved 1,000 concurrent 4K video transcoding streams and exceeded 1 million records per second in real-time stream processing. Data retrieval tests showed average response times for complex queries under 200ms. Under high concurrency, the average response time remained below 50ms, with 95% of requests processed within 100ms. Resource monitoring showed average CPU utilization at 65%, memory at 75%, and I/O at 60% during peak loads. Horizontal scaling tests verified linear expansion capabilities, with overall throughput increasing nearly threefold after node expansion. Load balancing tests confirmed an even distribution across nodes, with variance kept within 15%.

The cloud-based R&TV converged media data processing architecture achieves efficient handling of massive heterogeneous data through layered design and modular construction. By leveraging cloud computing, the architecture ensures high performance while providing excellent scalability and operational convenience. Practice demonstrates that this design effectively supports the development needs of converged media and serves as a valuable reference for technical innovation in the field. Future work will focus on optimizing processing algorithms, enhancing system intelligence, and strengthening data security mechanisms.

References

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