Multi-Source Heterogeneous Big Data Storage Technology for On-Board Intelligent Manufacturing with Edge-Cloud Collaboration

With the growing intelligence in aviation onboard product manufacturing, multi-source heterogeneous big data imposes greater demands on storage performance, security, and management efficiency. This paper proposes an edge-cloud collaborative framework to address these challenges in intelligent manufacturing scenarios. First, we design a data storage architecture with edge-cloud collaboration and develop an adaptive optimization strategy to meet real-time storage requirements. The architecture employs dynamic data distribution between edge nodes and cloud servers, ensuring low-latency processing for time-sensitive tasks while maintaining scalability. Secondly, based on the data storage architecture with edge-cloud collaboration, we propose a security assurance mechanism that integrates multireplica consistency verification and virtual private networks (VPNs). Thirdly, based on the data storage architecture with edge-cloud collaboration, an efficient management mechanism for multi-source heterogeneous big data is designed. With the help of data auditing and edge-cloud collaborative computing, efficient data management is achieved, improving data storage performance, system security capabilities, and data management efficiency.