Cross-region synchronization method for distributed databases in intelligent connected vehicle cloud control platform

To address the high-throughput and low-latency challenges in large-scale cross-region data synchronization for Intelligent Connected Vehicle (ICV) monitoring platforms, this study proposes an innovative architecture integrating distributed databases and message-oriented middleware. The multi-layer modular design incorporates ICV terminals, monitoring platforms, distributed databases, and synchronization components, employing a priority-based incremental synchronization strategy with distributed message queues to achieve efficient transmission of multimodal data including vehicle status and sensor information. The system implements dynamic load balancing, data classification/sorting, and asynchronous transmission mechanisms, demonstrating significant performance improvements: reducing synchronization latency to 89.4ms (a 69.3% enhancement) for 50,000 vehicle data records in cross-region tests, achieving 58.9ms P99 latency in collision warning scenarios, attaining 54,321 ops/sec throughput for OTA updates, and reducing resource consumption by over 40%. These results validate the proposed solution as a reliable cross-region data synchronization framework for ICV monitoring platforms.