A method for automatic synchronization of multiple databases on an AI platform

Abstract

This invention provides a method for automatic synchronization of multiple databases under an AI platform, belonging to the field of automatic database synchronization technology. This invention initializes the multi-database synchronization topology and deploys a change data capture module to monitor data changes in real time. The captured change records are converted into unified format synchronization messages and input into an intelligent scheduling optimization model for dependency analysis and transaction reordering based on dynamic programming. Data conversion rules are dynamically generated based on mode version differences, and Merkle tree hierarchical structure is used to verify data consistency. After conflict detection and circular dependency handling are performed at the target node, multi-level adaptive compression transmission and data writing are completed. Finally, periodic full reconciliation tasks ensure eventual consistency among the multiple databases, solving the technical problem of low transaction scheduling efficiency leading to synchronization performance bottlenecks during automatic multi-database synchronization.

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