In the process of data migration, it is necessary to ensure the consistency and integrity of data, avoid data loss or damage, and reduce the amount of duplicate data. Therefore, a hash graph based batch lossless database information migration method is proposed. The similarity of data between database is obtained based on the similarity of database data attributes and the optimal matching algorithm for bipartite graphs, the detection and deletion of duplicate data is completed based on the similarity. The hash values of each data after deduplication is calculated using a locally sensitive hash function, and a hash graph based on the hash values of all data is established. Conditional deep convolution is utitized to generate adversarial networks for batch migration of data, and the hash values of the migrated data is compared with the corresponding values in the hash graph. The migration is re-expanding for data with different hash values to ensure lossless migration of database information. The experimental results show that this method has a fast information transfer speed, low data duplication rate, and can ensure data consistency, indicating that it has achieved the research objectives and is feasible.

Hash graph, database information, non destructive migration, locally sensitive hash function, conditional deep convolution generates adversarial networks