2 results about "Hash list" patented technology

This invention discloses a method for pairing quantum keys and quantum ciphertext data packets. A first terminal assembles identification information into the front end of each quantum ciphertext data packet, and the front end of the quantum key corresponding to all quantum ciphertext data packets is assembled with the same identification information. A second terminal establishes a hash list, storing the assembled quantum ciphertext data packets and quantum keys in the hash list using the identification information, and then performs pairing and decryption. This invention improves the efficiency of pairing and decryption by adding an entry code (RID) and a message sequence number (MsgID) before transmitting the quantum ciphertext data packets and quantum keys, and then classifying and storing them in the hash list according to the message sequence number (MsgID). By finding quantum ciphertext data packets and quantum keys with the same entry code (RID) and message sequence number (MsgID) at the first terminal, pairing and quantum decryption are performed, saving a significant amount of reading time.

This invention relates to the field of artificial intelligence technology and can be applied to business system platforms in fields such as healthcare and fintech. It discloses a data anomaly identification method, apparatus, computer equipment, and storage medium. The method involves acquiring data to be identified input from a client, calculating the hash value of each data unit in the data, and generating a hash list of the data based on these hash values. A Merkle tree of the data is constructed based on the hash list, and the Merkle root is calculated from the Merkle tree and written to a blockchain. A first anomaly identification result is generated based on an on-chain anomaly identification strategy. A second anomaly identification result is generated based on a pre-trained data anomaly identification model. The first and second anomaly identification results are then fused to generate a comprehensive anomaly identification result, which is returned to the client. This enables efficient and accurate data anomaly identification.