Abstract: The present disclosure discloses a medical ETL task dispatching method, system and apparatus based on multiple centers. The method includes following steps: step S1: testing and verifying ETL tasks; step S2: deploying the ETL tasks to a hospital center, and dispatching the ETL tasks to a plurality of executors for execution; step S3: screening an executor set meeting resource demands of ETL tasks to be dispatched; step S4: calculating a current task load of each executor in the executor set; step S5: selecting the executor with a minimum current task load to execute the ETL tasks; and step S6: selecting, by the dispatching machine, the ETL tasks from executor active queues according to a priority for execution. The present disclosure selects the most suitable executor by analyzing a serving index as a task to be dispatched on a current dispatching machine.

Abstract: Discloses a method and an apparatus for visual construction of a knowledge graph system. In the present disclosure, data permission of a distributed client is determined through a central server. The central server obtains a master template of a knowledge graph system and sends it to the distributed client. The distributed client receives a natural language inputted by a user and parses to generate an abstract syntax tree. The user completes customization of a subtemplate of the knowledge graph system through visual operation. The distributed client encrypts the subtemplate and then sends it to the central server. When the knowledge graph system is to be used, any knowledge concept is inputted, the central server calls and decrypts the subtemplate and then searches a database, and a tree structure knowledge graph is generated and sent to the distributed client.

Abstract: Discloses a method and an apparatus for visual construction of a knowledge graph system. In the present disclosure, data permission of a distributed client is determined through a central server. The central server obtains a master template of a knowledge graph system and sends it to the distributed client. The distributed client receives a natural language inputted by a user and parses to generate an abstract syntax tree. The user completes customization of a subtemplate of the knowledge graph system through visual operation. The distributed client encrypts the subtemplate and then sends it to the central server. When the knowledge graph system is to be used, any knowledge concept is inputted, the central server calls and decrypts the subtemplate and then searches a database, and a tree structure knowledge graph is generated and sent to the distributed client.