An Automated Database Business Data Extraction Method Based on AI Agent and Knowledge Graph

Abstract

This invention provides an automated database business data extraction method based on AI Agent and knowledge graph, relating to the fields of artificial intelligence and data processing technology. The method includes: first, extracting metadata from a relational database and combining it with business concepts to construct a table structure knowledge graph; second, using an AI Agent to parse user natural language requirements, extracting key business concepts, and matching candidate tables, fields, and related paths by querying the knowledge graph; third, the AI ​​Agent automatically generating SQL query statements based on the matching results and in conjunction with data security rules; and finally, executing the SQL by calling the database interface through the Model Context Protocol (MCP), obtaining the result set, and exporting it to a file. This invention integrates knowledge graph semantics with AI Agent intent understanding to achieve fully automated extraction, solving problems of low efficiency, error susceptibility, and poor security compliance, thereby improving intelligence and security.

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