AI-based data warehouse quality automatic monitoring method and system

By automatically monitoring data warehouse change events and combining AI analysis to generate SQL data packets and execute them in an isolated environment, the problem of automatically generating rules and SQL in data quality inspection systems has been solved, achieving efficient and secure data warehouse quality monitoring and adapting to automated quality control of large-scale data warehouses.

CN122240602APending Publication Date: 2026-06-19BEIJING QINGSONG YIKANG INFORMATION TECHNOLOGY CO LTD
View PDF 0 Cites 0 Cited by

Patent Information

Application Number
CN202610699794.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-20
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing data quality inspection systems cannot automatically generate personalized quality inspection rules and SQL, and lack self-verification and self-optimization capabilities, resulting in low rule generation efficiency and poor accuracy, making it difficult to meet the needs of automated quality control in large-scale data warehouse environments.

Method used

By automatically monitoring new change events in the data warehouse, acquiring multi-source data and calling AI for matching analysis, generating SQL data packages, and executing them in an isolated or sandbox environment, the system automatically generates revised SQL until acceptance criteria are met, thus achieving automated quality monitoring.

Benefits of technology

Significantly reduce manual investigation costs, improve the timeliness of change response, ensure that SQL rules are compatible with business needs, safeguard operational security, shorten optimization cycles, improve anomaly handling efficiency, and adapt to the quality control needs of large-scale data warehouses.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122240602A_ABST
    Figure CN122240602A_ABST
Patent Text Reader

Abstract

This application discloses an AI-based automatic data warehouse quality monitoring method and system. The method includes: obtaining the object identifier of a newly emerging change event in the data warehouse, determining its corresponding target table, obtaining multi-source data corresponding to that table, calling AI to match it with a rule base, determining the target rules corresponding to the matched fields, and generating an SQL data package based on a preset output structure; executing the data package line by line in an isolated or sandbox environment, collecting the execution results, and if the results do not meet the conditions, generating a data correction request form and sending it back to AI to generate a corrected SQL, replacing the original entries, and re-executing; deploying entries that pass or reach the maximum number of iterations to obtain the quality results after scheduled execution; if the quality results are abnormal, automatically identifying the abnormal information and executing corresponding strategies according to the abnormality level. This method enables fully automated execution of the entire process from new table / new task awareness to quality monitoring deployment.
Need to check novelty before this filing date? Find Prior Art