Dlf-based ai trusted number inquiry device for production system

By using DLF's AI Trusted Data Query Device, the problems of low accuracy and data security risks in AI-generated SQL have been solved. It has achieved trusted generation and traceability of SQL, built a trusted traceability chain throughout the entire lifecycle, ensured that the generated SQL conforms to business logic, and provided a trusted report.

CN122284973BActive Publication Date: 2026-07-21JIANGSU ZHONGWEI TECH SOFTWARE SYST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JIANGSU ZHONGWEI TECH SOFTWARE SYST
Filing Date
2026-05-26
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Current AI-generated SQL suffers from low accuracy, lacks semantic enhancement and business constraint knowledge, resulting in inaccurate or non-compliant SQL statements, high data security risks, unauditable reports, poor interactivity, weak data source adaptability, and a lack of structured semantic models.

Method used

The DLF-based AI-trusted data query device generates OFD electronic certificates through a data configuration module, a data table parsing module, a semantic enhancement module, an interactive SQL generation module, a syntax pre-validation module, and a data execution module, ensuring the traceability and security of each link and building a trusted traceability chain.

Benefits of technology

It achieves accuracy and security in AI-generated SQL, ensuring that the generated SQL conforms to business logic, eliminating data security risks, providing a reliable report verification mechanism, improving data source adaptability and interactivity, and forming a reliable traceability chain throughout the entire lifecycle.

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Abstract

The application discloses an AI trusted data inquiry device based on a DLF production system, through a configuration management interface, data sources are selected, then the selected data table is automatically scanned, semantic expansion is carried out according to metadata, instant explanation is provided when a user inquires, corresponding SQL query statements are generated according to the understanding of the user intention and the data structure, natural language description is generated based on the generated SQL, the SQL statements are subjected to safety verification and explanation and data execution, and a result set is generated, when each module executes a function, corresponding OFD electronic vouchers are synchronously generated, input, output, metadata, snapshots and operation behavior information of the module are solidified, all OFD electronic vouchers are aggregated according to the order and logical relationship of the operation behavior chain to form a DLF trusted electronic voucher set, the application organically combines AI data inquiry and trusted electronic vouchers to generate corresponding OFD electronic vouchers.
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