CNAS laboratory risk identification method and system based on intelligent data analysis

The risk identification system, which utilizes intelligent data analysis, has solved the problems of scattered storage of multi-source data and reliance on human experience in CNAS laboratories. It has achieved precise matching of risk points with accreditation clauses and automated generation of internal audit plans, thereby improving risk management and ensuring the stability of CNAS accreditation.

CN122334952APending Publication Date: 2026-07-03STATE GRID ELECTRONIC COMMERCE TECH CO LTD +1
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Patent Information

Application Number
CN202610374580.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-25
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

The CNAS laboratory suffers from several problems, including scattered storage of heterogeneous data from multiple sources, reliance on human experience for risk identification, a lack of precise matching logic between risk points and accreditation clauses, low efficiency of manually developing internal audit plans, and a lack of follow-up on rectification and verification of results. These issues result in a low level of risk management and make it difficult to meet the continuous requirements of CNAS accreditation.

Method used

By constructing a risk identification system based on intelligent data analysis, we can achieve multi-source data collection and preprocessing, extract key features, use data analysis algorithms to identify potential risk points, combine them with the knowledge graph of approved terms for accurate matching, generate internal audit plans, and optimize the risk identification logic through rectification tracking and effect verification to form a closed-loop management.

Benefits of technology

It has improved the accuracy and proactivity of risk identification, enabled the automated generation of internal audit plans, enhanced the laboratory's risk control level and the operational efficiency of the quality system, and ensured the stability of CNAS accreditation.

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Abstract

The application provides a CNAS laboratory risk identification method and system based on intelligent data analysis, comprising collecting and preprocessing laboratory multi-source heterogeneous original data to generate standardized data; extracting risk key features to construct vectors, calling corresponding algorithms to identify potential risk points; matching risk feature vectors with recognized clause knowledge graphs to determine associated clauses and inspection item suggestions; generating internal audit plans containing annual internal audit plans and special inspection tables after classifying risk points; performing internal audits and tracking rectification non-conformities to verify rectification effects; feeding back verification results to optimize risk identification rules, and storing internal audit whole-process data as knowledge units to optimize clause matching logic. The method realizes multi-source data fusion, improves the accuracy and initiative of risk identification, realizes automatic generation of internal audit plans, and builds a self-learning closed-loop evolution system, which deeply integrates risk management and internal audit, and effectively guarantees the stability of CNAS laboratory accreditation qualifications.
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