A circuit board intelligent detection management system and method

By combining high-precision optical imaging with probe testing, a digital twin model of the circuit board is used to solve the problems of automation and intelligence in the circuit board testing system. This enables efficient and intelligent testing management, adapts to multi-variety, small-batch production, and supports production scheduling linkage.

CN122174784APending Publication Date: 2026-06-09MCCHONGDIWEI (HUIZHOU) ELECTRONICS CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
MCCHONGDIWEI (HUIZHOU) ELECTRONICS CO LTD
Filing Date
2026-02-02
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing circuit board testing systems rely on manual intervention, resulting in low testing efficiency, isolated data, insufficient intelligence, inability to adapt to multi-variety, small-batch production, and lack of deep integration with production execution systems.

Method used

A high-precision optical imaging system and a multi-channel probe testing unit work together to build a digital twin model of the circuit board, realize an adaptive probe testing strategy, upload data to a unified management platform, and achieve bidirectional data interaction with the production execution system.

Benefits of technology

It has achieved automation and intelligence in circuit board testing, improved testing efficiency, reduced the false negative rate, supported multi-variety, small-batch production, and enabled real-time communication and process optimization between testing data and production.

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Abstract

This invention discloses an intelligent testing management system and method for circuit boards, relating to the field of computer technology. During operation, the system simultaneously acquires the physical and electrical characteristics of the circuit board under test through optical imaging and probe testing. Based on these physical and electrical characteristics, and combined with pre-stored circuit board design files, a three-dimensional digital twin model containing geometric structure, network topology, and electrical attributes is generated, and the model undergoes consistency verification. Based on the model, probe paths are dynamically planned and test parameters are adaptively adjusted. Anomalies are intelligently identified and automatically triggered for retesting or isolation. All testing process data is uploaded to a unified data management platform, enabling bidirectional data interaction with the production execution system and achieving collaborative linkage with the production execution system. The system integrates high-precision imaging, multi-channel probes, a digital twin engine, a rule-based decision-making module, and a data management platform.
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