PCBA electrical performance detection method, apparatus and device, and storage medium

By performing feature extraction and graph neural network processing on the multi-channel timing electrical signals of PCBA circuit board, abnormal score values are generated, which solves the problem that the overall structure perception and local abnormality recognition in the prior art is not possible, and efficient electrical performance detection is achieved.

CN120370136AInactive Publication Date: 2025-07-25SHENZHEN QIANHENG ELECTRONICS CO LTD
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Patent Information

Application Number
CN202510517083.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-23
Publication Date
2025-07-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The prior art is difficult to take into account the overall structure perception and local abnormality recognition of complex PCBA circuit boards at the same time, resulting in misjudgment and missed detection.

Method used

By collecting multi-channel timing electrical signal data, signal sequence tensors are generated, multi-scale feature extraction and graph neural network processing are performed, combined with circuit diagram structure, abnormal score vectors are generated, and signal characterization model is constructed for electrical performance scoring.

Benefits of technology

The global electrical behavior detection of complex circuit boards is realized, the missed detection problem caused by topological dependence and dynamic interaction between time is solved, and the accuracy of detection is improved.

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Abstract

The invention provides a PCBA electrical performance detection method, device and equipment and a storage medium, and the method comprises the steps: collecting multi-channel time sequence electrical signals of a plurality of test nodes on a PCBA, and carrying out the synchronous processing of the signals, and generating a signal sequence tensor; performing multi-scale feature extraction on the tensor, and constructing a channel-level embedded vector group; mapping the embedded vectors to corresponding nodes of a PCBA circuit diagram structure to generate a circuit diagram node set; performing adjacent node feature deviation analysis based on node state representation extracted by a graph neural network in combination with an original signal to generate an abnormal score value vector; meanwhile, a label-free representation model is constructed; and finally, based on the score value and the representation model, performing electrical performance scoring and state judgment on the newly acquired signal. The method can be used for global electrical performance detection of a complex circuit board, solves the problem of missing detection caused by inconsideration of overall structure perception and local anomaly recognition, and improves the accuracy of electrical performance detection.
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