Power operation and maintenance personnel identity authentication method based on biometric recognition

By using a biometric identification technology that combines infrared imaging and bone conduction vibration sensors in the live-line maintenance scenario of ultra-high voltage substations, and adjusting the modal fusion ratio in real time, the problem of authentication failure caused by anti-arc mask and electromagnetic pulse interference was solved, and highly reliable identity authentication was achieved.

CN122416539APending Publication Date: 2026-07-17LUOHE POWER SUPPLY OF HENAN ELECTRIC POWER CORP
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
LUOHE POWER SUPPLY OF HENAN ELECTRIC POWER CORP
Filing Date
2026-04-17
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional biometric identification technologies fail in live-line maintenance scenarios at ultra-high voltage substations due to arc-proof masks and strong electromagnetic pulse interference. Existing multimodal fusion solutions fail to effectively utilize cross-modal physiological collaborative constraints, resulting in insufficient authentication robustness.

Method used

The study uses infrared imaging that can penetrate the mask coating and bone conduction vibration sensors to simultaneously collect periorbital vein texture and skull voiceprints. A dynamic weight matrix is ​​generated through a pre-trained environment assessment network to adjust the modality fusion ratio in real time, and a twin network is used for identity matching.

Benefits of technology

Achieving seamless and robust continuous identity verification in extreme environments avoids authentication failures caused by damage to a single modality, thus improving the reliability of identity authentication for power operation and maintenance personnel.

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Abstract

本申请涉及生物特征识别与电力安全管控技术领域,具体公开了一种基于生物特征识别的电力运维人员身份认证方法,其采用红外波段成像与骨传导振动传感相结合的双通道采集架构,无需脱卸防护装备即可获取眼周静脉纹理与头骨振动声纹。通过实时感知现场电磁脉冲强度与面罩光学畸变程度,构建环境参量驱动的自适应评估模型动态生成各模态置信度权重,避免单一模态受损导致认证失效。融合阶段利用动态权重对双模态特征进行自适应加权与跨维拼接降维,形成统一的多模态融合特征表达,最终通过孪生网络与预注册模板进行高维度量匹配并输出身份判决,从而突破面罩光学屏蔽与强电磁干扰的双重约束,实现极端电力作业场景下无感且高鲁棒性的连续身份核验。
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