摘要
针对传统电梯检验检测模式依赖人工经验、操作效率低、现场沟通不畅等问题,本文设计并验证了一种基于人工智能的电梯检验检测辅助助手。该系统以ESP32-S3为主控芯片,集成高精度麦克风、扬声器等硬件,结合语音活动检测、自动语音识别、大语言模型及文本转语音技术,实现自然语音交互功能。测试表明:在嘈杂环境下语音识别准确率达97%,合成语音自然度MOS值达4.5,系统响应实时性高(<2 s),续航达10 h。该设备显著提升了检验效率与决策科学性,解决了现场单手操作安全隐患及专业沟通障碍。未来将深化大语言模型模块、扩展多模态检测与离线部署能力,进一步推动电梯检验智能化发展。
To address issues in traditional elevator inspection and testing methods,such as reliance on manual experience,low operational efficiency,and poor on-site communication,this paper designs and validates an AI-powered elevator inspection and testing assistant.The system utilizes the ESP32-S3 as the main control chip,integrating hardware components including high-precision microphones and speakers.Combined with voice activity detection,automatic speech recognition,large language model,and text-to-speech technologies,it enables natural voice interaction.Tests demonstrate that in noisy environments,the speech recognition accuracy reaches 97%,the speech synthesis naturalness achieves a MOS(Mean Opinion Score)value of 4.5,the system exhibits high response real-time performance(<2 s),and the battery life lasts up to 10 h.This device significantly enhances inspection efficiency and decision-making scientificity,resolving safety hazards associated with single-handed operation and communication barriers for specialized knowledge on-site.Future work will focus on deepening the large language model module,expanding multimodal detection capabilities,and enabling offline deployment,further advancing the intelligent development of elevator inspection.
作者
吴洋
贾新
田界
褚士超
许露
WU Yang;JIA Xin;TIAN Jie;CHU Shichao;XU Lu(Zhong An Testing Group(Hubei)Co.,Ltd.,Wuhan,430000;Wuhan Mingchen Welding Non-destructive Testing Co.,Ltd.,Wuhan,430000;Hubei Special Equipment Inspection and Testing Institute Ezhou Institutional Division,Ezhou,436000;Zhongyi Wu Jian(Hubei)Inspection,Testing and Certification Co.,Ltd.,Wuhan,430000)
出处
《中国特种设备安全》
2025年第S1期45-50,共6页
China Special Equipment Safety
关键词
人工智能辅助助手
电梯检验检测
语音交互
嵌入式系统
大语言模型
AI-assisted assistant
Elevator inspection and testing
Voice interaction
Embedded system
Large language model(LLM)
