融合前端 AI、可视化与精准调控的检测仪器远程监控系统

Remote monitoring system for detection instruments featuring front-end AI, visualization, and precise regulation

摘要:

本研究针对传统高精度检测仪器监控系统在精度、实时性和扩展性上的技术瓶颈,设计了一款融合前端 AI 技术、可视化界面与精准调控功能的远程监控系统,从模块化硬件架构(监测、控制、前端交互、通信模块)和智能化软件功能(数据采集传输、数值识别、误差监测、AI 诊断预测、精准调控)两大维度实现系统搭建,通过工业级标准的多场景系统性测试,验证了该系统在监测误差(0.8℃以内)、长期漂移量、最远监控距离(超 8km)及AI 诊断性能(识别准确率 98.7%、诊断延迟 38ms)上均显著优于基于数字孪生和边缘控制逻辑的主流系统,解决了传统监控的误判漏检、人工干预成本高等问题,未来将进一步优化适配更多行业场景。

Abstract:

This study addresses the technical bottlenecks of traditional high-precision detection instrument monitoring systems in terms of accuracy, real-time performance, and scalability. It designs a remote monitoring system that integrates front-end AI technology, a visual interface, and precise control functions. The system is built from two major dimensions: a modular hardware architecture (monitoring, control, front-end interaction, and communication modules) and intelligent software functions (data acquisition and transmission, numerical identification, error monitoring, AI diagnosis and prediction, precise control). Through industrial-grade standard multi-scenario systematic testing, it is verified that the system significantly outperforms mainstream systems based on digital twins and edge control logic in terms of monitoring error (within 0.8℃), long-term drift, the longest monitoring distance (over 8km), and AI diagnosis performance (recognition accuracy rate of 98.7%, diagnosis delay of 38ms)**. It solves the problems of false positives and missed detections in traditional monitoring, as well as high costs of manual intervention. In the future, it will be further optimized to adapt to more industry scenarios.