AI“慧眼”破解机房运维痛点，树立资产识别新标杆

Core Insights - The article highlights the successful implementation of an intelligent asset identification system by Jining Mobile, leveraging multi-modal AI technology to address operational challenges in communication infrastructure management [1][8]. Group 1: Technological Advancements - The system utilizes YOLOv8 as its core architecture, incorporating attention mechanisms and cross-scale feature fusion strategies to enhance the precision and effectiveness of equipment detection, achieving an accuracy rate exceeding 97% for over 40 types of mainstream equipment [2]. - The innovative system combines YOLO target detection with a multi-modal visual model, enabling the accurate extraction of nameplate information from auxiliary equipment, achieving an overall recognition accuracy of over 98% [4]. Group 2: Operational Efficiency - The implementation of the intelligent identification system has significantly reduced the average inventory time in a single communication room from several hours to under 15 minutes, resulting in a more than tenfold increase in efficiency [8]. - The automation of asset identification has not only released human resources but also directly lowered operational costs, contributing to a greener and more efficient operational model [8]. Group 3: Future Directions - Jining Mobile aims to further integrate digital technologies with operational scenarios, expanding the system's applications in energy management, fault prediction, and digital twin technologies, thereby transitioning from reactive to proactive operational strategies [8].