STEM与日常科技·英语30篇（1）
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Edge Computing and the Need for Low Latency
边缘计算与低延迟场景
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When self-driving cars detect a pedestrian, they must react in under 100 milliseconds — too slow if data travels to a distant cloud server.自动驾驶汽车检测到行人时，必须在100毫秒内做出反应——若数据需传至遥远的云服务器，则响应过慢。
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Edge computing places processing power close to where data is generated, such as inside traffic cameras or factory machines.边缘计算将算力部署在数据生成地附近，例如交通摄像头或工厂设备内部。
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This reduces delay, or latency, by cutting transmission distance and avoiding network congestion during peak hours.此举通过缩短传输距离、避开高峰期网络拥堵，显著降低延迟。
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In smart factories, edge devices instantly adjust robotic arms when sensors spot misaligned parts on a conveyor belt.在智能工厂中，边缘设备一旦通过传感器发现传送带上零件错位，便立即调整机械臂。
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Video analytics for crowd monitoring also relies on edge AI to flag anomalies without sending raw footage online.人群监控的视频分析同样依赖边缘AI，可直接识别异常，无需上传原始视频。
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Unlike traditional cloud models, edge systems prioritize speed and local decision-making over centralized storage.与传统云模型不同，边缘系统更注重速度和本地决策，而非集中式存储。
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They often work alongside the cloud: edges handle urgent actions, while the cloud analyzes long-term trends.边缘系统常与云协同工作：边缘处理紧急任务，云端分析长期趋势。
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5G networks further boost edge computing by providing ultra-fast, reliable connections between devices and local servers.5G网络通过为设备与本地服务器之间提供超高速、高可靠的连接，进一步增强边缘计算能力。