AIOps 是把人工智能与机器学习应用于 IT 运维的方法论与平台体系，通过对日志、指标与链路的海量运维数据做智能分析实现异常检测、根因定位、容量预测与故障自愈，核心是让运维从被动响应转向主动预防，能力包括异常检测、告警收敛、根因分析、容量预测与运维 Copilot。它解决的是告警海量、真假难辨与故障定位高度依赖个人经验的问题，并不解决系统架构本身存在缺陷的问题。
AIOps 的核心是让数据代替人做判断，它可以帮助企业实现以下目标：
让异常在用户投诉之前被发现。模型基于历史基线识别偏离，异常出现即触发处置流程。模型以历史基线为参照判断偏离，节假日与促销期的负荷变化被显式建模，误报因此不随数据量增长而失控。
让告警从数量问题变成优先级问题。相似告警被自动聚合收敛，工程师只处理真正需要判断的少数。
让根因定位从跨系统翻查变成图谱推断。服务依赖关系与变更事件被关联，异常发生时直接指向可疑环节。运维知识库与对话层把历史故障的处理过程沉淀为可检索的经验，新工程师可以直接复用而非重新摸索。
让重复性处置被自动执行。已知故障模式配置了自愈动作，常见问题无需人工介入即可恢复。
总之，AIOps 可以帮助企业实现运维从人肉值班到智能运营的转变，把工程师从重复排查中解放出来，让系统在规模扩大与复杂度上升时仍能保持高可用，是大规模系统数字化时代的关键运营能力。
翻译
AIOps is the methodology and platform system that applies artificial intelligence and machine learning to IT operations, analyzing massive operational data across logs, metrics and traces to achieve anomaly detection, root cause localization, capacity prediction and self-healing. Its core is shifting operations from reactive response to proactive prevention, with capabilities covering anomaly detection, alert convergence, root cause analysis, capacity prediction and operations copilots. It solves the problems of alert floods that are hard to triage and of fault localization depending heavily on individual experience, but it does not solve the problem of defects in the system architecture itself.
At the heart of AIOps is letting data make the judgment, which can help companies achieve the following:
Detect anomalies before users complain. Models identify deviation from historical baselines and trigger handling the moment anomalies appear.
Turn alerts from a volume problem into a priority problem. Similar alerts are automatically aggregated and converged, so engineers handle only the few that truly need judgment.
Make root cause localization graph-based rather than cross-system searching. Service dependencies and change events are correlated, pointing directly at the suspect component when anomalies occur.
Execute repetitive remediation automatically. Known failure patterns have configured self-healing actions, so common problems recover without human intervention.
In short, AIOps can help enterprises shift operations from round-the-clock manual duty to intelligent operations, free engineers from repetitive troubleshooting, keep large systems highly available as scale and complexity grow, and constitute a key operational capability for the digital era.
