「英中对照」Willow面向数据中心的运营AI

Data centers span a wide spectrum, from small lab environments to hyperscale campuses. Somewhere in between lie colocation facilities hosting multiple customers and enterprise data centers supporting business applications. Despite their differences in scale, they all share a common challenge: managing redundancy, capacity, and infrastructure dependencies across a fragmented landscape of systems and data. 数据中心涵盖范围广泛，从小型实验室环境到超大规模园区。介于两者之间的，是托管多个客户的主机托管设施，以及支持业务应用的企业数据中心。尽管规模各异，它们都面临共同的挑战：在碎片化的系统和数据格局中管理冗余、容量和基础设施依赖关系。

This is where Operational AI transforms the equation. Willow’s rich ontology supports data center assets and relationships in the Knowledge Graph. Skills and Insights enable real-time monitoring and control. Building on this foundational data layer, facility teams can perform capacity management to optimize resource usage across power, cooling, and space.
这正是运营型 AI 改变局面的地方。Willow 丰富的本体为知识图谱中的数据中心资产和关系提供支持。Skills 与 Insights 可实现实时监控和控制。基于这一基础数据层，设施团队可开展容量管理，优化电力、制冷和空间的资源使用。

Why Ontology Matters本体为何重要

Data centers are highly interconnected systems. IT racks depend on upstream power infrastructure. Every kilowatt consumed generates heat that must be dissipated by cooling systems. Failures in one part of the system ripple across others. 数据中心是高度互联的系统。IT 机柜依赖上游电力基础设施。每消耗一千瓦电力，就会产生必须由制冷系统排走的热量。系统某一部分的故障会波及其他部分。 

By augmenting Willow’s ontology to include constructs like Data Halls to model spaces where IT Racks are housed, Busways for the electrical distribution, Automatic Transfer Switches (ATS) that automatically switch load between utility power and battery for Uninterruptible Power Supplies (UPS) and more, data centers can be represented in the Knowledge Graph. 通过扩展 Willow 的本体，加入 Data Halls（用于对容纳 IT 机柜的空间建模）、Busways（用于配电）、Automatic Transfer Switches (ATS)（在市电与电池之间自动切换负载，服务于 Uninterruptible Power Supplies (UPS)）等构造，数据中心便可在知识图谱中表示出来。 

When a problem occurs, it becomes very easy to instantly know the physical location of a Rack. If a PDU has a voltage spike, it is clear which UPS to check. When a breaker trips, it is clear which IT Racks are impacted. This representation enables teams to navigate from a high-level view down to granular assets, while preserving context along the way. 出现问题时，可立即轻松获知某个机柜的物理位置。如果 PDU 出现电压尖峰，就能明确应检查哪个 UPS。断路器跳闸时，可清楚知道哪些 IT 机柜受到影响。这种表示方式使团队能够从高层视图下钻到细粒度资产，同时在整个过程中保留上下文。 

Modeling twins with relationships in Willow’s ontology helps define redundancy in power systems- be it N+1 or 2N. Utility power feeds into UPS systems that connect to ATS units routing power to PDUs, which supply IT racks. 在 Willow 的本体中用关系为孪生体建模，有助于定义电力系统中的冗余——无论是 N+1 还是 2N。 市电接入 UPS 系统，UPS 系统连接 ATS 装置，由 ATS 将电力输送到为 IT 机柜供电的 PDU。

These relationships allow Willow to represent redundancy configurations such as N+1 with one extra component for failover, and alternatively 2N fully redundant systems, where two completely independent UPS systems are each capable of supporting 100% of the critical load. As an example, if the load requires 4 UPS modules, the 2N design deploys 8 modules, 4 in Path A and 4 in Path B, with either path able to carry the entire load independently. If one path fails, the other maintains uptime. Willow captures this redundancy explicitly, allowing teams to understand risk exposure and failover paths.这些关系使 Willow 能够表示各类冗余配置，例如带一个额外组件用于故障切换的 N+1，或者 2N 全冗余系统：两套完全独立的 UPS 系统各自都能承载 100% 的关键负载。 例如，如果负载需要 4 个 UPS 模块，2N 设计会部署 8 个模块，Path A 和 Path B 各 4 个，且任一路径都能独立承载全部负载。如果一条路径发生故障，另一条路径可维持正常运行。Willow 显式捕获这种冗余，使团队能够了解风险敞口和故障切换路径。

With Willow’s ontology in place, organizations can leverage a consistent data model across all systems.有了 Willow 本体，各组织便可在所有系统中利用一致的数据模型。

Unlocking Real-Time Monitoring and Operational Insights释放实时监控与运营洞察的价值

While ontology provides foundational structure, additional value is realized when it is combined with live telemetry being monitored 24×7 with Skills to generate Insights. Willow can integrate signals from building management systems (BMS), IoT sensors, and Data Center Infrastructure Management (DCIM) platforms into a unified data layer, transforming the digital twin into a real-time operational model.  本体提供了基础结构，但将其与借助 Skills 进行 24×7 监控以生成 Insights 的实时遥测数据相结合时，会实现更多价值。Willow 可将楼宇管理系统（BMS）、IoT 传感器和数据中心基础设施管理（DCIM）平台的信号集成到统一数据层，将数字孪生转化为实时运营模型。  

Live telemetry from these systems is normalized and processed with Skills and Insights, enabling visibility across critical operational domains. Cooling can be monitored continuously through CRAC performance, airflow patterns, and temperature gradients between hot and cold aisles. Subtle shifts, such as a widening temperature differential, can signal airflow imbalance or containment issues before they escalate. At a more granular level, equipment telemetry such as fan amperage or discharge air temperature provides early indicators of wear, inefficiency, or misconfiguration. Anomalies can be traced across upstream and downstream dependencies, revealing how localized issues propagate through the system. 来自这些系统的实时遥测数据通过 Skills 和 Insights 进行标准化和处理，实现跨关键运营领域的可见性。可通过 CRAC 性能、气流模式以及冷热通道间的温度梯度持续监控制冷。温差扩大等细微变化可能在气流失衡或遏制问题恶化前发出预警。在更精细的层面上，风机电流或送风温度等设备遥测数据可提供磨损、效率低下或配置错误的早期指标。可跨上下游依赖项追踪异常，揭示局部问题如何在系统中扩散。 

Power monitoring allows teams to track real-time load at the rack PDU level, power distribution across circuits, and variations that might signal imbalance or overload risk. Willow can ingest electrical data from meters, panelboards and switchboards. Per-phase current data can expose imbalances that average values might obscure, while harmonic distortion and reactive power can point to inefficiencies or stress on electrical infrastructure.电力监控使团队能够跟踪机柜 PDU 级别的实时负载、各电路的配电情况，以及可能预示失衡或过载风险的变化。Willow 可采集来自电表、配电盘和开关柜的电气数据。各相电流数据可暴露平均值可能掩盖的失衡，而谐波畸变和无功功率可指明电气基础设施的效率低下或压力。

Here we see the Excessive Switchboard Power Consumption Skill running on Service Switchboard A to continuously monitor power draw, generating an Insight when it exceeds adaptation baselines for normal operations. This is particularly useful for detection and diagnosis of transient events. 在这里我们看到  开关柜功耗过高 Skill 正在 Service Switchboard A 上运行，以持续监控功耗，并在功耗超出正常运行的适配基线时生成 Insight。这对于检测和诊断瞬态事件特别有用。 

When a spike is observed at a PDU, Willow can correlate it with upstream context, such as a recent ATS transfer or a fluctuation at the UPS, while simultaneously identifying downstream systems that may be affected. What would traditionally require manual investigation across multiple tools becomes a connected, explainable insight.  当在 PDU 处观测到峰值时，Willow 可将其与上游上下文关联，例如最近的 ATS 切换或 UPS 处的波动，同时识别可能受影响的下游系统。传统上需要跨多种工具进行人工调查的工作，将转变为一个互联且可解释的洞察。 

Actioning Insights ultimately enables a shift from reactive monitoring to proactive operations. Teams can detect early deviations from expected behavior, identify inefficiencies such as uneven cooling or unbalanced electrical loads, and perform root cause analysis by traversing the relationships modeled in the system. A temperature anomaly in a rack can be traced to a specific CRAC unit, just as a power irregularity can be followed upstream through the PDU, busway, and UPS to its source. 对 Insights 采取行动最终可实现从被动监控到主动运营的转变。团队可以检测到偏离预期行为的早期偏差，识别制冷不均或电力负载失衡等低效问题，并通过遍历系统中建模的关系执行根因分析。机柜中的温度异常可追溯至特定 CRAC 机组，正如电力异常可沿 PDU、母线槽和 UPS 向上游追踪至源头。 

The result is real-time situational awareness, as organizations gain a unified view of operations, enriched with context. This reduces time to resolution and improves operational efficiency.其结果是实现实时态势感知，因为组织可获得 enriched with context 的统一运营视图。这缩短了解决时间并提高了运营效率。

The Capacity Management Challenge容量管理挑战

Capacity management in data centers is multi-dimensional, requiring teams to balance power, cooling, and physical space. For example, adding new racks increases power demand, which in turn generates higher heat loads. This additional thermal output must be absorbed by cooling systems, which may themselves be constrained by airflow design or equipment limits. Without coordination, incremental changes in one area can create bottlenecks in another. Inefficiencies such as stranded capacity can occur, where power is available but cooling is not, or overprovisioning, which can lead to wasted resources. 数据中心的容量 管理 是多维度的，需要 团队在电力、制冷和物理空间之间取得平衡。 例如，增加新机架会提高电力需求，进而产生更高的热负荷。这 部分额外 热输出必须由制冷系统吸收，而制冷系统本身又可能受到气流设计或设备限制的制约。若缺乏协调，一个领域的增量变化可能会在另一个领域造成瓶颈。 搁浅容量等低效问题 可能出现， 即有 可用 电力但无制冷， 或 过度配置， 这 可能 导致资源浪费。 

Willow addresses these challenges by bringing all capacity dimensions into a unified analytical framework. This enables a holistic understanding of how capacity is allocated, consumed, and constrained across the environment.Willow 通过将所有容量维度纳入统一的分析框架来应对这些挑战。这有助于全面了解整个环境中容量如何被 分配、消耗和制约。

Teams can analyze peak and average power loads, assess utilization across UPS systems and PDUs, identify remaining headroom, and determine where additional workloads can be deployed. This level of visibility helps maximize existing infrastructure while maintaining resilience and operational safety. Cooling capacity can be evaluated alongside power by looking at peak and average loads on CRAC units. Organizations can assess how changes in workload distribution impact thermal stability. Physical space is also incorporated into the model, allowing teams to optimize density and determine how space is being used in relation to both power and cooling availability. 团队可以分析峰值和平均电力负载，评估各 UPS 系统和 PDU 的利用率，识别剩余裕量，并确定可在何处部署额外工作负载。这种级别的可见性有助于在保持弹性和运营安全性的同时，最大限度地利用现有基础设施。通过查看 CRAC 机组的峰值和平均负载，可结合电力评估制冷容量。组织可以评估工作负载分布变化对热稳定性的影响。物理空间也被纳入该模型，使团队能够优化密度，并根据电力和制冷的可用情况确定空间的使用方式。 

Willow Copilot introduces a natural language mechanism to interact with this data. Teams can prompt for racks with the highest electrical capacity, available headroom over current HVAC load, or rPDUs running over capacity, and more. Here’s an example:Willow Copilot 引入了一种自然语言机制来与此数据交互。团队可以通过提示查询电力容量最高的机架、相对于当前 HVAC 负载的可用裕量，或超容量运行的 rPDU 等内容。以下是一个示例：

By unifying data across power, cooling and space, Willow simplifies capacity management. Organizations can maximize infrastructure utilization, avoid unnecessary capital expenditures, and reduce the risk of outages caused by capacity constraints.通过统一 电力、制冷和空间的 数据，Willow 简化了容量管理。 组织可以最大限度提高基础设施 利用率，避免不必要的资本支出，并降低容量约束引发停机的风险。

Conclusion结论

Willow delivers operational efficiency for data centers with a rich ontology, real-time telemetry monitoring with Skills and Insights, and capacity management, all in a unified platform. Teams benefit from structure, visibility and AI-driven analytics across power, cooling, and space. This integrated approach enables organizations to evolve from fragmented tools and reactive workflows toward a more predictive and optimized model of operations. With Willow Copilot, these capabilities become even more accessible, allowing teams to explore complex infrastructure data through natural language and quickly uncover actionable insights. Operational AI paves the path towards improving efficiency, resilience, and decision-making across the board.Willow 为数据中心提升运营效率 ，凭借丰富的 本体、实时遥测 监控，并具备 Skills 和 Insights，以及容量 管理，全部集成在 一个 统一平台中。团队 受益 于结构化、 可视化 以及 AI 驱动的分析 ，覆盖电力、制冷和 空间。这种集成方法使 组织 能够 从 分散的工具和被动式工作流，转向更具预测性且更优化的运营模式。借助 Willow Copilot，这些能力变得更易使用，让 团队 能够通过自然语言探索复杂基础设施数据，并快速发掘可执行洞察。 运营 AI 铺平了通往 全面提升效率、韧性和决策水平的道路。

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