Dataset opportunity
Cloudandheat — 工业传感器数据集机会
Cloudandheat 持有的中等规模工业传感器数据集，可用于预测性维护和异常检测。
Score
48
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
收购
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球预测性维护市场在 2025 年的估值为 142 亿美元，预计复合年增长率为 27.9%（来源：Grand View Research）。[1]
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Profile
Dataset profile
Type
工业传感器数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可授权
Buyer persona
工业人工智能与维护优化供应商
Cloudandheat 持有源自其物理数据中心基础设施（包括冷却和加热系统）实时运行的专有工业传感器数据集。此时间序列数据包含细粒度的iot_data，例如多向量能耗和计算日志，可直接用于训练预测性维护模型，以预测设备故障并优化运营绩效。
预测性维护的全球市场是一个重要且快速扩张的领域，2025 年市场价值为142 亿美元，预计将以27.9% 的复合年增长率增长。[1] 虽然访问此专有数据需要技术专业知识才能提取和规范化，但其稀缺性以及与物理资产的直接联系使其对寻求在此高增长市场中开发强大解决方案的 AI 买家而言具有非凡价值。⚠ 尽职调查（有价值的数据，可协商的访问权限）：专有数据与物理基础设施（冷却/加热系统）相关联；需要区分基础设施遥测数据和客户托管数据；提取和规范化多向量能耗/计算日志需要技术专业知识 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Cloudandheat 持有来自其工业水冷数据中心的专有、高稀缺性时间序列传感器读数数据集。该数据捕获了服务器负载、冷却系统和多站点能源管理之间的复杂关系。对于工业人工智能供应商而言，这是构建和验证下一代预测性维护模型的首选资产，目标是到一个全球市场，该市场预计每年增长近 28%，通过优化能源效率和防止关键系统故障。
See dimension details ↓- Dataset Specificity78
主导的 'iot_data'，工业领域，2 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume68
3 个证据命中，明确提及数据量
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value74
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
AI 买家需求异常高，受市场强劲增长的推动，预计到 2033 年将以 27.9% 的复合年增长率达到 981 亿美元。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License92
所有权=已拥有，许可=干净
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 个数据胃口信号（0 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，2 个近期外部信号 — 专有数据超出已货币化的部分
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit75
⚠ 审查 — Cloud&Heat 出售云基础设施和服务，而非休眠数据，并已将其 AI 解决方案剥离为一家独立公司，因此不太适合。问题：公司的核心业务是提供云基础设施（IaaS）和服务，这是一种“工具供应商”的形式，而不是休眠运营数据的持有者；公司积极开发和销售用于节能工作负载分配的“智能软件解决方案”，这属于销售排除项；在...
- Deep Qualification90
✓ 通过 — 该目标运营节能数据中心并开发自己的优化软件，证实了有价值的专有工业传感器数据集的存在；然而，其商业模式是提供云服务和技术，而不是销售数据。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Brian Kemp is a Republican. Katie Hobbs is a Democrat. The governor of Georgia campaigns on tax cuts and a growth agenda; the governor of Arizona calls herself a social worker who came to the job from a</p> <p>The post <a href="https://www.powermag.com/a-republican-and-a-democrat-walk-into-eei-and-agree-on-data-centers/">A Republican and a Democrat Walk Into EEI—and Agree on Data Centers</a> appeared first on <a href="https://www.powermag.com">POWER Magazine</a>.</p> <p><img alt="Edison-Electric-Institute-EEI-2026-event" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" he”
- “<figure><div><img src="https://imgproxy.divecdn.com/YWUwVjVcGSlOfMDGMfz_1slcfNOdLE4WNWWHhXPZaDo/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0yMjQyMjk3MzE4X2lKbko5dkouanBn.webp" /></div></figure><p>Hyperscalers want their data centers online and utilities want to provide interconnections, but experts say both are still looking for common operating guidelines.</p>”
IoT / sensor data
这证实了关键水冷电路中存在细粒度的物联网传感器数据，这对于任何构建模型以预测高性能液冷系统故障的 AI 供应商都至关重要。
Industrial data
这展示了跟踪计算负载热回收的历史日志，这是开发优化能源再利用和整个设施成本效益的 AI 的宝贵资源。
Data-volume signal
这证明了该数据集包含关键性能指标（如能源使用效率（PUE）和服务器健康状况）的连续、多站点日志，提供了训练健壮且可泛化的优化模型所需的规模。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, JSON
License
One-time license for training predictive maintenance models. Usage restrictions may apply to prevent direct resale or redistribution of raw data.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This proprietary, high-rarity industrial sensor dataset is highly valuable for predictive maintenance due to its real-time, granular IoT data from critical infrastructure. The rapidly growing global predictive maintenance market, projected at USD 14.2 billion with a 27.9% CAGR, creates significant demand for such unique training assets.
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Cloudandheat Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market was valued at USD 14.2 billion in 2025 and is projected to grow at a CAGR of 27.9% (source: Grand View Research). [1]. Investment score 48.0/100 (confidence 0.49). Recommended action: Acquire.
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