Dataset opportunity
Luvside — 工业传感器数据集机会
Luvside 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
72.4
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
42%
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
2025年全球风力涡轮机预测性维护人工智能市场规模为28亿美元，复合年增长率为14.6%（来源：Wind Turbine Predictive Maintenance AI Market Research Report 2034）。[7]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-16
Comment Poweend veut valoriser ses petites éoliennes en autoconsommation
greenunivers.com ↗ - 📰press2026-06-16
Engie crée sa task force pour les centres de données
greenunivers.com ↗ - 📰press2026-06-16
Nerius Invest se mue en facilitateur de la décarbonation des PME
greenunivers.com ↗ - 📰press2026-06-16
Energy Dome, Salt River Project to build 19-MW CO2 battery system
utilitydive.com ↗ - 📰press2026-06-16
A New Coal Plant in the U.S.? Once Unthinkable, Now a Strong Maybe
powermag.com ↗
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.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
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
Luvside 拥有一个宝贵的时间序列数据集，该数据集由其物理风力涡轮机硬件上的工业传感器生成。这些专有的工业数据通过其“智能控制”监控系统收集，创建了一个集中且独特的数据流，即物联网数据。该数据集的结构捕获了连续的运行指标，如振动、温度和扭矩，非常适合用于预测性维护用例的 AI 模型训练，从而能够在组件发生故障之前进行预测。
其商业价值巨大，因为风能预测性维护领域的 AI 市场在 2025 年的估值为 28 亿美元，预计到 2034 年将增长到 104 亿美元，复合年增长率（CAGR）为 14.6%。[7] 这种高需求凸显了 Luvside 数据的价值。尽管由于其专有性质和硬件来源，访问需要进行谈判，但其稀缺性和直接适用性使其成为寻求在快速扩张的市场中开发先进 AI 解决方案的买家的引人注目的资产。⚠ 尽职调查（有价值的数据，可协商访问）：数据由物理风力涡轮机硬件生成；专有监控系统（智能控制）表明数据集中收集；工业物联网数据通常缺乏 GDPR 限制 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证实 Luvside 拥有其运行中的风力涡轮机的专有、实时传感器数据，包括转速和独特的功率曲线信息等关键参数。这正是工业 AI 供应商构建和优化预测性维护算法所需的燃料。获取这个稀有数据集为在快速扩张的 28 亿美元风力涡轮机 AI 市场中竞争提供了直接途径，该市场预计每年增长超过 14%。
See dimension details ↓- Dataset Specificity78
主导的“物联网数据”，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 Volume46
2 个证据命中
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 Demand92
全球预测性维护市场预计将从 2026 年的 175 亿美元增长到 2033 年的 981 亿美元，复合年增长率为 27.9%，这直接推动了对传感器数据以构建 AI 模型的需求。
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 Feasibility44
低难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength50
2 种证据类型，2 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 Orientation56
2 个数据需求信号（2 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，5 个近期外部信号 — 已货币化的专有数据之外
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 Audit100
✓ 良好目标 — Luvside 是一个理想的目标，因为它制造和销售小型风力涡轮机，这是一个运营业务，作为副产品生成有价值的专有传感器数据，关于性能和环境条件，没有证据表明将其作为核心产品出售。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
这些证据证实了关键涡轮机运行参数的实时时间序列数据的收集，这是任何预测性维护 AI 的基础输入。
Industrial data
这证实了在湍流风况下生成的特定功率曲线数据的存在，这是构建更健壮、更高效的性能优化模型的稀有且有价值的信号。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series
License
One-time license for use in AI model training and development for predictive maintenance within the wind energy sector.
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, real-time industrial sensor dataset from wind turbines is highly valuable due to its rarity and direct application in the rapidly growing wind energy predictive maintenance AI market, estimated at $2.8 billion in 2025. The unique IoT data stream enables critical AI model training for component failure anticipation.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Luvside Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Wind Turbine Predictive Maintenance AI market = $2.8 billion in 2025, CAGR 14.6% (source: Wind Turbine Predictive Maintenance AI Market Research Report 2034). [7]. Investment score 72.4/100 (confidence 0.42). Recommended action: Acquire.
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