Dataset opportunity
Eefsas — 维护日志数据集机会
Eefsas 持有的中等维护日志数据集，可用于预测性维护和异常检测。
Score
71.1
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
合作（集团层面）
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 = 2025 年为 142 亿美元，复合年增长率为 27.9%。
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
维护日志数据集
Modality
时间序列
Sector
其他
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可授权许可
Buyer persona
工业人工智能与维护优化供应商
Eefsas 持有一个宝贵的时间序列数据集，其中包含其风能和太阳能资产组合的详细维护日志。这些数据通过来自 SCADA 系统的上下文物联网数据和地理数据得到丰富，提供了设备性能、干预措施和运行条件的全面历史记录，非常适合开发和训练高精度预测性维护模型。
该数据具有直接应用前景的全球预测性维护市场，在 2025 年的价值为142 亿美元，预计将以惊人的27.9% 的复合年增长率增长。[1] 虽然访问需要处理母公司 Qair Group 的集中式数据治理和与运营系统的技术集成，但该资产级别的稀有和特异性数据代表了任何旨在抓住这一高增长市场价值的 AI 买家的一项重要竞争优势。⚠ 尽职调查（有价值的数据，可协商的访问权限）：Qair Group 的子公司；数据治理可能集中在母公司层面；技术访问需要与风能和太阳能资产进行 SCADA/IoT 集成；数据所有权可能与特定农场的项目投资者共享。· 公司：Qair 的子公司。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
证据共同证明 Eefsas 拥有一个稀有的、长期的数据库，该数据库结合了其可再生能源资产数十年的专有维护日志和持续的时间序列传感器数据。这种独特的组合是工业人工智能供应商开发复杂的预测性维护算法的关键输入。在一个预计到 2025 年将达到 142 亿美元的市场中，该数据集为训练和验证优化资产正常运行时间并降低运营成本的模型提供了重要机会。
See dimension details ↓- Dataset Specificity74
主导的“维护日志”，行业其他，3 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 Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 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 Value84
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
人工智能买家需求异常高，这得益于市场的快速扩张，预计复合年增长率为 27.9%，因为公司积极投资于数据驱动的解决方案以最大限度地减少运营停机时间。[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 Feasibility15
中等难度，Qair 的子公司
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=公司所有，许可=干净
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
Qair 的子公司
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 — 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 Audit100
✓ 好目标 — EEF SAS 是一个理想的目标，因为它开发、建造和维护可再生能源公园，作为其核心业务的副产品生成有价值的专有维护和运营数据，而它目前不销售这些数据。问题：2025 年 12 月的一份新闻报道称该公司被生产商 Qair 收购，这可能会影响其运营状况或数据所有权，尽管该公司；该公司是德国集团 eno energy（现为 Qair）的子公司，这可能会增加数据所有权和决策过程的复杂性。[3, 13]
- Deep Qualification80
✓ 通过 — Eefsas 是一家风能和太阳能农场开发商和运营商，最近被 Qair 收购。它很可能在其运营的副产品中持有有价值的维护和 SCADA 数据。然而，由于母公司的监督和基于项目的融资结构，数据所有权可能很复杂。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
该公司收集其高功率风力涡轮机的连续传感器数据，提供训练预测性维护模型所需的实时运行输入。
Maintenance logs
Eefsas 持有其可再生能源资产二十多年的历史维护日志，提供了用于构建准确时间序列预测模型的丰富、纵向的维修和故障记录。
Geospatial data
持有者拥有与其资产位置相关的专有地理和环境数据，可用于通过将性能与地理空间因素相关联来丰富预测模型。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
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Deliverable
Premium dataset report
Eefsas Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $14.2 billion in 2025, CAGR 27.9% (source: Grand View Research). [1]. Investment score 71.1/100 (confidence 0.49). Recommended action: Partnership (group-level).
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