Dataset opportunity
Satep — 维护日志数据集机会
Satep 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
69
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
数据共享协议
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%（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.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
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
受限
Legal
混合所有权 — GDPR 敏感（个人身份信息审查）
Buyer persona
工业人工智能与维护优化供应商
Satep 持有一个宝贵的时间序列数据集，其中包含其在暖通空调、管道和电气系统全国运营中的大量维护日志，包括物联网数据和其他工业数据。这些关于设备性能和干预措施的精细、真实世界数据为训练高精度预测性维护模型提供了坚实的基础，旨在在住宅和商业建筑系统发生故障之前预测其发生。
全球预测性维护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，预计复合年增长率 (CAGR) 为 27.9%。[1] 尽管存在数据分布在 8 个以上子公司、异构系统以及严格的客户信息 GDPR 要求等数据访问复杂性，但该数据集的独特范围及其在这一高增长市场的直接适用性使其成为寻求获得竞争优势的 AI 买家的一项稀有且战略性的资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据分布在多个区域子公司（8 家以上公司）中；包含需要严格 GDPR 合规性的住宅客户信息；技术数据可能存储在异构 ERP/维护管理系统中 · 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 Satep 持有一个专有数据集，其中包含来自大规模工业供暖、通风和空调 (CVC) 系统的维护日志。这种高稀有度的时间序列数据正是工业 AI 供应商构建和完善预测性维护算法所必需的。在一个年增长率接近 28% 的市场中，该数据集为优化资产性能和减少运营停机时间提供了关键的竞争优势。
See dimension details ↓- Dataset Specificity74
占主导地位的“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 Demand90
AI 买家需求异常高，这得益于市场爆炸式增长，预计复合年增长率为 27.9%，因为公司竞相采用数据驱动的维护策略。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
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 License28
所有权=混合，许可=GDPR 敏感
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 个数据胃口信号（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，1 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
✓ 良好目标 — Satep 是一家控股公司，收购并整合了当地暖通空调安装和维护中小型企业的网络，使得有价值的维护数据来源于底层运营公司，而非控股公司本身。[1]；实际的运营业务和数据生成（维护日志）存在于 Satep 收购的众多当地中小型企业中。[8, 9, 10]；目标是分散的；需要与 Satep 网络内的各个公司（例如 Le Thiec、Axe Énergies、Rhin Climatisation）进行互动；结构复杂，作为一个网络或集团而非单一运营实体运作，这可能会使数据交易复杂化。[2, 3]
- Deep Qualification80
✓ 通过 — Satep 是一家能源转型领域的服务公司，作为当地安装和维护公司网络的控股公司。它不将数据作为核心产品出售。“维护日志数据集”是其活动的连贯副产品，但由于其分布在 11 个以上子公司以及为超过 60,000 名住宅和专业客户提供服务而产生的 GDPR 敏感性，数据访问很复杂。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
这些证据证实了来自活跃的供暖、通风和空调 (CVC) 系统的维护日志的存在，提供了训练故障预测模型所必需的地面真实数据。
IoT / sensor data
该公司在现代热泵、太阳能解决方案和家庭自动化方面的工作表明，正在生成时间序列物联网数据，这对于将设备行为与维护事件相关联至关重要。
Industrial data
Satep 通过技术网络为超过60,000 名客户提供服务，证明了该数据集的潜在规模和多样性，为构建可推广的工业 AI 解决方案提供了坚实的基础。
press
- “<figure><div><img src="https://imgproxy.divecdn.com/Z5nV05zIHgUViXl_VeRfPj5PkcO1-rOAmzBDh6VKKNM/g:nowe:3:128/c:4168:2354/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0xMzAzMzk3NDcyLmpwZw==.webp" /></div></figure><p>The Texas Energy Fund has allocated more than $4 billion as part of the state’s effort to boost grid reliability. The award to SPS covers a drone-based pole inspection program and live monitoring.</p>”
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Satep 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 = $14.2B in 2025, CAGR 27.9% (source: Grand View Research). [1]. Investment score 69.0/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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