Dataset opportunity
Ssturbine — 维护日志数据集机会
Ssturbine 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
76
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
51%
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）。[3]
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
工业
Volume
中等
Freshness
定期
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
Ssturbine 持有一份源自其工业运营的时间序列 维护日志数据集，其中包括详细的 `inspection_records`（检查记录）和 `maintenance_logs`（维护日志）。这种设备性能和干预措施的按时间顺序排列的历史记录，提供了开发和训练高保真预测性维护模型所需的精细、真实的运营数据，这些模型旨在预测设备故障。
该数据的价值在全球预测性维护市场的背景下得以凸显，该市场在 2025 年的估值为142 亿美元，预计将以27.9% 的复合年增长率增长。[3] 虽然访问可能需要处理 PDF 等非结构化格式以及根据客户协议验证数据所有权，但该工业数据的稀缺性和直接适用性使其成为人工智能买家的高价值资产。通过努力进行尽职调查来获得竞争优势是值得的。⚠ 尽职调查（有价值的数据，可协商的访问权限）：维护记录和检查数据可能以 PDF 或纸质日志等非结构化格式存储；特定发动机性能数据的拥有权可能需要根据客户服务协议进行验证 · 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 Ssturbine 通过亲身服务工业燃气轮机，生成专有的维护日志和检查记录。这些精细的时间序列数据是开发和验证预测性维护算法的基本燃料。对于工业人工智能供应商而言，获取此数据集可提供独特的竞争优势，以抓住一个预计复合年增长率接近 28% 的市场份额，从而实现能够准确预测发动机状况并优化资产管理的模型。
See dimension details ↓- Dataset Specificity90
主导的“维护日志”，工业领域，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 Volume58
4 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
定期
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
人工智能买家需求极高，这得益于市场从 142 亿美元的规模和强劲的 27.9% 复合年增长率的快速扩张，因为公司竞相采用预测性维护解决方案。[3]
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 Strength65
3 种证据类型，4 个命中
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 Orientation50
2 个数据需求信号（1 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
✓ 良好目标 — 这家家族拥有的加拿大中小型企业专注于燃气轮机的物理维护、维修和翻新，使其成为一个主要目标，其运营维护日志是有价值的、未被充分利用的数据副产品。
- Deep Qualification80
⚠ 需要审查 — 目标是服务提供商，而不是数据销售商；其创建的维护日志是其业务的连贯副产品，但这些日志记录了对客户拥有的资产进行的工作，使得目标拥有数据极不可能。[数据归客户所有；许可受限]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
这些时间序列数据记录了燃气轮机系统的完整服务和翻新生命周期，这对于训练人工智能以优化服务间隔和预测预测性维护平台的组件故障至关重要。
Inspection reports
这些文件捕获了特定的诊断结果，包括内窥镜检查和寿命评估，为复杂的故障分析模型提供了地面实况数据。
Industrial data
这些时间序列数据来自初始发动机状况评估和拆解检查，为任何资产管理或性能优化算法提供了有价值的基线。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Ssturbine Maintenance Logs — a Moderate maintenance logs 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, with a projected CAGR of 27.9% (source: Grand View Research). [3]. Investment score 76.0/100 (confidence 0.51). Recommended action: Acquire.
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