Dataset opportunity
Edina — 数据维护日志机会
Edina 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
73.1
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
56%
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
全球预测性维护市场 = 2024 年为 106 亿美元，复合年增长率为 35.1%（来源：MarketsandMarkets）
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-07-28
Viridi BESS Installed at Oak Ridge Lab as Part of Grid Technology Research
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.
- 📣Press / announcement
首个参与国家电网平衡机制的小型发电机
source ↗
Profile
Dataset profile
Type
维护日志数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
混合所有权 — 许可清晰
Buyer persona
工业 AI 和维护优化供应商
来自 Edina 的此维护日志数据集提供了广泛的时间序列数据，非常适合预测性维护应用。它包含来自占有27%英国市场份额的燃气发动机的专有、聚合遥测数据和详细的维护历史记录，其中许多发动机运行在医院和数据中心等关键环境中。这为训练强大的 AI 模型提供了丰富、真实的基石。
全球预测性维护市场正在经历爆炸式增长，预计将从 2024 年的106 亿美元增长到 2029 年的 478 亿美元，复合年增长率高达 35.1% [1]。尽管由于数据的专有性质及其在客户站点上生成而存在访问复杂性，但其稀缺性和对这一高价值市场的直接适用性使其成为旨在获得竞争优势的 AI 买家的关键资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：EPAL 的子公司，EPAL 是印度 EESL 和英国 EnergyPro 的合资企业；数据由通常位于客户站点（医院、数据中心）的资产生成；专有层包括跨占 27% 英国市场份额的燃气发动机的聚合遥测和维护历史 · 公司：EnergyPro Assets Limited (EPAL) / EESL 的子公司。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Edina 拥有来自英国和爱尔兰大型发电资产组合的专有、高稀缺性维护日志和相应的实时物联网数据。这种历史故障记录和实时运行数据的独特组合是工业 AI 供应商构建下一代预测性维护解决方案的首选收购对象。在一个年增长率超过 35% 的市场中，此时间序列数据集为训练和验证真实资产性能的算法提供了关键机会，这是优化工业运营的关键差异化因素。
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 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
AI 买家需求异常高，这得益于全球预测性维护市场的快速扩张，预计该市场将以**35.1% 的复合年增长率**[1]增长。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility62
开放/API 访问
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
中等难度，EnergyPro Assets Limited (EPAL) / EESL 的子公司
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength74
4 种证据类型，4 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License58
所有权=混合，许可=清晰
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
EnergyPro Assets Limited (EPAL) / EESL 的子公司
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 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，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 Audit92
✓ 良好目标 — Edina 是一个强有力的目标，因为它设计、安装并提供发电厂的资产护理/维护，这是其核心业务，自然会产生有价值的维护日志数据作为副产品，并且它似乎不销售数据或 AI 软件作为产品。问题：该公司由印度电力部下属的大型能源服务公司 EESL（能源效率服务有限公司）拥有，这可能会使交易复杂化。
- Deep Qualification80
⚠ 需要审查 — Edina 是能源资产的服务提供商，而不是数据销售商；“维护日志数据集”是其核心业务的合理副产品，但其所有权是混合的/受限的，因为它是在客户站点上生成的，并且不符合指定的小众数据。[许可受限；实体不持有该小众数据的特征：识别的数据集是燃气发动机的“维护日志”，这与机械的预测性维护有关，而小众的“能源存储与电网现代化”则由“电池技术规格、电网集成项目、储能容量数据”定义。]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Developer portal
该公司的门户网站展示了其在交付大规模电池储能系统方面的工程专业知识，证实了其作为管理高价值工业资产的复杂技术运营商的地位。
IoT / sensor data
Edina 从工业燃气发动机捕获实时物联网数据，提供性能和消耗的详细时间序列指标，这对于训练预测性维护算法至关重要。
Maintenance logs
该公司持有大量发电资产的全面维护日志，提供了用于标记传感器数据的关键历史事件数据——即地面实况——以进行监督机器学习。
Industrial data
该数据集包括一级电池储能解决方案的性能和放电时间序列数据，展示了用于优化现代能源电网资产的宝贵工业数据来源。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Edina 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 = $10.6 billion in 2024, CAGR 35.1% (source: MarketsandMarkets). Investment score 73.1/100 (confidence 0.56). Recommended action: Partnership (group-level).
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