Dataset opportunity
Neieng — 维护日志数据集机会
Neieng 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
73.9
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
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 = 2025 年为 134 亿美元，复合年增长率为 23.2%（来源：Vertex AI Search 的市场分析报告）。[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
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 许可权待澄清
Buyer persona
工业人工智能与维护优化供应商
Neieng 持有一个全面的维护日志数据集，结构为时间序列数据。该数据集源自真实的 `industrial_data`、`iot_data` 和详细的 `maintenance_logs`，非常适合开发和训练预测性维护模型，因为它能随时间捕捉设备性能、故障事件和干预记录。
此类数据的商业价值体现在全球预测性维护市场上，该市场在 2025 年的估值为134 亿美元，预计将以23.2% 的复合年增长率增长。[1] 虽然访问需要处理专门的工程格式（ETAP、SKM、CAD）和潜在的客户保密条款，但该数据的稀有性及其在高性能工业人工智能应用中的直接适用性使其成为严肃买家的宝贵资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：存储在专门工程格式（ETAP、SKM、CAD）中的技术数据；工程服务协议中的潜在客户保密条款；数据高度技术化，需要领域专业知识才能提取和标记 · 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 Neieng 持有一个专有的、高稀有性的时间序列运行和维护日志数据集，用于高压工业设备。这正是工业人工智能供应商构建和验证复杂的预测性维护模型所需的真实数据。在一个预计到 2025 年将达到 134 亿美元的市场中，这个数据集——涵盖SCADA系统历史、现场测试和系统建模——为训练能够预测设备故障并占据重要市场份额的算法提供了一个难得的机会。
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 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 Demand92
买家需求极高，这得益于预测性维护市场的快速增长，该市场正以 23.2% 的复合年增长率扩张。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
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 License70
所有权=已拥有，许可=权利不明确
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，2 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 Audit100
✓ 良好目标 — 这是一个理想的目标；它是一家中小型工程公司，其核心业务是为电力系统提供服务，从而产生专有的维护和运行数据作为有价值的副产品。
- Deep Qualification80
⚠ 需要审查 — 该目标是一家专业的工程服务公司，数据是客户工作的副产品；所有权和访问权限可能受客户保密协议的限制，尽管最近的收购为战略变革创造了潜在的触发因素。[数据归公司客户所有；许可受限]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>The Federal Energy Regulatory Commission (FERC) has directed the North American Electric Reliability Corporation (NERC) to file one or more new or modified mandatory reliability standards governing the integration of computational loads—a category defined broadly enough to cover generative-AI data centers, cryptocurrency mines, and other information-technology facilities—by Dec. 31, 2026. FERC’s order, issued on July […]</p> <p>The post <a href="https://www.powermag.com/ferc-orders-mandatory-nerc-reliability-standards-for-data-center-and-other-computational-loads/">FERC Orders M”
- “<figure><div><img src="https://imgproxy.divecdn.com/Zq7JzSUFlJGoPD_J6xr4Vrq16g59-kdmMX8aWFIvEuI/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9WZXJub25fZGF0YV9jZW50ZXJfY29uc3RydWN0aW9uLmpwZw==.webp" /></div></figure><p>The June 18 show cause orders finally take aim at the load interconnection mess. But the customers who get power faster will be the ones who show up ready to flex, writes Shalin Savalia, a senior electrical engineer.</p>”
Industrial data
这表明来自全面的系统建模和电弧闪光研究的结构化数据，这对于训练人工智能理解复杂的、系统范围的故障场景非常有价值。
Maintenance logs
这是来自关键资产（如变压器）现场测试和调试的时间序列维护日志的直接证据，构成了任何预测性维护模型的基本真实数据。
IoT / sensor data
这证实了历史SCADA系统数据的可用性，提供了将设备行为与维护事件相关联并构建更准确模型所需的连续运行背景。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Neieng 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 = $13.4B in 2025, CAGR 23.2% (source: Market Analysis Report via Vertex AI Search). [1]. Investment score 73.9/100 (confidence 0.49). Recommended action: Acquire.
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