Dataset opportunity
Scale Energy — 维护日志数据集机会
由 Scale Energy 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
74.9
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在 2024 年的估值为 123 亿美元，预计复合年增长率为 29.7%（来源：Custom Market Insights）。[6]
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
工业
Volume
中等
Freshness
实时
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
Scale Energy 拥有一份来自其物理电池资产组合的宝贵时间序列 维护日志数据集。这些专有的 iot_data 从电池管理系统 (BMS) 和电网监控硬件中提取，提供精细的、真实的运营证据，非常适合开发和训练高保真预测性维护模型，以预测资产故障和优化性能。
全球预测性维护市场在 2024 年的估值为123 亿美元，预计将以29.7% 的复合年增长率增长。[6] 这一显著的市场增长凸显了买家对有效人工智能解决方案的强烈需求。尽管访问复杂性要求从专有系统中提取数据，但该工业数据的稀有性及其在减少昂贵的运营停机时间方面的直接适用性，使其成为能源和工业领域人工智能开发者的优质资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据由位于第三方工业现场的物理电池资产生成；访问需要从专有的电池管理系统 (BMS) 和电网监控硬件中提取。· 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 Scale Energy 拥有工业能源资产的专有维护日志，直接关联到相应的时序物联网传感器和工业能源消耗数据。这个独特、集成的数据库正是工业人工智能和维护优化供应商构建和验证下一代预测性维护模型所需要的。在一个预计年增长近 30% 的全球市场中，获取这些数据为优化资产性能和预测故障提供了关键的竞争优势。
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 Demand90
人工智能买家需求异常高，这得益于预测性维护市场的快速增长（预计复合年增长率为 29.7%），而此类时序工业数据是该市场必不可少且稀缺的资源。[6]
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 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 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 Orientation22
0 数据需求信号（0 类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，5 个近期外部信号 — 已货币化的专有数据之外
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 — Scale Energy 是一个良好目标，因为它为工业客户安装和运营电池存储系统，产生运营数据作为副产品，并且似乎不将数据或人工智能软件作为核心产品销售。问题：该公司的核心业务是提供全资能源存储解决方案，而不是数据产品。“维护日志数据集”是潜在的副产品
- Deep Qualification80
✓ 通过 — 该目标是一家服务提供商，安装和运营电池存储系统，使得“维护日志数据集”作为运营副产品的存在极有可能。然而，数据所有权和访问权限不明确，因为数据是在第三方站点上生成的，并且具有专有性
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>The International Hydropower Association (IHA) said global installed hydropower capacity reached 1,469 GW in 2025 after the addition of 28 GW of new capacity during the year, including a record 11.6 GW of pumped storage. Pumped storage capacity surpassed 200 GW globally for the first time.</p> <p>The post <a href="https://www.powermag.com/pumped-storage-additions-lead-global-hydropower-growth/">Pumped Storage Additions Lead Global Hydropower Growth</a> appeared first on <a href="https://www.powermag.com">POWER Magazine</a>.</p> <p><img alt="Fig1-wudongde-china-aerial-jun21-GE-Renewable-Ener”
- “<figure><div><img src="https://imgproxy.divecdn.com/y2JmMuEEhThfqWk7g2bWHi_FFAepyB6c76o-AeFkTTM/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0xOTI2MjI3OTQ4LmpwZw==.webp" /></div></figure><p>Relative certainty around tax policy and demand from large load customers are among factors driving the country’s energy storage boom, according to two reports out this month.</p>”
- “<p>L’Union française de l’électricité (UFE) a organisé ce mardi 23 juin son grand raout annuel, le dernier avant la prochaine élection présidentielle. Les patrons d’EDF, Engie et TotalEnergies y ont participé mais, pour une fois, chacun à une table-ronde différente. Céline Stein, PDG d’Octopus en France, issé au 4e rang des fournisseur derrières les trois […]</p> <p>L’article <a href="https://www.greenunivers.com/2026/06/reseaux-appels-doffres-nucleaire-les-coulisses-du-colloque-de-lufe-427550/">Réseaux, appels d’offres EnR, nucléaire… : les coulisses du col”
IoT / sensor data
证据表明来自物联网传感器的时序数据监测电网稳定性，为人工智能模型将外部条件与资产健康联系起来提供了必要的运营背景。
Industrial data
这证实了工业能源消耗的时序数据的存在，这对于根据实际运营强度对资产压力进行建模和预测故障至关重要。
Maintenance logs
这些证据证实了工业电池系统的专有维护日志的存在，作为训练和验证任何预测性维护算法所必需的真实数据。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
JSON, CSV
License
One-time license for internal use in developing and training predictive maintenance models.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This proprietary, high-rarity time-series IoT data from industrial battery maintenance logs is highly valuable for predictive maintenance model development. The significant and growing market for predictive maintenance, driven by industrial AI demand, supports a premium valuation.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Scale Energy 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 $12.3 Billion in 2024, with a projected CAGR of 29.7% (source: Custom Market Insights). [6]. Investment score 74.9/100 (confidence 0.49). Recommended action: Acquire.
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