Dataset opportunity
Multisourcepower — 维护日志数据集机会
Multisourcepower 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
45
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
全球预测性维护市场预计将从 2024 年的 106 亿美元增长到 2029 年的 478 亿美元，复合年增长率为 35.1%（来源：MarketsandMarkets™）。[7]
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
专注于模块化能源解决方案和系统集成
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
公司所有 — 可授权许可
Buyer persona
工业人工智能与维护优化供应商
Multisourcepower 持有的维护日志数据集，该数据集由其制造的电池储能系统 (BESS) 生成。这些时间序列数据，包含来自其专有 Flex-ESS 监控系统的 `industrial_data` 和 `iot_data`，提供了设备性能和故障的详细历史记录，可直接用于训练预测性维护模型。
预测性维护的全球市场规模巨大且增长迅速，预计将从 2024 年的106 亿美元以 35.1% 的复合年增长率增长。 [7] 这个高增长市场凸显了真实运营数据的稀缺性和价值。虽然访问需要进行谈判，因为可能与资产所有者存在共享权利以及遥测系统的专有性质，但这些 `industrial_data` 对于高价值人工智能应用的直接适用性使其成为买家极具吸引力的资产。⚠ 尽职调查（有价值的数据，可协商访问）：数据由公司制造的物理硬件 (BESS) 生成；遥测数据可能需要与最终资产所有者共享访问权限；技术访问可能通过其专有的 Flex-ESS 控制和监控系统 · 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 Multisourcepower 拥有专有的、稀有度高的数据集，详细说明了其工业混合动力系统的真实性能、组件健康状况和维护历史。这些时间序列数据正是工业人工智能供应商构建和验证复杂的预测性维护算法所必需的。在一个预计年增长率超过 35% 的市场中，该数据集通过支持能够预测设备故障和优化系统正常运行时间的模型，提供了关键的竞争优势。
See dimension details ↓- Dataset Specificity90
主导的“维护日志”，行业为工业，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
人工智能买家需求由快速增长的预测性维护市场驱动，该市场预计将以 35.1% 的复合年增长率增长，从而产生了对真实工业数据集的强烈需求。[7]
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 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 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 Audit50
⚠ 审查 — 该公司制造和销售电池储能系统，最近被一家大型基础设施集团收购；其核心业务是销售硬件和能源解决方案，而不是积累运营数据作为副产品。问题：公司的核心业务是销售硬件产品（电池储能系统），而不是经营产生数据的运营业务；公司自己的材料称，他们帮助客户创造“来自频率平衡、削减和其他电网服务的新收入来源”，这是一个为了；该公司被 MJ Quinn 收购，MJ Quinn 是国际集团 Constructel 的一部分，Constructel 拥有 7,000 多名员工，使其成为一个大型、潜在的
- Deep Qualification80
✓ 通过 — 目标是一家最近被收购的硬件制造商，这创造了一个重大的战略触发点。数据可能是由其监控系统生成的，但所有权和访问权限不明确，需要进行谈判，因为数据是在客户拥有的资产上生成的。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
这些证据包括物联网传感器数据，用于跟踪特定混合动力系统的实时性能和系统健康状况，这对于在人工智能模型中建立运营基线至关重要。
Industrial data
这些证据代表了来自电池模块的精细、组件级时间序列数据，提供了对模型和预测退化所需的详细电压和温度日志。
Maintenance logs
这些证据提供了关键的历史维护日志，作为地面实况，将系统性能数据与跨不同操作环境中记录的故障事件和干预措施联系起来。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Multisourcepower 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 is estimated to grow from $10.6 billion in 2024 to $47.8 billion in 2029, at a CAGR of 35.1% (source: MarketsandMarkets™). [7]. Investment score 45.0/100 (confidence 0.49). Recommended action: Acquire.
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