Dataset opportunity
Eco Stor — 维护日志数据集机会
Eco Stor 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
48
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
63%
Action
许可
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 年的价值为 92.1 亿美元，预计从 2026 年到 2035 年的复合年增长率为 26.19%（来源：Precedence Research）。[2]
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
中等
Accessibility
开放/API
Legal
公司所有 — 许可干净
Buyer persona
工业人工智能与维护优化供应商
Eco Stor 持有一个详细的维护日志数据集，以时间序列模式呈现，该数据集源自其大规模电池存储资产。这些`工业数据`和`物联网数据`的集合构成了一个全面的`知识库`，捕获了真实的设备性能、退化模式和运行事件，因此非常适合开发和验证预测性维护算法。
该数据所处的市场预计到 2035 年将达到942.7 亿美元，以26.19% 的复合年增长率增长。[2] 虽然由于与物理资产、电网运营商协议和专有数字孪生相关联，访问权限复杂，但这确保了数据的稀缺性和高价值。对于人工智能买家来说，这代表了一个独特的机会，可以获得难以复制的数据集，并在快速扩张的能源和公用事业领域建立竞争优势。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据与物理电池资产和电网运营商协议相关联；使用专有数字孪生，这可能会使原始数据提取复杂化；运营数据部分取决于当地电网条件和监管框架 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明了 Eco Stor 系统地捕获和分析其工业能源存储系统的细粒度、时间序列运行数据。数据包括明确的维护和维修日志、历史负荷曲线以及物联网传感器数据，所有这些都由其内部数据科学家进行整理。对于工业人工智能供应商而言，此数据集是训练高价值预测性维护模型的直接输入，而这在年增长率超过 26% 的市场中至关重要。获取此数据为优化资产性能和防止昂贵的故障提供了显著的竞争优势。
See dimension details ↓- ICP Audit75
⚠ 审查 — 尽管 Eco Stor 是一家产生有价值的专有维护和运营数据（来自其电池存储园区）的中小企业，但它不是一个好的目标，因为其官方公司宗旨包括开发和销售用于运营这些系统的软件，这意味着它已经销售了衍生产品 问题：公司合法注册的公司宗旨明确包括“开发和销售用于运营大型电池存储系统的软件”；公司活跃
- 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 Rarity58
专有领域数据（开放降低稀缺性）
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume64
5 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
人工智能买家需求极高，这得益于市场以 26.19% 的复合年增长率快速扩张，对专业工业数据产生了紧迫需求，以构建预测模型。[2]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility78
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 Feasibility66
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 Strength86
5 种证据类型，5 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 个数据胃口信号（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 — 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. - Deep Qualification90
⚠ 需要审查 — Eco Stor 是资产开发商和运营商，而不是数据销售商；它持有其大规模电池园区产生的专有运营数据，这对于开发预测性维护算法是可行的，但受到其物理性质和电网运营商协议的限制。[许可受限]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Downloads / exports
这些证据表明该公司维护与其建筑和财务运营相关的结构化表格记录，这表明有一个组织数据治理的基础，对于确保数据来源很有价值。
Knowledge base / docs
该公司明确表示，它会为与服务提供商协调的工作创建安全文档，证实了捕获服务活动和干预措施的文本记录流程。
IoT / sensor data
这证实了其自己的数据科学家收集和分析了能源存储系统的时间序列技术数据，为用于性能优化的有价值的物联网传感器数据提供了直接证据。
Industrial data
该公司分析了包括负荷曲线和电压在内的历史时间序列数据，这是对工业资产行为进行建模以用于人工智能应用的特定、细粒度的运营数据。
Maintenance logs
这是对系统组件安全记录的维护和维修日志的直接确认，代表了训练预测性维护算法所需的核心真实数据。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, JSON
License
One-time license for predictive maintenance algorithm development and validation, with restrictions on redistribution and commercial use of raw data.
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 dataset's value is driven by its rarity, direct application in the high-growth predictive maintenance sector, and the complexity of sourcing. The strong market growth for predictive maintenance algorithms fuels demand for such granular industrial time-series data.
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
Eco Stor 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 9.21 billion in 2025, projected to grow at a CAGR of 26.19% from 2026 to 2035 (source: Precedence Research). [2]. Investment score 48.0/100 (confidence 0.63). Recommended action: License.
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