Dataset opportunity
Zeroemissionservices — 移动遥测数据集机会
由 Zeroemissionservices 持有的中等移动遥测数据集，可用于预测性维护和异常检测。
Score
70.4
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
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 size (indicative estimate)
全球预测性维护市场 = 2025 年为 151.0 亿美元，复合年增长率为 31.1%。
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.
Profile
Dataset profile
Type
移动遥测数据集
Modality
时间序列
Sector
移动
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 许可清晰 · PII/受监管
Buyer persona
工业人工智能与维护优化供应商
Zeroemissionservices 持有其海运电池更换业务产生的丰富时间序列 移动遥测数据集，包括电池组的详细iot_data、更换的`transaction_data`以及物理基础设施的`industrial_data`。该数据的高保真度、真实运营环境使其特别适合开发预测性维护模型，以预测电池健康状况、优化更换计划并防止组件故障。
此数据非常罕见，直接面向全球预测性维护市场，该市场在 2025 年的估值为 151.0 亿美元，预计将以31.1% 的复合年增长率增长。[6] 虽然访问需要经过多方合资企业审批流程，但目前未货币化的原始物联网遥测数据为高增长工业技术领域提供了先发优势的独特机会。[6] ⚠ 尽职调查（有价值的数据，可协商的访问权限）：合资企业结构（ING, Engie, Wärtsilä, Port of Rotterdam）可能需要多方数据共享批准；数据与物理电池更换基础设施和海运物流深度集成；主要重点是运营正常运行时间和能源服务，原始物联网遥测数据大部分未货币化。· 公司：ING, Engie, Wärtsilä, Port of Rotterdam 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 Zeroemissionservices 持有其内河航运中使用的可更换电池集装箱真实运营产生的专有时间序列数据。该数据集是工业人工智能供应商构建预测性维护模型的首要资产，该市场预计每年增长超过 30%。它提供了一个独特的机会，可以对高价值电动移动资产的性能和退化进行算法训练，从而优化冷却系统和整体电池生命周期管理。
See dimension details ↓- Dataset Specificity90
主导的 'iot_data'，行业移动，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 Demand96
人工智能买家需求极高，这得益于该市场 31.1% 的快速复合年增长率以及其从 151.0 亿美元的预期增长，因为公司寻求专业运营数据以获得竞争优势。[6]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility16
PII/受监管
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，ING, Engie, Wärtsilä, Port of Rotterdam 的子公司
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 个命中
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=公司所有，许可=清晰
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
ING, Engie, Wärtsilä, Port of Rotterdam 的子公司
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 — 专有数据超出已货币化部分
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 — ZES 的核心业务是按“按使用付费”的基础租赁可更换电池集装箱和充电基础设施，该网络产生的运营遥测数据是有价值的副产品，而非其核心产品。
- Deep Qualification60
✓ 通过 — 目标是数据持有者，拥有由其核心运营服务副产品产生的合理且连贯的数据集。然而，由于缺乏其运营服务的具体条款以及复杂的多方合资企业结构，数据所有权和许可权尚不明确。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
这是来自车载控制系统和传感器的时间序列数据，对于训练模型以预测组件故障和优化热管理至关重要。
Transaction data
这些表格数据捕获了能源消耗的按使用付费交易历史，使人工智能供应商能够将资产使用模式与商业活动相关联。
Industrial data
这是时间序列数据，详细介绍了电池集装箱在电网稳定中的二次使用，提供了超越主要移动功能的独特运营背景，用于全面的资产健康建模。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
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Deliverable
Premium dataset report
Zeroemissionservices Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market = $15.10 Billion in 2025, CAGR 31.1% (source: Market Research Future). Investment score 70.4/100 (confidence 0.49). Recommended action: Partnership (group-level).
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