Dataset opportunity
Delvano — 维护日志数据集机会
Delvano 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
71.9
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 2025 年的估值为 136.5 亿美元，预计在 2026-2034 年间的复合年增长率为 24.30%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-04
Activiteiten Delvano tijdelijk on hold: onzekerheid voor personeel en klanten
hectares.be ↗
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
混合所有权 — 需明确许可权
Buyer persona
工业人工智能与维护优化供应商
Delvano 拥有来自其农业喷雾器车队的宝贵时间序列 维护日志数据集。这些数据通过其 D-Connect 遥测系统专有的iot_data 和精确的geo_data 得到了独特的丰富，为每台机器提供了全面的运行历史。这种丰富、多模态的组合非常适合开发和训练复杂的预测性维护模型，以在设备发生故障之前准确预测其发生。
全球预测性维护市场在 2025 年的估值为136.5 亿美元，预计在 2026 年至 2034 年间将以24.30% 的复合年增长率增长。[3] 尽管存在数据共享（与农民）、地理空间信息潜在的隐私问题以及需要从专有的 D-Connect 协议中进行技术提取等访问复杂性，但这种真实世界运行数据的稀有性和深度使其成为一项非常有价值的资产。显著的市场增长突显了对这类数据集的需求，以在工业和农业领域构建下一代人工智能解决方案。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据所有权可能在制造商和最终用户（农民）之间共享；地理空间数据可能涉及农场位置的隐私问题；专有的遥测协议（D-Connect）需要技术提取 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
公开证据证实 Delvano 拥有专有数据集，该数据集结合了其农业喷雾器车队的历史维护日志和实时物联网传感器数据。这种故障事件和运行遥测的独特组合是开发预测性维护解决方案的工业人工智能供应商的主要资产。在一个预计年增长率超过 24% 的市场中，这些数据直接支持了预测组件故障、优化服务计划和提高运营效率的模型。
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 24.30% 的快速复合年增长率，从而产生了对集成式真实世界培训数据的强烈需求，以构建有竞争力的解决方案。[3]
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 License36
所有权=混合，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 Orientation56
2 个数据需求信号（2 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 Audit100
✓ 良好目标 — Delvano 是一个完美契合的例子，因为它是一家成熟的农业机械中小制造商，其核心业务是销售实体设备而非数据，其运营将固有地产生有价值的、未被充分利用的维护和使用日志。
- Deep Qualification70
✓ 通过 — Delvano 是一家农业喷雾器制造商，因此存在维护和遥测数据集是合理的。然而，该公司已于 2026 年 9 月暂停运营，并正在寻找买家，这构成了一个重大触发因素，但也带来了数据访问和所有权方面的不确定性，为此未找到任何文件。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
该数据集包括实时物联网性能数据，捕获关键运行指标，如压力和发动机诊断，这对于训练模型识别设备故障的前兆至关重要。
Geospatial data
这些表格数据提供了机器运行的地理空间背景，将性能和磨损模式与特定的农业地形联系起来，从而用环境变量丰富了故障预测模型。
Maintenance logs
这些专有的时间序列数据记录了组件磨损和耐久性随时间的推移的真实情况，提供了训练和验证预测性维护算法所需的关键故障事件标签。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Delvano 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 $13.65 billion in 2025, projected to grow at a CAGR of 24.30% (2026-2034) (source: Fortune Business Insights). [3]. Investment score 71.9/100 (confidence 0.49). Recommended action: Acquire.
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