Dataset opportunity
Dinnissen — 维护日志数据集机会
Dinnissen 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
73.3
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 142 亿美元，预计复合年增长率为 27.9%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-09-28
Dinnissen neemt Rotoflo over
metaalmagazine.nl ↗ - 📰press2026-09-28
Dinnissen neemt Rotoflo over voor silo-uitvoer van vrij tot moeilijk stromende bulkmaterialen
kunststofenrubber.nl ↗
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
Dinnissen 拥有来自其专有工业混合机和涂布机在客户工厂运行的宝贵维护日志数据集。这些时间序列数据，包含其“Dinnissen Automation”软件的详细iot_data和操作日志，为构建和训练高保真预测性维护算法提供了丰富的基础。
预测性维护的全球市场规模巨大且正在迅速扩张，2025 年市场价值为 142 亿美元，预计将以27.9% 的复合年增长率增长。[2] 尽管由于与客户可能存在数据共享所有权，访问权限需经协商，但该真实世界工业数据的稀有性和直接适用性使其成为寻求在此高增长市场中创造价值的 AI 买家的优质资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据由安装在客户现场的工业机器（混合机、涂布机）生成；工艺数据的归属可能与客户合同共享或受限；访问通过其专有的“Dinnissen Automation”和“Smart Process”软件进行中介。· 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 Dinnissen 持有一个稀有且专有的数据集，该数据集结合了其全球工业机器车队的历史维护日志、实时工艺数据和精细的物联网传感器馈送。这些丰富、多模态的时间序列数据正是工业人工智能和维护优化供应商构建和验证高精度预测性维护模型所需的。在一个预计到 2025 年将超过 142 亿美元的市场中，该数据集通过提供预测设备故障和优化工业运营所需的真实数据，提供了显著的竞争优势。
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 Demand95
AI 买家需求异常高，这得益于在以 27.9% 的复合年增长率增长的市场中降低运营停机时间的迫切需求。[2]
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 Orientation73
3 个数据需求信号（3 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，2 个近期外部信号 — 超出已货币化部分的专有数据
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 Qualification80
⚠ 需要审查 — Dinnissen 是一家工具供应商，销售工业加工机械和集成生产线。它提供“Dinnissen Productivity Platform”进行远程监控，该平台处理客户数据。然而，数据是在客户自己的生产过程中生成并与其相关的，因此属于客户所有。转售数据的访问权限极不可能，且未发现相反的条款。[数据归客户所有]
- ICP Audit92
✓ 良好目标 — 绝佳目标：Dinnissen 是一家工业加工机械的中小型制造商，其本身会产生有价值的维护和运营数据作为副产品，并且似乎不将数据或智能作为核心产品进行销售。问题：该公司提供“Dinnissen Productivity Platform”，为客户提供数据报告和远程监控。[13, 16] 需要对此进行验证。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Industrial data
这是来自混合和计量等核心工业操作的实时工艺数据，为开发异常检测模型的 AI 供应商提供了正常的机器行为的关键基线。
Maintenance logs
这是来自全球车队的维护事件和性能指标的全面历史记录，提供了训练和验证预测性维护算法所需的关键真实标签。
IoT / sensor data
这是精细的物联网传感器数据和自动化控制日志，提供了构建能够识别设备故障细微前兆的复杂预测模型所需的高频输入。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Dinnissen 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 $14.2 billion in 2025, with a projected CAGR of 27.9% (source: Grand View Research). [2]. Investment score 73.3/100 (confidence 0.49). Recommended action: Acquire.
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