Dataset opportunity
Hospital Engineering — 维护日志数据集机会
Hospital Engineering 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
42.5
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到 2035 年将达到 1061 亿美元，而 2025 年为 134 亿美元，复合年增长率为 23.2%。
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-07-13
UAB "SLAUGIVITA" — Latvia – Medical equipments – “Sensorās istabas aprīkojuma iegāde un uzstādīšana”
ted.europa.eu ↗ - 📰press2026-07-07
Valsts sabiedrība ar ierobežotu atbildību "Nacionālais rehabilitācijas centrs "Vaivari"" — Latvia – Medical equipments – Medicīnas tehnoloģiju un aprīkojuma piegāde
ted.europa.eu ↗
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
医疗保健
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 许可权待澄清
Buyer persona
工业人工智能与维护优化供应商
Hospital Engineering 持有一个全面的时间序列数据集，其中包含各种医疗设备的详细维护日志。此独特集合还包括相关的采购和工业数据，提供了开发和训练高精度预测性维护算法所需的精确、真实世界的证据，从而能够预测设备故障的发生。
预测性维护的全球市场正在迅速扩张，预计到 2035 年将达到1061 亿美元，强劲的复合年增长率为 23.2%。[1] 这个有价值的数据集代表了利用这一增长的重大机会。虽然由于客户保密和专有制造商规范，访问受到尽职调查的限制，但这些详细维护日志对于寻求竞争优势的 AI 买家而言，其稀有性和直接适用性使其成为一项关键资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：技术数据可能受客户保密协议的约束；医疗设备规格是制造商的专有信息，但维护日志由公司持有；出于安全原因，基础设施蓝图是敏感的 · 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持有方拥有详细记录医院设备完整生命周期的专有数据集，从采购和技术规划到长期维护概念。这正是工业人工智能供应商构建和验证专门的医疗保健领域的预测性维护模型所需的数据。随着全球预测性维护市场预计到 2035 年将增长到 1000 亿美元以上，这个稀有、高保真的数据集在高价值垂直领域提供了重要的先发优势。
See dimension details ↓- Dataset Specificity90
主导的“维护日志”，医疗保健行业，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 Freshness46
定期
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
AI 买家需求异常高，这得益于一个预计复合年增长率为 23.2% 的市场，因为组织越来越多地采用人工智能来防止代价高昂的设备停机时间。[1]
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 个数据胃口信号（2 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus70
盈余=中等，2 个近期外部信号 — 超出已货币化数据的专有数据
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. - ICP Audit42
⚠ 审查 — 该公司的核心业务是销售医院管理软件和咨询服务，而不是运营医院，这使其成为供应商且不适合。问题：该公司通过其母公司“German Healthcare Engineering GmbH”明确销售“智慧医院规划 (SHP)”和“智慧医院维护 (SHM)”软件；其业务模式是为医疗保健行业提供软件、咨询和培训，这属于“销售情报”排除标准。[；该公司不运营医院或自行进行维护；它为他人提供工具和咨询。因此，它不持有专有；一家德国商业登记处的记录显示，截至 2025 年年中，‘Hospital Engineering GmbH’有一名临时破产管理人，表明存在潜在的财务问题。
- Deep Qualification70
✓ 通过 — 目标是医院开发的服务提供商，包括设备维护，这使得“维护日志数据集”的存在具有合理性。然而，数据很可能由其客户在严格保密下拥有，并且该公司正在进行破产程序，这严重复杂化了任何数据交易。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
持有方为医疗和实验室技术创建时间序列维护概念，提供训练预测性故障模型所需的核心运营数据。
Procurement / tenders
这些证据证实了详细的设备清单和采购数据的存在，这些数据提供了关键的资产特定信息，以丰富用于人工智能应用的维护数据集。
Industrial data
该公司对核心医院基础设施（包括暖通空调和医用气体）的技术规划，生成了围绕关键医疗资产的运行环境的关键时间序列数据。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Hospital Engineering Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the healthcare domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market to reach $106.1 Bn by 2035, from $13.4 Bn in 2025, at a CAGR of 23.2% (source: Vertex AI Search). Investment score 42.5/100 (confidence 0.49). Recommended action: Acquire.
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