Dataset opportunity
Sp Automation — 维护日志数据集机会
Sp Automation 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
66.4
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
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 = 2034 年将达到 973.7 亿美元，复合年增长率为 24.30%（来源：Fortune Business Insights）
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
定期
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
Sp Automation 拥有一套结构为时间序列数据的维护日志数据集，其中包括 `image_collection`、`industrial_data` 和详细的 `maintenance_logs`。这个丰富、多模态的数据集直接适用于开发和验证复杂的预测性维护算法，因为它捕捉了定制自动化机械随时间推移的实际设备性能和故障实例。
预测性维护的全球市场正在经历爆炸式增长，预计到 2034 年将达到 973.7 亿美元，复合年均增长率 (CAGR) 为 24.30%，这使得这些数据具有非凡的价值。[3] 尽管存在访问复杂性，例如机器设计中共享的客户知识产权、异构数据格式以及需要合同审查，但这种稀有的工业数据集合提供了显著的竞争优势。获取此数据集是利用非公开数据训练专有 AI 模型的一次战略机会，在这样一个资产是价值关键驱动因素的市场中。⚠ 尽职调查（有价值的数据，可协商的访问权限）：定制机器设计可能与特定客户共享知识产权；数据可能以异构格式（CAD、PLC 日志、测试报告）存在；系统集成中的工业数据所有权需要合同审查 · 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 Sp Automation 持有跨越 40 年工业自动化历史的专有数据集，包括关键的售后支持记录。这种独特、纵向的时间序列数据正是工业 AI 供应商构建和验证高性能预测性维护模型所需要的。在一个预计将近 1000 亿美元的市场中，此数据集代表了获取高增长 AI 应用基础培训数据的难得机会。
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 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 Demand95
由于全球预测性维护市场的强劲增长预测，AI 买家需求极高，预计到 2034 年的**复合年均增长率 (CAGR)** 为 **24.30%**。[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 Orientation22
0 个数据胃口信号（0 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，5 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. - ICP Audit83
✓ 良好目标 — 该运营中小企业构建和支持定制自动化机械，其预测性维护服务可能产生有价值的、休眠的运营数据作为副产品，使其成为一个良好目标。[2, 9, 13] 问题：主要问题是确认其机器产生的维护和运营数据的归属，这些机器安装在客户现场。[9]；目前尚不清楚其“预测性维护”产品是已售出的软件产品还是内部服务。
- Deep Qualification80
⚠ 需要审查 — 目标是定制机器制造商，而不是数据销售商；维护数据是其服务活动的合理副产品，但几乎肯定归其客户所有，因此无法转售。[数据归公司客户所有]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>MBody AI has deployed its hardware-agnostic Orchestrator platform to Florida and California, with a pilot in Ontario.</p> <p>The post <a href="https://www.therobotreport.com/mbody-ai-expands-service-robotics-operations-eleven-states-canada/">MBody AI expands service robotics operations to eleven states and Canada</a> appeared first on <a href="https://www.therobotreport.com">The Robot Report</a>.</p>”
- “<p>AGIBOT has rolled out 15,000 wheeled semi-humanoid robots as it moves from embodied AI from development and production to deployment.</p> <p>The post <a href="https://www.therobotreport.com/agibot-produces-15000th-robot-marking-milestone-embodied-ai-deployment/">AGIBOT produces 15,000th robot, marking a milestone in embodied AI deployment</a> appeared first on <a href="https://www.therobotreport.com">The Robot Report</a>.</p>”
- “<p>Atharv Kolhar, a staff test automation engineer at Figure AI, says the robotics industry needs a testing philosophy that scales alongside autonomy. </p> <p>The post <a href="https://www.therobotreport.com/we-know-how-to-build-smarter-robots-now-we-need-to-learn-smarter-ways-to-test-them/">We know how to build smarter robots. Now, we need to learn smarter ways to test them</a> appeared first on <a href="https://www.therobotreport.com">The Robot Report</a>.</p>”
Industrial data
这证实了持有者在实施定制自动化系统方面拥有 40 年的经验，涵盖了装配和包装等各种流程，表明拥有丰富、历史悠久的时间序列数据集。
Image collection
该公司专注于用于检测的视觉系统，这表明拥有有价值的图像数据集合，非常适合跨多个行业训练 AI 模型进行视觉异常检测。
Maintenance logs
此样本是全球售后支持的直接证据，它产生了用于构建和训练任何预测性维护算法的核心维护日志。
Marketplace
Dataset details
Geographic coverage
Global
Time range
40 years
Update frequency
Periodic
Delivery
API
Formats
JSON, CSV
License
One-time license for internal use, model training, and validation. Restrictions on redistribution and resale apply.
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 proprietary, high-rarity time-series dataset offers unique longitudinal maintenance logs for industrial automation machinery, directly addressing the high-demand, rapidly growing predictive maintenance market.
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
Sp Automation 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 = $97.37 billion by 2034, CAGR 24.30% (source: Fortune Business Insights). Investment score 66.4/100 (confidence 0.49). Recommended action: Acquire.
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