Dataset opportunity
Mobilityservice — 维护日志数据集机会
Mobilityservice 持有的海量维护日志数据集，可用于预测性维护和异常检测。
Score
76.2
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
65%
Action
数据共享协议
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 134 亿美元，复合年增长率为 23.2%。
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
出行
Volume
大型
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — GDPR 敏感（个人身份信息审查）
Buyer persona
工业人工智能与维护优化供应商
Mobilityservice 持有一个全面的维护日志数据集，结构为时间序列。该数据集整合了来自车辆遥测的详细 `maintenance_logs`（维护日志）、`inspection_records`（检查记录）、`transaction_data`（交易数据）和 `iot_data`（物联网数据），使其非常适合开发预测性维护人工智能模型。这些数据提供了车辆健康和维修历史的丰富纵向视图。
该数据在全球预测性维护市场中具有极高的价值，该市场在 2025 年的估值为134 亿美元，预计将以 23.2% 的复合年增长率增长。[1] 尽管存在数据访问复杂性，例如需要符合 GDPR 的驾驶员数据匿名化、与 OEM 共享遥测权以及分布式服务记录的专有性质，但该集成数据集的稀有性和深度为旨在减少非计划停机时间的买家提供了显著的竞争优势。⚠ 尽职调查（有价值的数据，可协商的访问权限）：与驾驶员相关的数据需要严格的 GDPR 匿名化；车辆遥测可能涉及与 OEM 的共享权；维护记录是专有的，但分布在服务合作伙伴之间 · 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 Mobilityservice 拥有丰富、专有的数据集，涵盖了大型、多样化车队的完整运营生命周期。该资产的核心是来自维护日志和真实电动汽车车队运营的详细时间序列数据，直接支持先进的预测性维护模型。对于工业人工智能供应商而言，该数据集是开发用于组件故障和维护优化的、高精度算法的直接途径。获取此数据可在全球预测性维护市场中获得显著的竞争优势，该市场预计到 2025 年将达到 134 亿美元。
See dimension details ↓- Dataset Specificity100
主导的“维护日志”，出行行业，4 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 Rarity70
专有领域数据（开放会降低稀有度）
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume70
6 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 Value94
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
人工智能买家需求由市场的快速扩张驱动，预计复合年增长率为 23.2%，因为公司越来越多地采用预测性维护来降低运营成本和资产停机时间。[1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility14
开放/API 访问
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility48
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 Strength89
5 种证据类型，6 个命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License62
所有权=公司所有，许可=GDPR 敏感
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 Surplus92
盈余=高 — 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 Audit92
✓ 良好目标 — 这家 B2B 汽车租赁公司专注于电动汽车，是一个有力的目标，因为它作为核心运营业务的副产品生成有价值的维护和使用数据，并且似乎不销售数据或情报产品。问题：虽然该公司似乎是一家中小企业，但在公开来源中未找到确切的员工人数；该公司提供驾驶员应用程序，这可能是向客户提供数据驱动服务的初步步骤，但目前似乎专注于基本服务（
- Deep Qualification70
✓ 通过 — 该目标是一家汽车租赁公司，使其成为维护和遥测日志的潜在数据持有者。然而，由于涉及驾驶员（GDPR）、服务合作伙伴和 OEM，数据所有权是混合的，许可权不明确，需要大量的法律尽职调查。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Downloads / exports
该公司通过白皮书下载捕获潜在客户生成信息，展示了其在电动汽车领域的成熟专业知识和受众参与度。
Maintenance logs
持有者通过管理大型、多样化车队的所有维护、维修和技术检查来生成专有时间序列数据，这是任何预测性维护模型的关键基础事实。
IoT / sensor data
该数据集包括来自电动汽车车队的真实物联网数据，捕获了关键的行为信号，如充电模式和续航里程表现，这对于优化电动汽车维护计划至关重要。
Inspection reports
标准化的检查记录记录了租赁周期结束时车辆的状况、损坏和磨损情况，为故障分析和残值建模提供了结构化特征数据。
Transaction data
持有者拥有关于租赁定价和二手车估值的历史交易数据，使人工智能模型能够将维护成本直接与车辆的总拥有成本和财务绩效联系起来。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Mobilityservice Maintenance Logs — a Large maintenance logs dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market = $13.4 billion in 2025, CAGR 23.2% (source: Report via Vertex AI Search). [1]. Investment score 76.2/100 (confidence 0.65). Recommended action: Data Sharing Agreement.
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