Dataset opportunity
Electrifiedgarage — 维护日志数据集机会
Electrifiedgarage 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
78.8
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)
全球汽车预测性维护市场在 2024 年的估值为 46.6 亿美元，预计复合年增长率为 17.5%（2025-2034 年）（来源：Global Market Insights Inc.）。[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（PII 审查）
Buyer persona
工业 AI 和维护优化供应商
Electrifiedgarage 持有一个全面的维护日志数据集，结构为时间序列。该数据集包含来自电动汽车的详细 `event_streams`（事件流）、`inspection_records`（检查记录）、`iot_data`（物联网数据）和 `knowledge_base`（知识库）。这些数据结合了诊断故障代码和随时间推移的维修操作，非常适合用于训练预测性维护模型，以在组件发生故障之前进行预测。
这些数据在全球汽车预测性维护市场中具有极高的价值，该市场估计在 2024 年价值46.6 亿美元，预计将以17.5% 的复合年增长率增长。[2] 虽然访问需要克服客户 PII/VIN 的去标识化以及 OEM 提供的诊断数据的敏感性等复杂性，但这些真实电动汽车维护记录的稀有性和深度为开发强大的 AI 解决方案提供了显著的竞争优势。⚠ 尽职调查（有价值的数据，可协商访问）：数据集包含需要去标识化的客户 PII 和 VIN；数据主要存储为单独的服务档案和诊断日志；关于使用 OEM 提供的诊断数据的潜在敏感性 · 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 Electrifiedgarage 拥有专有的、稀有度高的电动汽车维护日志数据集，涵盖Tesla和Rivian车辆。数据包括详细的诊断、电池健康评估以及复杂的超出保修期维修的记录的故障模式。对于工业 AI 供应商而言，这是构建和验证预测性维护模型的重要资产，在预测的汽车市场中，该市场预计将以 17.5% 的复合年增长率增长，通过解锁对真实组件故障的洞察，提供独特的竞争优势。
See dimension details ↓- ICP Audit100
✓ 良好目标 — 这是一个理想的目标：一家拥有两个地点的独立电动汽车维修专家，其核心业务是车辆服务，作为副产品生成专有的维护和诊断数据，没有任何出售的迹象。
- Deep Qualification80
✓ 通过 — 该目标是一家车辆维修服务公司，而非数据销售商；它持有有价值的维护日志作为其核心业务的副产品，但由于第三方工具和 OEM 数据敏感性，数据权利很复杂。
- Dataset Specificity100
占主导地位的“维护日志”，行业出行，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 Rarity94
专有领域数据
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
AI 买家需求极高，受市场快速扩张的推动，预计复合年增长率为 17.5%，因为公司寻求最大限度地减少停机时间和维护成本。[2]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
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 Feasibility44
低难度，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 次命中
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=已拥有，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 数据胃口信号（0 类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，3 个近期外部信号 — 超出已货币化的专有数据
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.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>Tesla reported 26% revenue growth and reaffirmed plans to begin Semi production this year despite sharply higher investment spending.</p> <p>The post <a href="https://www.freightwaves.com/news/teslas-28b-quarter-fueled-by-record-deliveries-semi-production-ramp">Tesla’s $28B quarter fueled by record deliveries, Semi production ramp</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
- “Tesla a franchi les 100 milliards de dollars de chiffre d'affaires sur douze mois glissants, porté par une hausse de 25 % de ses livraisons mondiales. Mais les promotions et plusieurs coûts exceptionnels ont pesé sur la rentabilité, faisant reculer le bénéfice net de 17 % au deuxième trimestre 2026.”
- “Le constructeur américain ouvre un magasin éphémère dans le centre commercial de Paris-La Défense, tandis qu'il inaugure également un pop-up store estival au Touquet (62). Ce développement s'accompagne de l'implantation de nouvelles bornes de recharge près de l'aéroport de Roissy-CDG (95).”
Knowledge base / docs
这些文本证据代表了一个结构化的知识库，详细说明了技术人员使用的诊断程序和文档标准，向买家保证了数据的一致性和可靠性。
Maintenance logs
这些是时间序列维护日志，涵盖了完整的服务生命周期，从日常任务到Tesla和Rivian车型的复杂超出保修期诊断，对于训练故障预测算法至关重要。
IoT / sensor data
这些时间序列数据由详细的电池健康状态评估和动力总成扫描组成，提供了对电动汽车电池退化和性能进行建模所需的原始诊断遥测数据。
Inspection reports
这些是结构化的检查记录，记录了车辆状况并捕获了关键的故障代码审查，提供了直接将症状与特定系统故障联系起来的标记数据点。
Event streams
这些时间序列数据代表了用于识别重复故障模式的聚合事件流，提供了一个预先分析的层，非常适合优化车队维护策略和识别系统性问题。
Marketplace
Dataset details
Detailed schema & sample available on access request.
Want this data?
Request access — we broker a secure deal room. Operator-reviewed, no automatic sharing.
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
Electrifiedgarage 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 for vehicles market was estimated at $4.66 billion in 2024, with a projected CAGR of 17.5% (2025-2034) (source: Global Market Insights Inc.). [2]. Investment score 78.8/100 (confidence 0.65). Recommended action: Data Sharing Agreement.
From the marketplace
Explore live data opportunities
Meximpex — 公司记录语料库机会
View opportunity →工业Moltexflex — 监管记录数据集机会
View opportunity →移动出行Musc — 工业运营数据集机会
View opportunity →