Dataset opportunity
Bump Charge — 维护日志数据集机会
由 Bump Charge 持有的中等规模维护日志数据集，可用于预测性维护和异常检测。
Score
70
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
56%
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 = 到 2030 年达到 1300 亿美元，复合年增长率 21% (2024-2030)
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
移动出行
Volume
中等
Freshness
实时
Rarity
高 (专有)
Accessibility
受限
Legal
混合所有权 — GDPR敏感 (PII审查)
Buyer persona
工业人工智能和维护优化供应商
Bump Charge 拥有丰富的维护日志数据集，主要以时间序列模式呈现，这对于出行领域的预测性维护极具价值。该数据集通过整合地理数据、物联网数据、维护日志和交易数据而独具特色，提供了资产性能和运营背景的全面视图。这种细粒度、多模式的数据对于开发能够预测设备故障、优化维护计划和延长资产寿命的复杂人工智能模型至关重要。
汽车行业的预测性维护市场预计到2030年将超过1300亿美元，从2024年起以惊人的21%复合年增长率增长。如此巨大的市场规模和增长凸显了人工智能买家对能够将停机时间减少30-50%并将维护成本降低20-40%的数据的强烈需求。利用此类数据的解决方案每月每资产成本为50-200美元，或每年每关键资产成本为1,500美元。尽管作为一家投资公司（DIF Capital Partners）的子公司，并且包含GDPR敏感数据（这使数据成本增加约20%），但该数据集的稀有性和深度使其在实现显著运营效率和成本降低方面具有极高的价值。⚠ 尽职调查（有价值的数据，可协商访问）：投资公司（DIF Capital Partners）的子公司；数据集包含GDPR敏感个人数据 · 公司：DIF Capital Partners 的子公司。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Bump Charge 拥有电动汽车充电基础设施的专有且稀有的维护日志数据集，提供对预测性维护模型至关重要的时间序列数据。这些独特的数据直接满足了工业人工智能和维护优化供应商的需求，使他们能够进入快速增长的1300亿美元全球汽车预测性维护市场。其对资产健康和运营模式的洞察对于优化新兴电动汽车充电生态系统中的正常运行时间并降低成本具有极高价值，使其成为专注于出行和基础设施可靠性的人工智能买家及时且具有战略意义的收购。
See dimension details ↓- Dataset Specificity100
主导的‘维护日志’，出行领域，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 Volume58
4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
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 Demand92
人工智能驱动的预测性维护市场严重依赖数据，预计从2025年到2032年将以39.5%的复合年增长率增长，表明对相关数据集的需求非常高且不断增长。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/受监管
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
中等难度，DIF Capital Partners 的子公司
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength74
4种证据类型，4个命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
所有权=混合，许可=GDPR敏感
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence50
DIF Capital Partners 的子公司
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0个数据需求信号 (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，3个近期外部信号 — 超出已货币化范围的专有数据
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 Audit92
✓ 良好目标 — Bump Charge 是一家电动汽车充电基础设施运营商，其核心运营业务会产生有价值的维护日志数据作为副产品，并且似乎不将其作为主要产品出售，这使其成为数据市场的一个良好目标。问题：尽管 Bump Charge 成立于2021年，是一家初创公司，但其在2022年获得了1.8亿欧元的巨额融资，并制定了雄心勃勃的扩张计划（部署25,000个充电；提示中提到了“维护日志数据集机会”
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “Porté par des primes à l'achat, le marché allemand est resté stable en mai 2026, avec 239 448 unités (+0,1 %), grâce aux véhicules électriques qui ont progressé de 39,3 %. Mais cette dynamique bénéficie surtout aux marques étrangères, faute d'écoscore.”
- “<figure><div><img src="https://imgproxy.divecdn.com/9AN7qQk1bR0ziOjpB_hUg2_FZbQJ-hLoCvjgotdk6Dw/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0xNTkwNjE2NTg4XzEuanBn.webp" /></div></figure><p>Certain light-duty vehicles have the potential to earn around $3,000 per summer, and school buses $12,000, by enrolling in the state’s virtual power plant, a state program manager said.</p>”
- “En mai 2026, pour la première fois, les livraisons de voitures électriques ont été majoritaires sur les canaux BtoB. Une nouvelle illustration du bouleversement énergétique en cours dans les flottes automobiles.”
IoT / sensor data
此证据详细说明了智能电动汽车充电站的实时和历史性能指标，为优化资产利用率和网络管理提供了关键的运营洞察。
Transaction data
此数据捕获了付费充电会话的交易详情，包括时间和能源消耗，直接支持计费、收入管理和用户行为分析。
Geospatial data
此证据表明可提供与路线信息集成的地理空间数据，从而实现其网络内电动汽车充电的网络优化和用户引导。
Maintenance logs
此核心数据集包含电动汽车充电基础设施的时间序列维护日志，详细记录了与终端预订、监控和盈利能力跟踪相关的活动，在开发预测性维护解决方案方面备受追捧。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
JSON, Time Series
License
One-time license for internal use in predictive maintenance model development and deployment.
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 dataset of EV charging maintenance logs, enriched with IoT and transaction data, is critical for predictive maintenance in the rapidly growing mobility sector. Its unique, multi-modal nature directly addresses demand from Industrial AI and maintenance optimization vendors.
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
Bump Charge Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Automotive Predictive Maintenance Market = $130 Billion by 2030, CAGR 21% (2024-2030). Investment score 70.0/100 (confidence 0.56). Recommended action: Data Sharing Agreement.
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