Dataset opportunity
Sparkcharge — 移动遥测数据集机会
Sparkcharge 持有的中等规模移动遥测数据集，可用于预测性维护和异常检测。
Score
76.1
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
全球汽车预测性维护市场预计到 2033 年将达到 123 亿美元，复合年增长率为 20.5%（2026-2033 年）。[15]
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-12
Renaut, Stellantis et Volkswagen unissent leurs voix pour infléchir le "Made in Europe"
journalauto.com ↗ - 📰press2026-06-12
Véhicule de fonction : les règles du jeu se précisent pour les modèles électriques écoscorés
journalauto.com ↗ - 📰press2026-06-12
Bornes : une autre association alerte sur l’opacité tarifaire de la recharge
journalauto.com ↗ - 📰press2026-06-11
Distribution automobile : l’heure délicate des successions familiales
journalauto.com ↗ - 📰press2026-06-11
Stellantis dope une Charger avec une batterie solide
journalauto.com ↗
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
移动遥测数据集
Modality
时间序列
Sector
移动
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可授权
Buyer persona
工业人工智能与维护优化供应商
Sparkcharge 拥有宝贵的移动遥测数据集，以时间序列模式呈现。该数据集直接来自 Sparkcharge 的专有物理硬件 Roadie 和 PowerHub 系统，捕获真实的 `event_streams`、`geo_data` 和 `iot_data`。其在预测性维护用例中的核心优势在于跨多种电动汽车型号收集的高分辨率电池放电和健康遥测数据，为开发和训练预测算法提供了丰富的基础。
全球汽车预测性维护市场预计到 2033 年将达到123 亿美元，复合年增长率为20.5%。[15] 虽然访问此数据集需要协商，因为其中一部分已用于 SparkAI 的运营优化，但这种复杂性凸显了其稀缺性和战略价值。该数据集独特的来源和详细的遥测数据为寻求在快速增长的市场中构建卓越预测性维护解决方案的 AI 买家提供了独特的竞争优势。[15] ⚠ 尽职调查（有价值的数据，可协商访问）：数据由专有物理硬件（Roadie、PowerHub）生成；SparkAI 已将部分数据用于运营优化；数据集包含跨多种电动汽车型号的高分辨率电池放电和健康遥测数据 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Sparkcharge 的证据证明其拥有大规模、专有数据集，捕获了数百万次按需电动汽车充电事件。这些独特的时间序列和遥测数据是 AI 供应商构建电动汽车电池和充电硬件预测性维护模型的关键资产。在预计将超过 120 亿美元的汽车预测性维护市场中，该数据集提供了预测电池退化、优化车队运营和创建高价值 AI 解决方案所需的真实信号。
See dimension details ↓- Dataset Specificity90
主导的 'iot_data'，行业移动，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 Freshness82
实时/流式传输
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 Demand85
汽车预测性维护市场，该市场基本依赖于移动遥测数据，预计在 2023 年至 2033 年间将以 23.9% 的强劲复合年增长率增长，表明买家需求非常强劲且不断增长。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
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 License92
所有权=已拥有，授权=干净
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 个数据需求信号（2 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，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
⚠ 审查 — SparkCharge 的核心业务是销售移动电动汽车充电硬件和捆绑的“充电即服务”（CaaS），其中包括用于管理充电运营的软件平台，使其成为智能的销售者且不适合。问题：该公司的主要产品是“充电即服务”（CaaS），这是硬件、能源和软件的捆绑产品。[3, 9, 12]；CaaS 产品包括一个软件平台，提供实时监控、数据洞察和自动报告。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
该数据集包含来自公司移动充电硬件的精细物联网传感器遥测数据，直接证明了能源交付和电池健康，可用于对组件级性能进行建模。
Event streams
此证据证实了大规模事件流，详细记录了超过 630 万千瓦时的交付量，其中包括宝贵的车辆特定充电配置文件和使用模式，这对于训练强大的AI 模型至关重要。
Geospatial data
该数据集包含表格地理空间数据，精确识别了车队车辆在何处以及何时需要离网充电，从而能够构建预测能源需求和优化物流的模型。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Rolling 12 months
Update frequency
Real-time
Delivery
API
Formats
JSON, Time Series
License
One-time license for predictive maintenance model development and training, with restrictions on redistribution of raw data.
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 dataset's value is driven by its high-resolution, proprietary EV battery telemetry, crucial for the rapidly growing predictive maintenance market. The real-time freshness and exclusive nature of this data, sourced directly from hardware, position it as a premium asset.
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
Sparkcharge Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Vehicle Predictive Maintenance market is projected to reach $12.3 billion by 2033, growing at a CAGR of 20.5% (2026-2033). [15]. Investment score 76.1/100 (confidence 0.49). Recommended action: Acquire.
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