Dataset opportunity
Trinityenergy — 工业传感器数据集机会
Trinityenergy 持有的中等规模工业传感器数据集，可用于预测性维护和异常检测。
Score
48
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在 2025 年的估值为 142 亿美元，预计复合年增长率为 27.9%（2026-2033 年）（来源：Grand View Research）。[1]
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
工业传感器数据集
Modality
时间序列
Sector
工业
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
Trinityenergy 持有一个宝贵的时间序列数据集，该数据集由其专有的模块化能源系统和微电网生成的 `industrial_data`、`event_streams` 和 `iot_data` 组成。这种精细的工业传感器数据非常适合开发和训练高精度预测性维护模型，从而能够在设备发生故障之前进行预测。
预测性维护的全球市场正在经历显著扩张，2025 年市场价值为142 亿美元，预计将以27.9% 的复合年增长率增长。[1] 虽然访问需要进行谈判，因为遥测所有权可能与商业客户共享，并且数据存储在内部平台中，但该工业数据的稀有性及其对高增长市场的直接适用性使其成为人工智能买家的主要资产。⚠ 尽职调查（有价值的数据，可协商访问）：数据由专有的模块化能源系统和微电网生成；遥测所有权可能与商业客户（车队、酒店业）共享，具体取决于服务合同；数据很可能存储在内部监控和调试平台中。· 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 Trinityenergy 拥有来自工业可再生能源系统实际运行的专有时间序列数据，包括太阳能发电、存储和电动汽车充电基础设施。这种高稀有度的数据集直接适用于开发预测性维护解决方案的 AI 供应商，以应对快速扩张的能源行业。在一个预计将超过 142 亿美元的市场中，这些数据提供了训练模型所需的真实情况，以优化资产性能、预测故障并释放显著的运营效率。
See dimension details ↓- 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，许可=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 — 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. - Dataset Specificity90
主导的“iot_data”，行业工业，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 次证据命中
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 Demand90
人工智能买家需求极高，这得益于预测性维护市场的快速增长，预计复合年增长率为 27.9%。[1]
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 Feasibility44
低难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - ICP Audit67
⚠ 审查 — 该公司的核心业务是销售和实施智能、监控能源系统，其中分析和性能数据是产品的重要组成部分，使其成为智能供应商，而不是休眠数据持有者。问题：核心业务是销售智能：公司的服务明确包括“分析”和“监控”以报告现场性能，这是一种自我服务；品牌混淆的可能性很高：全球有多个不同的公司名为“Trinity Energy”，包括一家在印度专注于智能电表的公司；数据是核心产品的一部分：该公司将“先进的硬件和软件集成到为客户提供的单一、连贯的解决方案中”，这意味着数据是
- Deep Qualification80
✓ 通过 — Trinity Energy 作为模块化能源系统的服务提供商运营，工业传感器数据的生成是其监控服务的合理副产品；但是，数据所有权可能与客户混合，并且没有找到明确的数据许可条款。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<figure><div><img src="https://imgproxy.divecdn.com/T0Dx-Dse_0slS4uKYoAvwButYuiNLFrS36AvjjcRuXs/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9TYW50YV9DbGFyaXRhX3Jvb2Z0b3Bfc29sYXIuanBn.webp" /></div></figure><p>The announcement comes as ratepayer and clean energy advocates push capacity-hungry hyperscalers to subsidize residential solar, batteries and energy efficiency.</p>”
- “<figure><div><img src="https://imgproxy.divecdn.com/KP-NZsE3EojMbciTvTHP9YO7oY_yJgVY9INp39Hf1ug/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0xMjI0OTEyMTA4X1JPQ05hbnAuanBn.webp" /></div></figure><p>“Regardless of the cost allocation methodology that is chosen, there remains a glaring cross-class subsidization occurring to the benefit of new [large load] customers,” said State Corporation Commission attorney Andrew Major.</p>”
- “<figure><div><img src="https://imgproxy.divecdn.com/uCtgVszpaTIDRRQ7A85xq8v-xRXoiIyM7WpG36OW2Ns/g:ce/rs:fill:1600:900:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9zdG9uZV9yaWRnZV9kYXRhX2NlbnRlci5qcGc=.webp" /></div></figure><p>The technology is there. The institutions are not. FERC’s July 23 technical conference on the PJM Interconnection’s governance is where that gap gets addressed — or doesn’t, writes Jeanine Johnson, a former member of the grid operator’s board.</p>”
IoT / sensor data
这些证据证实了来自太阳能发电系统的物联网数据的存在，提供了宝贵的精细运营信号，可用于训练资产监控和性能优化模型。
Industrial data
该信号指向涵盖可再生能源系统完整生命周期的工业级数据，这对于构建优化系统级效率和可靠性的强大模型至关重要。
Event streams
该数据集包括来自特定高价值操作（如电动汽车充电和备用电源激活）的事件流，这对于开发精确的异常检测算法至关重要。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Trinityenergy Industrial Sensor — a Moderate industrial sensor dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at USD 14.2 billion in 2025, with a projected CAGR of 27.9% (2026-2033) (source: Grand View Research). [1]. Investment score 48.0/100 (confidence 0.49). Recommended action: Acquire.
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