Dataset opportunity
Sruav — 传感器遥测数据集商机
由 Sruav 持有的中等规模传感器遥测数据集，可用于预测性维护和异常检测。
Score
69.4
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
采购
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
全球预测性维护市场 = 2025年为156亿美元，预计到2034年将达到910.4亿美元，复合年增长率（2026-2034）为21.01%
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.
- ✨Signal
使用机器学习进行无人机探测和识别
source ↗
Profile
Dataset profile
Type
传感器遥测数据集
Modality
时间序列
Sector
其他
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可授权
Buyer persona
工业人工智能和维护优化供应商
Sruav 拥有一个传感器遥测数据集，具有时间序列模式，其开发者门户、事件流和物联网数据均可证明。该数据集捕获各种资产的连续运行参数，非常适合预测性维护应用，能够检测预示潜在故障的异常和模式。将此数据与 AI/ML 模型集成，可以实现主动干预，显著减少设备停机时间并优化运营效率。
全球预测性维护市场预计到 2034 年将达到910.4 亿美元，从 2026 年到 2034 年的复合年增长率 (CAGR) 为 21.01%。这一可观的市场增长突显了对高质量传感器数据以支持 AI/ML 模型的高需求，这些模型可将计划外停机时间减少 35-45%，将维护成本降低 5-10%。尽管由于敏感的国防/安全部门数据和客户数据（军事、执法）限制而存在访问复杂性，但此类专业数据的稀缺性和关键性质使其在提高这些行业的运营效率和任务就绪度方面具有极高的价值。⚠ 注意（有价值的数据，可协商访问）：敏感的国防/安全部门数据；客户数据（军事、执法）可能有特定的访问限制 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Sruav 提供高度专有的传感器遥测数据集合，主要为时间序列模式，源自专注于无人机探测和中和的高级电子战和网络化平台。此独特数据集对于旨在开发尖端预测性维护解决方案的工业人工智能和维护优化供应商来说具有极高的价值。随着全球预测性维护市场预计到 2034 年将超过 910 亿美元，这种高稀缺性数据为寻求立即创新和占领市场份额的买家提供了显著的竞争优势。
See dimension details ↓- Dataset Specificity62
主导的“物联网数据”，行业其他，2 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
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 Value74
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
全球预测性维护市场严重依赖传感器遥测数据进行人工智能/机器学习分析，预计从 2026 年到 2033 年的复合年增长率 (CAGR) 为 27.9%。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility62
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 Feasibility4
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=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 Orientation39
1 个数据需求信号（1 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 — SteelRock Technologies 开发和部署反无人机系统和无人机平台，作为其运营业务的副产品生成传感器遥测数据，并且似乎不将其数据或派生情报作为其核心产品进行销售。问题：未明确确认 SME 地位，未提供具体员工人数或收入数据，但他们似乎不是大型企业。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Developer portal
来自开发者门户的这一证据展示了 Sruav 在电子战系统和网络化平台方面的基础专业知识，为他们传感器数据的复杂来源提供了关键背景。
IoT / sensor data
这直接证实了与射频探测和自主威胁中和相关的时间序列数据的可用性，这对于预测性维护应用高度相关。
Event streams
这些事件流进一步验证了时间序列数据的存在，强调了其在机器学习中用于无人机识别和探测的应用，突显了其在高级分析模型中的效用。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Continuous
Delivery
API
Formats
Time Series, JSON
License
One-time license for predictive maintenance and AI/ML model development. Usage restrictions may apply.
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 sensor telemetry dataset is highly valuable for predictive maintenance applications, driven by strong demand in a rapidly growing market. Its rarity and real-time freshness command a premium.
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
Sruav Sensor Telemetry — a Moderate sensor telemetry dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = $15.60 billion in 2025, projected to reach $91.04 billion by 2034, with a CAGR of 21.01% (2026-2034). Investment score 69.4/100 (confidence 0.49). Recommended action: Acquire.
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