Dataset opportunity
Gurusystems — 传感器遥测数据集机会
Gurusystems 持有的大型传感器遥测数据集，可用于预测性维护和异常检测。
Score
65.5
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
60%
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
全球预测性维护市场 = 2025年438.8亿美元，复合年增长率26.2% (2025-2035) (来源: Market Research Future)
Recent dated external facts that triggered this opportunity — auditable provenance.
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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
传感器遥测数据集
Modality
时间序列
Sector
其他
Volume
大
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
聚合/第三方 — GDPR 敏感（个人身份信息审查）
Buyer persona
工业AI和维护优化供应商
Gurusystems 拥有丰富的传感器遥测数据集，包含从客户拥有的热网收集的时间序列数据。这些广泛的物联网数据，通过数据量和开发者门户得到证明，捕获了对理解设备随时间行为至关重要的连续运行参数。其结构化特性使其非常适合预测性维护应用，能够识别供暖基础设施中细微的异常和退化模式。
预测性维护市场在2025 年价值 438.8 亿美元，预计到2035 年将达到 4496 亿美元，复合年增长率为 26.2%，这表明此类数据具有显著的商业价值。尽管在协商数据使用协议和管理与个人能源消耗相关的GDPR 敏感信息方面存在复杂性，但这种高质量数据的稀缺性及其在减少计划外停机和维护成本方面的直接适用性，使其对人工智能买家来说具有非凡的价值。⚠ 尽职调查（有价值的数据，协商访问权）：数据从客户拥有的热网收集，需要特定的数据使用协议；包含与个人能源消耗相关的 GDPR 敏感信息。· 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 Gurusystems 拥有源自真实热网的独特且专有的时间序列传感器遥测数据集，以高频率捕获。这些详细的关键运行参数的粒度数据正是工业人工智能和维护优化供应商开发和完善先进预测性维护模型所需要的。随着全球预测性维护市场预计到 2025 年将达到 438.8 亿美元，该数据集为在快速扩张的行业中获得竞争优势提供了及时且宝贵的机会。
See dimension details ↓- 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 Volume86
6 个证据命中，明确提及数据量
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
人工智能驱动的预测性维护市场依赖于传感器遥测数据，预计从 2025 年到 2032 年的复合年增长率为 39.5%，这表明对此类数据集的买家需求非常高。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility32
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 Strength80
4 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 License10
所有权=聚合，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 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，5 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. - ICP Audit58
⚠ 审核 — Gurusystems 不是一个好的目标，因为他们的核心业务是销售数据分析平台和从他们收集的数据中获得的智能，这明确排除了 d-nvest 的范围。问题：Gurusystems 的核心业务是为热网提供硬件和数据分析平台，这涉及到销售其产品（如 Guru）所衍生的智能和分析；他们收集的数据不是休眠的，而是活跃使用的，并作为其产品的一部分进行销售。
- Dataset Specificity62
占主导地位的“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.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
这些证据直接证实了 Gurusystems 使用专有硬件从热网捕获详细时间序列数据的能力，提供了对预测性维护和性能分析至关重要的关键传感器读数。
Developer portal
这指的是 Gurusystems 的面向公众的开发者信息，展示了其技术对住宅开发商和热供应商系统性能的影响，这表明了对专注于运营改进的合作伙伴的价值。
Data-volume signal
这证实了每五分钟从其 Hub 设备捕获的性能数据的高频率，提供了先进预测建模和实时洞察所需的粒度细节。
Regulatory records
这表明收集的数据支持遵守热网的监管合规性和行业行为准则，为在受监管环境中运营的组织增加了显著价值。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series
License
One-time license for predictive maintenance use cases, with potential restrictions on redistribution or resale.
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 rarity as proprietary sensor telemetry from client-owned heat networks, coupled with large volume and real-time freshness, catering to the rapidly growing predictive maintenance market.
Detailed schema & sample available on access request.
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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
Gurusystems Sensor Telemetry — a Large sensor telemetry dataset (Time Series modality) in the other domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market = USD 43.88 Billion in 2025, CAGR 26.2% (2025-2035) (source: Market Research Future). Investment score 65.5/100 (confidence 0.6). Recommended action: Data Sharing Agreement.
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