Dataset Overview
| Dataset Name | Industry Solution Dataset |
| Total Data Volume | 22,800 (program documents + industry reports + case studies + technical white papers) |
| Covering industry | Construction/engineering, IT/internet, manufacturing, finance, energy, healthcare, education, agriculture, logistics |
| Type of programme | Digital solutions / informatization construction solutions / industry studies / feasibility analysis / technology selection |
| Time Span | 2019–2026 |
| Data Source | Consulting firm reports, industry associations, public company announcements, government industry planning |
| Annotation Method | Industry analysts + field experts audit |
Detailed Description of Data Dimensions
| Field Name | Data Type | Value Range / Description |
|---|---|---|
| doc_id | String(32) | Document_UNIQUE_TECH__Evil _UNIQUE_TECH___ |
| industry | Enum | CONSTRUCTION | IT | MFG | FINANCE | ENERGY | HEALTHCARE | EDUCATION | AGRICULTURE | LOGISTICS |
| doc_type | Enum | SOLUTION | REPORT | CASE_STUDY | WHITEPAPER | FEASIBILITY | TECH_SELECTION |
| title | Text | Document Title |
| publish_year | Integer | Year issued |
| source_type | Enum | CONSULTING | ASSOCIATION | LISTED_CO | GOV_PLANNING | ACADEMIC |
| key_technologies | Array | Core technology fields covered |
| market_size_ref | Object | {value, unit, year, source} |
| competitive_landscape | Array | [{company, market_share, strength, weakness}] |
| key_insights | Array | List of core summary fields |
De-identified JSON Data Sample
[ { "doc_id": "SOL-2024-IT-08231", "industry": "IT", "doc_type": "SOLUTION", "title": "[a large enterprise][digitization]Solutions", "publish_year": 2024, "source_type": "CONSULTING", "key_technologies": ["[]", "[]data", "AI[]", "[]Services[]", "data[]"], "market_size_ref": {"value": 5800, "unit": "[]", "year": 2024, "source": "[]"}, "competitive_landscape": [ { "company": "[]", "market_share": 18.5, "strength": "Full-stack[]", "weakness": "[]"}, { "company": "[]", "market_share": 22.3, "strength": "[]", "weakness": "[]"} ], "key_insights": ["Enterprisedigitization''''", "datadigitization"] }, { "doc_id": "SOL-2023-CONST-01562", "industry": "CONSTRUCTION", "doc_type": "CASE_STUDY", "title": "[]platform", "publish_year": 2023, "source_type": "ASSOCIATION", "key_technologies": ["IoT", "BIM", "AI[]", "[]"], "market_size_ref": {"value": 320, "unit": "[]", "year": 2023, "source": "[]"}, "competitive_landscape": [ { "company": "[]", "market_share": 25.0, "strength": "BIM[]", "weakness": "AI[]"} ], "key_insights": ["[]Lower[]45%", "BIM+IoT[]"] }, { "doc_id": "SOL-2024-ENERGY-03421", "industry": "ENERGY", "doc_type": "REPORT", "title": "2024[digitization]reports", "publish_year": 2024, "source_type": "ASSOCIATION", "key_technologies": ["[]", "[]", "[]AI"], "market_size_ref": {"value": 18000, "unit": "[]", "year": 2024, "source": "[]"}, "competitive_landscape": [ { "company": "[]", "market_share": 15.2, "strength": "[]", "weakness": "[digitization]"} ], "key_insights": ["digitization", "AI[]3-5%"] }, { "doc_id": "SOL-2025-FIN-00789", "industry": "FINANCE", "doc_type": "WHITEPAPER", "title": "[finance]IndustriesLarge Models[]（2025）", "publish_year": 2025, "source_type": "CONSULTING", "key_technologies": ["[]LanguageModels", "RAG", "Knowledge Graph", "Intelligent Risk Control"], "market_size_ref": {"value": 1200, "unit": "[]", "year": 2025, "source": "[]Consultants"}, "competitive_landscape": [ { "company": "[]", "market_share": 20.1, "strength": "[finance]Scenes[]", "weakness": "Compliance[]"} ], "key_insights": ["[finance]Large Models[]Intelligent Customer Service and []Domains[]", "Compliance[]Large Models[finance]"] }, { "doc_id": "SOL-2024-HEALTH-01234", "industry": "HEALTHCARE", "doc_type": "FEASIBILITY", "title": "[]Researchreports", "publish_year": 2024, "source_type": "GOV_PLANNING", "key_technologies": ["HIS[]", "AI-Assisted Diagnosis", "[]", "5G[medical]"], "market_size_ref": {"value": 980, "unit": "[]", "year": 2024, "source": "[]"}, "competitive_landscape": [ { "company": "[]", "market_share": 12.8, "strength": "[]", "weakness": "AI[]"} ], "key_insights": ["Electronic Medical Records", "AIImaging[]30%[]"] }
]
AI Training Use Cases
Auto-generated industry solutions
Applied model: LLM + RAG + Multi-agent (AutoGen / CrewAI). Automatically generates customized solution documents based on industry knowledge bases.
Industry trend analysis and forecasting
Applied model: Sentence-BERT + time series forecasting. Key trend signals are extracted from large industry reports, and the direction of technological development is projected.
Competitive Intelligence Analysis
Models applied: NER + knowledge graph + GNN. Automatically builds a knowledge graph of industry competition patterns to identify market opportunities and threats.
Technical Selection to Support Decision-Making
Models applied: Decision Tree + Multi-Criteria Decision Analysis (AHP/TOPSIS). A technical insights model based on historical project data.
Get This Dataset
The industry solution datasets are authorized by industry and document type.
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