Enterprise AI Analysis
Multi-Source Data Fusion for Identifying Top Meteorological Experts
Against the backdrop of rapid advancements in climate change and smart meteorology, the meteorological field is increasingly reliant on high-end, scarce experts. This paper proposes a multi-source data fusion-based method for identifying meteorological experts in high-end, scarce fields, along with a corresponding expert database system architecture. By integrating heterogeneous data sources including academic papers, collaboration networks, patents, research projects, and academic services, we develop a multidimensional evaluation model centered on academic influence, network influence, and industry contribution.
Executive Impact at a Glance
Our analysis reveals the transformative potential of multi-source data fusion for expert identification and management in critical fields like meteorology.
0.90
Correlation with Manual Selection
87.2%
Identification Accuracy
85.6%
Recommendation Accuracy
1.1s
Average Retrieval Response Time
Deep Analysis & Enterprise Applications
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Comprehensive Data Fusion for Expert Identification
The proposed system integrates multi-source data (academic papers, collaboration networks, patents, research projects, academic services) into a unified fusion framework. It employs an AHP-entropy weight method to determine indicator weights across academic influence, network influence, and industry contribution, establishing comprehensive scoring and classification rules. The system is built on a three-tier architecture supporting automatic identification, intelligent recommendation, and visual analysis.
Superior Accuracy and Robust Expert Profiling
Experiments with 1,000 meteorological experts showed the multi-source fusion model significantly outperforms single-method approaches, achieving a 0.90 correlation coefficient with manual results. The expert hierarchy follows a distinct pyramid structure, confirming robust macro-level rationality. Identified high-end experts exhibit strong academic influence (1.8x general group), high network centrality, and significant industry contributions (patents, major projects).
Efficient & Stable System Operation
The system demonstrates high efficiency and reliability: 87.2% accuracy in identifying high-end and scarce experts, 85.6% recommendation accuracy, and an average retrieval response time of 1.1 seconds. It also shows good stability and robustness, with an average expert level change of less than 3% per year and a retention rate of over 95% for high-precision experts under small data disturbances.
0.90
Correlation Coefficient of Multi-Source Fusion Model with Manual Selection Results
Enterprise Process Flow
Unified Collection & Preprocessing of Multi-Source Data
→
Construct Fusion Dataset
→
Extract Core Metrics (Academic, Network, Industry Influence)
→
Apply Multi-Criteria Weighting (AHP-Entropy)
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Automated Expert Screening & Classification
→
Deployment into Expert Database System
| Method | Correlation Coefficient | Key Advantages of Fusion Model |
|---|---|---|
| Single Bibliometrics | 0.60 |
|
| Network Analysis Method | 0.72 |
|
| Multi-Source Fusion Model (This Paper) | 0.90 |
|
Case Study: National Meteorological Talent Initiative
A leading national meteorological agency faced challenges identifying high-end, scarce experts crucial for advancing climate modeling and disaster prediction. Their existing methods were manual, subjective, and failed to capture the full spectrum of expert contributions—academic, collaborative, and industrial. By implementing a system based on Multi-Source Data Fusion, integrating data from publications, projects, patents, and service records, the agency successfully deployed an intelligent expert database. This resulted in a 87.2% accuracy in identifying top-tier talent, significantly streamlining the selection process and enabling more effective allocation of human capital to critical national projects, transforming talent management from reactive to strategic.
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Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your Implementation Roadmap
Our structured approach ensures a smooth and effective integration of multi-source expert identification into your operations.
Phase 1: Data Integration & Preprocessing
Consolidate heterogeneous data sources (papers, patents, projects, services), clean, standardize, and build the fusion dataset for comprehensive expert profiling.
Phase 2: Model Development & Calibration
Construct the multidimensional evaluation model, determine indicator weights using AHP-Entropy, and establish robust scoring and classification rules tailored for high-end talent.
Phase 3: System Architecture & Deployment
Design and implement the three-tier expert database system, enabling automated identification, intelligent recommendation, and visual analysis of expert networks.
Phase 4: Validation & Optimization
Conduct simulation experiments to validate identification accuracy and system performance, continuously refine the model, and ensure engineering applicability.
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