Expert Matching System for Power Grid Technology Project Evaluation

Abstract

Evaluating power grid technology projects requires an efficient expert matching system to ensure accurate assessments. Current manual or semi-automatic approaches struggle with multi-domain batch processing due to computational complexity and inadequate handling of heterogeneous expertise requirements. This paper proposes a system using domain relevance vectors and the Kuhn-Munkres (KM) algorithm to optimize cross-domain expert selection for concurrent projects. The system integrates TF-IDF-based domain labeling, entropy-weighted expert profiling, and KM-based global assignment to address the batch multi-domain matching problem in power grid project evaluation. Validated with data from one power grid research institute (600 experts and 1,000 projects), the system outperforms traditional methods such as Greedy and Random Selection, particularly in scenarios with diverse project domains. The KM algorithm achieves higher domain coverage than conventional approaches, ensuring balanced expertise allocation across all technical fields within project batches while maintaining operational feasibility.

Keywords

Matching system / technology projects / power grid / Kuhn-Munkres algorithm / batch assignment

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Systems Engineering Society of China and Springer-Verlag GmbH Germany

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