Bridging Quantitative Scoring and Qualitative Grading: A Mapping Framework for Intelligent System Evaluation

In current evaluations of intelligent system capabilities, there is a problem with the difficulty of unifying quantitative scoring results with qualitative grading standards, which severely limits cross-platform and cross-task intelligent comparisons and grading certifications. To address this issue, this paper proposes a hierarchical parameter partitioning equivalent mapping method (HPP-EM) to construct a mapping mechanism from quantitative scores to qualitative grades. The method comprises three modules: Nonlinear Interval Partitioning (NIP) constructs intelligent capability intervals based on the principle of diminishing capability margins; Quantification-to-Grade Mapping (QGM) introduces a set of criteria to determine mapping rules; and Dynamic Threshold Generation (DTA) calculates the lower score thresholds and total score thresholds for each grade based on indicator weights. The model is interpretable and supports mapping across multiple metrics and multi-level systems. Experimental validation was conducted using evaluation data from a deep-space unmanned system, demonstrating that HPP-EM accurately reflects system intelligence levels and exhibits good adaptability and scalability. This paper provides a traceable, structured mapping scheme for unified intelligence level assessment.