Effective collaboration between UAV operators and intelligent control systems is crucial for the successful completion of tasks. To address the operational safety risks of UAVs during the expansion of the low-altitude economy, this study proposes and establishes a hierarchical competency assessment system for UAV operators. The system is based on the competency framework for operators proposed in the “Regulations on the Management of Civil Unmanned Aerial Vehicle Operators,” and, considering the differing competency requirements for micro/small, medium, and large UAVs in typical scenarios, 12 core competency indicators are identified. Grey relational analysis and the Delphi method are used for comprehensive weighting, and the Lagrange operator is introduced to integrate subjective and objective weights, creating a comprehensive weight model. The results show that the core competencies for mainframe operators are focused on problem decision-making and team leadership. Medium UAV operators emphasize program execution and automated management, while micro/mini UAV operators focus on situational awareness and anomaly handling. This research provides theoretical support for differentiated training assessments and lays the foundation for the industry to establish a dynamic competency evaluation mechanism that integrates human-machine collaboration.