Research on an Intelligent Decision Support Methodology for Complex Equipment System Architecture Design

Decisions made during the architecture design phase are critical to the development of complex equipment systems, as they often define the performance boundaries and guide subsequent research directions. To address challenges in early architecture design decision-making for complex equipment, such as difficulties in mathematical representation, low efficiency in solution generation, design space combinatorial explosion, and the challenge of balancing the diverse needs of multiple stakeholders, this paper proposes intelligent decision support techniques for complex equipment system architecture design. The proposed framework involves six key stages of architecture decision-making: (1) architecture decision representation based on morphological matrices; (2) system architecture generation driven by the integration of domain knowledge and reinforcement learning; (3) architecture design space reduction using constraint management; (4) multi-stage optimization of large-scale system architecture decisions; (5) multi-objective trade-offs in system architecture considering stakeholder preferences; and (6) digital evaluation and verification of architecture decisions driven by the fusion of mechanism models and data. Finally, a typical case study on the stage separation design of a launch vehicle demonstrates the feasibility and effectiveness of the proposed framework and key techniques, providing theoretical and technical references for the practical application of architecture decision-making in complex equipment systems.