Strategic Command Center Algorithm: A "Qi-Zheng" Dual-Layer Framework for Dynamic Decision-Making in Non-Stationary Adversarial Environments

This paper presents the Strategic Command Center Algorithm, an advanced decision-making framework rooted in the "Qi-Zheng" philosophy to address the challenges of non-stationary adversarial systems. Breaking through the limitations of stability-oriented traditional control systems, the algorithm integrates quantum statistical mechanics, non-equilibrium physics, and game theory to realize dynamic balance via "controlled active entropy increase". Its core innovation lies in the dual-layer architecture: the "Zheng" layer (ground state) adopts PPO reinforcement learning to ensure energy-efficient and stable long-term decision returns; the "Qi" layer (excited state) acts as a meta-controller, triggering controlled randomness or deceptive actions to avoid adversarial traps when system vulnerability or entropy rises. Complemented by Bayesian update for belief iteration, multi-mode dynamic switching (Conservative/Balanced/Aggressive/Deceptive), and three-tier resource optimization (core/support/reserve), the system achieves cognitive anti-interference capability and cost-effective strategic space expansion. Experimental results validate its superiority in win rate and adaptive resource allocation compared to pure PPO algorithms, with cross-disciplinary applications in cybersecurity, market competition, evolutionary games, and complex system dynamics. This work redefines adversarial decision-making from passive stability maintenance to active dynamic adaptation, providing a reproducible paradigm for non-equilibrium system control.