飚刹推拉量子叠加态理论动态平衡数学表达式 BSPP (Biao-Sha-Push-Pull) Quantum Superposition Theory: A Unified Framework for Multi-Scale Dynamic Balance Based on the 167ms Temporal Lattice

Dynamic balance is a universal phenomenon in natural and engineered systems. However, a unified framework connecting microscopic quantum superposition to macroscopic classical perception has remained elusive. This paper introduces the BSPP (Biao-Sha-Push-Pull) Theory, an axiomatic framework for multi-scale dynamic balance founded upon the 167ms temporal lattice unit (τ₀). The theory's core comprises five fundamental axioms. These axioms extend the concept of quantum superposition to the macroscopically perceivable "Biao-Sha" superposition state and provide a standardized four-step resolution process for any system meeting the three criteria of perceptibility, discretizability, and normalizability. A defining feature of this framework is its extreme physical simplicity: the core algorithm can be implemented on an FPGA chip utilizing only 42 Look-Up Tables (LUTs) with a power consumption of less than 5mW. Experimental validation spans three distinct levels: 1) At the neuroscience level, electroencephalography (EEG) demonstrates the synchronous enhancement of γ and δ waves under BSPP acoustic stimulation, confirming the computable resolution of macroscopic emotional contradictions. 2) At the quantum physics level, a modified photoelectric effect experiment reveals a smooth transition in wave-particle duality dependent on the observation time window, with data precisely aligning with theoretical predictions. 3) At the engineering level, an Embedded Balance Arbiter (EBA) based on BSPP improves fuel efficiency by 12.3% in hybrid electric vehicle energy management. The BSPP Theory achieves, for the first time, a formal unification of Western quantum science and Eastern Yin-Yang Five Phases philosophy on the fundamental issue of dynamic balance at mathematical, hardware, and empirical levels. It provides a computable, verifiable, and engineerable solution for cross-scale complex systems.