<p>Rock collapse disasters, characterized by their widespread distribution, high frequency, and sudden occurrence, represent one of the most challenging geological hazards for real-time monitoring and early warning. Current approaches based on macroscopic displacement measurements prove inadequate for timely detection of impending rock collapses. To address this gap, this study proposed a novel slope dynamics theory grounded in Newton’s second law and an intelligent monitoring system employing microchip-based sensor devices (Microchip Pile, MCP). Additionally, an integrated safety monitoring incorporating both technological and managerial components was developed. This paper systematically investigated five critical dimensions: (1) dynamic modeling of rock collapse instability, (2) monitoring indices and warning thresholds, (3) sensor architecture, (4) predictive system implementation, and (5) practical engineering validations. First, development of a dynamic rock slope model derived from Newtonian mechanics, overcoming limitations of conventional limit equilibrium analysis, with a generalized instability formulation characterizing dynamic parameter evolution. Second, creation of the MCP sensor platform was created to enable multi-parametric detection (stress/strain, displacement, vibration) during collapse process. Furthermore, a risk mitigation strategy integrating sensor networks with proactive geotechnical management protocols was implemented. Field deployments across 500 + engineering projects in China demonstrated system efficacy, successfully triggering alerts for 50 + collapse events and resolving fundamental scientific challenges in geological hazard prediction.</p>