A Multi-Source Data-Driven Framework for Environmental Adaptability Assessment and Dynamic Optimization of Complex Equipment

The performance and reliability of complex equipment operating in extreme environments, particularly at high altitudes, are critical for operational success and safety. Traditional assessment methods often struggle with the dynamic and coupled nature of environmental stressors and equipment responses. This paper proposes a multi-source data-driven framework to address these challenges. The framework integrates data from equipment sensors, environmental monitoring sources (including satellite and ground-based systems), and operational/maintenance logs using a spatio-temporal alignment approach. It employs a dynamic weighting method combining the Entropy Weight Method (EWM) and Analytical Hierarchy Process (AHP) for adaptability quantification, adjusting parameter importance based on real-time conditions. A novel hybrid machine learning architecture, HybridML-ADAPT, combining Random Forest and LSTM, is introduced for modeling complex interactions and temporal dependencies to predict equipment adaptability levels and performance degradation. Enhanced anomaly detection mechanisms incorporating environmental context are used to improve reliability. The framework was validated through a case study involving high-altitude deployed electronic monitoring systems. Results demonstrate significant improvements, including a 34.2% increase in power degradation prediction accuracy, an 85.7% increase in Mean Time Between Failures (MTBF), and a 61.9% reduction in fault detection delay following framework-guided optimizations. This research provides a robust methodology for assessing and enhancing equipment resilience in challenging high-altitude conditions.