## 5 Discussion
Our integrated approach, combining distributed optical fiber sensing with DIC and BPPS technologies, offers superior spatial and temporal resolution while maintaining measurement accuracy within 8.88% deviation, significantly improving upon the 15-20% error margins commonly reported in the existing literature. Furthermore, while previous studies have primarily focused on either pre-failure or post-failure behavior, our theoretical framework uniquely addresses both phases through a unified mathematical model. This comprehensive approach enables continuous monitoring across the entire deformation process, from initial elastic deformation through to complete failure. The establishment of a quantitative strain threshold (4000 \(\mu\)e) for fracture initiation provides a more precise criterion compared to traditional qualitative assessments. Additionally, our methodology's ability to capture the characteristic \"U-shaped\" pressure distribution with high fidelity represents a significant improvement over the existing approaches that often struggle to maintain accuracy in highly disturbed zones. Contemporary monitoring systems frequently face challenges in real-time data interpretation and early warning capabilities. Our dual monitoring approach, validated through extensive physical modeling, addresses these limitations by providing continuous, high-resolution data with clear quantitative thresholds for safety assessment. The integration of optical fiber sensing with advanced data inversion techniques offers unprecedented insights into the dynamic evolution of mining-induced stress fields, establishing a more robust foundation for mining safety assessment and control strategies compared to current industry standards. However, it is important to acknowledge that our approach shares some common limitations with the existing technologies, particularly in addressing sensor-formation coupling efficiency and geological anisotropy effects. Future research directions could focus on enhancing these aspects while maintaining the demonstrated advantages in measurement accuracy and comprehensive monitoring capabilities. The methodology's successful validation through physical modeling experiments, while maintaining consistent accuracy across various advancement stages, positions it as a significant advancement in the field of mining safety monitoring and stress analysis. Based on the above analysis, future research should focus on the following directions: conducting in-depth studies on fiber-rock interface coupling mechanisms to establish more accurate strain transfer models and incorporating rock mass anisotropy characteristics into the inversion model to enhance engineering practicality. Specifically, tensor-based strain analysis will be used to integrate anisotropic rock mass properties, and surface treatment techniques (such as micro-groove processing) will be used to improve the fiber-rock coupling efficiency and optimize strain transfer. In addition, rapid inversion methods will be developed that consider dynamic effects to improve the characterization of engineering dynamic responses, and intelligent algorithms will be introduced, such as machine learning to enhance the adaptability and accuracy of the inversion process. Specifically, machine learning techniques, such as neural networks, will be explored, and the inversion process will be optimized through high-resolution experimental data training, so as to realize the adaptive adjustment of parameters under heterogeneous geological conditions. Finally, more engineering case validations will be conducted to establish comprehensive engineering application guidelines. Through these in-depth studies, the fiber-optic-driven goaf stress field inversion method can be further improved to provide more reliable theoretical and technical support for safe and efficient mining operations.