Intelligent evaluation of coal mine solid filling effect using fuzzy logic and improved D-S evidence theory

Addressing the inherent fuzziness and uncertainty in filling outcomes, this paper proposes a novel method for evaluating the effectiveness of solid filling operations in coal mines by integrating Interval Type-2 Fuzzy Logic Systems (IT2FLS) with an improved Dempster-Shafer (D-S) evidence theory. Initially, local data fusion is conducted using IT2FLS-Adam, where interval type-2 fuzzy sets are employed to fuzzify input features, and the Adam optimizer is utilized for parameter optimization. This allows for preliminary judgments on filling effects from various perspectives based on local features. To overcome the limitations of local fusion, an improved D-S evidence theory is adopted, which effectively handles conflicting evidence by incorporating the Wasserstein distance and Deng entropy to combine the judgments from local features, achieving global data fusion. Experimental results demonstrate that the proposed method attains a remarkable accuracy of 92.9% in global fusion tasks, surpassing traditional methods. This study provides a data fusion framework for filling workfaces, integrating multi-sensor data and addressing the complexities and uncertainties associated with filling processes, thereby making a significant contribution to the intelligent monitoring and management of coal mine filling operations.