Early Warning of Coal Mine Production Environment Safety Risks Based on Multi-Source Information Feature Fusion

As intelligent mining develops, utilizing coal mine production monitoring data for early warnings has become a crucial means of ensuring safety in mining operations. Assisting decision-makers in making scientific choices through multi-source and massive data is a complex yet vital task. Based on multi-source information fusion, a model for the coal mine production environment is proposed in this paper. It is designed to provide early warnings regarding the safety status of coal production environments in order to assist management and control personnel in making scientific decisions. Firstly, data integration of multi-source heterogeneous datasets was conducted. Multi-source heterogeneous data collected by various types of monitoring sensors in coal mines were analyzed, including temperature, dust, wind speed, vibration energy, and gas. Based on this, the factors influencing coal mine production safety were identified. These factors were then screened through factor analysis to determine the index. An early warning index system for coal mine production environment safety was established. The index weight was established by the principal component analysis method, and the index system for coal mine production environment safety and early warning systems was established. Secondly, based on BP neural networks, a multi-input single-output feature-level fusion model and a multi-input multi-output feature-level fusion model were constructed. Based on the above model, the safety warning for coal mine production environments was implemented. The accuracy of model was 89.29%. Based on multi-source information fusion, the early warning system for coal mine production environments was constructed. The system exhibited good feasibility. It could assist management and control personnel in making scientific decisions.