基于多传感器数据融合的煤矿安全状态评估

为了保障矿井工人的生命安全，减少经济损失，提出一种基于多传感器数据融合技术的煤矿安全状态评估方法。先使用基于均值的分批估计预处理方法对井下的瓦斯浓度、温度、风速、一氧化碳、粉尘等多种传感器采集的数据进行综合处理，得到第一级融合结果，再利用D-S证据理论消除评估过程中的不确定性，提高评估的准确性。通过具体的案例，验证了本方法的可行性。实验结果表明，该评估方法的准确性很高，能够为矿井安全状态的评估与判断提供决策支持。

Assessment of Coal Mine Safety State Based on Multi-sensor Data Fusion

In order to protect the safety of the coal mine workers and reduce economic losses, an assessment method for coal mine safety status based on multi-sensor data fusion was proposed. The data from sensors of gas concentration, temperature, wind speed, carbon monoxide, dust and others from the underground was first handled, the batch estimate pretreatment methods based on the average value were used to get the first level fusion result, and then the D-S evidence theory was used to eliminate the un- certainty in the assessment and improve the accuracy. At last, a specific case was used to verify the feasibility of the proposed method. The result shows that the assessment method is of a high degree of accuracy, and it can provide decision support for the assessment and judgment of the state of coal mine safety.