Human-caused accident evaluation of coal mine safety relying on ant colony clustering algorithm

Through the analysis of numerous case samples, it is easy to find that the numerous reasons for the frequent occurrence of coal mine accidents are the human factors, and human-caused accidents have become the main cause of coal mine safety. The causes of human-caused accidents are very complex as well as the characteristic mechanisms of accidents are difficult to extract and carry out accurate data measurement, so the traditional engineering safety measurement methods have great limitations. They cannot accurately evaluate the causes of human-caused accidents in coal mines in the problem of high-dimensional pattern recognition. Based on this, the main work of the study focused on selecting the algorithm with good performance of simulated evolution-ant colony clustering algorithm as the technical support for the evaluation work, combining with the current mine production practice, four categories of factors from individual worker factors, environmental factors, life event factors and biological rhythm factors were clustered and analysed, scientific classification patterns as well as mathematical models were established to quantitatively analyse. The analysis of human misbehaviour is analysed and predicted. A behaviour correction model is established based on the human causes derived from the analysis so as to achieve the effect of accident prevention.