Overflow warning and remote monitoring technology based on improved random forest

Overflow accident is one of the accidents with the greatest impact on safe drilling. An unclear diagnosis of an overflow accident or improper post-processing may cause serious drilling accidents. Based on this, a remote monitoring platform for overflow accidents is proposed. The diagnosis method of overflow accidents was researched and optimized, and a random forest overflow accident identification and classification model based on bat algorithm optimization was established. This model can optimize the optimal parameter combination and has the ability to handle a large number of eigenvalues. Through comparative analysis, the optimization model has higher accuracy, and the accuracy rate is increased by 6.67%.