es and advantages/disadvantages of different models in terms of early warning capabilities. It is found that in scenarios where the demand for characteristic parameters is low and the data sources are stable，machine learning algorithm models exhibit superior accuracy and timeliness in the early warning of drilling complex accidents. However，when faced with challenges such as high computational complexity，poor data quality，and unstable data sources，especially in predicting lost circulation during drilling and early-warning complex well kick accidents，the models suffer from high rates of false alarms and frequent missed alarms，and few field application cases are reported. To promote the field application of drilling complex accident early warning techniques and drilling digitalization，it is necessary to strengthen research on data quality improvement and algorithm optimization. Despite the numerous challenges，big data and artificial intelligence still open up broad prospects for early warning technologies.

Real-time monitoring of bit wear is crucial for accelerating drilling operations. However，it is challenging to measure on-site parameters that directly reflect levels of bit wear. Currently，there are few means of monitoring bit wear，and in most cases，determination of bit wear is empirically performed by technicians. Quantitatively evaluating the wear of PDC bit has always been a difficult task. The evaluation of bit wear is primarily based on rock breaking efficiency and mechanical specific energy. In this study，a model is proposed for real-time monitoring of PDC bit wear，based on a physical model to calculate mechanical specific energy. Moreover，the wavelet analysis and clustering algorithm are utilized to characterize the bit wear process. Finally，a monitoring model based on Gated Recurrent Unit （GRU） neural network is established，which maps drilling parameters to bit wear levels with 95% accuracy. The model is tested using data from Well A in Xinjiang Oilfield，which demonstrates the capability of the model to accurately estimate current bit wear levels. This model provides a solution for bit wear monitoring，aiding engineers in determining the optimal timing for bit replacement and thereby ensuring higher drilling efficiency.