Intelligent Early Warning Algorithm for Abnormal Work Orders of Key Nodes Based on Frequent Itemsets

In order to accurately analyze the abnormal data of work orders and give an early warning, and improve the management level of work orders, an intelligent early warning algorithm of work orders in key nodes based on frequent itemsets is proposed. Under frequent itemsets, the support and confidence are analyzed to realize direct mining of frequent itemsets, and the association rule reconstruction function of candidate itemsets is constructed. Based on the association rule reconstruction function, the abnormal characteristics of key node work orders are determined. Calculate the similarity between real-time features and abnormal features of data, and based on this, extract key node work orders; Determine the early warning level of key node work orders from two aspects: severity and urgency. The normalized target value is solved, the weight is updated, and the confidence anomaly coefficient is set according to the work order business requirements of key nodes, so as to obtain the early warning result of work order anomaly of key nodes. The experimental results show that this algorithm can accurately identify and warn the abnormal work orders of key nodes, with high accuracy and reliability, and send out early warning information in time to remind staff to deal with the abnormal work orders of key nodes in time.