China’s economy has entered a new normal

supply side reform and the battle for environmental protection have continued to advance

international trade unilateralism has risen

traditional industries are facing many challenges

and the risk of electricity tariff recovery for enterprise customers is increasing.Therefore

predicting the overdue risk of power consumption of enterprises and minimizing the impact on the company's operations are becoming more and more urgent. At present

overdue risk management and control methods can only rely on the historical arrears of enterprises to conduct risk judgments

and lack of deep exploration of user power consumption trends and payment historyleads to the lack of accuracy and timeliness of enterprise overdue risk judgment. To solve the above problems

from the single perspective of enterprise electricity data

based on DBSCAN clustering algorithm

constructing the typical electricity consumption mode of the industry

and adopting the idea of k nearest neighbor

defining the pattern distance measurement function

introducing the concept of risk level

and proposing a power pattern based electricity overdue risk prediction model for enterprises to realize the self evolving electrical deviation based on overdue risk level with degree 4 and risk prediction. Theoretical analysis and experimental results verify the effectiveness of the proposed method.