Research on big data anomaly mining method for power grid operation and maintenance based on reinforcement learning algorithm

As the scale of development of power grids continues to expand, the issue of their safe operation has received much attention. The problem of low accuracy in the face of network attacks exists in the big data anomaly mining method of grid operation and maintenance. A big data anomaly mining method of grid operation and maintenance based on reinforcement learning algorithm is designed. The method is based on a reinforcement learning algorithm, which is used to obtain early warning data, develop an offline analysis function, calculate an increase matrix of key nodes, construct a big data anomaly identification model and optimise the mining process. Experimental results: The accuracy of the designed mining method is 11.92%, 14.57% and 13.02% higher than the average of the other three methods.