Electric power spot service system fault prediction method and device, computer equipment and storage medium

The invention discloses an electric power spot service system fault prediction method and device, computer equipment and a storage medium, and the method comprises the steps: receiving real-time equipment parameter time sequence data of hardware equipment in an electric power spot service system, inputting a first type of deep neural network to obtain the fault probability of the hardware equipment, wherein the first-class deep neural network is obtained through pre-construction, and comprises the steps of receiving equipment parameter time sequence data of hardware equipment in an electric power spot service system before a fault, and constructing an equipment parameter dictionary according to the equipment parameter time sequence data; performing vectorization processing on the equipment parameters by using the equipment parameter dictionary to obtain vectorized equipment parameter time sequence data and training data; and carrying out fine-tuning transfer learning on the training data by using the deep neural network to obtain the first-class deep neural network. According to the invention, safe operation and reliable operation of an electric power spot market and a business system are guaranteed.