Power enterprise risk identification model based on convolutional neural network and adaptive comparison algorithm

Abstract

Combined with the actual characteristics of risk identification in electric power enterprises, a convolutional neural network model suitable for load sequence data prediction is determined. Particle Swarm Optimization (PSO) algorithm is used to transform the convolutional neural network (convolutional neural network) to improve the global Optimization ability and convergence speed. Simulation results show that CNN can effectively extract sample information through its convolutional layer and pool layer. After particle swarm optimization, it also achieves good results in prediction accuracy and prediction speed. Secondly, classical interpretation combination model (ISM) is used to analyze the structure of the risk system of electric power enterprises, and the link relationship model of the risk of electric power enterprises is constructed. Through the structural analysis of risk and risk factors, the paper finds out the mutual influence relationship between risk and risk factors, and further finds out the risk chain and risk source. The classical explanatory structure model is extended to the fuzzy set, and then the influence intensity model of power enterprise risk is built. This model considers the influence of risk intensity when analyzing the risk relationship of electric power enterprises, and gives different risk link relations based on different impact intensity. Through comparative analysis, the relationship between the link relationship model and the influence intensity model of the risk of electric power enterprises is obtained. Put forward the sequence similarity matching algorithm based on adaptive search window (ADTW), average algorithm using Piecewise gathered (Piecewise Aggregate Approximation, PAA) strategy for sequence sampling sequence, low precision and low calculation precision sequence alignment of paths, and according to the change of gradient on the low precision of distance matrix forecast path deviation, expand the scope of limiting path search window; Then, the algorithm gradually improves the sequence accuracy, corrects the path in the search window, calculates the new search window, and finally realizes the fast solution of DTW distance and similarity alignment path.