Research on the Application of Digital Audit in Power Grid Investment Based on Big Data and Neural Networks

This study aims to explore the application of big data and neural network technologies in the digital audit of power grid investments. By constructing a neural network-based analytical model, this paper effectively integrates and analyzes large-scale datasets to enhance audit efficiency and accuracy. The research first identifies and categorizes the main risk factors in power grid investments, then uses big data technology to collect and preprocess related data. On this basis, a neural network model is developed to analyze potential risks and abnormal patterns in the data. Experimental results show that this neural network model can effectively identify and predict risks and issues that may arise in power grid investments, significantly improving the accuracy and timeliness of the audit.