IASB Framework: Construction of Data Asset Accounting System Based on PO-BP Model - Haixia Guo (HighTech and Innovation Journal)
This study aims to construct a data asset accounting system based on the International Accounting Standards Board (IASB) framework, addressing the challenges in identifying, measuring, and reporting data assets within traditional accounting systems. By integrating the Political Optimization (PO) algorithm with the Back Propagation (BP) neural network, we propose a novel PO-BP model to enhance the accuracy and efficiency of data asset valuation. The PO algorithm optimizes the weights and biases of the BP neural network, improving its global search and local development capabilities. Experimental validation using open-source datasets demonstrates that the PO-BP model outperforms traditional models (e.g., BP, GWO-BP, and SSA-BP) in terms of convergence speed, prediction accuracy, and stability, achieving an average relative error of 0.2292% and a coefficient of determination R² of 0.9957. This study innovatively combines the PO algorithm with BP neural network, offering a robust technical approach for data asset value assessment. The findings provide significant theoretical support for advancing data asset accounting and practical guidance for enterprise decision-making during digital transformation. Future research will explore the model's adaptability to diverse industry data and dynamic market environments.