Construction of A Big Data-Driven Risk Identification, Assessment, and Precise Control System for Electricity Bill Accounting

Ensuring the security of electricity revenue and improving operational efficiency are critical issues in modern power systems, particularly under increasingly complex electromagnetic energy transmission environments and data-intensive grid operations. To address the lagging response and passive management characteristics of traditional electricity bill accounting risk control, this study proposes a big data-driven “identification–evaluation–control” framework for electricity bill accounting. By integrating multi-source operational data, machine learning techniques, and process reengineering strategies, the proposed framework transforms conventional accounting management into a proactive and high-precision risk control system. The study clarifies the core architecture, technical support mechanisms, and implementation pathway of the system, emphasizing data fusion, risk identification, quantitative assessment, precise intervention, and dynamic optimization. Furthermore, the framework enables accurate prediction, scientific evaluation, and efficient control of electricity bill accounting risks through continuous data-driven decision-making. The proposed approach provides a practical solution for power enterprises facing increasingly diversified settlement mechanisms and operational uncertainties, while offering methodological references for intelligent risk management and reliable operation in data-centric power and energy systems.