Multi Source Data Fusion Driven Bidding Business Prediction System for State Grid Corporation of China

With the in-depth promotion of the digital transformation in the power industry, the tendering business of the State Grid Corporation of China is characterized by a surge in multi-source heterogeneous data and the complexity of business associations. This study proposes a tendering business prediction system driven by multi-source data fusion, aiming to construct an intelligent prediction model by deeply integrating multi-dimensional data resources such as historical tendering data, market dynamics, policies and regulations, supplier information, and the macro economy, so as to improve the capabilities of identifying tendering demands and anticipating risks. Aiming at the problems of single data dimension and insufficient generalization ability of traditional tendering prediction models, this study innovatively proposes a prediction framework based on the deep fusion of multi-source heterogeneous data and ensemble learning.