To address the issues of insufficient dimensions, lack of objectivity, and weak coupling with incident data in current power supply enterprise risk assessments, a comprehensive systematic risk assessment method based on generalized information is proposed. Firstly, complex risk factors faced by power supply enterprise are systematically analyzed. A comprehensive systematic risk assessment framework based on generalized information is established, enabling a multi-dimensional characterization of safety risks. On the basis, considering the differences between assessment dimensions and risk inter-coupling, an indicator weighting method integrating fault tree logic structures and expert knowledge is proposed to accurately determine the importance weights of each dimension. Subsequently, recognizing the impact of assessment indicator characteristic cycles on systemic risk, data features embedded within incident samples are deeply mined. A risk dynamic identification model based on accident energy mapping is developed, and an improved grey wolf optimizer algorithm is proposed to solve the model. This enhanced algorithm incorporates a large-scale incident sample-driven convergence mechanism and introduces chaotic initialization along with a cosine-based dynamic optimization mechanism to improve population diversity and balance global exploration and local exploitation capabilities across different solution stages, which significantly enhances solution efficiency and precision. Finally, the effectiveness of the proposed method is validated through a case study on a regional power supply enterprise.