针对供电企业风险评估维度不全、缺乏客观性、与事故样本耦合程度不足等问题，提出了一种基于广义信息的供电企业系统性风险综合评估方法。首先，系统梳理了供电企业面临的复杂交错的风险因素，提出了基于广义信息的系统性风险综合评估体系，实现了供电企业安全风险的多维度表征。在此基础上，考虑评估维度与风险耦合差异性，提出了结合事故树逻辑结构与专家经验的评估指标定权方法，以准确刻画各评估维度的重要性。然后，考虑评估指标特征周期对系统性风险的影响，深度挖掘事故样本蕴含的数据特征，提出了基于事故能量映射的风险动态辨识模型，并提出改进灰狼算法求解以实现风险动态辨识。该算法融合了大规模事故样本驱动收敛的思路，并引入混沌机制和余弦型动态寻优机制提升种群多样性和均衡不同求解阶段的全局搜索与局部寻优能力，有效提升了求解效率和精度。最后，以某供电企业为研究对象验证了所提方法的有效性。

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.