【摘要】 尾矿库风险评价具有随机性、模糊性和评价过程中不确定因素众多的特征，为有效保障尾矿库运行安全，提出了基于组合赋权-云模型的尾矿库风险评价方法。首先，从系统分析视角出发，运用物理-事理-人理(WSR)系统理论，从主体因素、客体因素和组织形式三个方面探讨尾矿库安全运行影响因素，构建基于WSR的尾矿库风险评价指标体系；其次，根据改进G1法与CRITIC法分别对评价指标进行主客观赋权，由博弈论算法计算权重最优的组合系数，得出组合权重；最后，结合云模型理论，计算各指标相对隶属度和综合特征值，基于最大隶属度原则划分风险等级，以云图的形式呈现可视化效果显著。以云南某尾矿库为例进行评价方法验证，结果与实际情况相吻合，并与未知测度法等方法进行对照，研究表明方法具有合理性和可测性。更多还原

【Abstract】 The risk evaluation of tailing ponds is characterized by randomness, fuzziness and numerous uncertainties in the evaluation process.In order to effectively guarantee the operational safety of tailing ponds, a tailing pond risk evaluation method based on a combined assignment-cloud model is proposed.From a system analysis perspective, this paper applies the physical-substance-human-reason(WSR)system theory to explore the factors affecting the safe operation of tailings ponds from three aspects: subject factors, object factors and organisational forms, and constructs a WSR-based tailings pond risk evaluation index system.Secondly, the improved G1 method and CRITIC method are used to assign subjective and objective weights to the evaluation indicators respectively, and the game theory algorithm calculates the combination coefficients with the best weights to derive the combination weights.Finally, the cloud model theory is combined to calculate the relative affiliation and comprehensive characteristic value of each indicator, and the risk level is classified based on the principle of maximum affiliation, and the visualisation effect is presented in the form of a cloud chart.The evaluation method is validated using a tailings pond in Yunnan Province as an example, and the results are consistent with the actual situation, and compared with unknown measures and other methods.更多还原