露天矿行车事故预测方法及应用

为有效解决露天矿山行车事故预测模型建模时,易受小样本数据、离群数据规模影响,导致模型精度损失、算法抗噪容差能力及收敛速度下降等问题,提出一种基于二次惩罚项修正(PTS)的改进支持向量回归机模型(WLSSVR)。根据训练样本的数据分布特性,研究了服从露天矿山现实应用场景的二次惩罚项,进一步提高回归机模型的抗噪容差能力;考虑非线性预测模型影响因子选择困难的问题,研究了数据降维及因子分析方法,并将主成分分析方法引入到输入数据预处理算法中,以保证算法可得到理想的输入;针对传统回归机模型易受核参数选择影响,易导致模型早熟和收敛速度慢等问题,研究了粒子群惯性因子、学习因子的自适应迭代形式,提出了一种应用改进粒子群算法优化回归机模型核参数的方法。以露天矿行车事故频次预测为例,进行了预测和对比实验。实验结果表明:引入PTS模型的测试集预测结果明显优于不采用PTS策略的预测结果。这说明,应用文中提出的二次惩罚策略和参数优化算法对复杂系统的事故预测问题研究是可行且有效的。

The prediction method of traffic accident and its application in open-pit mine based on the PTS-WLSSVR model

To effectively solve the problems of the accuracy loss of prediction models for traffic accident prediction of open-pit mines, the decrease of the algorithm''s capability of anti-noise tolerance and convergence rate caused by small sample data and outliers, we propose a modified support vector regression model based on penalized trimmed squares (PTS). According to the data distribution characteristics of the training samples, the penalized trimmed squares submitted to the application scenario of open-pit mine is studied to improve the anti-noise tolerance capability of the regression model. In consideration of the difficulties of the nonlinear prediction model impact factor selection, the method of principal component analysis is introduced into the preprocessing algorithm to reduce the data dimension and ensure that the algorithm can get ideal input data. In view of the problems of premature and slow convergence speed caused by the nuclear parameter selection, the inertial factor and the learning factor of particle swarm are studied and an improved particle swarm algorithm to optimize nuclear parameters regression of the model is proposed. The prediction and comparison experiments are carried out in the case of the accident frequency prediction of open-pit mine. The experimental results show that the test set prediction results of the PTS model are better than those without the PTS policy model.This indicates that the modified penalized trimmed squares strategy and parameter optimization algorithm proposed in this paper is feasible and effective for the study of accident prediction of complex systems.