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采用传统方法进行海量激光数据结构鲁棒性检测,很难同时兼具检测周期短、检测误差低且行之有效。为此,提出云计算环境下的海量激光数据结构鲁棒性检测方法,搭建了云计算环境下海量激光数据处理模型。模型使用逆向云计算对海量激光数据的鲁棒性数据结构集合以及异常数据结构集合进行分类,给出结构鲁棒性检测样本,并使用量化方法和结构鲁棒性检测语言对样本进行分析,给出结构鲁棒性检测结果。实验结果说明,所提方法的检测周期短、检测误差低,能够为管理人员的决策工作提供合理的参考建议。
Using the traditional method to detect the robustness of the mass laser data structure, it is difficult to combine both the short detection cycle, the low detection error and effective. Therefore, the detection method of mass laser data structure robustness in cloud computing environment is put forward, and the massive laser data processing model in cloud computing environment is established. The model uses reverse cloud computing to classify robust data structures and abnormal data structures of mass laser data, and gives structural robustness detection samples. The quantitative analysis method and structural robustness detection language are used to analyze the samples and give Out of structure robustness test results. The experimental results show that the proposed method has short detection cycle and low detection error, which can provide reasonable reference for managers’ decision-making.