论文部分内容阅读
在云计算环境下对钢铁产能资源信息进行优化调度,可以有效实现产能经济数据预测,缓解钢铁产能过剩。提出一种基于交互信息自适应关联特征提取的云计算下钢铁产能资源的优化调度模型。在云计算环境下,对钢铁产能数据进行语义特征分解,对数据信息进行趋化重采样熵编码,实现适应关联特征提取,提高资源调度的信息覆盖度。仿真结果表明,该模型能有效提高钢铁产能资源的调度效率和云计算执行效率,性能优越。
Optimal scheduling of steel production resource information in the cloud computing environment can effectively predict the production economic data and ease the excess steel production capacity. This paper proposes an optimal scheduling model of steel production resources under the cloud computing based on the adaptive association feature extraction of interactive information. In the cloud computing environment, the steel capacity data is decomposed by semantic features, and the data information is entropy encoded by chemotaxis and re-sampling, so as to adapt to the correlation feature extraction and improve the information coverage of resource scheduling. The simulation results show that the model can effectively improve the scheduling efficiency of steel production resources and cloud computing execution efficiency, and has superior performance.