新时代智能制造业发展影响因素的作用机理研究

On Mechanism of Factors Influencing Development of Intelligent Manufacturing in New Era

产业结构升级背景下,智能制造将为我国工业发展注入源源不断的动力,但是由于我国智能制造行业起步较晚,大多数企业均是由传统制造业转型而来,各个企业的智能化效率参差不齐,因此对各个企业的智能化发展影响因素进行筛选并预测具有重要的现实意义。通过GASP算法对样本数据进行训练预测,结果显示:第一,通过皮尔逊相关性检验,剔除非显著性的智能化效率的影响因素,进一步利用GASP算法进行预测,比不剔除后的效果要更加好;第二,与传统的BP神经网络方法相比,GASP算法在制造企业智能化效率影响因素筛选和预测中具有更加显著的优越性。第三,GASP算法在制造企业智能化效率影响因素筛选和预测中实际值和预测值的偏差范围很小,在可以接受的范围内,因此将其运用到制造企业智能化效率的预测和影响因素的筛选中具有较强的适用性。

With the upgrade of industrial structure, intelligent manufacturingfunctions as a sustaining drive for China’s industrial development. However, since China’s intelligent manufacturing industry has only developed for a short period, most enterprises are transformed from traditional manufacturing, the intelligence level of each enterprise being pretty uneven. Thus it is of great significance to screen out the factors affecting each enterprise’s intelligence development and make related prediction. This article adopts GASP algorithm tomake predictionand test the sample data. The results show thatfirstly, it is more effective, compared with the case without removal of the insignificant factors, to remove the insignificant factors affecting intelligence efficiency through Pearson correlation test and then adopt the GASP algorithm for prediction. Secondly, in comparison withthe traditional BP neural network method,the GASP algorithm has prominent advantages in screening and predicting the factors affecting intelligence efficiency of manufacturing enterprises. Thirdly, the GASP algorithm yieldscertain deviations between the actual value and the predicted value in screening and predicting factors affecting intelligence efficiency of manufacturing enterprises. However, the deviation issmalland acceptable.Therefore, it is feasible to apply the GASP algorithm to the screening and predication of factorsaffecting intelligence efficiency of manufacturing enterprises.