The Evaluation Prediction System for Urban Advanced Manufacturing Development

Abstract

With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

Keywords

• urban advanced manufacturing
• evaluation system
• machine learning method
• the push–pull-mooring theory
• the life cycle theory