PLC course performance evaluation based on machine learning and image feature retrieval

PLC is an indispensable technology for modern automation. The future social development will require a large number of PLC technical talents, so higher requirements are put forward for the teaching of PLC courses in colleges and universities. The intelligence and practical effect of the PLC course evaluation system are particularly important. Based on this, this article combines machine learning and image feature retrieval to construct a PLC course performance evaluation system. Moreover, this paper introduces the smoothness of the multispectral image, the smoothness of the blur function and the smoothness between the blur functions of adjacent spectral images as constraints and uses the gradient of the image blur kernel to express the smoothness of the image blur kernel itself. In addition, this article constructs the model system architecture according to the teaching requirements of the course and analyzes its realization process. Finally, in order to verify the performance of the model, this paper conducts system performance verification experiments through practical teaching methods and analyzes the results with statistical methods. The research results show that the PLC performance evaluation system constructed in this paper has a certain effect.