Sustainable manufacturing process thermal energy environment detection and green economy development based on image classification

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With a growing global focus on sustainability, manufacturing needs to ensure productivity while reducing resource consumption and environmental impact. This paper discusses the thermal energy environment detection method based on image classification in order to improve the energy efficiency monitoring in the manufacturing process and promote the development of green economy. In this study, deep learning technology was used to train convolutional neural network (CNN) to classify thermal environment data, collect thermal data images from multiple manufacturing processes, and label them. The pretreatment technology is used to improve the data quality, and the classification effect is optimized through multi-level network structure. The training and verification process of the model ensures its reliability in practical application scenarios. The experimental results show that the proposed image classification method achieves high accuracy in thermal environment detection, which is obviously superior to the traditional method. Through real-time monitoring of thermal energy status, timely identification of abnormal conditions, so as to provide effective data support for the manufacturing process, and promote the realization of energy conservation and emission reduction goals. Therefore, the thermal environment detection based on image classification provides a new technical path for the sustainable development of the manufacturing industry, and helps enterprises realize the transformation and upgrading of the green economy.