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Loosening of bolts, which is a common form of failure in bolted connections, causes relative slippage between the connected surfaces. The bolts fail under the action of external shear forces due to fatigue and breakage, thereby affecting the service performance and connection strength of the equipment, potentially resulting in major accidents. At present, condition monitoring, which is used to detect the tightness of bolt connections, has obtained acceptable results; however, most of them are still carried out under laboratory conditions and cannot be applied to engineering. In addition, effective remedial measures should be implemented after detecting bolt looseness. On the basis of such problems, a multi-bolt looseness monitoring method based on machine vision and deep learning is proposed. At the same time, shape memory alloy is used in the design of a structure that actively compensates for loose bolts. This method realises bolt recognition of the bolt connection structure through video monitoring and looseness monitoring of multi-target bolts at the same time. When the system detects that the bolts are loosened, an alarm signal is issued and, at the same time, the control device is activated to compensate, to increase the time available for repair time and to ensure the service performance of major equipment.

COVID-19 accelerated the growth of the digital economy and digital transformation across the globe. Meanwhile, it also created a higher demand for productivity in the real economy. Hence, the correlation between the digital economy and green productivity is worth studying as COVID-19 prevention becomes the norm. The digital economy overcomes the limitations imposed by traditional factors of production on economic growth and empowers innovative R&D and resource allocation in all aspects. This study delved into the digital economy by focusing on its green value at different levels of development. The study gathered the green-productivity indices and the principal components of the digital economy for each prefecture-level city in China from 2011 to 2019 and meticulously portrayed their trends in spatial and temporal figures. Meanwhile, regression models were used to verify the mechanism through which digital-economy development influences the changes in green productivity. The results showed that: (1) a higher level of digital economy helps to increase urban green total-factor productivity (GTFP) and that the conclusions of this paper still held after potential endogeneity problems were solved through the instrumental-variables approach; (2) the digital economy will drive an increase in urban GTFP by upgrading firms’ production technologies and that digital-economy development encourages green patent applications from firms; and (3) as the digital economy develops, it will also drive urban GTFP increases by removing polluting enterprises from the market and that the higher the level of digital-economy development, the greater the number and probability of polluting enterprises exiting the market. In view of this study’s results, the government should increase the importance of the digital economy, strengthen the role of the digital economy in promoting urban green development, and provide more guidance on regional green development with the help of the digital economy.