Human survival and development are constantly responding to the environment, and human participation has changed the original appearance of the entire natural environment to a certain extent. In recent years, pollution has intensified, and many countries have paid more attention to and actively controlled pollution, including air pollution, water pollution, solid waste and urban noise. Although pollution control has improved in recent years, urban environmental quality has improved significantly and developed steadily, many problems need further management to improve environmental quality. Effective environmental monitoring measures can provide a good basis for decision-making and implementation of environmental management, which is an effective measure for scientific evaluation of environmental management work of governments at all levels. According to the requirements of modern management, relevant government environmental monitoring agencies must change their monitoring strategies, actively introduce the concepts and methods of supervision and control, and improve the overall monitoring efficiency. In order to better carry out environmental monitoring, this paper uses symmetric connection network and feedback neural network to carry out environmental monitoring research. This paper constructs the structure of the Internet of Things for environmental monitoring, introduces the node structure and network system framework of wireless sensor networks, and classifies environmental images using symmetric connection networks. In this paper, feedback neural network is used for data fusion of the Internet of Things for environmental monitoring. In the experimental part, the environmental monitoring system is used for air monitoring experiment. The experimental results show that the monitoring system has good environmental monitoring effect, and the accuracy rate of air quality monitoring is 86.7%, with a high accuracy rate.

Kim Cheolgi. Feedback Neural Network and Symmetrical Connection Network in Human Environment. Nature Environmental Protection (2021), Vol. 2, Issue 1: 50-59. https://doi.org/10.38007/NEP.2021.020106.

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