Abstract
With the development of science and technology and the improvement of industrialization, the development of more and more industries has interacted to form many industrial clusters. For the healthy development and safety of various industries, environmental quality monitoring and management in industrial clusters is of utmost importance. Currently, domestic and foreign enterprises and related departments are vigorously developing smart environment platforms. The purpose of this paper is to design an intelligent environment platform for industrial clusters based on cloud computing technology. This article first uses the methods of literature research and network investigation to collect relevant literature and research results and organizes statistics on the collected information. Then, through case analysis to study the needs and overview of the construction of smart environment platforms in industrial clusters, it uses the wireless smart sensing technology of the Internet of Things and the information interaction technology of the Internet to provide data to cloud computing through mobile terminals, and cloud computing provides various types on demand data service. Then, according to the demand analysis of the intelligent environment platform, the functional modules and operation procedures of the intelligent environment platform are designed. The main monitoring content is water quality testing, air quality testing, garbage disposal and soil quality testing, etc. Finally, this article processes, analyzes, and predicts the detected sampled data through cloud computing. It can also locate and track abnormal data. Through the curve fitting of the data, the environmental conditions in the area can be predicted. Experiments show that the prediction accuracy rate is as high as 93.8%, which plays an important role in monitoring and preventing environmental pollution.
Highlights
Environmental pollution problems were mainly aimed at the pollution of air and water quality caused by waste gas, heat, sewage, and dust particles generated in the production workshop during industrial production
It caused environmental pollution and endangered people’s health and industrial development. erefore, people pay more and more attention to the environmental monitoring of industrial clusters while paying attention to the industrial production environment [1]. e main purpose of this research is to analyze and predict the environment under the information interaction based on intelligent sensor technology
Analyze the necessity of building a smart environment platform based on cloud computing based on the hazards and losses caused by environmental pollution in industrial clusters, and determine the functional requirements of the smart environment platform, including environmental monitoring, location tracking, environmental information sharing, and environmental pollution prediction
Summary
In the intelligent environment platform of industrial clusters, in order to reduce the error caused by the real-time monitoring of changes in environmental factors in various industrial areas, the environmental data values obtained by the detection should be more authentic and reliable. For the temperature and humidity of the industrial area, the distribution of PM2.5 in the air, and other environmental values that change over time, after collecting the original data, the number of samples can be reduced by the abovementioned methods, and the algorithm can be used to upload these. Based on the difficulty of data collection in the environmental monitoring of the current industrial clusters, the large scale of sampled data, and the large amount of data, traditional storage, and processing technologies cannot meet the needs of real-time monitoring and updating of environmental data, research the introduction of cloud computing technology and some artificial intelligence technologies to build industrial clusters. The amount of original sampling data obtained by wireless sensor sampling is large, and some data have measurement errors, which are not conducive to analysis. erefore, in the process of processing these data by the platform, some algorithms are used to transform the data, reduce the range of sampled data, and retain high accuracy
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