场地环境大数据采集和机器学习方法在污染智能识别中的应用研究进展

doi:10.19741/j.issn.1673-4831.2021.0668

Abstract

Abstract: Due to the rapid development of big data technology, the amount and type of data used to analyze and mine site pollution characteristics and formation mechanisms have also increased significantly. The traditional methods of acquiring, cleaning and mining site environmental data are difficult to meet the requirements of big data storage and processing. In recent years, it has become a research hotspot to use machine learning algorithms to mine multi-source heterogeneous site data to realize pollution identification at site and regional scales. The status and deficiencies of big data acquisition, processing and mining on intelligent identification of site pollution are systematically reviewed. Then, the application countermeasures to obtain site environmental data using 5G and the Internet, terminal information collection, web crawler, and natural language processing methods are proposed. Finally the key technologies of site multi-source site data integration and fusion and the future intelligent identification mode of site pollution in China are prospected.

Key words: site pollution identification, big data on the ecological environment, machine learning, information technology

CLC Number:

LU Xiao-song, WANG Guo-qing, LI Xu-zhi, DU Jun-yang, SUN Li. Research Progress of Big Data of Site Environment Acquisition and Machine Learning Method in Pollution Intelligent Identification[J]. Journal of Ecology and Rural Environment, 2022, 38(9): 1101-1111.

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Research Progress of Big Data of Site Environment Acquisition and Machine Learning Method in Pollution Intelligent Identification

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