黄河流域是"能源流域"，兼具生态环境治理和经济社会发展的重任，涉煤产业场地类型、数量及特征的精准智能识别是流域能源资源-低碳发展-生态保护的关键基础问题。研究融合多源数据与深度学习算法，从流域-基地-场地尺度对黄河流域 13个大型煤电基地的煤基场地特征精准解析，获得煤电基地高精度、高质量的本底信息，提出一种实时实景智能识别涉煤产业空间特征的新方法。①筛选Google image、GF-6影像、Sentinel-2影像等多源数据，采集 13个大型煤电基地煤基场地样本，构建煤炭场地(露天)、煤炭场地(井工)、煤电场地、煤化工场地 4类数据集，涵盖 21种样本类型。按照每种样本六面体设定 6×10个样本，共计 1 260个场地样本，分析得出最适样本数量-最高识别效率-最优识别模型的置信区间为 80%～86%。②建立了煤基场地类型量化模型(Coal-based Site Classification Quantitative Model，CSCQM)和煤基场地范围特征模型(Coal-based Site Range Characteristic Model，CSRCM)，模型平均精准度为 0。837。明析了黄河流域涉煤产业场地本底信息，提出Google image底图叠加场地智能识别模型解算结果的高精度场地智能识别方法。③解析了流域神东煤炭-煤电产业集聚区精准本底数据，依据遥感生态指数(Remote Sens-ing Based Ecological Index，IRSE)分析，煤基场地分布 2km核心区地表生态质量受煤炭、煤电产业影响明显，5 km缓冲区则影响不明显，而 8km控制区基本不受煤炭、煤电产业影响，从而给出了"动态修复"与分区域、分阶段重点治理等低碳路径。④解析了流域宁东煤炭-煤电-煤化工产业集聚区精准本底数据，2022年煤炭场地 17。81 km2、占比 34。1%，煤化工场地 22。3 km2、占比42。6%，煤电场地 12。2 km2、占比 23。3%，煤化工场地>煤炭场地>煤电场地。进而采用PSR(Pressure-State-Response)模型得到风险管控综合得分 53。93分，较 2003年提高了 27。2%。划分生态维护区、生产监测预警区、损毁修复重建区、其他调控区的分区管控模式。研究为涉煤产业煤基场地潜在污染控制、场地治理及区域生态修复提供技术方法与实践支撑。
Precise intelligent recognition method and application of coal-power-chemical industry sites characteristics in Yellow River Basin
The Yellow River Basin is an energy basin that has the dual responsibility of ecological environment gov-ernance and economic and social development.The precise and intelligent recognition of the categories,numbers and characteristics of coal-related industrial sites is a key basic issue for energy resources-low carbon development-ecological protection in the basin.This study integrated the multi-source data and deep learning algorithms to precisely analyze the characteristics of coal-based sites in 13 large-scale coal-fired power bases in the Yellow River Basin from the basin-base-site scale,obtained the high-precision and high-quality background information of coal-power bases,and proposed a new method of real-time real-scene intelligent recognition of spatial characteristics of coal-related industries.In this study,①Multi-source data such as Google image,GF-6 image,Sentinel-2 image,etc.were collected as coal-based site samples from 13 large-scale coal-fired power bases to build four datasets of coal mine sites(open-pit),coal mine sites(under-ground),coal-power sites,and coal chemical sites,covering 21 categories of samples.According to each type of sample,6×10 samples were set for each hexagonal cell,totaling 1260 site samples.The confidence interval of the optimal sample number-highest recognition efficiency-optimal recognition model was 80%-86%.②A coal-based site classification quantitative model(CSCQM)and a coal-based site range characteristic model(CSRCM)were established.The average accuracy of the models was 0.837.The background information of coal-related industrial sites in the Yellow River Basin were clarified,and a high-precision site intelligent recognition method based on Google image base map overlaying site intelligent recognition model calculation results was proposed.③The precise background data of the Shendong coal-power industrial agglomeration area in the basin were analyzed.Analyzed by remote sensing based ecological index(RSEI),the surface ecological quality of the 2 km core area of coal-based sites was significantly affected by coal mine and coal-power industries,while the 5 km buffer zone was not significantly affected,and the 8 km control zone was basically not affected by coal mine and coal power industries.Thus,the low-carbon pathways such as dynamic remediation and key management by region and stage were proposed.④The precise background data of the Ningdong coal-power-chemical industrial agglomeration area in the basin were analyzed.In 2022,the area of coal mine sites covered an area of 17.81 km2,accounting for 34.1%of the total area,the area of coal chemical sites covered an area of 22.3 km2,accounting for 42.6%of the total area,and the area of coal-power sites covered an area of 12.2 km2,accounting for 23.3%of the total area.The area ratio was coal chemical sites>coal mine sites>coal-power sites.Then,using the PSR(Pressure-State-Response)model,the comprehensive score of risk management was obtained as 53.93 points,which was 27.2%higher than that in 2003.A zoning management mode of ecological maintenance zone,production monitoring and early warning zone,dam-age repair and reconstruction zone,and other regulation zone were implemented.The study provided some technical meth-ods and practical support for the potential pollution control,site management and regional ecological restoration of coal-related industrial sites.
Yellow River Basincoal-based sitemulti-source dataAI modelprecise and intelligent identification
董霁红、王立兵、冯晓彤、王蕾、刘峰、翟文、THOMAS Kienberger
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中国矿业大学环境与测绘学院,江苏徐州 221116
矿山生态修复教育部工程研究中心,江苏徐州 221116
中国煤炭学会,北京 100013
国家能源集团战略规划部,北京 100011
Department of Environmental and Energy Process Engineering,Montanuniversität Leoben,A-8700 Leo-ben,Austria
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