一种工业热源提取方法:利用温度特征模板的BP神经网络  

An Industrial Heat Source Extraction Method:BP Neural Network Using Temperature Feature Template

作　　者：李博 范俊甫[1] 韩留生[1] 孙广伟 张大富[1] 张盼盼[2] LI Bo;FAN Junfu;HAN Liusheng;SUN Guangwei;ZHANG Dafu;ZHANG Panpan(School of Civil and Architectural Engineering,Shandong University of Technology,Zibo 255000,China;National Ocean Technology Center,Tianjin 300112,China)

机构地区：[1]山东理工大学建筑工程学院,淄博255000 [2]国家海洋技术中心,天津300112

出　　处：《地球信息科学学报》2022年第3期533-545,共13页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42171413);国家重点研发计划项目(2017YFB0503500);山东省自然科学基金项目(ZR2020MD015、ZR2020MD018);山东省重大科技创新工程项目(2019JZZY020103);山东理工大学青年教师发展支持计划项目(4072-115016)。

摘　　要：针对年尺度热异常数据提取工业热源的方法存在数量和空间精细化程度不足的问题,使用VIIRS Active Fire数据,提出了一种基于温度特征模板的BP神经网络工业热源提取方法。该方法以京津冀及周边地区为试验区,首先,根据工业热源空间聚集性特征,使用OPTICS算法划分热源对象;其次,根据热源的热辐射特征,构建工业热源与非工业热源温度特征模板;最后,以温度特征模板、热源统计特征等作为参数,使用BP神经网络提取工业热源对象。结果表明:(1)本文提出的基于温度特征模板的BP神经网络算法的工业热源提取精度达到了96.31%,与时间滤波、逻辑回归方法相比较,工业热源提取精度分别提高了8.45%、7.53%;(2)2015—2020年京津冀及周边地区6省市工业热源数量整体减少了27.46%;河北省工业热源对象数量和热异常点数量年均减少了8.06%和7.44%,相对于其他省市减少幅度最大;山东、天津的工业热源集中度分别提高了25.72%、86.64%,说明两地工业转型升级政策取得较显著成效;(3)唐山、邯郸、吕梁和长治4个城市工业热源对象数量占试验区全部的31.37%,为京津冀及周边地区工业热源主要分布城市;临汾、太原等7个城市工业热源聚集程度和能源消耗程度高于其他城市;北京、周口等11个城市工业热源聚集程度和能源消耗程度低于其他城市;(4)2020年1—5月,京津冀及周边地区工业热异常点数量相对于2019、2021年同期保持不变或增加,新冠疫情对试验区工业热源无显著影响;2020年1、2月武汉工业热异常点数量与2019、2021年同期相比数量减少了66.67%以上,2020年3—5月工业热异常点数量低于2019年同期,2020年1—5月新冠疫情对武汉市工业热源影响显著。该研究反映了京津冀及周边地区工业热源发展的现状及趋势,能够为降低能耗和提高第二产业集中度等相关政策的制定与调整提供有价值的参考。Aiming at the problem of insufficient quantity and spatial refinement in the extraction of industrial heat source from annual scale thermal anomaly data,a neural network industrial heat source extraction method based on temperature feature template is proposed by using VIIRS active fire data.This study took BeijingTianjin-Hebei and its surrounding areas as the study area,Firstly,according to the spatial aggregation characteristics of industrial heat sources,the heat source objects were divided by the OPTICS algorithm.Secondly,according to the thermal radiation characteristics of the heat sources,the temperature characteristic template of industrial heat sources and non-industrial heat sources were constructed.Finally,the BP neural network was used to extract industrial heat source objects using the temperature feature template and heat source statistical characteristics as parameters.The results show that:(1)the extraction precision of industrial heat source of the neural network algorithm of temperature feature template proposed in this paper reached 96.31%.Compared with time filtering and logistic regression methods,the extraction precision of industrial heat sources was improved by 8.45%and 7.53%,respectively;(2)From 2015 to 2020,the number of industrial heat sources in the six provinces and cities in Beijing-Tianjin-Hebei and its surrounding areas decreased by 27.46%.The number of industrial heat source objects and heat anomalies in Hebei Province decreased by 8.06%and 7.44%annually,respectively,which was the largest decrease compared with other provinces and cities.The concentration of industrial heat sources in Shandong and Tianjin increased by 25.72%and 86.64%,respectively,indicating that the industrial transformation and upgrade policies in the two places have achieved remarkable results;(3)Tangshan,Handan,Lvliang,and Changzhi accounted for 31.37%of the total industrial heat sources in the study area,which are the main cities in Beijing-Tianjin-Hebei and its surrounding areas.The degree of industrial heat

关 键 词：工业热源 OPTICS算法 温度特征模板 BP神经网络 VIIRS Active Fire 京津冀及周边地区 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TK11[自动化与计算机技术—控制科学与工程]