卫星热红外温度反演钢铁企业炼钢月产量估算模型

doi:10.6046/zrzyyg.2020399

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摘要

钢铁业是国民经济发展中重要组成部分,掌握钢铁企业月产量有利于开展宏观调控及合理分配资源。以钢铁企业的月产量为研究对象,运用景观格局指数的理论和方法,利用卫星热红外遥感数据表面温度反演后的分级结果,结合厂房矢量数据来获取表面温度异常值和热力景观分布参数,以此提出并建立钢铁企业炼钢月产量估算模型。再结合华中和华北两个典型钢铁企业实际月产量数据,根据最小二乘拟合分别求估算模型,模型的决定系数(R²)大于0.9。分析后验差检验结果可知,该估算模型精度等级为二级; 且在95%的置信度下,实际产量值均落在估算值的置信区间内,综合反映本文提出的炼钢月产量估算模型精度较高。

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	李特雅
	宋妍
	于新莉
	周圆锈

关键词 ：热红外遥感, 景观格局指数, 炼钢月产量估算模型, 后验方差检验

Abstract：

The iron and steel industry is a very important part in economic development. Obtaining the knowledge of the monthly production of steel companies is conducive to the macro control of the economy and the rational allocation of resources. In this paper, a monthly production estimation model for steel companies was proposed based on the grading results of the surface temperature obtained from the inversion of satellite thermal infrared data as well as the theory and method of landscape pattern indices. The surface temperature anomalous values and the thermal landscape distribution parameters of steel companies can be calculated according to the vector data of the spatial framework of steel companies. Based on this and the actual monthly production data of two typical steel companies in Central China and North China, the estimation model was established through the least-squares fitting, and the coefficient of determination (R²) of the model was greater than 0.9. According to the posterior variance test results, the accuracy of the estimation model proposed in this study is level 2. Meanwhile, the actual production values all fall within the 95% confidence interval of the estimation values. All these comprehensively reflect the monthly production model proposed in this paper are highly accurate.

Key words： thermal infrared remote sensing landscape pattern index steelmaking monthly production estimation model posterior variance test

收稿日期: 2020-12-14 出版日期: 2021-12-23

ZTFLH:

TP79

基金资助:国防科工局民用“十三五”航天预先研究项目“星载高分辨率红外高光谱相机及应用技术”(D040104)

通讯作者: 宋妍

作者简介: 李特雅(1996-),男,硕士研究生,主要研究方向为热红外遥感应用技术。Email: liteya@cug.edu.cn。

引用本文:

李特雅, 宋妍, 于新莉, 周圆锈. 卫星热红外温度反演钢铁企业炼钢月产量估算模型[J]. 自然资源遥感, 2021, 33(4): 121-129.
LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2020399 或 https://www.gtzyyg.com/CN/Y2021/V33/I4/121

Fig.1 炼钢厂厂房热辐射示意图

Fig.2 炼钢月产量估算模型建模流程图

Tab.1 参数与月产量实际值相关性

Tab.2 拟合精度等级

Tab.3 表面孤立温度区及其四邻域表面温度值

Fig.3 表面孤立温度区箱线图分析

时间	残差Y_j- $Y j 0$	残差百分比(Y_j- $Y j 0$ )/ Y_j×100%
2017年2月	-4.409	-3.50%
2017年10月	-1.889	-1.35%
2017年12月	-0.601	-0.45%
2018年7月	-4.372	-3.13%
2018年9月	-3.800	-2.76%
2018年10月	1.736	1.24%
2018年11月	1.554	1.12%
2020年2月	-7.575	-6.47%
均值	-2.419	-1.91%

Tab.4 验证用实际值与SMPE值残差及残差百分比

Fig.4 验证用实际值与SMPE值曲线

参数	$y -$	$e -$	S₁	S₂	C	P
数值	134.080	-2.419	7.772	3.019	0.388	1

Tab.5 企业A的SMPE精度验证参数

Fig.5 SMPE值置信区间(置信度=95%)

Fig.6 表面孤立温度区箱线图分析

时间	残差Y_j- $Y j 0$	残差百分比(Y_j- $Y j 0$ )/ Y_j×100%
2013年7月	3.163	4.52%
2013年8月	3.369	4.22%
2014年7月	0.770	1.01%
2016年5月	1.019	1.39%
2017年4月	-0.826	-1.06%
均值	1.499	2.02%

Tab.6 验证用实际值与SMPE残差及残差百分比

Fig.7 验证用炼钢月产量实际值与SMPE曲线

参数	$y -$	$e -$	S₁	S₂	C	P
均值	75.528	1.499	3.604	1.576	0.438	1

Tab.7 企业B SMPE模型精度验证参数

Fig.8 SMPE值置信区间(置信度=95%)

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