变形监测数据处理与预测分析系统设计与实现
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摘要
变形监测是为了监视地表、工程建筑物及设施等变形体的变形情况而进行的长期性、重复性的测量工作,其关键在于捕捉变形体的变形敏感部位和各观测周期间变形观测点的变形信息。虽然现在GPS测量方法、摄影测量方法以及其他测量方法都用在该领域得到了较好的运用。但传统的常规大地测量方法仍然是人类进行工程建筑物变形监测的主要手段。在一些有关的技术领域内,其他技术(如现代空间定位技术)尚无法替代传统的常规大地测量方法。
变形监测的观测精度要求高,数据处理分析复杂,需要一个高精度的数据处理软件进行监测数据处理。同时,由于变形监测数据量大,质量控制及规范化、标准化管理难度大,需要对数据进行分类归档存储。而现有的变形监测系统在功能上不能同时满足数据处理和管理功能,对于某些工程,需要对变形信息进行预测分析,例如高速铁路沉降评估,因此,设计一个集数据处理,管理和预测分析于一体的系统显得十分重要。
本文系统地研究了变形监测网数据处理方法和相关预测分析模型。用基于最小二乘原理的迭代算法改进指数曲线模型、三点法模型等模型的计算方法。并研究基于时空的内插修正模型,实现区域地面沉降的沉降数据修正。
通过上述研究,采用VC#.NET作为开发工具,以MYSQL为基本的数据库系统,以Excel和文本形式作为成果输出格式,以.jpg和.dwg格式作为图形输出格式,研发了变形监测数据处理与预测分析系统,该系统的主要功能是处理和管理变形监测数据,数据处理完成后输出完整的精度统计信息,并可对变形信息进行预测分析,同时系统还提供变形数据和预测信息成果输出功能。该系统由数据处理子系统、数据管理子系统、图形绘制子系统、预测分析子系统、成果输出子系统和区域沉降数据修正子系统六个子系统组成。在对实测数据处理和预测分析的基础上,验证本系统数据处理模块和预测分析等模块运行的正确性。
Deformation monitoring is a long-term and repeating survey work that aims at monitoring movements of the earth's surface, engineering buildings and establishments, which is catching the deformation information in the sensitive deformation positions and each periodic survey. GPS, photogrammetry and other survey methods play an important role in this area. Conventional survey cannot be replaced in some area (modern space location), and still major means of engineering structure's deformation monitoring.
As the deformation monitoring requires higher accuracy, the data processing is quite complicated, monitoring data processing needs a high-precision data processing software. Meanwhile, massive data, quality control and standardized management are difficult, and need to classify stored data. The existing deformation monitoring systems cannot satisfy data processing and management at the same time. Some engineering, such as the high-speed railway settlement evaluation need prediction analysis deformation. Therefore, it is extremely important to develop an integrated system with data processing, management and prediction analysis.
In this thesis, the data processing methods of deformation monitoring network and algorithm of prediction model are systematically studied, by Iterative Algorithm of least squares method improved some of the algorithm of index curve model and three points mode prediction model. The research on spatiotemporal correction model is useful for correcting the regional settlement data.
On the above basis of the research, the deformation monitoring data processing and prediction analysis system are developed, here the VC#.NET is adopted as the tool, MYSQL are as the basic database system, Excel and text form are as the result output formats,. jpg and.dwg are as the graphics output formats. The main functions of this system are deformation monitoring data processing and management. We can get overall precision statistics information when the system data processing completed, and prediction information, and realize the deformation results output. It consists of five modules:data processing module, data management module, graphics draw module, prediction analysis module, output result module and regional settlement data correction. Finally, Through the use of system processing data and prediction analysis, we can verify the correctness of data process module and prediction analysis module.
变形监测的观测精度要求高,数据处理分析复杂,需要一个高精度的数据处理软件进行监测数据处理。同时,由于变形监测数据量大,质量控制及规范化、标准化管理难度大,需要对数据进行分类归档存储。而现有的变形监测系统在功能上不能同时满足数据处理和管理功能,对于某些工程,需要对变形信息进行预测分析,例如高速铁路沉降评估,因此,设计一个集数据处理,管理和预测分析于一体的系统显得十分重要。
本文系统地研究了变形监测网数据处理方法和相关预测分析模型。用基于最小二乘原理的迭代算法改进指数曲线模型、三点法模型等模型的计算方法。并研究基于时空的内插修正模型,实现区域地面沉降的沉降数据修正。
通过上述研究,采用VC#.NET作为开发工具,以MYSQL为基本的数据库系统,以Excel和文本形式作为成果输出格式,以.jpg和.dwg格式作为图形输出格式,研发了变形监测数据处理与预测分析系统,该系统的主要功能是处理和管理变形监测数据,数据处理完成后输出完整的精度统计信息,并可对变形信息进行预测分析,同时系统还提供变形数据和预测信息成果输出功能。该系统由数据处理子系统、数据管理子系统、图形绘制子系统、预测分析子系统、成果输出子系统和区域沉降数据修正子系统六个子系统组成。在对实测数据处理和预测分析的基础上,验证本系统数据处理模块和预测分析等模块运行的正确性。
Deformation monitoring is a long-term and repeating survey work that aims at monitoring movements of the earth's surface, engineering buildings and establishments, which is catching the deformation information in the sensitive deformation positions and each periodic survey. GPS, photogrammetry and other survey methods play an important role in this area. Conventional survey cannot be replaced in some area (modern space location), and still major means of engineering structure's deformation monitoring.
As the deformation monitoring requires higher accuracy, the data processing is quite complicated, monitoring data processing needs a high-precision data processing software. Meanwhile, massive data, quality control and standardized management are difficult, and need to classify stored data. The existing deformation monitoring systems cannot satisfy data processing and management at the same time. Some engineering, such as the high-speed railway settlement evaluation need prediction analysis deformation. Therefore, it is extremely important to develop an integrated system with data processing, management and prediction analysis.
In this thesis, the data processing methods of deformation monitoring network and algorithm of prediction model are systematically studied, by Iterative Algorithm of least squares method improved some of the algorithm of index curve model and three points mode prediction model. The research on spatiotemporal correction model is useful for correcting the regional settlement data.
On the above basis of the research, the deformation monitoring data processing and prediction analysis system are developed, here the VC#.NET is adopted as the tool, MYSQL are as the basic database system, Excel and text form are as the result output formats,. jpg and.dwg are as the graphics output formats. The main functions of this system are deformation monitoring data processing and management. We can get overall precision statistics information when the system data processing completed, and prediction information, and realize the deformation results output. It consists of five modules:data processing module, data management module, graphics draw module, prediction analysis module, output result module and regional settlement data correction. Finally, Through the use of system processing data and prediction analysis, we can verify the correctness of data process module and prediction analysis module.
引文
[1]黄声享等.变形监测数据处理[M],武汉：武汉大学出版社,2003：1-3.
[2]吴世棋,孔健,张德强.近景摄影测量在隧道变形监测中的应用[J],铁路航测,1994(4)：6-8.
[3]王静瑶,吴云.现代地壳运动与地震监测预报研究现状和发展趋势[J],地球科学进展,2000,15(1)：84-89.
[4]王晓华,胡友健,柏柳.变形监测研究现状综述[J],测绘科学,2006,31(2).
[5]肖栾,胡友健,王晓华.GPS技术在变形监测中的应用综述[J],工程地球物理学报,2005,2(2)：160-165.
[6]周江文.监测网的拟稳平差[M],北京：测绘出版社,1987.
[7]陈永奇.变形观测数据处理[M],北京：测绘出版社,1988.
[8]黄声享.监测网的稳定性分析[J],测绘信息与工程,2001(3)：16-19.
[9]岳建平,方露,黎昵.变形监测理论与技术研究进展[J],测绘通报,2007(7).
[10]刘大杰.论亏秩自由网平差[J],武汉测绘学报,1981(10)：8-13.
[11]陶本藻.自由网平差与变形分析[M],武汉：武汉大学出版社,2000：5-12,78-84.
[12]京沪高速铁路线下工程沉降变形观测及评估实施细则[Z],成都：中铁二院,西南交通大学,2008.
[13]陈永奇,吴子安,吴中如.变形监测分析与预报[M],北京：测绘出版社,1998.
[14]Gabriel A K, Goldstein R M, Zebker H A. Mapping small elevation changes over large areas:Differential radar interferometry[J]. Journal of Geophysical Research,1989,94 (B7).
[15]http://www.dreamincode.net/code/snippet1677.htm.
[16]范东明.非线性最小二乘参数平差的非线性规划算法研究[J],西南交通大学学报,2001,36(5)：478-479.
[17]艾则孜·买提吐尔逊.变形监测网优化设计的研究[D],河海大学硕士学位论文,2006：3-8.
[18]赵显富.变形监测成果数据库管理系统的研制[J],测绘通报,2001(4).
[19]A SAOKA A. Observational procedure of settlement prediction[J]. Soils and Foundations,1978,18(4).
[20]肖文,范志平,蔡仁澜等.灰色系统GM(1,1)模型在建筑物变形监测中的应用[J],地理空间信息,2010(2).
[21]白迪谋,李仲勤.工程建筑物变形观测和变形分析[M],成都：西南交通大学出 版社,2002.
[22]彭广亮.变形监测数据处理与分析方法研究[D],辽宁工程技术大学硕士学位论
[23]陈德智,何李.变形监测信息管理与工程安全预警系统简介[J],山西建筑,2009,35 (35).
[24]李猛,谢正文,付艳.最优沉降组合预测模型及应用[J],西部探矿工程,2008(8).
[25]汪延彬,张献州.京沪高铁天津地区地表沉降空间插值方法研究[J],北京：测绘通报,2009(12).
[26]张正禄.工程测量学[M],武汉：武汉大学出版社,2005.
[27]刘祖强,张正禄,邹启新.工程变形监测分析预报的理论与实践[M],北京：中国水利水电出版社,2008.
[28]李斌,朱健.非等间隔灰色GM(1,1)模型在沉降数据分析中的应用[J],2007,32(4)：52-53.
[29]Dr Craig D,HILL,KarlD,SIPPEL.Modem Deformation Monitoring:A Multi Sensor Approach[J].FIG XXII international Congress,Washington,D.C.USA, April 19-26,2002.
[30]Roman Kadaj and Marek Plewako.A New Mode ling Approach to Geodetic Deformation Analysis,The 8th FIG International Symposium on Deformation Measurements,1996.
[31]孙昊月,王清,林坚民.双曲线法和指数曲线法推算软土地基沉降量的准确性比较[J],煤炭技术,2010,29(1).
[32]http://www.codeproject.com/KB/database/ConnectCsharpToMysql.aspx.
[33]Liu zhibin.Settlement prediction of deep excavation based on Gray Markow chain Model[J]. Geology&ExPloratioin,2002(11).
[34]Huang mint.prediction of resolution and homposition of measured displacement during slope excavation[J]. Chinese Journal of Rock Mechanics and Engineer.2003(8): 1320-1323.
[35]冯文凯,刘汉超.修正双曲线法在路基沉降变形初期阶段的应用探[J],地质灾害与环境保护,2010(3).
[36]周靖.Visual C# 2005从入门到精通[M].北京：清华大学出版社,2008.
[37]李敏,李知峰.变形监测网网型分析[J].南昌：江西测绘,2007(3).
[38]邹自力.建筑物沉降变形监测系统的建立与变形分析[J].地矿测绘,1999(20).
[39]CAI Qian-Guang etc.Deformation Monitor and Analysis of High-rise Building[J].第四届中俄城市与矿山论文集,2006.9.
[40]吕慧,俞永成.变形监测网网型结构对拟稳平差法分析结构的影响[J].工程勘察, 2006(9).
[41]吴子安.工程建筑物变形监测数据处理[M],北京：测绘出版社,1998.
[42]Chrzanowski, Adam, Szostak-Chrzanowski, Anna. Automation of deformation monitoring techniques and integration with prediction modeling[J], Geomatica,2010, 64(2).
[43]Xu Jin-Jun, Wang Hai-Cheng, Luo Yu-Zhen, etc. Deformation monitoring and data processing of landslide based on 3D laser scanning[J], Yantu Lixue/Rock and Soil Mechanics,2010,31(7).
[44]Qu Wan-Bo, Liu Xin-Rong, Huang Rui-Jin, etc. Deformation monitoring and its control in a shallow-buried large-span tunnel constructed using the pile-beam-arch method[J], Tumu Jianzhu yu Huanjing Gongcheng/Journal of Civil, Architectural and Environmental Engineering,2009,31(1).
[45]Wang Zhi-wang, Zhang Bao-jun, LI Di. Dam deformation monitoring model based on genetic algorithm of neural network[J], Rock and Soil Mechanics,2003(1).
[46]Zhou Dingheng, Cao Liqiao, Qu Haifeng etc. Deformation monitoring and control of super large section highway tunnel with different surrounding rocks [J], Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering,2009, 28(12).
[47]Zhao Erfeng, Jin Yongqiang. Dam deformation monitoring model and forecast based on hierarchical diagonal neural network[J],2008 International Conference on Wireless Communications,Networking, and Mobile Computing, WiCOM 2008,2008.
[48]Fornaro G, Pauciullo A, Serafino F. Deformation monitoring over large areas with multipass differential SAR interferometry:A new approach based on the use of spatial differences [J], international Journal of Remote Sensing,2009,30(6).
[49]DeLoach.Stephen R Continuous deformation monitoring with GPS[J], Journal of Surveying Engineering,1989,115(1).
[50]Liu Chao, Wang Jian, Hu Hong etc. Research on real-time correcting model of multipath in GPS dynamic deformation monitoring [J], Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University,2010,35(4).
[51]邹双朝,王新洲,邹进贵等.变形监测综合数据处理系统的研制[J],工程勘察,2003(4).
[52]宋力杰.测量平差程序设计[M],北京：国防工业出版社,2009.
[53]A Chrzanowski. Development trends in deformation Monitoring and Analysis The 8th FIG International Symposium on Deformation Measurements,1996.
[54]建筑变形测量规范[M],北京：中华人民共和国建设部,2008.
[2]吴世棋,孔健,张德强.近景摄影测量在隧道变形监测中的应用[J],铁路航测,1994(4)：6-8.
[3]王静瑶,吴云.现代地壳运动与地震监测预报研究现状和发展趋势[J],地球科学进展,2000,15(1)：84-89.
[4]王晓华,胡友健,柏柳.变形监测研究现状综述[J],测绘科学,2006,31(2).
[5]肖栾,胡友健,王晓华.GPS技术在变形监测中的应用综述[J],工程地球物理学报,2005,2(2)：160-165.
[6]周江文.监测网的拟稳平差[M],北京：测绘出版社,1987.
[7]陈永奇.变形观测数据处理[M],北京：测绘出版社,1988.
[8]黄声享.监测网的稳定性分析[J],测绘信息与工程,2001(3)：16-19.
[9]岳建平,方露,黎昵.变形监测理论与技术研究进展[J],测绘通报,2007(7).
[10]刘大杰.论亏秩自由网平差[J],武汉测绘学报,1981(10)：8-13.
[11]陶本藻.自由网平差与变形分析[M],武汉：武汉大学出版社,2000：5-12,78-84.
[12]京沪高速铁路线下工程沉降变形观测及评估实施细则[Z],成都：中铁二院,西南交通大学,2008.
[13]陈永奇,吴子安,吴中如.变形监测分析与预报[M],北京：测绘出版社,1998.
[14]Gabriel A K, Goldstein R M, Zebker H A. Mapping small elevation changes over large areas:Differential radar interferometry[J]. Journal of Geophysical Research,1989,94 (B7).
[15]http://www.dreamincode.net/code/snippet1677.htm.
[16]范东明.非线性最小二乘参数平差的非线性规划算法研究[J],西南交通大学学报,2001,36(5)：478-479.
[17]艾则孜·买提吐尔逊.变形监测网优化设计的研究[D],河海大学硕士学位论文,2006：3-8.
[18]赵显富.变形监测成果数据库管理系统的研制[J],测绘通报,2001(4).
[19]A SAOKA A. Observational procedure of settlement prediction[J]. Soils and Foundations,1978,18(4).
[20]肖文,范志平,蔡仁澜等.灰色系统GM(1,1)模型在建筑物变形监测中的应用[J],地理空间信息,2010(2).
[21]白迪谋,李仲勤.工程建筑物变形观测和变形分析[M],成都：西南交通大学出 版社,2002.
[22]彭广亮.变形监测数据处理与分析方法研究[D],辽宁工程技术大学硕士学位论
[23]陈德智,何李.变形监测信息管理与工程安全预警系统简介[J],山西建筑,2009,35 (35).
[24]李猛,谢正文,付艳.最优沉降组合预测模型及应用[J],西部探矿工程,2008(8).
[25]汪延彬,张献州.京沪高铁天津地区地表沉降空间插值方法研究[J],北京：测绘通报,2009(12).
[26]张正禄.工程测量学[M],武汉：武汉大学出版社,2005.
[27]刘祖强,张正禄,邹启新.工程变形监测分析预报的理论与实践[M],北京：中国水利水电出版社,2008.
[28]李斌,朱健.非等间隔灰色GM(1,1)模型在沉降数据分析中的应用[J],2007,32(4)：52-53.
[29]Dr Craig D,HILL,KarlD,SIPPEL.Modem Deformation Monitoring:A Multi Sensor Approach[J].FIG XXII international Congress,Washington,D.C.USA, April 19-26,2002.
[30]Roman Kadaj and Marek Plewako.A New Mode ling Approach to Geodetic Deformation Analysis,The 8th FIG International Symposium on Deformation Measurements,1996.
[31]孙昊月,王清,林坚民.双曲线法和指数曲线法推算软土地基沉降量的准确性比较[J],煤炭技术,2010,29(1).
[32]http://www.codeproject.com/KB/database/ConnectCsharpToMysql.aspx.
[33]Liu zhibin.Settlement prediction of deep excavation based on Gray Markow chain Model[J]. Geology&ExPloratioin,2002(11).
[34]Huang mint.prediction of resolution and homposition of measured displacement during slope excavation[J]. Chinese Journal of Rock Mechanics and Engineer.2003(8): 1320-1323.
[35]冯文凯,刘汉超.修正双曲线法在路基沉降变形初期阶段的应用探[J],地质灾害与环境保护,2010(3).
[36]周靖.Visual C# 2005从入门到精通[M].北京：清华大学出版社,2008.
[37]李敏,李知峰.变形监测网网型分析[J].南昌：江西测绘,2007(3).
[38]邹自力.建筑物沉降变形监测系统的建立与变形分析[J].地矿测绘,1999(20).
[39]CAI Qian-Guang etc.Deformation Monitor and Analysis of High-rise Building[J].第四届中俄城市与矿山论文集,2006.9.
[40]吕慧,俞永成.变形监测网网型结构对拟稳平差法分析结构的影响[J].工程勘察, 2006(9).
[41]吴子安.工程建筑物变形监测数据处理[M],北京：测绘出版社,1998.
[42]Chrzanowski, Adam, Szostak-Chrzanowski, Anna. Automation of deformation monitoring techniques and integration with prediction modeling[J], Geomatica,2010, 64(2).
[43]Xu Jin-Jun, Wang Hai-Cheng, Luo Yu-Zhen, etc. Deformation monitoring and data processing of landslide based on 3D laser scanning[J], Yantu Lixue/Rock and Soil Mechanics,2010,31(7).
[44]Qu Wan-Bo, Liu Xin-Rong, Huang Rui-Jin, etc. Deformation monitoring and its control in a shallow-buried large-span tunnel constructed using the pile-beam-arch method[J], Tumu Jianzhu yu Huanjing Gongcheng/Journal of Civil, Architectural and Environmental Engineering,2009,31(1).
[45]Wang Zhi-wang, Zhang Bao-jun, LI Di. Dam deformation monitoring model based on genetic algorithm of neural network[J], Rock and Soil Mechanics,2003(1).
[46]Zhou Dingheng, Cao Liqiao, Qu Haifeng etc. Deformation monitoring and control of super large section highway tunnel with different surrounding rocks [J], Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering,2009, 28(12).
[47]Zhao Erfeng, Jin Yongqiang. Dam deformation monitoring model and forecast based on hierarchical diagonal neural network[J],2008 International Conference on Wireless Communications,Networking, and Mobile Computing, WiCOM 2008,2008.
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