基于C/S的气田巡井考勤睑测子系统的设计与实现
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摘要
苏里格气田现场数据采集基本靠人工完成,巡井人员亲临现场采集井口生产数据,由于油气生产井数量多且分布范围广,人工巡井不仅加重工人劳动强度,且影响到现场监控与采油数据的实时性和准确性。本文针对目前苏里格气田落后的巡检管理方式,以实现对油田井站生产信息的远程检测和管理为目标,设计一套新型的基于C/S结构的电子巡井系统,其对于实现油气井管理自动化、提高工作效率、保证数据采集准确性及加强现场事故应急处理反应速度等都具有非常重要的意义。
本文首先研究了巡井系统国内外研究现状,并在此基础上针对苏里格气田巡井现状,提出了基于C/S结构的苏里格气田新型电子巡井系统设计目标及系统总体架构。为便于设计实现及后期运行管理,将整个系统按功能划分为若干子系统。因时间、经验等所限,本文仅对苏里格气田电子巡井系统的考勤子系统及视频异常检测子系统进行了详细设计和实现,以及功能测试和分析。因考勤信息并发执行处理涉及系统安全性问题,本文对其应对策略专门进行研究。由于视频异常检测涉及到整个气田的视频数据,如采用传统视频异常检测算法,系统的处理效率不高。本文采用了一种改进的视频检测差分算法以提高系统处理速度。本文第一章绪论部分介绍了巡井系统的发展概况,目前国内外巡井系统的发展状况,开发技术和特点,以及苏里格气田电子巡井系统的应用背景和现状。第二章对基于C/S结构的苏里格气田新型电子巡井系统进行了总体设计,包括系统设计目标与开发思想,在此基础上搭建系统架构,并对系统进行功能子系统的划分,同时介绍了本系统开发的相关技术。第三章和第四章分别对考勤子系统及视频异常检测子系统进行了详细设计和实现,其中数据库采用SQL Server设计实现,界面及处理算法采用VC++ 6.0编码实现,同时对所完成的系统进行了功能测试及分析。第五章结论部分对全文工作及不足之处做出总结,并提出下一步的研究方向。
本文为油田的现代化建设和数字化管理,以及电子巡井相关技术未来的研究和发展方向提供了良好的借鉴。
Nowadays, the field data of Sulige Oil and Gas Field mainly were gathered manually, and the efficiency and accuracy of the field monitoring and gas producing data were influenced badly. So it was important and necessary to research and design a whole new kind of based of C/S structured electronic and intelligent patrolling and checking system.
This thesis gave a general description of the development of patrolling and checking system. Under discussion of traditional solutions, a new whole scheme was put forward. In this thesis, the mainframe of the system was discussed, and the entire system was divided into functional sub-systems. And this thesis only gave the specific realization of the work attendance checking sub-system and video abnormality monitoring and detection sub-system. Functional test showed the sub-systems fit the expected goals. Also in this thesis, the work attendance checking information parallel processing strategy and a type of improved video abnormality detection differential algorithm was discussed.
The chapter 1 mainly discussed the common techniques of the patrolling and checking system. The chapter 2 gave a description of the structure design and sub-systems division of the whole system. The chapter 3 and 4 separately showed the detailed design and implementation of work attendance checking sub-system and video abnormality monitoring and detection sub-system. The chapter 5 gave the summary of this thesis, and point out the next round research direction.
This thesis’s research about newly electronic and intelligent patrolling and checking system provided successful experiences for the oil and gas field production modernization and related tech researches.
本文首先研究了巡井系统国内外研究现状,并在此基础上针对苏里格气田巡井现状,提出了基于C/S结构的苏里格气田新型电子巡井系统设计目标及系统总体架构。为便于设计实现及后期运行管理,将整个系统按功能划分为若干子系统。因时间、经验等所限,本文仅对苏里格气田电子巡井系统的考勤子系统及视频异常检测子系统进行了详细设计和实现,以及功能测试和分析。因考勤信息并发执行处理涉及系统安全性问题,本文对其应对策略专门进行研究。由于视频异常检测涉及到整个气田的视频数据,如采用传统视频异常检测算法,系统的处理效率不高。本文采用了一种改进的视频检测差分算法以提高系统处理速度。本文第一章绪论部分介绍了巡井系统的发展概况,目前国内外巡井系统的发展状况,开发技术和特点,以及苏里格气田电子巡井系统的应用背景和现状。第二章对基于C/S结构的苏里格气田新型电子巡井系统进行了总体设计,包括系统设计目标与开发思想,在此基础上搭建系统架构,并对系统进行功能子系统的划分,同时介绍了本系统开发的相关技术。第三章和第四章分别对考勤子系统及视频异常检测子系统进行了详细设计和实现,其中数据库采用SQL Server设计实现,界面及处理算法采用VC++ 6.0编码实现,同时对所完成的系统进行了功能测试及分析。第五章结论部分对全文工作及不足之处做出总结,并提出下一步的研究方向。
本文为油田的现代化建设和数字化管理,以及电子巡井相关技术未来的研究和发展方向提供了良好的借鉴。
Nowadays, the field data of Sulige Oil and Gas Field mainly were gathered manually, and the efficiency and accuracy of the field monitoring and gas producing data were influenced badly. So it was important and necessary to research and design a whole new kind of based of C/S structured electronic and intelligent patrolling and checking system.
This thesis gave a general description of the development of patrolling and checking system. Under discussion of traditional solutions, a new whole scheme was put forward. In this thesis, the mainframe of the system was discussed, and the entire system was divided into functional sub-systems. And this thesis only gave the specific realization of the work attendance checking sub-system and video abnormality monitoring and detection sub-system. Functional test showed the sub-systems fit the expected goals. Also in this thesis, the work attendance checking information parallel processing strategy and a type of improved video abnormality detection differential algorithm was discussed.
The chapter 1 mainly discussed the common techniques of the patrolling and checking system. The chapter 2 gave a description of the structure design and sub-systems division of the whole system. The chapter 3 and 4 separately showed the detailed design and implementation of work attendance checking sub-system and video abnormality monitoring and detection sub-system. The chapter 5 gave the summary of this thesis, and point out the next round research direction.
This thesis’s research about newly electronic and intelligent patrolling and checking system provided successful experiences for the oil and gas field production modernization and related tech researches.
引文
[1]王铁流,王瑛,丁开慧.采用ADμC812实现超大容量油井数据采集与存储.电子产品世界,2002,(8):116-117
[2]陈洁.油田采油系统无功功率补偿方案设计.工矿自动化,2009,(8):122-125
[3]吴革生,王效明,宋汉华,等.气井井口智能生产控制系统.新疆石油天然气, 2008,4(8):126-136
[4]李晓岩.油井多参数远程监控系统的设计与实现.中国石油大学(华东):硕士学位论文, 2009:4-5
[5]郝晓弘,徐维涛.基于FF协议智能变送器的设计与开发.仪表技术与传感器, 2003,(5): 6-7
[6]张莲蓬.先进的井下监测仪.国外油田工程,2001,17(10):47-48
[7]敬蜀蓉,李鑫,等..抽油机示功图无线测试技术.石油钻采工艺,2004,26(4):79-81
[8]马维华.提高多机数据采集系统的通信可靠性.数据采集与处理,2002,17(4):441-444
[9]刘春,万阳,王仲璃..国际油价上涨“路线图”.国际石油经济,2008,8:4-9
[10]康学峰,杨厚荣,孙永兴.永置式井下智能多参数监测装置.石油仪器,2007,21(2):8-10
[11]冉新权,何光怀.关键技术突破_集成技术创新,实现苏里格气田规模有效开发.天然气工业,2007,27(12):1-5
[12]苏海平,杨光,等.苏里格气田节能减排地面设计.石油工程建设,2010,36(1):139-140
[13] S.G Gibbs. Utility of Motor-Speed Measurements in Pumping-Well Analysis and Control. SPE 13198, 1987:199-208
[14] M.Depenbrock. Direct Self-Control (DSC) of Inverter-Fed Induction Machine. IEEE Trans on PE, Vo1.P3, 1988, 4:420-429
[15] Takahashi L. High-performance of direct torque control of an induction motor. IEEE Trans on Ind. Vo1.252, 1989:257-264
[16]蒲东兵,杜常瑞,等.基于DSP的人脸识别门禁系统设计与实现.仪器仪表学报,2005, (8):397-399
[17]高伟.电力线路巡检管理系统的研究.重庆大学硕士论文,2004:1
[18]邹颖颖,苗克坚,陆寅.基于射频识别的门禁考勤系统研究.微处理机,2008, (6):180-182
[19]刘铮,张克.非接触式IC卡中的射频识别技术.贵州工业大学学报,2002(1):54-58
[20]孟志清,郭云飞.基于数据库应用软件的一类并行数据库管理应用系统的结构设计.计算机工程与应用,1998,(8):3-5
[21]吴建,郑潮,汪杰.UML基础与Rose建模案例.人民邮电出版社,2005:99-111
[22]孙文娟.企业绩效管理系统设计与实现.华东师范大学专业硕士学位论文,2009:51-56
[23]钟勇波,段永宏,雷曼.电子考勤系统在化工企业的实现.企业技术开发,2006, (6):20-22
[24]王瑞,于速,张雨.Visual C++数据库系统开发完全手册.人民邮电出版社,2006:459-466
[25]孙秀娟,吴建,尹翠霞,王传江.基于iButton技术的矿用门禁考勤系统设计.煤炭科学技术,2009,(3):83-86
[26]章毓晋.图像工程(下册) :图像理解与计算机视觉.北京:清华大学出版社,2000:9-13
[27]杨耿,和卫星.运动目标图像识别与跟踪系统的研究.计算机测量与控制,2005,13( 3):267-269
[28]王锦,于俊清,段萌远.基于感兴趣区域的运动分割算法.微处理机,2008,(3):139-141
[29]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,25(3):225-237
[30] Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1778-1792.
[31] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. Froc of the 6th European Conference on Computer Vision, London, 2000: 751-767.
[32] Javed O, Shah M. Tracking and object classification for automated surveillance. Froc of the 7th European Conference on Computer Vision, Springer, 2002: 343-357.
[33] Chen Yunqiang, Rui Yong, Huang T S. Multicolor HMM-UKF for real-time contour backing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1525-1529.
[34] Yang Tao, Li S Z, Pan Quan, et al. Real-time multiple objects tracking with occlusion handling in dynamic scenes. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 2005, 1:970-975.
[35] Wang Yongzhong, Liang Yan, Zhao Chunhui, et al. Adaptive multi-cue kernel tracking. Proceedings of IEEE International Conference on Multimedia and Expo, Beijing, 2007:1814-1817.
[36]凌志刚,赵春晖,等.基于视觉的人行为理解综述.计算机应用研究,2008,25(9):2570-2578.
[37] Aggarwal J K, Cai Q. Human motion analysis: A review. Computer Vision and Image Understanding, 1999, 73(3): 428-440.
[38]陈凤东,洪炳铬.基于动态阀值背景差分算法的日标检测方法.哈尔滨工业大学学报,2005,37(7): 883-884, 955
[39]潘石柱,交伟群,王星.基于自适应背景的实时运动物体检测.计算机应用,2004,24(10):94-96
[40]于成忠,朱骏,袁晓辉..基于背景差法的运动日标检测..东南大学学报(自然科学版),2005,35(2):159-161
[41]关海英,阮秋琦.序列图像中运动日标的检测.铁道学报,1996,18(6):32-35
[42]张宇.基于差分和肤色图像的人脸检测算法.电子科技大学学报,2005,34(4):497-500
[43]刘灵丽.指纹图像的分割算法.湘南学院学报,2004,25(5):72-75.
[44] Jing Zhang,Y'mg Shan,Mao-zu Guo.An Edge-Detection Method for Moving Objects. 2007 IEEE International Conference on Automation and Logistics. Jinan, Shandong, China. August 18-21, 2007
[45] Wang Dongsheng, Li Zaiming. Task-flow analysis of video object detection forC6X implementation, Communication, Circuits and Systems, IEEE 2002 International Conference on, Volume: 2, 29 June-1 July 2002, Pages: 1267-1270.
[46] T.Meier, K.N.Ngan. Video segmentation for content-based coding, IEEE Trans. Circuits and Systems for Vdeo Technology, vo1.9, pp.1190-1203, Dec.1999.
[47] D.G.Sim, O.K.Kwon, R.H.Park. Object matching algorithm using robust Hausdorff distance measure, IEEE Trans. Image Processing, vo1.8, pp.425-429, Mar.1999.
[48] C.Toklu, A.M.Tekalp, A.T.Erdem. Semi-automatic video object segmentation in the presence of occlusion. IEEE Trans. Circuits and Systems for Video Technology, vo1.10, pp.624-629, Jun.2000.
[49]沈定刚,戚飞虎.智能型目标识别系统.红外与毫米波学报,1995,14(3):182-1880
[50] Chao He, Ahalt S C. Object tracking using the Gabber wavelet transform and the golden section algorithm, IEEE Trans. On Multimedia, 2002, 4(4): 528-538.
[51]王宾,潘建寿,梁雁冰.基于Matrox卡的视频图像运动检测.西北大学学报(自然科学网络版) ,2004,3(2):6-8
[52] Chen NianNian, Li Bo, Fan Yong, Lin MaoSong. A Segmentation Algorithm for Thermals Paint Image. Proceedings of 2006 ICMLC.2006, 8
[53] Ye Zhang, Zhi-jing Liu. Irregular Behavior Recognition Based on Treading Track. IEEE ICWAPR-2007. (Index by EI). Published
[2]陈洁.油田采油系统无功功率补偿方案设计.工矿自动化,2009,(8):122-125
[3]吴革生,王效明,宋汉华,等.气井井口智能生产控制系统.新疆石油天然气, 2008,4(8):126-136
[4]李晓岩.油井多参数远程监控系统的设计与实现.中国石油大学(华东):硕士学位论文, 2009:4-5
[5]郝晓弘,徐维涛.基于FF协议智能变送器的设计与开发.仪表技术与传感器, 2003,(5): 6-7
[6]张莲蓬.先进的井下监测仪.国外油田工程,2001,17(10):47-48
[7]敬蜀蓉,李鑫,等..抽油机示功图无线测试技术.石油钻采工艺,2004,26(4):79-81
[8]马维华.提高多机数据采集系统的通信可靠性.数据采集与处理,2002,17(4):441-444
[9]刘春,万阳,王仲璃..国际油价上涨“路线图”.国际石油经济,2008,8:4-9
[10]康学峰,杨厚荣,孙永兴.永置式井下智能多参数监测装置.石油仪器,2007,21(2):8-10
[11]冉新权,何光怀.关键技术突破_集成技术创新,实现苏里格气田规模有效开发.天然气工业,2007,27(12):1-5
[12]苏海平,杨光,等.苏里格气田节能减排地面设计.石油工程建设,2010,36(1):139-140
[13] S.G Gibbs. Utility of Motor-Speed Measurements in Pumping-Well Analysis and Control. SPE 13198, 1987:199-208
[14] M.Depenbrock. Direct Self-Control (DSC) of Inverter-Fed Induction Machine. IEEE Trans on PE, Vo1.P3, 1988, 4:420-429
[15] Takahashi L. High-performance of direct torque control of an induction motor. IEEE Trans on Ind. Vo1.252, 1989:257-264
[16]蒲东兵,杜常瑞,等.基于DSP的人脸识别门禁系统设计与实现.仪器仪表学报,2005, (8):397-399
[17]高伟.电力线路巡检管理系统的研究.重庆大学硕士论文,2004:1
[18]邹颖颖,苗克坚,陆寅.基于射频识别的门禁考勤系统研究.微处理机,2008, (6):180-182
[19]刘铮,张克.非接触式IC卡中的射频识别技术.贵州工业大学学报,2002(1):54-58
[20]孟志清,郭云飞.基于数据库应用软件的一类并行数据库管理应用系统的结构设计.计算机工程与应用,1998,(8):3-5
[21]吴建,郑潮,汪杰.UML基础与Rose建模案例.人民邮电出版社,2005:99-111
[22]孙文娟.企业绩效管理系统设计与实现.华东师范大学专业硕士学位论文,2009:51-56
[23]钟勇波,段永宏,雷曼.电子考勤系统在化工企业的实现.企业技术开发,2006, (6):20-22
[24]王瑞,于速,张雨.Visual C++数据库系统开发完全手册.人民邮电出版社,2006:459-466
[25]孙秀娟,吴建,尹翠霞,王传江.基于iButton技术的矿用门禁考勤系统设计.煤炭科学技术,2009,(3):83-86
[26]章毓晋.图像工程(下册) :图像理解与计算机视觉.北京:清华大学出版社,2000:9-13
[27]杨耿,和卫星.运动目标图像识别与跟踪系统的研究.计算机测量与控制,2005,13( 3):267-269
[28]王锦,于俊清,段萌远.基于感兴趣区域的运动分割算法.微处理机,2008,(3):139-141
[29]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,25(3):225-237
[30] Sheikh Y, Shah M. Bayesian modeling of dynamic scenes for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1778-1792.
[31] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. Froc of the 6th European Conference on Computer Vision, London, 2000: 751-767.
[32] Javed O, Shah M. Tracking and object classification for automated surveillance. Froc of the 7th European Conference on Computer Vision, Springer, 2002: 343-357.
[33] Chen Yunqiang, Rui Yong, Huang T S. Multicolor HMM-UKF for real-time contour backing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1525-1529.
[34] Yang Tao, Li S Z, Pan Quan, et al. Real-time multiple objects tracking with occlusion handling in dynamic scenes. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 2005, 1:970-975.
[35] Wang Yongzhong, Liang Yan, Zhao Chunhui, et al. Adaptive multi-cue kernel tracking. Proceedings of IEEE International Conference on Multimedia and Expo, Beijing, 2007:1814-1817.
[36]凌志刚,赵春晖,等.基于视觉的人行为理解综述.计算机应用研究,2008,25(9):2570-2578.
[37] Aggarwal J K, Cai Q. Human motion analysis: A review. Computer Vision and Image Understanding, 1999, 73(3): 428-440.
[38]陈凤东,洪炳铬.基于动态阀值背景差分算法的日标检测方法.哈尔滨工业大学学报,2005,37(7): 883-884, 955
[39]潘石柱,交伟群,王星.基于自适应背景的实时运动物体检测.计算机应用,2004,24(10):94-96
[40]于成忠,朱骏,袁晓辉..基于背景差法的运动日标检测..东南大学学报(自然科学版),2005,35(2):159-161
[41]关海英,阮秋琦.序列图像中运动日标的检测.铁道学报,1996,18(6):32-35
[42]张宇.基于差分和肤色图像的人脸检测算法.电子科技大学学报,2005,34(4):497-500
[43]刘灵丽.指纹图像的分割算法.湘南学院学报,2004,25(5):72-75.
[44] Jing Zhang,Y'mg Shan,Mao-zu Guo.An Edge-Detection Method for Moving Objects. 2007 IEEE International Conference on Automation and Logistics. Jinan, Shandong, China. August 18-21, 2007
[45] Wang Dongsheng, Li Zaiming. Task-flow analysis of video object detection forC6X implementation, Communication, Circuits and Systems, IEEE 2002 International Conference on, Volume: 2, 29 June-1 July 2002, Pages: 1267-1270.
[46] T.Meier, K.N.Ngan. Video segmentation for content-based coding, IEEE Trans. Circuits and Systems for Vdeo Technology, vo1.9, pp.1190-1203, Dec.1999.
[47] D.G.Sim, O.K.Kwon, R.H.Park. Object matching algorithm using robust Hausdorff distance measure, IEEE Trans. Image Processing, vo1.8, pp.425-429, Mar.1999.
[48] C.Toklu, A.M.Tekalp, A.T.Erdem. Semi-automatic video object segmentation in the presence of occlusion. IEEE Trans. Circuits and Systems for Video Technology, vo1.10, pp.624-629, Jun.2000.
[49]沈定刚,戚飞虎.智能型目标识别系统.红外与毫米波学报,1995,14(3):182-1880
[50] Chao He, Ahalt S C. Object tracking using the Gabber wavelet transform and the golden section algorithm, IEEE Trans. On Multimedia, 2002, 4(4): 528-538.
[51]王宾,潘建寿,梁雁冰.基于Matrox卡的视频图像运动检测.西北大学学报(自然科学网络版) ,2004,3(2):6-8
[52] Chen NianNian, Li Bo, Fan Yong, Lin MaoSong. A Segmentation Algorithm for Thermals Paint Image. Proceedings of 2006 ICMLC.2006, 8
[53] Ye Zhang, Zhi-jing Liu. Irregular Behavior Recognition Based on Treading Track. IEEE ICWAPR-2007. (Index by EI). Published