分布式光纖安防檢測(cè)系統(tǒng)的信號(hào)識(shí)別方法研究
發(fā)布時(shí)間：2018-12-23 08:00
【摘要】：隨著社會(huì)的快速發(fā)展,通信電纜、輸油管道、輸氣管道、高壓電網(wǎng)以及軍用、民用安防系統(tǒng)等基礎(chǔ)設(shè)備、設(shè)施的安全監(jiān)測(cè)等問(wèn)題已日趨成為影響經(jīng)濟(jì)發(fā)展和社會(huì)穩(wěn)定的重要因素。因此,針對(duì)各種危及電纜、管道安全及安防系統(tǒng)中非法入侵行為的及時(shí)發(fā)現(xiàn)和有效定位具有重要的研究?jī)r(jià)值和現(xiàn)實(shí)意義。本課題來(lái)源于與天津大學(xué)合作的國(guó)家973項(xiàng)目“光纖智能傳感網(wǎng)實(shí)驗(yàn)平臺(tái)關(guān)鍵技術(shù)及其應(yīng)用的基礎(chǔ)研究”課題,負(fù)責(zé)其中“惡劣環(huán)境對(duì)連續(xù)分布式傳感網(wǎng)偏振及定位影響研究”子項(xiàng)目。本文以提高M(jìn)ach-Zehnder干涉儀光纖安防系統(tǒng)擾動(dòng)事件識(shí)別精確度為目標(biāo),采用小波多分辨率分析和神經(jīng)網(wǎng)絡(luò)等手段,對(duì)采集得到的分布式光纖傳感安防系統(tǒng)的監(jiān)測(cè)數(shù)據(jù)進(jìn)行信號(hào)特征提取以及信號(hào)識(shí)別等研究,實(shí)現(xiàn)了對(duì)外界環(huán)境的下雨、敲擊、攀爬等信號(hào)的識(shí)別,對(duì)提高安防監(jiān)測(cè)系統(tǒng)中入侵行為的準(zhǔn)確定位提供了研究基礎(chǔ)。本文主要完成了以下幾方面的工作：(1)采用小波變換,對(duì)分布式光纖傳感安防系統(tǒng)的監(jiān)測(cè)數(shù)據(jù)實(shí)現(xiàn)了多分辨率信息提取,通過(guò)小波能譜實(shí)現(xiàn)了信號(hào)特征的向量提取,獲得了能較好反映各種安全事件的本質(zhì)特征,對(duì)下雨、敲擊、攀爬等信號(hào)實(shí)現(xiàn)了初步識(shí)別。(2)深入理解神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),將BP神經(jīng)網(wǎng)絡(luò)識(shí)別方法用于對(duì)安防檢測(cè)信號(hào)類型的識(shí)別。設(shè)計(jì)了4種改進(jìn)后的BP神經(jīng)網(wǎng)絡(luò)的MATLAB程序,并運(yùn)用獲得的樣本數(shù)據(jù)的特征量對(duì)其分別進(jìn)行訓(xùn)練,對(duì)訓(xùn)練結(jié)果進(jìn)行比較分析,選取最佳BP網(wǎng)絡(luò)訓(xùn)練方法。(3)對(duì)實(shí)驗(yàn)現(xiàn)場(chǎng)采集的監(jiān)測(cè)數(shù)據(jù)進(jìn)行了大量實(shí)驗(yàn)研究,證明了算法的有效性及可靠性,基于BP神經(jīng)網(wǎng)絡(luò)的識(shí)別率達(dá)到90%。
[Abstract]:With the rapid development of society, communication cables, oil pipelines, gas pipelines, high-voltage power grids, military and civilian security systems and other basic equipment, Safety monitoring of facilities has become an important factor affecting economic development and social stability. Therefore, it is of great research value and practical significance to detect and locate the illegal intrusion behavior in the cable, pipeline safety and security system. This topic comes from the national 973 project "key Technology and basic Research on the Application of Optical Fiber Intelligent Sensor Network experiment platform", which is in cooperation with Tianjin University. Responsible for the "adverse environment on the continuous distributed sensor network polarization and positioning research" subproject. In order to improve the accuracy of disturbance event identification in Mach-Zehnder interferometer optical fiber security system, wavelet multi-resolution analysis and neural network are used in this paper. The signal feature extraction and signal recognition of the monitoring data of distributed optical fiber sensing security system are studied, and the recognition of rain, knock, climbing and other signals in the outside environment is realized. It provides a research basis for improving the accurate location of intrusion behavior in security monitoring system. The main work of this paper is as follows: (1) Multiresolution information is extracted from the monitoring data of distributed optical fiber sensor security system by wavelet transform, and vector extraction of signal features is realized by wavelet spectrum. The essential characteristics of various security events are obtained, and the initial recognition of the signals such as rain, knocking, climbing and so on is achieved. (2) the neural network structure is deeply understood. The BP neural network recognition method is used to identify the type of security detection signal. Four improved MATLAB programs of BP neural network are designed, and the training results are compared and analyzed by using the characteristic quantity of the obtained sample data. The optimal BP network training method is selected. (3) A large number of experimental studies are carried out on the monitoring data collected from the experiment site, which proves the validity and reliability of the algorithm. The recognition rate based on BP neural network reaches 90 points.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP274;TN253
本文編號(hào)：2389659
[Abstract]:With the rapid development of society, communication cables, oil pipelines, gas pipelines, high-voltage power grids, military and civilian security systems and other basic equipment, Safety monitoring of facilities has become an important factor affecting economic development and social stability. Therefore, it is of great research value and practical significance to detect and locate the illegal intrusion behavior in the cable, pipeline safety and security system. This topic comes from the national 973 project "key Technology and basic Research on the Application of Optical Fiber Intelligent Sensor Network experiment platform", which is in cooperation with Tianjin University. Responsible for the "adverse environment on the continuous distributed sensor network polarization and positioning research" subproject. In order to improve the accuracy of disturbance event identification in Mach-Zehnder interferometer optical fiber security system, wavelet multi-resolution analysis and neural network are used in this paper. The signal feature extraction and signal recognition of the monitoring data of distributed optical fiber sensing security system are studied, and the recognition of rain, knock, climbing and other signals in the outside environment is realized. It provides a research basis for improving the accurate location of intrusion behavior in security monitoring system. The main work of this paper is as follows: (1) Multiresolution information is extracted from the monitoring data of distributed optical fiber sensor security system by wavelet transform, and vector extraction of signal features is realized by wavelet spectrum. The essential characteristics of various security events are obtained, and the initial recognition of the signals such as rain, knocking, climbing and so on is achieved. (2) the neural network structure is deeply understood. The BP neural network recognition method is used to identify the type of security detection signal. Four improved MATLAB programs of BP neural network are designed, and the training results are compared and analyzed by using the characteristic quantity of the obtained sample data. The optimal BP network training method is selected. (3) A large number of experimental studies are carried out on the monitoring data collected from the experiment site, which proves the validity and reliability of the algorithm. The recognition rate based on BP neural network reaches 90 points.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP274;TN253
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