本文選題：無線傳感網絡　切入點：水質監(jiān)測和評估　出處：《寧夏大學》2017年碩士論文　論文類型：學位論文

【摘要】：水是生命之源,對人類的生存和發(fā)展起著至關重要的作用。隨著工業(yè)的進步,工廠污水排放不斷增加,致使水污染情勢日益嚴峻,實現(xiàn)水環(huán)境保護與管理的重要措施之一就是對水環(huán)境進行有效的監(jiān)測和評估。本文分析了當前國內外水環(huán)境監(jiān)測系統(tǒng)的研究狀況,針對企業(yè)排污監(jiān)測的特點,將數(shù)據(jù)融合算法和無線傳感器相結合應用于工廠污水監(jiān)測系統(tǒng)中,提出了基于數(shù)據(jù)融合的工廠污水無線監(jiān)測系統(tǒng)。本文的主要工作如下:(1)介紹了目前國內外水環(huán)境監(jiān)測系統(tǒng)的技術水平和研究現(xiàn)狀,綜合無線傳感網絡技術和數(shù)據(jù)融合算法,提出將數(shù)據(jù)融合算法和無線傳感網絡相結合應用于工廠污水監(jiān)測系統(tǒng)中。(2)本文以淮安某一氨氮化肥廠為研究對象,設計多層次的數(shù)據(jù)融合算法,將溫度、PH、氨氮、溶解氧、濁度五個污水參數(shù)進行數(shù)據(jù)融合,評估出污水等級。本文采用分布式檢測結構,分別在數(shù)據(jù)層、特征層、決策層進行數(shù)據(jù)融合。數(shù)據(jù)層采用格拉布斯準則和中位值平均法對單個傳感器多次測量結果進行融合,剔除失真數(shù)據(jù),提高測量精度;特征層采用自適應加權算法對監(jiān)測區(qū)域內多個同類傳感器的數(shù)據(jù)進行融合,以求得該區(qū)域各個參數(shù)的一個整體特征值;決策層采用GA-BP神經網絡對五個特征值進行融合得出污水等級。(3)系統(tǒng)硬件電路設計。包括檢測節(jié)點、網關、監(jiān)測中心。各個部分采用模塊化的思想進行設計,檢測節(jié)點包括傳感器模塊和組網通信模塊,傳感器模塊負責對各參數(shù)數(shù)據(jù)的采集,組網通信模塊采用CC2530負責數(shù)據(jù)的融合和無線收發(fā);網關采用CC2530+STM32+GPRS,CC2530負責組網通信,STM32負責數(shù)據(jù)融合處理,GPRS負責數(shù)據(jù)的遠程發(fā)送;監(jiān)測中心采用電腦進行操作,不需要硬件設計。(4)軟件設計。對系統(tǒng)硬件的各個部分進行相應的軟件設計,在檢測節(jié)點處首先采用格拉布斯準則剔除可疑數(shù)據(jù),然后利用中位值平均法濾波,提高傳感器采集數(shù)據(jù)的可靠性和精度;網關處使用自適應加權算法對多個同類傳感器數(shù)據(jù)進行融合,以得出一個最優(yōu)值;監(jiān)測中心:采用GA-BP神經網絡,首先用遺傳算法去優(yōu)化BP神經網絡,然后將訓練好的GA-BP神經網絡用于對水質的等級評估,得出水環(huán)境的一個整體指標。最后使用LabView進行上位機友好界面的編寫和Web對外發(fā)布。(5)對設計的整個污水監(jiān)測系統(tǒng)進行測試,評估系統(tǒng)性能。測試結果表明本文將數(shù)據(jù)融合算法和無線傳感網絡相結合設計出的監(jiān)測系統(tǒng),不僅通信穩(wěn)定、可擴展性好而且能準確對污水進行等級評估,具有可靠性高、通用性強、準確性高等特點,具有良好的應用前景。
[Abstract]:Water is the source of life and plays a vital role in the survival and development of human beings. With the progress of industry, the discharge of sewage from factories is increasing, and the situation of water pollution is becoming more and more serious. One of the important measures to realize the protection and management of water environment is to effectively monitor and evaluate the water environment. This paper analyzes the current research situation of water environment monitoring system at home and abroad, and aims at the characteristics of sewage monitoring in enterprises. The data fusion algorithm and wireless sensor are combined in the plant sewage monitoring system. This paper presents a wireless monitoring system for factory sewage based on data fusion. The main work of this paper is as follows: 1) the technical level and research status of water environment monitoring system at home and abroad are introduced, and the wireless sensor network technology and data fusion algorithm are integrated. Data fusion algorithm and wireless sensor network are applied to plant sewage monitoring system. In this paper, a certain ammonia nitrogen fertilizer plant in Huai'an is taken as the research object, and a multi-level data fusion algorithm is designed. The five parameters of turbidity are fused to evaluate the grade of sewage. In this paper, the distributed detection structure is used in the data layer, the characteristic layer, The data layer uses Grubbs criterion and median average method to fuse the multiple measurement results of a single sensor to eliminate the distorted data and improve the measurement accuracy. The feature layer uses adaptive weighting algorithm to fuse the data of several similar sensors in the monitoring area to obtain a global characteristic value of each parameter in the region. The decision layer uses the GA-BP neural network to fuse the five eigenvalues to get the design of the hardware circuit of the system, including the detection node, the gateway and the monitoring center. Each part is designed with the idea of modularization. The detection node includes sensor module and network communication module. The sensor module is responsible for the collection of each parameter data, and the network communication module uses CC2530 for data fusion and wireless transceiver. The gateway adopts CC2530 STM32 GPRSN CC2530 to be responsible for data fusion processing and remote data transmission, and the monitoring center uses computer to operate, and STM32 is responsible for data fusion and data transmission. There is no need for hardware design. (4) Software design for each part of the system hardware is carried out. At the detection node, the Grubbs criterion is used to eliminate the suspicious data, and then the median average method is used to filter the suspicious data. Improve the reliability and accuracy of sensor data collection; Gateway uses adaptive weighting algorithm to fuse multiple similar sensor data to obtain an optimal value; Monitoring Center: using GA-BP neural network, First, the BP neural network is optimized by genetic algorithm, and then the trained GA-BP neural network is used to evaluate the water quality. Finally, we use LabView to compile the friendly interface of the host computer and Web to release the whole sewage monitoring system. The test results show that the monitoring system designed by combining the data fusion algorithm with the wireless sensor network is not only stable in communication, good in expansibility, but also accurate in evaluating the level of sewage, and has high reliability. It has the characteristics of high generality and high accuracy, so it has a good application prospect.
【學位授予單位】：寧夏大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP274

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