發(fā)布時(shí)間：2018-03-04 14:33

  本文選題：無線傳感網(wǎng)絡(luò)　切入點(diǎn)：水質(zhì)監(jiān)測和評(píng)估　出處：《寧夏大學(xué)》2017年碩士論文　論文類型：學(xué)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(guān)重要的作用。隨著工業(yè)的進(jìn)步,工廠污水排放不斷增加,致使水污染情勢日益嚴(yán)峻,實(shí)現(xiàn)水環(huán)境保護(hù)與管理的重要措施之一就是對水環(huán)境進(jìn)行有效的監(jiān)測和評(píng)估。本文分析了當(dāng)前國內(nèi)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(diǎn),將數(shù)據(jù)融合算法和無線傳感器相結(jié)合應(yīng)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(nèi)外水環(huán)境監(jiān)測系統(tǒng)的技術(shù)水平和研究現(xiàn)狀,綜合無線傳感網(wǎng)絡(luò)技術(shù)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(wǎng)絡(luò)相結(jié)合應(yīng)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(shè)計(jì)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(gè)污水參數(shù)進(jìn)行數(shù)據(jù)融合,評(píng)估出污水等級(jí)。本文采用分布式檢測結(jié)構(gòu),分別在數(shù)據(jù)層、特征層、決策層進(jìn)行數(shù)據(jù)融合。數(shù)據(jù)層采用格拉布斯準(zhǔn)則和中位值平均法對單個(gè)傳感器多次測量結(jié)果進(jìn)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(yīng)加權(quán)算法對監(jiān)測區(qū)域內(nèi)多個(gè)同類傳感器的數(shù)據(jù)進(jìn)行融合,以求得該區(qū)域各個(gè)參數(shù)的一個(gè)整體特征值;決策層采用GA-BP神經(jīng)網(wǎng)絡(luò)對五個(gè)特征值進(jìn)行融合得出污水等級(jí)。(3)系統(tǒng)硬件電路設(shè)計(jì)。包括檢測節(jié)點(diǎn)、網(wǎng)關(guān)、監(jiān)測中心。各個(gè)部分采用模塊化的思想進(jìn)行設(shè)計(jì),檢測節(jié)點(diǎn)包括傳感器模塊和組網(wǎng)通信模塊,傳感器模塊負(fù)責(zé)對各參數(shù)數(shù)據(jù)的采集,組網(wǎng)通信模塊采用CC2530負(fù)責(zé)數(shù)據(jù)的融合和無線收發(fā);網(wǎng)關(guān)采用CC2530+STM32+GPRS,CC2530負(fù)責(zé)組網(wǎng)通信,STM32負(fù)責(zé)數(shù)據(jù)融合處理,GPRS負(fù)責(zé)數(shù)據(jù)的遠(yuǎn)程發(fā)送;監(jiān)測中心采用電腦進(jìn)行操作,不需要硬件設(shè)計(jì)。(4)軟件設(shè)計(jì)。對系統(tǒng)硬件的各個(gè)部分進(jìn)行相應(yīng)的軟件設(shè)計(jì),在檢測節(jié)點(diǎn)處首先采用格拉布斯準(zhǔn)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(wǎng)關(guān)處使用自適應(yīng)加權(quán)算法對多個(gè)同類傳感器數(shù)據(jù)進(jìn)行融合,以得出一個(gè)最優(yōu)值;監(jiān)測中心:采用GA-BP神經(jīng)網(wǎng)絡(luò),首先用遺傳算法去優(yōu)化BP神經(jīng)網(wǎng)絡(luò),然后將訓(xùn)練好的GA-BP神經(jīng)網(wǎng)絡(luò)用于對水質(zhì)的等級(jí)評(píng)估,得出水環(huán)境的一個(gè)整體指標(biāo)。最后使用LabView進(jìn)行上位機(jī)友好界面的編寫和Web對外發(fā)布。(5)對設(shè)計(jì)的整個(gè)污水監(jiān)測系統(tǒng)進(jìn)行測試,評(píng)估系統(tǒng)性能。測試結(jié)果表明本文將數(shù)據(jù)融合算法和無線傳感網(wǎng)絡(luò)相結(jié)合設(shè)計(jì)出的監(jiān)測系統(tǒng),不僅通信穩(wěn)定、可擴(kuò)展性好而且能準(zhǔn)確對污水進(jìn)行等級(jí)評(píng)估,具有可靠性高、通用性強(qiáng)、準(zhǔn)確性高等特點(diǎn),具有良好的應(yīng)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(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP274

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