發(fā)布時間：2018-08-11 21:33

【摘要】：“養(yǎng)魚先養(yǎng)水”,養(yǎng)殖環(huán)境的好壞直接關(guān)系到養(yǎng)殖品種的發(fā)育和生長,因?yàn)轲B(yǎng)殖水域是養(yǎng)殖品種的生活環(huán)境,從而決定著養(yǎng)殖品種產(chǎn)量和質(zhì)量的高低。近年來,隨著水產(chǎn)養(yǎng)殖種類的不斷增多,規(guī)�；�、集約化程度的不斷提高以及養(yǎng)殖密度的增加,養(yǎng)殖水域病害發(fā)生率越來越高,水質(zhì)環(huán)境也日趨惡化,由此容易引發(fā)水產(chǎn)品質(zhì)量安全問題。因此,為了能夠及時掌握和估計養(yǎng)殖品種水質(zhì)環(huán)境狀況,需要迫切地構(gòu)建一種水產(chǎn)養(yǎng)殖水質(zhì)環(huán)境監(jiān)測和預(yù)測系統(tǒng),從而采取有效的措施調(diào)控水質(zhì),達(dá)到高效和安全生產(chǎn),并保障水產(chǎn)品質(zhì)量安全。結(jié)合實(shí)際情況來看,采用現(xiàn)有的水質(zhì)預(yù)測監(jiān)測方法存在不及時性、缺乏可靠性、成本高等問題,已遠(yuǎn)遠(yuǎn)不能實(shí)現(xiàn)上述需求。以規(guī)�；�、科學(xué)化為主要方式是未來水產(chǎn)養(yǎng)殖的發(fā)展趨勢,從而實(shí)現(xiàn)水產(chǎn)品的高成活率和高質(zhì)量。針對上述問題,本文結(jié)合物聯(lián)網(wǎng)技術(shù),研究了水產(chǎn)養(yǎng)殖水質(zhì)、水產(chǎn)品流通、銷售環(huán)節(jié)環(huán)境參數(shù)的自動監(jiān)測及預(yù)測系統(tǒng),完成了水產(chǎn)養(yǎng)殖水質(zhì)、物流和銷售環(huán)境數(shù)據(jù)和監(jiān)測點(diǎn)位置信息數(shù)據(jù)的采集、存儲、處理和轉(zhuǎn)發(fā),實(shí)現(xiàn)養(yǎng)殖環(huán)境實(shí)時監(jiān)控,并根據(jù)傳感器監(jiān)測的實(shí)時水質(zhì)參數(shù)進(jìn)行預(yù)測預(yù)警。本文主要工作歸納如下:(1)通過傳感器得到某一固定時間段的水質(zhì)參數(shù)數(shù)據(jù),針對數(shù)據(jù)流傳遞的水產(chǎn)養(yǎng)殖水質(zhì)參數(shù)可能存在的數(shù)據(jù)質(zhì)量問題,提出了基于滑動窗口的水質(zhì)參數(shù)異常檢測算法,對原始數(shù)據(jù)進(jìn)行異常點(diǎn)排除。(2)深入分析了單整自回歸移動平均(ARIMA)模型和深度信念網(wǎng)絡(luò)(DBN)模型特點(diǎn)的基礎(chǔ)上,建立了一種適用于水質(zhì)參數(shù)預(yù)測的組合預(yù)測模型ARIMA-DBN,并以溶解氧為例與單一的ARIMA模型和DBN分別進(jìn)行了比較分析,驗(yàn)證了組合預(yù)測模型ARIMA-DBN的準(zhǔn)確性和有效性。(3)在上述組合預(yù)測模型研究基礎(chǔ)上,設(shè)計并實(shí)現(xiàn)了水產(chǎn)養(yǎng)殖、水產(chǎn)品流通及銷售環(huán)節(jié)環(huán)境參數(shù)的自動監(jiān)測及預(yù)警系統(tǒng)。該系統(tǒng)可以對水產(chǎn)養(yǎng)殖及流通全過程的主要環(huán)境因子實(shí)施有效監(jiān)測,并利用組合預(yù)測模型ARIMA-DBN對關(guān)鍵環(huán)境因子進(jìn)行預(yù)測,若預(yù)測結(jié)果發(fā)現(xiàn)異常,則提前預(yù)警。
[Abstract]:The quality of the culture environment is directly related to the development and growth of the breed, because the aquaculture water is the living environment of the breed, which determines the yield and quality of the breed. In recent years, with the increase of aquaculture species, scale, intensive degree and density of aquaculture, the incidence of diseases in aquaculture waters is becoming higher and higher, and the water quality environment is deteriorating day by day. This will easily lead to aquatic product quality and safety problems. Therefore, in order to grasp and estimate the water quality environment of aquaculture varieties in time, it is necessary to establish an environmental monitoring and forecasting system for aquaculture water quality urgently, so as to take effective measures to regulate water quality and achieve high efficiency and safety in production. And to ensure the quality and safety of aquatic products. According to the actual situation, the existing methods of water quality prediction and monitoring are not timely, lack of reliability, high cost and so on. In order to realize the high survival rate and high quality of aquatic products, it is the development trend of aquaculture in the future to take the scale and science as the main way. In view of the above problems, this paper studies the automatic monitoring and forecasting system for the environmental parameters of aquaculture water quality, aquatic product circulation and sales link, combining with the technology of Internet of things, and accomplishes the aquiculture water quality. The collection, storage, processing and forwarding of logistics and sales environment data and location information data of monitoring points are carried out to realize the real-time monitoring of aquaculture environment, and the real-time water quality parameters monitored by sensors are used to predict and early warning. The main work of this paper is summarized as follows: (1) the data quality problem of aquiculture water quality parameters transmitted by data stream can be solved by obtaining the water quality parameter data of a fixed time period through the sensor. An anomaly detection algorithm for water quality parameters based on sliding window is proposed. (2) the characteristics of single integral autoregressive moving average (ARIMA) model and depth belief network (DBN) model are deeply analyzed. A combined forecasting model, ARIMA-DBN, which is suitable for water quality parameter prediction, is established and compared with a single ARIMA model and a DBN model, taking dissolved oxygen as an example. The accuracy and validity of the combined prediction model ARIMA-DBN are verified. (3) based on the research of the combined prediction model, the automatic monitoring and warning system of the environmental parameters in aquaculture, aquatic product circulation and marketing is designed and implemented. The system can effectively monitor the main environmental factors in the whole process of aquaculture and circulation, and predict the key environmental factors by using the combined forecasting model (ARIMA-DBN). If the prediction results are abnormal, early warning will be given.
【學(xué)位授予單位】：上海海洋大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S959;TP274

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