本文選題：海洋觀測浮標 + 數據采集控制系統(tǒng)　； 參考：《山東大學》2014年碩士論文

【摘要】：海洋觀測是海洋科學的重要組成部分。通過海洋水文氣象的觀測,人們能夠全面、及時、準確地掌握海洋環(huán)境的變化規(guī)律以及人類活動對海洋環(huán)境的影響,為海洋科學研究、海洋工程建設、海洋資源開發(fā)、海洋環(huán)境保護、航海安全保證、海洋災害預防提供了基礎資料和科學依據,因而對此的研究有著極為重要的意義。由于海洋環(huán)境的高度復雜性,對于觀測技術提出了較高的要求。 本文針對進行遠洋氣象和水文觀測而研制的深海浮標,在對其采集參數性質和數據傳輸方式等功能需求進行充分分析的基礎上,設計了一套數據采集系統(tǒng)。該系統(tǒng)采用嵌入式技術,分時對海洋氣象環(huán)境中的氣溫、氣壓、濕度、雨量、風速風向和長波短波輻射等氣象參數以及水文環(huán)境中的水溫、鹽度和海流進行采集和存儲。同時,利用GPS模塊對系統(tǒng)進行賦時。文中分硬件和軟件兩個部分對數據采集系統(tǒng)做了設計。 為了在AD轉換過程中消除氣象參數采集中可能的誤差,本文選用卡爾曼濾波方法進行濾波。在結合AD轉換對卡爾曼濾波方程進行化簡后將其應用在AD轉換后取得的AD值數據集合的濾波,結果表明濾波效果良好,可以獲取較為準確的數值。 對海洋參數進行觀測的目的是更好地研究海洋從而為人類活動服務。本文將浮標獲取到的氣象和水文參數應用于季風的研究之中,研究包括季風模式的識別和一年兩次的爆發(fā)點的確定。在介紹HMM的基本算法以后,文中選取具有代表性的幾個海洋參數進行兩種季風模式下的HMM學習。按每天數據進行學習得到的模型在非季風爆發(fā)階段的季風模式的識別中得到比較精確的結果。為了進一步確定季風的爆發(fā)點,選取小時值進行學習建模。在對爆發(fā)月份以及前后兩個月份的小時平均值進行識別后,通過統(tǒng)計每天夏季風識別小時數的個數并與按天得到的結果進行比較,最終確定了季風的爆發(fā)點,從而為季風的研究提供了一個有益的參考。
[Abstract]:Ocean observation is an important part of marine science. Through the observation of marine hydrology and meteorology, people can comprehensively, timely and accurately grasp the changing laws of the marine environment and the impact of human activities on the marine environment. The protection of marine environment, the guarantee of navigation safety and the prevention of marine disasters provide basic information and scientific basis, so the study of marine disasters is of great significance. Because of the high complexity of marine environment, high requirements for observation technology are put forward. In this paper, a data acquisition system is designed for the deep-sea buoy developed for ocean-going meteorological and hydrological observation. Based on the analysis of its collection parameter properties and data transmission mode, a set of data acquisition system is designed. The system adopts embedded technology to collect and store the meteorological parameters such as temperature, pressure, humidity, rainfall, wind speed, wind direction and long wave shortwave radiation in the marine meteorological environment, and the water temperature, salinity and current in the hydrological environment. At the same time, the GPS module is used to schedule the system. The data acquisition system is designed in two parts: hardware and software. In order to eliminate the possible errors in the acquisition of meteorological parameters during AD conversion, Kalman filtering method is used in this paper. The Kalman filter equation is simplified with AD conversion and applied to the filtering of AD value data set after AD conversion. The result shows that the filtering effect is good and the more accurate value can be obtained. The purpose of observing ocean parameters is to better study the ocean and serve human activities. In this paper, the meteorological and hydrological parameters obtained from buoys are applied to the study of monsoon, which includes the identification of monsoon patterns and the determination of bimonthly burst points. After introducing the basic algorithm of hmm, several typical ocean parameters are selected for hmm learning under two monsoon modes. The model, which is based on the daily data, is proved to be more accurate in the recognition of the monsoon pattern in the non-monsoon burst stage. In order to further determine the onset point of the monsoon, the learning model was established by selecting the hour value. After identifying the hourly average of the onset month and the two months before and after, the onset point of the monsoon is determined by counting the number of hours recognized by the summer monsoon every day and comparing it with the results obtained by the day. It provides a useful reference for the study of monsoon.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP274.2;P715.2

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