本文選題：物聯(lián)網(wǎng) + 智能航海避碰��； 參考：《天津理工大學(xué)》2017年碩士論文

【摘要】：隨著“一帶一路”戰(zhàn)略的提出,“21世紀(jì)海上絲綢之路”構(gòu)想將中國海運事業(yè)提升至一個新高度。海運量增長的同時也給安全航海帶來前了所未有的挑戰(zhàn)。目前,航海安全主要依靠值班駕駛員在雷達(dá)、自動識別系統(tǒng)(AIS)、自動雷達(dá)標(biāo)繪儀(ARPA)等輔助設(shè)備協(xié)助下對碰撞危險進(jìn)行判斷。但船-船相碰事故有增無減,帶來極大的財產(chǎn)損失和人員傷亡,如何更有效地預(yù)防海上船舶碰撞事故的發(fā)生成為亟待解決的問題。為提高航海安全性,各國學(xué)者對智能避碰決策做了大量工作并取得一定成果。而隨著物聯(lián)網(wǎng)技術(shù)在智能交通、智能家居、健康監(jiān)測等多個領(lǐng)域的逐步滲入,整個社會變得更加智能化。本文通過深入研究物聯(lián)網(wǎng)和智能算法的相關(guān)理論,將其關(guān)鍵技術(shù)綜合應(yīng)用到智能航海避碰中。提出了基于物聯(lián)網(wǎng)架構(gòu)的智能航海避碰系統(tǒng)模型,設(shè)計了一種可以實時預(yù)測船舶碰撞風(fēng)險度的智能航海避碰算法,該算法可提前“預(yù)知”船舶在下一時刻抵達(dá)位置處的碰撞風(fēng)險程度并在危險來臨前推送避碰優(yōu)先序列。本文所做的工作如下:(1)提出了基于物聯(lián)網(wǎng)架構(gòu)的智能航海避碰系統(tǒng)模型。該模型包括感知層、傳輸層和應(yīng)用層。其中,感知層以性價比更高的智能傳感器模塊為主體對各類航海信息進(jìn)行實時采集;傳輸層以物聯(lián)網(wǎng)中間件及Zigbee協(xié)議為基礎(chǔ)進(jìn)行信息傳輸。最后由應(yīng)用層的智能避碰算法分析、預(yù)測船舶碰撞風(fēng)險并作出避碰優(yōu)先序列決策。(2)通過深入研究國內(nèi)外智能避碰算法,基于層次分析法(AHP)和BP人工神經(jīng)網(wǎng)絡(luò)算法設(shè)計了一種改進(jìn)的智能航海避碰算法。該算法加入了環(huán)境因子,使整個算法可根據(jù)實際環(huán)境的改變而實時調(diào)整策略,具有實際應(yīng)用價值。首先,本文基于AHP確定影響安全航海的各環(huán)境因素所占的權(quán)重,將采集到的各影響因素值與其權(quán)重相結(jié)合,動態(tài)確定環(huán)境因子;根據(jù)感知層采集到的實時數(shù)據(jù)與環(huán)境因子綜合確定最佳預(yù)測時間T。其次,運用BP神經(jīng)網(wǎng)絡(luò)算法學(xué)習(xí)船舶的航海習(xí)慣;基于訓(xùn)練完成的BP神經(jīng)網(wǎng)絡(luò)預(yù)測T時刻后船舶可能抵達(dá)的位置坐標(biāo)P。最后,根據(jù)航海學(xué)知識建立模型,判斷P處的碰撞風(fēng)險度;根據(jù)不同船舶與本船之間碰撞的風(fēng)險度大小建立避碰優(yōu)先列表,供值班駕駛員參考。(3)本文基于MATLAB平臺對改進(jìn)的智能航海避碰算法所有步驟進(jìn)行建模仿真,以渤海海域內(nèi)漁船、貨船等實際船舶的歷史航行記錄數(shù)據(jù)作為測試用例進(jìn)行實驗,評估驗證本文提出的改進(jìn)算法的可用性和有效性。
[Abstract]:With the development of "Belt and Road" strategy, the concept of "21st Century Maritime Silk Road" will elevate China's maritime transport industry to a new height. The increase in sea traffic also poses a challenge to safe navigation. At present, navigational safety mainly depends on the assistant equipment such as radar, automatic identification system (AISN), automatic radar plotter (ARPAA) and so on, to judge collision risk. However, the accidents of ship-ship collision are increasing, which brings great loss of property and casualties. How to prevent the collision of ships at sea more effectively becomes an urgent problem to be solved. In order to improve the safety of navigation, scholars from various countries have done a lot of work on intelligent collision avoidance decision and achieved certain results. With the gradual infiltration of Internet of things technology in intelligent transportation, smart home, health monitoring and other fields, the whole society has become more intelligent. In this paper, the relevant theories of Internet of things and intelligent algorithm are deeply studied, and its key technologies are applied to intelligent navigation collision avoidance. This paper presents an intelligent navigation collision avoidance system model based on the Internet of things, and designs an intelligent navigation collision avoidance algorithm which can predict ship collision risk in real time. The algorithm can predict the collision risk of the ship at the next moment and push the collision avoidance priority sequence before the danger comes. The work of this paper is as follows: 1) A model of intelligent navigation collision avoidance system based on the Internet of things is proposed. The model includes perceptual layer, transport layer and application layer. Among them, the sensing layer takes the intelligent sensor module with higher performance and price ratio as the main body to collect all kinds of navigation information in real time; the transmission layer is based on the middleware of the Internet of things and the Zigbee protocol for information transmission. Finally, by analyzing the intelligent collision avoidance algorithm of application layer, forecasting ship collision risk and making collision avoidance priority sequence decision. Based on the analytic hierarchy process (AHP) and BP artificial neural network (Ann), an improved intelligent navigation collision avoidance algorithm is designed. The environment factor is added to the algorithm, which makes the whole algorithm can adjust the strategy in real time according to the change of the actual environment, so it has practical application value. First of all, based on AHP, this paper determines the weight of the environmental factors which affect the safe navigation, and combines the collected values of the factors with their weights to determine the environmental factors dynamically. According to the real-time data collected from the perception layer and the environmental factors, the best prediction time T is determined. Secondly, the BP neural network algorithm is used to study the navigation habits of ships, and based on the BP neural network completed by the training, the coordinates of the ship's possible arrival position after T time can be predicted. Finally, according to the navigational knowledge, the collision risk degree at P is judged, and the collision avoidance priority list is established according to the collision risk degree between different ships and their own ships. This paper models and simulates all steps of the improved intelligent navigation collision avoidance algorithm based on MATLAB platform, and takes the historical navigation record data of fishing vessels and freighters in the Bohai Sea as test cases. The evaluation verifies the availability and effectiveness of the proposed improved algorithm.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.44;TN929.5

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