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基于云計算的高速公路交通安全預警系統的研究與設計

論文編號:lw201903181119296924 所屬欄目:計算機安全論文 發布日期:2019年03月18日 論文作者:論文網
摘要
中國的高速公路交通安全形勢由于它通車里程的迅速增加、大型的貨運流動問題威脅著我們的生活。很多的交通問題,如:突發事件、道路堵塞等狀況,已呈現出快速增長的趨勢。對交通事故進行實時、準確、及時的判斷和預測是減少高速公路交通事故、提高安全水平的有效途徑。如何建立有效的高速公路交通安全預警系統,有效地預測即將發生的交通事故,及時、快速、準確發布預警信息給用戶;利用預警系統監控的高速公路,獲取實時數據,利用數據處理迅速消除潛在危害是從根源上減少交通事故率的關鍵。因此,本文的目的是構建一個完整的交通安全預警系統,它集交通數據采集、數據處理、安全評價和預警信息于一體。
(1)以云計算為基礎,介紹了它的概念、特點、模板和應用;同時,對現有的高速公路交通安全預警系統框架理論進行了分析和總結,分析了云計算的關鍵技術和云計算在交通領域的應用。
(2)重點解析了物聯網技術在交通安全領域中的應用,闡述物聯網的相關理論,同時研究基于物聯網的交通安全預警系統的結構,包括基于物聯網和基于GIS的信息處理的采集系統,為以云端框架為基礎的高速公路交通安全預警系統的設計提供了經驗。
(3)解析了針對高速公路的預警處理和預警系統,對構建一個合理的基于云端框架的高速公路交通安全預警系統,提出了總體架構與邏輯結構,著重闡述了基于物聯網平臺的信息采集系統及信息發布系統。
(4)針對雨雪等惡劣天氣條件下路面狀況的檢測和預警問題,采用樸素貝葉斯分類器對路面狀況進行分類。首先,提取圖像中道路的區域,然后提取道路圖像的熵、能量、對比度、平均亮度和平均飽和度,采用樸素貝葉斯分類器對道路區域進行分類,對雨雪較多的路段進行報警處置。
(5)設計了高速公路智能視頻監控系統。該系統能夠實現道路路面影像的采集、運動目標檢測和追蹤、車輛信息統計和道路雨雪路況檢測等作用,可用于異常路況和惡劣路況的報警處理。
關鍵詞:高速公路交通安全,預警系統,云計算,運動目標檢測
Abstract
The traffic safety situation of expressway in China is becoming more and more serious due to the rapid increase of its opening mileage and the flow of people logistics, many traffic problems, such as sudden events, traffic jams and so on, have shown a fast increasing trend. The effective way to reduce the highway traffic accident and improve its safety level is the realistic, accurate, timely judgment and predict the impending traffic accident. How to build efficient and powerful expressway traffic safety early warning system to effectively predict the impending traffic accident, timely, fast and accurate release of early warning information to the user; using early-warning system to monitor the expressway, obtain real-time data, and quickly eliminate the potential harm by processing data is the key to reduce the traffic accident rate from the root. Therefore, the aim of this paper is to construct a complete traffic safety early warning system, which integrates traffic data collection, data processing, safety evaluation and early warning information.
(1) Starting from the basic concept of cloud computing, this paper introduces its concept, characteristics and model and its application. At the same time, analyzed and summarized the existing framework theory of expressway traffic safety early warning system, and analyzed the key technology of cloud computing and the application of cloud computing in traffic field.
(2) Focusing on the application of IoT technology in the field of traffic safety, this paper introduces the theory of IoT correlation and studies the structure of the early warning system of traffic safety based on IoT, including the collection system based on IoT and the information processing system based on GIS, which provides the experience support for the design of expressway traffic safety early warning system based on cloud architecture.
(3) Analyze the demand of expressway early warning management and early warning system, construct a reasonable early warning system of expressway traffic safety based on cloud structure, put forward its general frame and logical structure, emphatically introduce the information collection system and information releasing system based on the Internet of things.
(4) In view of the problem of road surface condition detection and early warning in the condition of rain and snow, the simple Bayes classifier is used to classify the road state. Firstly, the region of the road in the image is extracted, and then the entropy, energy, contrast, average brightness and average saturation of the road image are extracted, and the simple Bayes classifier is used to classify the road area, and the road with more wet and slippery snow is alarm.
(5) The Intelligent video Surveillance system of Expressway is designed. The system can accomplish the function of road background extraction, motion target detection and tracking, vehicle information statistics and road rain and snow condition detection in traffic video, and can be used for alarming treatment of abnormal traffic condition and bad road condition.
Keywords:Highway Traffic Safety,Early Warning System,Cloud,Moving target detection
目錄
摘要 4
關鍵詞 4
Abstract

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