本文選題：群控電梯系統(tǒng)　切入點：模式識別　出處：《沈陽建筑大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著高層建筑的不斷增多,電梯作為現(xiàn)代化建筑中必不可少的垂直交通工具,越來越受到人們的重視,人們對于電梯的性能和服務(wù)質(zhì)量的要求也日益增加。一個合格的電梯系統(tǒng),在運(yùn)行的過程中不僅要考慮乘客對樓層去向的需求,還應(yīng)當(dāng)考慮到乘客在候梯期間和乘梯期間的心理變化,以及系統(tǒng)能耗等更加全面的問題。計算機(jī)技術(shù)的發(fā)展為智能算法在電梯技術(shù)上的應(yīng)用提供了硬件基礎(chǔ),然而目前沒有有效的方法能夠合理安排調(diào)度電梯資源,電梯系統(tǒng)在這些方面研究還處于起步階段,尚有很大的研究空間,基于以上目的,電梯的調(diào)度算法研究具有重要的社會意義和經(jīng)濟(jì)意義。本文首先研究了群控電梯在電梯行業(yè)中的地位和發(fā)展現(xiàn)狀,然后從群控電梯系統(tǒng)出發(fā),通過建立群控電梯系統(tǒng)的數(shù)學(xué)模型,歸納了群控電梯系統(tǒng)常見的四種交通模式,而后將隨機(jī)森林算法應(yīng)用到群控電梯系統(tǒng)中,準(zhǔn)確地辨識出了群控電梯系統(tǒng)的交通模式。論文主要包括以下幾個方面的內(nèi)容：(1)分析群控電梯系統(tǒng)工作原理,研究電群控梯系統(tǒng)的發(fā)展趨勢及亟待解決的問題。通過對各種方法分析比較,最終確定了采用以隨機(jī)森林算法和匈牙利算法相結(jié)合的群控電梯調(diào)度方案。(2)以實際電梯為例,通過對在運(yùn)行過程中客流分布情況進(jìn)行分析,將群控電梯系統(tǒng)的交通模式分為四種：上行高峰交通模式、下行高峰交通模式、層間交通模式及空閑交通模式。分析這四種交通模式的特點,采用隨機(jī)森林算法算法對群控電梯進(jìn)行交通模式識別后,再使用匈牙利算法來確定群控電梯派梯方案。(3)更加深入地分析和研究群控電梯系統(tǒng),按照不同的交通模式,針對系統(tǒng)的多目標(biāo)性進(jìn)行子評價函數(shù)的加權(quán),從而構(gòu)建出具有單目標(biāo)性質(zhì)的群控電梯系統(tǒng)數(shù)學(xué)模型,并同時提出該數(shù)學(xué)模型的約束條件、決策變量和目標(biāo)函數(shù)。(4)采用隨機(jī)森林算法對群控電梯系統(tǒng)進(jìn)行模式識別。通過對數(shù)據(jù)系統(tǒng)分析,得出電梯系統(tǒng)的交通模式,利用隨機(jī)森林算法根據(jù)數(shù)據(jù)系統(tǒng)訓(xùn)練一組決策樹,將真實電梯數(shù)據(jù)帶入該決策樹進(jìn)行決策分析,由決策樹投票決策出當(dāng)前電梯的交通模式。(5)采用匈牙利算法,對已經(jīng)由隨機(jī)森林算法計算出交通模式的群控電梯系統(tǒng)進(jìn)行派梯方案的設(shè)計,以實現(xiàn)對群控電梯系統(tǒng)的多目標(biāo)優(yōu)化。(6)通過計算機(jī)仿真,驗證基于隨機(jī)森林和匈牙利算法的群控電梯系統(tǒng)模式識別和派梯方案的有效性和優(yōu)越性。(7)總結(jié)本文研究進(jìn)展,展望下一步工作內(nèi)容。
[Abstract]:With the increasing of high-rise buildings, elevators, as an indispensable vertical vehicle in modern buildings, have been paid more and more attention to. There is also a growing demand for elevator performance and quality of service. A qualified elevator system should not only take into account passengers' needs for floor movements during operation, Consideration should also be given to the psychological changes of passengers while waiting for and taking the ladder, as well as to more comprehensive problems such as system energy consumption. The development of computer technology provides a hardware basis for the application of intelligent algorithms in elevator technology, However, there is no effective method to arrange the elevator resources reasonably. The elevator system is still in its infancy, and there is still a lot of research space. The study of elevator scheduling algorithm has important social and economic significance. Firstly, this paper studies the status and development status of group control elevator in elevator industry, and then starts from the group control elevator system. By establishing the mathematical model of the group control elevator system, four common traffic modes of the group control elevator system are summarized, and then the stochastic forest algorithm is applied to the group control elevator system. The traffic mode of group control elevator system is identified accurately. This paper mainly includes the following contents: 1) analyzing the working principle of group control elevator system. This paper studies the development trend of the electric group control elevator system and the problems to be solved urgently. Through the analysis and comparison of various methods, it is finally determined to adopt the group control elevator dispatching scheme, which combines the stochastic forest algorithm and the Hungarian algorithm, as an example, taking the actual elevator as an example. By analyzing the distribution of passenger flow in the operation process, the traffic mode of group control elevator system is divided into four types: uplink peak traffic mode, downlink peak traffic mode, downlink peak traffic mode, After analyzing the characteristics of the four traffic modes, the stochastic forest algorithm is used to recognize the traffic patterns of the group control elevators. Then the Hungarian algorithm is used to determine the group control elevator dispatching scheme. (3) the group control elevator system is analyzed and studied more deeply. According to different traffic modes, the sub-evaluation function is weighted according to the multi-objective nature of the system. Thus, the mathematical model of group control elevator system with single objective property is constructed, and the constraint conditions of the mathematical model are put forward at the same time. Decision variable and objective function. 4) the random forest algorithm is used to recognize the pattern of the elevator group control system. By analyzing the data system, the traffic pattern of the elevator system is obtained, and a group of decision trees are trained according to the data system by using the stochastic forest algorithm. Taking the real elevator data into the decision tree for decision analysis, the decision tree votes to decide the current elevator traffic mode. (5) the Hungarian algorithm is used. In order to realize the multi-objective optimization of the group control elevator system, which has been calculated by the stochastic forest algorithm, the elevator system of group control has been designed by computer simulation. Verify the effectiveness and superiority of pattern recognition and ladder allocation scheme based on stochastic forest and Hungarian algorithm) summarize the research progress in this paper and look forward to the next work.
【學(xué)位授予單位】：沈陽建筑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU857

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