發(fā)布時間：2018-03-24 16:38

  本文選題：移動目標(biāo)　切入點：時空同現(xiàn)模式　出處：《北方工業(yè)大學(xué)》2017年碩士論文

【摘要】：在現(xiàn)有目標(biāo)跟蹤技術(shù)的高速發(fā)展中,移動目標(biāo)如行人、車輛和輪船等的軌跡時空數(shù)據(jù)可以被有效記錄,這些時空數(shù)據(jù)中蘊含著大量潛在的有價值的模式及知識,這些潛在的模式在城市規(guī)劃、國防軍事、基于位置的服務(wù)等方面具有非常重要的研究價值。為發(fā)現(xiàn)移動目標(biāo)的活動規(guī)律,本文采用同現(xiàn)模式分析方法進行共現(xiàn)分析,并在此基礎(chǔ)上分析共現(xiàn)模式的部分周期性,本文的主要研究內(nèi)容如下。針對如何從時空數(shù)據(jù)中發(fā)現(xiàn)有效模式的提取問題,本文提出了基于雙層網(wǎng)絡(luò)的時空同現(xiàn)模式挖掘算法。從行人、車輛和輪船移動目標(biāo)的時空軌跡數(shù)據(jù)中發(fā)現(xiàn)時空同現(xiàn)模式需要計算時空興趣度,為了簡化時空興趣度的計算方式,本文根據(jù)時空數(shù)據(jù)的特性提出雙層時空網(wǎng)絡(luò)建模方法,該時空網(wǎng)絡(luò)有效保存了時空對象及時空元素之間的時空關(guān)系,在計算時空興趣度時可以快速計算出時空頻繁度。根據(jù)元素網(wǎng)絡(luò)層可以減少大量冗余候選模式,從而減少計算量并降低內(nèi)存空間的開銷。本文在時空網(wǎng)絡(luò)的基礎(chǔ)上提出了基于時空網(wǎng)絡(luò)的同現(xiàn)模式挖掘算法,該算法考慮了元素的有效周期,提出能表征模式的時空頻繁度的權(quán)重特征值計量度,并采用模式鏈保存了全部時空同現(xiàn)模式集。實驗表明,采用相同數(shù)據(jù)集并獲取相同結(jié)果時,該算法相比Celik的算法及Wang等的算法運行效率較高。針對移動目標(biāo)活動的部分周期性問題,本文提出移動目標(biāo)部分周期性共現(xiàn)模式自適應(yīng)挖掘算法。行人、車輛及輪船三類移動目標(biāo)的共現(xiàn)活動通常具有部分周期性,本文將部分周期性模式分析應(yīng)用在移動目標(biāo)的共現(xiàn)規(guī)律研究中,提出部分周期性共現(xiàn)模式自適應(yīng)挖掘算法。該算法依據(jù)元素有效周期加入了模式有效度,改進了共現(xiàn)頻率的計算方式,算法的自適應(yīng)體現(xiàn)在周期跨度及置信參數(shù)的確定中,依據(jù)時空數(shù)據(jù)的時間框架及初始化給出了周期跨度自適應(yīng)確定方法,并依據(jù)最大周期跨度及保留全部部分周期性共現(xiàn)模式的原則,給出了置信參數(shù)自適應(yīng)確定方法,然后根據(jù)周期先驗性質(zhì),先判定較長模式的部分周期性再考慮子集模式的部分周期性,減少了共現(xiàn)分析中的共現(xiàn)頻率計算,實驗表明,本文所提的算法與Apriori-like算法及Naive算法相比,能自適應(yīng)準(zhǔn)確計算出周期跨度及置信參數(shù),并保留了全部的部分周期性共現(xiàn)模式,提高了挖掘模式的運行效率。
[Abstract]:With the rapid development of the existing target tracking technology, the track time and space data of moving targets such as pedestrians, vehicles and ships can be effectively recorded. These spatiotemporal data contain a lot of potentially valuable patterns and knowledge. These potential models have very important research value in urban planning, national defense and military, location-based services, etc. In order to find out the moving target activity law, this paper uses co-occurrence mode analysis method to carry out co-occurrence analysis. Based on the analysis of the periodicity of co-occurrence mode, the main contents of this paper are as follows. In this paper, an algorithm of spatio-temporal cooccurrence pattern mining based on double-layer network is proposed. It is found from the space-time track data of moving objects of pedestrians, vehicles and ships that the spatio-temporal interest degree needs to be calculated, in order to simplify the calculation method of spatio-temporal interest degree. Based on the characteristics of spatio-temporal data, a two-layer spatio-temporal network modeling method is proposed in this paper. The spatio-temporal network effectively preserves the spatio-temporal relationship between spatio-temporal objects and space-time elements. The spatio-temporal frequency can be calculated quickly when calculating spatio-temporal interest. According to the element network layer, a large number of redundant candidate patterns can be reduced. In this paper, based on the space-time network, we propose an algorithm of cooccurrence pattern mining based on space-time network, which takes into account the effective period of elements. The weighted eigenvalue measurement of spatio-temporal frequency is proposed, and all spatio-temporal co-occurrence pattern sets are preserved by pattern chain. The experiment shows that when the same data set is used and the same results are obtained, This algorithm is more efficient than Celik algorithm and Wang algorithm. In this paper, an adaptive mining algorithm of moving target partial periodic co-occurrence pattern is proposed. The co-occurrence of three moving targets of vehicles and ships usually has partial periodicity. In this paper, the partial periodic pattern analysis is applied to the study of the co-occurrence law of moving targets. A partial periodic co-occurrence pattern adaptive mining algorithm is proposed, which adds the pattern validity degree according to the effective period of elements, and improves the calculation method of co-occurrence frequency. The adaptive algorithm is reflected in the determination of period span and confidence parameters. According to the time frame and initialization of spatiotemporal data, the method of adaptive determination of periodic span is given. According to the principle of maximum period span and the principle of preserving the periodic co-occurrence mode of all parts, the adaptive determination method of confidence parameters is given. Then, according to the property of periodic priori, the partial periodicity of long mode is determined first, then the partial periodicity of subset mode is considered, which reduces the calculation of co-occurrence frequency in co-occurrence analysis. The experiment shows that the algorithm proposed in this paper is compared with Apriori-like algorithm and Naive algorithm. The period span and confidence parameters can be calculated adaptively and accurately, and all of the periodic co-occurrence patterns are retained, which improves the running efficiency of mining patterns.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13

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