本文關(guān)鍵詞： 室內(nèi)定位 性能 聚類(lèi) 數(shù)據(jù)降維 內(nèi)存數(shù)據(jù)庫(kù) 分布式集群　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著智能手機(jī)的普及和移動(dòng)互聯(lián)網(wǎng)浪潮的來(lái)臨,室內(nèi)定位服務(wù)因其在為用戶提供便捷的定位導(dǎo)航的同時(shí),也能從收集的定位數(shù)據(jù)中挖掘出有價(jià)值的商業(yè)信息,進(jìn)而在智能家居、智慧商城和公共安全應(yīng)急響應(yīng)等領(lǐng)域中扮演著日益重要的角色。然而當(dāng)其應(yīng)用在高鐵站、購(gòu)物中心或會(huì)展中心等大型室內(nèi)環(huán)境中時(shí),較多的無(wú)線接入熱點(diǎn)和移動(dòng)終端用戶會(huì)給定位服務(wù)帶來(lái)很大的計(jì)算和存儲(chǔ)壓力,比如規(guī)模大維度高的訓(xùn)練樣本和數(shù)據(jù)量大并發(fā)性強(qiáng)的數(shù)據(jù)吞吐,傳統(tǒng)的定位算法和單機(jī)架構(gòu)都難以支撐這樣的業(yè)務(wù)場(chǎng)景。因此針對(duì)這些不足,本文分別從計(jì)算密集和10密集兩個(gè)角度,對(duì)大型環(huán)境下室內(nèi)定位服務(wù)的定位算法性能和數(shù)據(jù)存儲(chǔ)模型的IO性能進(jìn)行了研究和改進(jìn),具體的工作如下:1)提出了一種基于二次聚類(lèi)的子空間劃分算法。該算法通過(guò)將位置指紋相似度高的樣本數(shù)據(jù)聚類(lèi)到同一個(gè)較小規(guī)模的子空間中,降低了 kNN分類(lèi)器在位置指紋最近鄰發(fā)現(xiàn)過(guò)程中的樣本搜索范圍,初步提高了定位算法的性能。為了得到高質(zhì)量的類(lèi)簇,本文根據(jù)室內(nèi)定位數(shù)據(jù)樣本的特點(diǎn)對(duì)kmeans算法做了初始化質(zhì)心的改進(jìn),實(shí)驗(yàn)結(jié)果表明相較于隨機(jī)質(zhì)心的kmeans算法,本文提出的二次聚類(lèi)算法得到的類(lèi)簇在總凝聚度上要高出18.7%。2)提出了一種降維改進(jìn)的位置指紋定位算法。該算法首先對(duì)AP熱點(diǎn)的掃描頻數(shù)建立對(duì)數(shù)正態(tài)分布模型,通過(guò)去除RSSI特征向量中對(duì)定位結(jié)果影響較小的弱無(wú)關(guān)項(xiàng)實(shí)現(xiàn)降維,在低維向量空間上采用kNN分類(lèi)器得到定位坐標(biāo),進(jìn)一步提高了算法的性能。與其它定位算法的對(duì)比實(shí)驗(yàn)結(jié)果表明,本文的改進(jìn)算法在保障定位精度的前提下,RSSI特征向量的平均維度只有原始樣本的13%,具有顯著的性能優(yōu)勢(shì)。3)提出了 Redis-MySQL混合存儲(chǔ)模型。對(duì)于讀寫(xiě)頻繁、熱點(diǎn)性強(qiáng)的實(shí)時(shí)定位數(shù)據(jù),選用讀寫(xiě)更快、基于內(nèi)存的Redis作為存儲(chǔ)介質(zhì),相較于傳統(tǒng)的關(guān)系數(shù)據(jù)庫(kù)能夠?qū)崿F(xiàn)更快的數(shù)據(jù)查詢;同時(shí)對(duì)于非熱點(diǎn)數(shù)據(jù),根據(jù)生產(chǎn)者消費(fèi)者模型,設(shè)計(jì)了基于分布式消息隊(duì)列RabbitMQ的異步機(jī)制,有效地將定位數(shù)據(jù)持久化任務(wù)從定位引擎上解耦,提高后者對(duì)于大規(guī)模數(shù)據(jù)量的定位業(yè)務(wù)支撐能力。實(shí)驗(yàn)結(jié)果表明,本文提出的存儲(chǔ)模型在實(shí)時(shí)位置查詢的響應(yīng)時(shí)間上的加速比達(dá)到1.48,而且定位引擎在異步持久化過(guò)程中的IO阻塞時(shí)間上只有同步方式的10%,在短時(shí)間內(nèi)數(shù)據(jù)量爆發(fā)的情況下能夠?qū)ySQL起到一定的緩沖作用。4)設(shè)計(jì)了基于水平分片策略的Redis集群方案。該方案對(duì)Redis進(jìn)行分布式擴(kuò)展,通過(guò)哈希映射的方式將不同移動(dòng)終端、不同地圖環(huán)境的定位數(shù)據(jù)路由到不同的Redis節(jié)點(diǎn)上,實(shí)現(xiàn)并行的數(shù)據(jù)查詢。實(shí)驗(yàn)結(jié)果表明,Redis集群方案在高并發(fā)的業(yè)務(wù)場(chǎng)景中對(duì)于前端請(qǐng)求的響應(yīng)時(shí)間具有良好的加速比。
[Abstract]:With the popularity of smart phones and the advent of mobile Internet, indoor positioning services can mine valuable business information from the collected location data while providing users with convenient positioning and navigation, and then in the smart home. Smart Mall and public safety emergency response play an increasingly important role. However, when it is used in large indoor environments such as high-speed rail stations, shopping centers or convention and exhibition centers, More wireless access hot spots and mobile terminal users will bring a lot of computing and storage pressure to the location services, such as large-scale training samples with large dimensions and high concurrent data throughput. It is difficult to support such business scenarios by traditional localization algorithms and single computer architecture. Therefore, in view of these shortcomings, this paper respectively from the point of view of computation-intensive and 10-intensive, The performance of location algorithm and IO performance of data storage model in large scale indoor positioning service are studied and improved. The main work is as follows: (1) A subspace partition algorithm based on quadratic clustering is proposed. It reduces the sample search range of kNN classifier in the process of location fingerprint nearest neighbor discovery, and improves the performance of the localization algorithm. According to the characteristics of indoor positioning data samples, the kmeans algorithm is improved to initialize the centroid. The experimental results show that compared with the random centroid kmeans algorithm, In this paper, a reduced dimension improved location fingerprint location algorithm is proposed, which is based on the clustering algorithm proposed in this paper. Firstly, a logarithmic normal distribution model is established for the scanning frequency of AP hot spots. The dimensionality reduction is achieved by removing the weak irrelevant items in the RSSI eigenvector which have little influence on the localization results. The positioning coordinates are obtained by using the kNN classifier in the low dimensional vector space. Compared with other localization algorithms, the experimental results show that, The improved algorithm proposed in this paper has only 13th dimension of the original sample, and has significant performance advantage. 3) A hybrid Redis-MySQL storage model is proposed. For real-time location data with frequent reading and writing, hot spot is strong. Using Redis based on memory as storage medium, it can realize faster data query compared with traditional relational database. At the same time, for non-hot data, according to producer consumer model, An asynchronous mechanism based on distributed message queue RabbitMQ is designed to decouple the localization data persistence task from the location engine effectively, and improve the location service support ability of the latter for large-scale data. The experimental results show that, The storage model proposed in this paper has a speedup ratio of 1.48 on the response time of real-time location query, and the location engine has only 10 times of synchronous mode in IO blocking time during asynchronous persistence, and in a short period of time, the amount of data explodes. In this paper, a Redis cluster scheme based on horizontal slicing strategy is designed, which can buffer the MySQL to a certain extent. This scheme extends Redis distributed. The location data of different mobile terminals and different map environments are routed to different Redis nodes by hash mapping. The experimental results show that the Redis cluster scheme has a good speedup ratio to the front-end request response time in the high concurrent business scenario.
【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN92;TP311.13

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