【摘要】：近年來(lái),隨著我國(guó)老齡化的加速發(fā)展,高血壓的發(fā)病率日益升高。高血壓是多種疾病的誘發(fā)因素,做好對(duì)高血壓患者體征數(shù)據(jù)的采集和分析,對(duì)于預(yù)防高血壓及其并發(fā)癥有著重要的作用。如何采集患者實(shí)時(shí)的體征數(shù)據(jù),存儲(chǔ)這些數(shù)據(jù),并根據(jù)數(shù)據(jù)分析與預(yù)測(cè)患者的血壓趨勢(shì)等問(wèn)題亟需應(yīng)對(duì)和解決。云計(jì)算、物聯(lián)網(wǎng)和“互聯(lián)網(wǎng)+”的飛速發(fā)展,4G和無(wú)線網(wǎng)絡(luò)的快速覆蓋,Android和可穿戴智能設(shè)備的快速普及,都為解決上述問(wèn)題提供了便利條件�；贏ndroid的血壓數(shù)據(jù)采集系統(tǒng)圍繞數(shù)據(jù)采集、分析與預(yù)警,從數(shù)據(jù)采集、數(shù)據(jù)存儲(chǔ)、數(shù)據(jù)分析三個(gè)層次來(lái)解決上述問(wèn)題。該系統(tǒng)實(shí)現(xiàn)了數(shù)據(jù)采集內(nèi)容及時(shí)準(zhǔn)確,傳輸渠道通暢,分析預(yù)警快速有效,模塊擴(kuò)展方便易用。論文的研究工作圍繞著數(shù)據(jù)采集、數(shù)據(jù)存儲(chǔ)和數(shù)據(jù)算法三方面展開(kāi)。數(shù)據(jù)采集方面,對(duì)藍(lán)牙血壓計(jì)的協(xié)議進(jìn)行了分析,基于Android平臺(tái)設(shè)計(jì)異構(gòu)藍(lán)牙數(shù)據(jù)采集系統(tǒng),支持多種藍(lán)牙協(xié)議的解析,通過(guò)抽象上層的工作流程,使用策略模式設(shè)計(jì)了易于添加藍(lán)牙設(shè)備的協(xié)議解析方案。數(shù)據(jù)存儲(chǔ)方面,Android端通過(guò)網(wǎng)絡(luò)將數(shù)據(jù)上傳到云平臺(tái),設(shè)計(jì)了使用MongoDB建立血壓存儲(chǔ)數(shù)據(jù)庫(kù)集群的方案,實(shí)現(xiàn)了服務(wù)器資源的橫向擴(kuò)展。數(shù)據(jù)算法方面,根據(jù)高血壓數(shù)據(jù)的特點(diǎn),將時(shí)間序列預(yù)測(cè)法應(yīng)用到血壓數(shù)據(jù)的預(yù)測(cè)上,使用向量自回歸算法設(shè)計(jì)了預(yù)測(cè)方案和模型,預(yù)測(cè)結(jié)果較為準(zhǔn)確。最后,完成了系統(tǒng)功能性界面的設(shè)計(jì)實(shí)現(xiàn),實(shí)現(xiàn)了歷史記錄和預(yù)警信息的可視化,并進(jìn)行了整個(gè)系統(tǒng)的聯(lián)調(diào)測(cè)試。本論文將Android、藍(lán)牙和云平臺(tái)相結(jié)合,實(shí)現(xiàn)了血壓數(shù)據(jù)采集、存儲(chǔ)、分析的流程化操作。采集系統(tǒng)支持多種異構(gòu)藍(lán)牙設(shè)備,存儲(chǔ)系統(tǒng)具有較高的擴(kuò)展性,預(yù)警系統(tǒng)可以較好的分析患者的疾病風(fēng)險(xiǎn)。本系統(tǒng)的實(shí)現(xiàn),對(duì)于醫(yī)療大數(shù)據(jù)的采集和分析有一定借鑒意義。
[Abstract]:In recent years, with the accelerated development of aging in China, the incidence of hypertension is increasing. Hypertension is the inducing factor of many diseases. It is very important to collect and analyze the data of physical signs of hypertension patients for the prevention of hypertension and its complications. How to collect and store the data of patients' physical signs, and how to analyze and predict the trend of patients' blood pressure according to the data, and so on, should be dealt with and solved urgently. The rapid development of cloud computing, the Internet of things and the Internet of things, the rapid coverage of 4G and wireless networks, and the rapid spread of Android and wearable smart devices all provide a convenient solution to these problems. The blood pressure data acquisition system based on Android solves the above problems from three levels: data acquisition, analysis and early warning, data acquisition, data storage and data analysis. The system realizes timely and accurate data collection, smooth transmission channel, fast and effective analysis and early warning, and easy to use. This paper focuses on data acquisition, data storage and data algorithm. In the aspect of data acquisition, the protocol of bluetooth sphygmomanometer is analyzed. The heterogeneous Bluetooth data acquisition system is designed based on Android platform, which supports the analysis of various Bluetooth protocols. A protocol resolution scheme which is easy to add Bluetooth device is designed by using policy mode. In the aspect of data storage, the Android terminal uploads the data to the cloud platform through the network, designs the scheme of establishing the blood pressure storage database cluster with MongoDB, and realizes the lateral expansion of the server resources. In terms of data algorithm, according to the characteristics of hypertension data, the time series prediction method is applied to the prediction of blood pressure data. The prediction scheme and model are designed by using vector autoregressive algorithm, and the prediction results are more accurate. Finally, the design and implementation of the functional interface of the system is completed, and the visualization of historical records and warning information is realized. In this paper, Android, Bluetooth and cloud platform are combined to realize blood pressure data acquisition, storage and analysis. The acquisition system supports a variety of heterogeneous Bluetooth devices, and the storage system is highly scalable, and the early warning system can better analyze the disease risk of patients. The realization of this system has certain reference significance for the collection and analysis of medical big data.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP277;TP393.09;TP316

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本文編號(hào)：2370480