中国医疗设备
中國醫療設備
중국의료설비
CHINA MEDICAL EQUIPMENT
2015年
4期
15-17,21
,共4页
医疗设备%主成分分析模型%温度监测模型%数据挖掘%主元空间
醫療設備%主成分分析模型%溫度鑑測模型%數據挖掘%主元空間
의료설비%주성분분석모형%온도감측모형%수거알굴%주원공간
medical equipment%principal component analysis%temperature monitoring model%data mining%principal component space
将具有保温功能的医疗设备的温度控制在设定值的合适范围内具有重要意义。传统的温度监测方法主要是人工判断，对主观经验依赖性较大，而且过于简单，遇到复杂情况将无法判断温度的可靠性。本文基于主成分分析（PCA）模型，提出了一种新的医疗设备温度监测模型，即基于PCA模型构造多个温度传感器监测数据之间的相关关系，进而构造了相关统计量来监测医疗设备的温度是否正常。将该模型应用于蓝光箱的箱温数据分析，结果表明，该模型可以有效地监测蓝光箱的箱温，当箱温出现异常时，能够及时地发出故障警报，使得异常状况在最短的时间内被发现和处理。该模型可运用到各类温度数据需要进行监测的医疗设备中，具有较强的实用性。
將具有保溫功能的醫療設備的溫度控製在設定值的閤適範圍內具有重要意義。傳統的溫度鑑測方法主要是人工判斷，對主觀經驗依賴性較大，而且過于簡單，遇到複雜情況將無法判斷溫度的可靠性。本文基于主成分分析（PCA）模型，提齣瞭一種新的醫療設備溫度鑑測模型，即基于PCA模型構造多箇溫度傳感器鑑測數據之間的相關關繫，進而構造瞭相關統計量來鑑測醫療設備的溫度是否正常。將該模型應用于藍光箱的箱溫數據分析，結果錶明，該模型可以有效地鑑測藍光箱的箱溫，噹箱溫齣現異常時，能夠及時地髮齣故障警報，使得異常狀況在最短的時間內被髮現和處理。該模型可運用到各類溫度數據需要進行鑑測的醫療設備中，具有較彊的實用性。
장구유보온공능적의료설비적온도공제재설정치적합괄범위내구유중요의의。전통적온도감측방법주요시인공판단，대주관경험의뢰성교대，이차과우간단，우도복잡정황장무법판단온도적가고성。본문기우주성분분석（PCA）모형，제출료일충신적의료설비온도감측모형，즉기우PCA모형구조다개온도전감기감측수거지간적상관관계，진이구조료상관통계량래감측의료설비적온도시부정상。장해모형응용우람광상적상온수거분석，결과표명，해모형가이유효지감측람광상적상온，당상온출현이상시，능구급시지발출고장경보，사득이상상황재최단적시간내피발현화처리。해모형가운용도각류온도수거수요진행감측적의료설비중，구유교강적실용성。
It was important to ensure the temperature control of medical equipment with the heat preservation function within an acceptable range of the setting value. The temperature monitoring was traditionally carried out by artiifcial judgment, which mainly depended on subjective experiences. Also, it was not easy to make an accurate decision in case of complex situations. In this paper, a PCA-based (Principal-Component-Analysis-Based) multivariate statistical model was proposed and applied for temperature monitoring of the medical equipment. In the new model, connections between the monitoring data of temperature sensors was constructed on the basis of PCA model so as to monitor the status of the temperature of medical equipment through use of corresponding statistical data. After application of the new model to analysis of the temperature of the blue light box, it demonstrated its effectiveness, quick responses and alert to the abnormal conditions, which could ensure the abnormalities are found and processed in the shortest time. In practice, the proposed model had strong practicality and could also be used in various medical equipment that needed to monitor the temperature data.
