【摘要】：隨著水利信息化系統(tǒng)遷入云端之后,由設(shè)備或人為、環(huán)境等各種主客觀原因造成采集到云數(shù)據(jù)中心的數(shù)據(jù)中含有大量的"臟數(shù)據(jù)"(如亂序、異常、相似重復(fù)、誤報、不完整、邏輯錯誤等),這些大量的"臟數(shù)據(jù)"會給應(yīng)用系統(tǒng)帶來高額的處理費用,延長響應(yīng)時間,甚至會導致數(shù)據(jù)分析異常,降低決策支持系統(tǒng)的準確率,嚴重影響系統(tǒng)服務(wù)質(zhì)量,難以支撐上層應(yīng)用。本文結(jié)合項目中的實際情況給出了清洗這些臟數(shù)據(jù)的流程和方法,并通過實際數(shù)據(jù)和實驗方案驗證了本數(shù)據(jù)清洗方案的有效性,大大改善了水利信息化系統(tǒng)預(yù)測預(yù)警的效率。
[Abstract]:As the water conservancy information system moves into the cloud, the data collected into the cloud data center contain a large amount of "dirty data" (such as disorder, anomaly, similar repetition, false alarm, incomplete) caused by various subjective and objective reasons, such as equipment, human beings, environment and so on. The large amount of "dirty data" will bring high processing cost to the application system, prolong the response time, even lead to abnormal data analysis, reduce the accuracy of decision support system, and seriously affect the quality of service of the system. It is difficult to support the upper application. According to the actual situation of the project, this paper gives the flow and method of cleaning these dirty data, and validates the validity of the data cleaning scheme through the actual data and experimental scheme, which greatly improves the efficiency of prediction and early warning of water conservancy information system.
【作者單位】： 四川信息職業(yè)技術(shù)學院;
【分類號】：TV21;TP311.13
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本文編號：2305022