计算机技术与发展
計算機技術與髮展
계산궤기술여발전
COMPUTER TECHNOLOGY AND DEVELOPMENT
2015年
2期
225-229
,共5页
流程控制%RFID%NFA%数据清洗%路径约束%数据挖掘
流程控製%RFID%NFA%數據清洗%路徑約束%數據挖掘
류정공제%RFID%NFA%수거청세%로경약속%수거알굴
process control%RFID%NFA%data cleaning%path constraints%data mining
针对生产检测流程控制中,产品可能会沿着多种工位流动,且工位的执行顺序是复杂的问题,文中提出了一种基于RFID及其路径约束的生产检测流程控制方法。该方法在产品上贴有RFID标签,在产品经过的生产检测工位上安装有阅读器;产品按固定的工位操作顺序进行生产检测,当贴有标签的产品经过每一个工位时就能被阅读器识别,从而判定产品经过的生产检测流程是否按照规定的顺序执行,将这一顺序称之为路径约束。使用NFA构造贴有RFID标签的产品在生产检测流程中所需要遵守的工位执行顺序(路径约束)。利用构造好的路径,按照设计好的算法先过滤RFID数据流,使用清洗后的数据流进行生产检测流程的控制。实验结果表明该算法能有效地控制生产检测的流程。
針對生產檢測流程控製中,產品可能會沿著多種工位流動,且工位的執行順序是複雜的問題,文中提齣瞭一種基于RFID及其路徑約束的生產檢測流程控製方法。該方法在產品上貼有RFID標籤,在產品經過的生產檢測工位上安裝有閱讀器;產品按固定的工位操作順序進行生產檢測,噹貼有標籤的產品經過每一箇工位時就能被閱讀器識彆,從而判定產品經過的生產檢測流程是否按照規定的順序執行,將這一順序稱之為路徑約束。使用NFA構造貼有RFID標籤的產品在生產檢測流程中所需要遵守的工位執行順序(路徑約束)。利用構造好的路徑,按照設計好的算法先過濾RFID數據流,使用清洗後的數據流進行生產檢測流程的控製。實驗結果錶明該算法能有效地控製生產檢測的流程。
침대생산검측류정공제중,산품가능회연착다충공위류동,차공위적집행순서시복잡적문제,문중제출료일충기우RFID급기로경약속적생산검측류정공제방법。해방법재산품상첩유RFID표첨,재산품경과적생산검측공위상안장유열독기;산품안고정적공위조작순서진행생산검측,당첩유표첨적산품경과매일개공위시취능피열독기식별,종이판정산품경과적생산검측류정시부안조규정적순서집행,장저일순서칭지위로경약속。사용NFA구조첩유RFID표첨적산품재생산검측류정중소수요준수적공위집행순서(로경약속)。이용구조호적로경,안조설계호적산법선과려RFID수거류,사용청세후적수거류진행생산검측류정적공제。실험결과표명해산법능유효지공제생산검측적류정。
For the problem that in production testing process control,the goods may flow along with a variety of workstations,and the or-der of execution is complex,propose a production testing process control method based on RFID with path constraint. With RFID tags in a product,this method needs the product pass on the station which is equipped with RFID readers and through the fixed location with se-quence. The readers can recognize it when the product passing each workstation,then determine whether the product after the workstation is in accordance with right order,and this order is called path constraint. Use the NFA to construct the workstation execution sequences ( path constraints) which the moving product with RFID tags needs to follow. Applying the path that has been constructed well,filter the RFID data with the algorithm designed,and then control the flow with the cleaned data. Experimental results show that the algorithm can control the production testing process effectively.