Data Analysis and Evaluation Based on RFID Considering K-Means Algorithm of GPGPU


  • Juan Li


As for the missing data in the material data collected by radio frequency identification
(RFID), this phenomenon greatly reduces the correctness of the results used in RFID. In
view of this phenomenon of missing reading data, the solution is to take the original
RFID data reading as a unit, and expand the window to smooth according to the previous
reading data of the tag itself. In this behavior, a lot of redundant data that has nothing to
do with the query is parsed and perspective. In addition, it involves multiple RFID
applications. Therefore, the correctness of analysis and perspective is lower. To solve
the above problems, the first is to transfer the RFID data from the data layer to the RFID
data layer, and take it as the processing granularity. Three kinds of computing data and
evaluation analysis of GPU k-means algorithm are launched. By taking the predicted
GPGPU sequence correlation, the future events are analyzed, evaluated and judged.