Research on multi-task parallel PLC control based on data flow clustering algorithm

Multi-task parallel data has more categories and huge data streams, which leads to its poor control ability and increases the IoT access burden and communication overhead. In order to improve the stability of data flow and standardize the data flow access process, a multi-task parallel data control method based on data flow clustering algorithm is proposed. Firstly, the data acquisition and monitoring system is used to obtain the IoT data streams from three aspects: identification, acquisition and verification; then the Euclidean distance between the dynamic trends of each data stream is calculated, so that the data stream similarity measure is unified; the optimized data streams are substituted into the clustering algorithm, which reasonably corresponds to the task request according to the data stream timestamps; and finally, the PLC controller is used to realize the multi-task parallel data control. The experimental results show that the proposed method has high scalability and high acceleration ratio.