This application provides a standard working time dynamic self-tuning method based on the PID
algorithm, applied in the field of production management technology. It obtains a dataset of measured time consumption for a target task over multiple execution cycles, preprocesses the dataset to remove outliers, and obtains an effective working time
feature set. The logical deviation between this effective working time
feature set and the current target reference value is determined, and a
PID control algorithm is executed based on this deviation to achieve precise control of the working
time data. During the iterative update of the target reference value, an ELM prediction model is used to dynamically adjust the
control parameters of the
PID control algorithm based on historical deviation trends. Finally, when the fluctuation range of the target reference value and the prediction error meet a preset threshold, the final standard working time is output, ensuring the stability and accuracy of the standard working time. This method has the
advantage of dynamically adapting to changes in the production environment and improving the accuracy and stability of the standard working time.