Establishment of a self-learning mechanism for multi-time scale forecasting of concrete distributor transport capacity

The screw transport capacity forecasting model is used to give the control system a forecast setting of the transport capacity prior to production, in the concrete pouring control system for the production of precast concrete components. The model can approximate the screw transport process and give reasonable forecasts of transport capacity, but it is difficult to adapt to the changing conditions of pouring production and give accurate forecasts. Therefore, this paper analyses the influence of deviations in transport capacity forecasts on pouring control, based on the process of the concrete pouring production process, combined with a screw transport capacity forecasting model and a concrete placement control system. Then, the self-learning principle and self-learning mechanism for transport capacity forecasting are designed, and the multi-time scale self-learning mechanism for transport capacity forecasting is established based on the exponential smoothing self-learning method. The establishment of this self-learning mechanism will enable the self-learning method to be combined with the transport capacity forecasting model to improve the applicability of the transport capacity forecast values and to lay the foundation for the development of intelligent concrete pouring control systems.