Abstract: A process for controlling a conveyor line for general cargo is provided, the conveyor line including a plurality of consecutive conveyor line portions, each of which is driven by a drive. The drives are controlled by a computing unit using a machine learning model. The machine learning model accomplishes this by getting first input data on the basis of current operating information from at least one further conveyor line that it does not control. The machine learning model has previously been trained using second input data on the basis of operating information of the at least one further conveyor line. The operating information of the at least one further conveyor line in this instance relates to measured values from sensors for detecting general cargo and speeds of conveyor line portions.

Abstract: A process for controlling a conveyor line for general cargo, the conveyor line including a plurality of consecutive conveyor line portions , each of which is driven by a drive. One or more sensors for detecting general cargo are located on at least some of the conveyor line portions. The drives are controlled by means of a computing unit using a machine learning model. The machine learning model accomplishes this by repeatedly receiving input data including a vector of a fixed length, each vector element being associated with a section of the conveyor line and indicating a current proportional occupancy of the respective section by an item of general cargo. Each conveyor line portion is split into a plurality of the sections of identical size. An apparatus or a system for data processing, a computer program, a computer-readable data carrier and a data carrier signal is also provided.

Abstract: Using the example of a logistics system including a plurality of parallel conveyor lines for piece goods, which each lead to a combining unit in the conveying direction, it is provided how the temporally and spatially extremely complex control of such an industrial installation can be simulated with the aid of neural networks such that the temporal and spatial dependences are also reliably identified by the neural network. This is effected by digital stopwatches which are applied to the neural network in addition to sensor data from the logistics system and are reset to an initial value whenever motion detectors indicate the passage of a package.

Abstract: A method for configuration of a controlled drive application of a logistics system. The logistics system includes parallel conveying paths for piece goods. Each conveying path includes sub-conveying paths which are each accelerated or delayed to merge the piece goods on a single output conveying path with defined spacing. A system model of the logistics system is firstly determined by operating data of the logistics system which include sensor values of the logistics system and changes to control variables. A control function is determined, which includes configuration data for the drives, with at least one control action being performed on the precondition of one or more performance features that are to be achieved in the system model, during which control action the operating data is simulated for a plurality of time steps.