Abstract: Methods, devices, and assemblies for controlling pressure in a supply grid are provided. The supply grid is suitable for supplying fluid to loads. The supply grid has first sensors for measuring the flow and/or the pressure of the fluid at first locations in the supply grid and a pump for pumping the fluid or a valve for controlling the flow of the fluid. The method includes: measuring the flow and/or pressure of the fluid at the first locations in the supply grid by the first sensors; predicting the pressure at the second location in the supply grid using a self-learning system based on the measured flows or pressures, wherein the self-learning system is trained to predict the pressure at a specified location in the supply grid; and actuating the pump or the valve at least also based on the pressure predicted by the trained system at the second location.

Abstract: A training data generator includes an interface to read symbols extracted from digital system plans for technical systems, wherein each of the system plans represents the design and/or the functionality of a technical system by of symbols, and the system plans are identical. The training data generator also includes a storage module which is designed to store the extracted symbols, a selection module which is designed to randomly select a symbol sub-quantity of the stored symbols using a random generator, a generator which is designed to generate at least one synthetic system plan on the basis of the selected symbol sub-quantity, and an output module to output the at least one synthetic system plan as training data in order to train a trainable image detection module, wherein the trainable image detection module is designed to generate a digital system plan using an analog system plan of a technical system.

Abstract: A method for detecting a leakage of fluid in a supply grid is disclosed. The method includes measuring a flow of the fluid at first locations by first sensors; predicting a flow at a second location by a self-learning system based on the measured flows, wherein the self-learning system has been trained to predict the flow at a specified location in a supply grid; measuring an actual flow of the fluid at the second location by a second sensor located at the second location; ascertaining a difference between the actual flow measured at the second location and the flow at the second location predicted by the trained system; and outputting a notification of an assumed leakage when the ascertained difference is greater than a specified threshold. Devices and assemblies for detecting a leakage of fluid in a supply grid are also disclosed.

Abstract: A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

Abstract: Methods, devices, and assemblies for controlling pressure in a supply grid are provided. The supply grid is suitable for supplying fluid to loads. The supply grid has first sensors for measuring the flow and/or the pressure of the fluid at first locations in the supply grid and a pump for pumping the fluid or a valve for controlling the flow of the fluid. The method includes: measuring the flow and/or pressure of the fluid at the first locations in the supply grid by the first sensors; predicting the pressure at the second location in the supply grid using a self-learning system based on the measured flows or pressures, wherein the self-learning system is trained to predict the pressure at a specified location in the supply grid; and actuating the pump or the valve at least also based on the pressure predicted by the trained system at the second location.

Abstract: To control a technical system having multiple system components, multiple functional modules access a common data model. The functional modules in this case include an interface module, a simulation module and an output module. The data model includes data components associated with the system components and simulation model data for a simulation model of the technical system. The functional modules are actuated by a flow controller, wherein the flow controller and a respective functional module have functional-module-specific selection data transmitted between them that the respective functional module takes as a basis for accessing model data of the data model. The interface module continually captures operating data of the technical system and stores them in the data model by means of selection-data-specific access.

Abstract: A supply network for supplying consumers with a medium is provided. Based on a reduction of data and a subsequent reconstruction of at least one pressure and/or flow value, the state at least at one further location in the supply network can be determined without having to be measured. An improved actuation of the at least one pump and/or of the at least one valve is thus possible. A device for pressure control in a supply network is also provided.

Abstract: To control a technical system having multiple system components, multiple functional modules access a common data model. The functional modules in this case include an interface module, a simulation module and an output module. The data model includes data components associated with the system components and simulation model data for a simulation model of the technical system. The functional modules are actuated by a flow controller, wherein the flow controller and a respective functional module have functional-module-specific selection data transmitted between them that the respective functional module takes as a basis for accessing model data of the data model. The interface module continually captures operating data of the technical system and stores them in the data model by means of selection-data-specific access.

Abstract: A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

Abstract: A method includes the following steps: observing a first state vector including state variables in a physical system A; determining a first prediction vector based on the first state vector, with a data driven model for system A; determining a second prediction vector based on the first state vector, with a physics based model for system A; training a prediction fusion operator to determine a third prediction vector based on the first and second prediction vectors; validating the prediction fusion operator on the third prediction vector and another first state vector, the other first state vector concerning the same time as the third prediction vector.

Abstract: A supply network for supplying consumers with a medium is provided. Based on a reduction of data and a subsequent reconstruction of at least one pressure and/or flow value, the state at least at one further location in the supply network can be determined without having to be measured. An improved actuation of the at least one pump and/or of the at least one valve is thus possible. A device for pressure control in a supply network is also provided.

Abstract: Method for generating boundary conditions for at least one model for the simulation of at least one civil infrastructure, said method comprising the process steps: (a) mapping of spatially distributed installations connected to the at least one civil infrastructure onto a data structure; (b) typification of the spatially distributed installations; and (c) determination of boundary conditions for the at least one model by means of the spatially distributed installations that have been typified.