Abstract: A method and an apparatus for generating sensor data for controlling an autonomous vehicle in an environment is provided, such as driverless transport vehicles in a factory for example. Sensor positions of static sensors and the sensors of autonomous vehicles are defined in a global coordinate system on the basis of an environment model, such as a BIM model for example. Sensor data is centrally generated in this global coordinate system for all sensors as a function of these sensor positions. The sensor data is then transformed into a local coordinate system of an autonomous vehicle and transferred for controlling the autonomous vehicle.

Abstract: A method including a) receiving a computer generated data set of sequencing constraints describing a software system to be executed on an automation system and including software components and runnable function entities distributed over the number of computing nodes; b) generating a transition matrix from the data set of sequencing constraints, the transition matrix having a plurality of matrix elements each of them describing, by a transition value, a transition from a runnable function entity to another runnable function entity; c) receiving a computer generated communication matrix describing communication links between the computing nodes in the automation system; d) generating a Markov chain out of the data set of sequencing constraints and the communication matrix; e) generating a distribution function from the Markov chain describing used resources of the computing nodes by the software components and runnable function entities; and f) optimizing the allocation of resources.

Abstract: In order to identify manipulations of cyber-physical-systems, in which cyber-security attacks or manual attacks, can be identified in real-time, it is proposed with regard to (i) a cyber-physical-system with an embedded, distributed and complex system structure and providing sensor/actor-signal-information depicting a behavior of the cyber-physical-system during operation or commissioning, and (ii) a Digital-Twin-Unit, creates and executes a digital twin replicating the behavior of the cyber-physical-system and consequently producing replicated sensor/actor-signal-information by simulating the cyber-physical-system and when the cyber-physical-system and the Digital-Twin-Unit are run in parallel, (1) to detect cyclically a deviation in the behavior of the cyber-physical-system, (2) to determine an environmental model impacts on the deviation due to external and environmental conditions and based on external information, (3) to identify a manipulation of the cyber-physical-system if—for each detection cycle the

Abstract: A method to orchestrate configurations of simulations based on digital twin models of modular plants such that the configurations of the model-based simulations are generated and deployed automatically, is proposed and based on a logical “System Structure and Parameterization” functionality including a “Functional Mock-up Interface”-functionality combined with a “Functional Mock-up Unit”-functionality to generate for distributed operational-technology-applications of the modular plants simulated model components including assignment rules for automation data captured due to automation of the plants and assigned to the distributed operational-technology-applications, and deploying the simulated model components on the distributed operational-technology-applications by using the SSP-functionality with “Functional Mock-up Unit”-functionalities as part of the FMI-functionality for the distributed operational-technology-applications and implementing for the distributed operational-technology-applications and as pa

Abstract: To automate alarm management in modular production installations, provided is, with respect to a modular production installation, for which, for its modular, manufacturer-independent construction and operation, “process equipment assembly” modules, controlled by a programmable PEA controller and simulated by means of a configurable PEA simulation, are integrated into the production installation, by means of a “module type package <MTP>” mechanism for the PEA module description, in the course of a process orchestration which is standardized the following is carried out in the alarm management: —all states of the modular production installation with respect to triggered faults of possible fault cases of the production installation are virtually run through and simulated; —simulation faults that occur are logged concomitantly by means of alarms, which provide identification and an alarm message log is created and/or alarm chains are identified and alarm relationships in the alarm chains are determined.

Abstract: The invention relates to a method for producing a test data record (12) for an algorithm (1), comprising the following steps: 1.1 a trained artificial intelligence (8) is provided. 1.2 the artificial intelligence (8) is stimulated, particularly using a random signal (11) and/or using a quasi linearity property for visual concepts, and 1.3 the artificial intelligence (8) produces at least one test data record (14), which comprises image data (15) and action regions (16) associated with the image data (1g) and/or sensor data and action regions associated with the sensor data. The invention furthermore relates to a method for operating a system for the automated, image-dependent control of a device and an apparatus for carrying out the aforementioned method. Finally, the invention relates to a control system for a device which comprises such an apparatus, and a computer program product, a computer-readable medium, the production of a data storage device and the use of an artificial intelligence (8).

Abstract: An apparatus, such as a virtual sensor, for measuring a value of a physical variable of a technical system includes a generative machine learning model that is trained, e.g., on the basis of generative adversarial networks (GANs for short), in such a way as to take at least one value of a first physical variable of a technical system as a basis for generating and outputting at least one value of a second physical variable of the technical system. The apparatus is configured in such a way as to use the generative machine learning model to generate and output a value of a second physical variable of the technical system on the basis of a measured value of a first physical variable of the technical system.

Abstract: To identify manipulations of cyber-physical-systems in real-time to avoid or prevent damages to the cyber-physical systems, it is proposed with regard to (i) a cyber-physical-system with an embedded, distributed and complex system structure and providing sensor/actor-signal-information depicting a behavior of the cyber-physical-system during operation or commissioning, and (ii) a Digital-Twin-Unit, which in the course of “Model-based Digital-Twin-Representation” of the cyber-physical-system creates and executes a digital twin replicating the behavior of the cyber-physical-system and consequently producing replicated sensor/actor-signal-information by simulating the cyber-physical-system, and when the cyber-physical-system and the Digital-Twin-Unit are run in parallel, to detect cyclically a deviation in the behavior of the cyber-physical-system by comparing information by information the sensor/actor-signal-information with the replicated sensor/actor-signal-information, to identify a manipulation of the cybe

Abstract: To automate alarm management in modular production installations, provided is, with respect to a modular production installation for which, for its modular, manufacturer—independent construction and operation, “process equipment assembly” modules, controlled by a programmable PEA controller and simulated by means of a configurable PEA simulation, are integrated into the production installation, by means of a “module type package <MTP>” mechanism for the PEA module description, in the course of a process orchestration which is standardized the following is carried out in the alarm management: —all states of the modular production installation with respect to triggered faults of possible fault cases of the production installation are virtually run through and simulated; —simulation faults that occur are logged concomitantly by means of alarms, which provide identification and an alarm message log is created and/or alarm chains are identified and alarm relationships in the alarm chains are determined.

Abstract: A method and a device for the computer-assisted simulation of a modular technical system, wherein for each real module of the modular technical system, a corresponding virtual module is generated, is provided. The virtual module includes a controller for controlling a module-specific process and at least one module-specific simulation model, to which model type information is assigned, for simulating the model-specific process. The virtual module is designed to simulate the module-specific process that is controlled by the controller by means of the module-specific simulation model and/or the controller of the real module, and is provided for a computer-assisted simulation of the modular technical system as a data container. In particular, the following allows a virtual start-up of modular process systems.

Abstract: A computer-implemented method for controlling a technical system is provided, including: —reading in hardware configuration parameters and a value of a real-time requirement of a control unit, —reading in hardware configuration parameters of a computing unit for artificial intelligence, —reading in a control application for controlling the technical system, the control application being configured to generate an input value for the control unit in accordance with an artificial intelligence, —determining a processing time of the control application for execution of the control application on the computing unit for artificial intelligence, considering the hardware configuration parameters of the control unit and the hardware configuration parameters of the computing unit for artificial intelligence, —checking the determined processing time based on the value of the real-time requirement of the control unit and outputting a check result, and —the control application, depending on the check result, for the contro

Abstract: A method and a device for configuring a controller and to a method and a controller for controlling a technical system by means of a data-based control model is provided, in particular a model based on reinforcement learning. This data-based control model is configured using a model-predictive control model. Configuration parameters of the data-based control model are set by mapping the model-predictive control model onto the data-based control model in such a way that the data-based control model reproduces the output data of the model predictive control model depending on state data of the technical system read in, and determines optimized control parameters configured in this way. A computationally intensive training procedure for configuring the data-based control model can thus be avoided.

Abstract: A method and an apparatus for generating sensor data for controlling an autonomous vehicle in an environment is provided, such as driverless transport vehicles in a factory for example. Sensor positions of static sensors and the sensors of autonomous vehicles are defined in a global coordinate system on the basis of an environment model, such as a BIM model for example. Sensor data is centrally generated in this global coordinate system for all sensors as a function of these sensor positions. The sensor data is then transformed into a local coordinate system of an autonomous vehicle and transferred for controlling the autonomous vehicle.

Abstract: A computer-implemented method for generating a computer-readable model for a technical system on the basis of text-based specification data for the technical system, the text-based specification data specifying at least one system condition is provided. A plurality of model components for the computer-aided modelling of the technical system are provided, each model component being assigned a model component identifier. By means of generative adversarial networks, model components are selected, using their model component identifiers, from the plurality of model components on the basis of the text-based specification data, and a computer-readable model for the technical system is generated from the selected model components in such a way that model data of the computer-readable model satisfy at least one system condition specified in the text-based specification data.

Abstract: A method and a device for the computer-assisted simulation of a modular technical system, wherein for each real module of the modular technical system, a corresponding virtual module is generated, is provided. The virtual module includes a controller for controlling a module-specific process and at least one module-specific simulation model, to which model type information is assigned, for simulating the model-specific process. The virtual module is designed to simulate the module-specific process that is controlled by the controller by means of the module-specific simulation model and/or the controller of the real module, and is provided for a computer-assisted simulation of the modular technical system as a data container. In particular, the following allows a virtual start-up of modular process systems.

Abstract: The invention relates to a method for producing a test data record (12) for an algorithm (1), comprising the following steps: 1.1 a trained artificial intelligence (8) is provided, 1.2 the artificial intelligence (8) is stimulated, particularly using a random signal (11) and/or using a quasi linearity property for visual concepts, and 1.3 the artificial intelligence (8) produces at least one test data record (14), which comprises image data (15) and action regions (16) associated with the image data (1g) and/or sensor data and action regions associated with the sensor data. The invention furthermore relates to a method for operating a system for the automated, image-dependent control of a device and an apparatus for carrying out the aforementioned method. Finally, the invention relates to a control system for a device which comprises such an apparatus, and a computer program product, a computer-readable medium, the production of a data storage device and the use of an artificial intelligence (8).

Abstract: Provided is a method and device for determining the cause of a fault in an electrical circuit by a graph-based circuit diagram simulation model of the electrical circuit. A fault is simulated in the electrical circuit and resulting connected components, and respective potential values and/or phase values of the graph vertices of the resulting connected components, are determined by reference to the modified graph-based circuit diagram simulation model, and, on the basis of the potential values and/or phase values of the graph vertices determined, the switching behavior of the electrical circuit is represented by the further addition and/or removal of at least one further graph edge. The resulting potential and/or phase values for specified graph vertices are outputted in the form of simulated output signals. The simulated output signals are compared with reference output signals for the electrical circuit, and the cause of the fault is outputted.

Abstract: A method including a) receiving a computer generated data set of sequencing constraints describing a software system to be executed on an automation system and including software components and runnable function entities distributed over the number of computing nodes; b) generating a transition matrix from the data set of sequencing constraints, the transition matrix having a plurality of matrix elements each of them describing, by a transition value, a transition from a runnable function entity to another runnable function entity; c) receiving a computer generated communication matrix describing communication links between the computing nodes in the automation system; d) generating a Markov chain out of the data set of sequencing constraints and the communication matrix; e) generating a distribution function from the Markov chain describing used resources of the computing nodes by the software components and runnable function entities; and f) optimizing the allocation of resources.