Method for creating a digital twin of an installation or device, wherein a first data source containing automation engineering data in relation to automation and/or an automation plan of the installation or device or parts thereof is present, and wherein the automation engineering data comprise data from at least two data categories, the method being characterized by the following steps: a.) identifying component data clusters (310, 320, 330, 340, 250) within the data from the first data source using a clustering method in the form of a machine learning method, wherein the component data clusters (310, 320, 330, 340, 250) are able to be associated with or are associated with component types or component ID information in relation to the installation or device, b.) assigning, in automated or semi-automated fashion, a respective component type designation (312, 322, 352) or a respective component ID information designation (314, 334, 344, 354) to at least one of the identified component data clusters (310, 320, 330, 340, 250), wherein the automated or semi-automated assignment is carried out using a cluster association database and/or a cluster association neural network, c.) creating and storing the digital twin of the installation or device, comprising arranging and/or storing the information, generated according to method step b.), in relation to the component type designation or component ID information designation assigned in each case to the at least one of the identified component data clusters, as a digital twin database. wherein a first data source containing automation engineering data in relation to automation and/or an automation plan of the installation or device or parts thereof is present, and wherein the automation engineering data comprise data from at least two data categories, the method being characterized by the following steps: a.) identifying component data clusters (310, 320, 330, 340, 250) within the data from the first data source using a clustering method in the form of a machine learning method, wherein the component data clusters (310, 320, 330, 340, 250) are able to be associated with or are associated with component types or component ID information in relation to the installation or device, b.) assigning, in automated or semi-automated fashion, a respective component type designation (312, 322, 352) or a respective component ID information designation (314, 334, 344, 354) to at least one of the identified component data clusters (310, 320, 330, 340, 250), wherein the automated or semi-automated assignment is carried out using a cluster association database and/or a cluster association neural network, c.) creating and storing the digital twin of the installation or device, comprising arranging and/or storing the information, generated according to method step b.), in relation to the component type designation or component ID information designation assigned in each case to the at least one of the identified component data clusters, as a digital twin database. a.) identifying component data clusters (310, 320, 330, 340, 250) within the data from the first data source using a clustering method in the form of a machine learning method, wherein the component data clusters (310, 320, 330, 340, 250) are able to be associated with or are associated with component types or component ID information in relation to the installation or device, b.) assigning, in automated or semi-automated fashion, a respective component type designation (312, 322, 352) or a respective component ID information designation (314, 334, 344, 354) to at least one of the identified component data clusters (310, 320, 330, 340, 250), wherein the automated or semi-automated assignment is carried out using a cluster association database and/or a cluster association neural network, c.) creating and storing the digital twin of the installation or device, comprising arranging and/or storing the information, generated according to method step b.), in relation to the component type designation or component ID information designation assigned in each case to the at least one of the identified component data clusters, as a digital twin database.