Method for Generating a Digital Twin of a System or Device

Abstract

A method for generating a digital twin of a system or device includes identifying component data clusters within the first data source, where the component data clusters are assigned or assignable component types or component ID information relating to the system or device, allocating a respective component type designation or a respective component ID information designation to at least one of the identified component data clusters, and generating and storing the digital twin of the system or device.

Claims

1.-17. (canceled)

18. A method for creating a digital twin of an installation or device, a first data source containing automation engineering data related to at least one of automation and an automation plan of the installation or device or parts thereof being present, and the automation engineering data comprising data from at least two data categories, the method comprising: a.) identifying component data clusters within the automation engineering data, the component data clusters be associable with or being associated with component types or component ID information related to the installation or device; b.) assigning a respective component type designation or a respective component ID information designation to at least one of the identified component data clusters; and c.) creating and storing the digital twin of the installation or device.

19. The method as claimed in claim 18, wherein subsequent to method step a.) and before method step c.), the method further comprises: a1.) identifying subcomponent data clusters within the identified component data clusters, wherein the subcomponent data clusters are able to be associated with or associated with subcomponent types or subcomponent ID information related to the installation or device; and wherein subsequent to method step a1.) and before method step c.), the method further comprising: b1.) assigning a respective subcomponent type designation or a respective subcomponent ID information designation to at least one of the identified subcomponent data clusters.

20. The method as claimed in claim 18, wherein subsequent to at least one of method step a.) and a1.), the method further comprising: b2.) identifying relationship information of the component data clusters identified in accordance method step a.) by evaluating at least one of the automation engineering data and additional information regarding these data, and/or identifying relationship information between subcomponent data clusters identified in accordance with method step a1.) by evaluating at least one of the data from the automation engineering data and the additional information regarding these data; and wherein during method step c.), the digital twin is additionally created utilizing identified relationship information.

21. The method as claimed in claim 19, wherein subsequent to at least one of method step a.) and a1.), the method further comprises: b2.) identifying relationship information of the component data clusters identified in accordance method step a.) by evaluating at least one of the automation engineering data and additional information regarding these data, and/or identifying relationship information between subcomponent data clusters identified in accordance with method step a1.) by evaluating at least one of the data from the automation engineering data and the additional information regarding these data; and wherein during method step c.), the digital twin is additionally created utilizing identified relationship information.

22. The method as claimed in claim 18, wherein a second data source from the following data sources is present: mechanical computer-aided design data at least one of (i) related to a mechanical and/or spatial plan of the device or installation or parts thereof and (ii) related to a mechanical and/or spatial design of the device or installation or parts thereof, electrical computer-aided design data at least one of (i) related to an electrical plan and/or circuit diagram of the device or installation or parts thereof and (ii) related to an electrical design and/or implemented circuit diagram of the device or installation or parts thereof, robotics data related to at least one of a plan and a design of one or more robots of the device or installation, and description data related to at least one of a plan and/or design of the device or installation or parts thereof; wherein the method further comprises, before the creation of the digital twin in accordance with step c.): aa.) identifying component data clusters within the second data source, the component data clusters being associable with or associated with component types or component ID information related to the installation or device; bb.) assigning a respective component type designation or a respective component ID information designation to at least one of the component data clusters identified in method step aa.); bbb.) associating at least one of the component data clusters and subcomponent data clusters of the automation engineering data identified with the component data clusters identified in method step bb.).

23. The method as claimed in claim 22, wherein subsequent to method step aa.) and before method step bbb.), the method further comprises: aa1.) identifying identified subcomponent data clusters within the component data clusters, subcomponent types or subcomponent ID information related to the installation or device being associable with or being associated with the subcomponent data clusters; and bb1.) assigning a respective subcomponent type designation or a respective subcomponent ID information designation to at least one of the identified subcomponent data clusters.

24. The method as claimed in claim 22, wherein subsequent to at least one of method step aa.) and aa1.), the method further comprises: bb2.) identifying at least one of (i) relationship information between component data clusters identified in accordance with method step aa.) by evaluating at least one of the data from the second data source and additional information regarding these data, (ii) relationship information between subcomponent data clusters identified according to method step aa1.) by evaluating the data from at least one of the second data source and additional information regarding these data; and wherein during method step c.), the digital twin is additionally created utilizing the identified relationship information.

25. The method as claimed in claim 23, wherein subsequent to at least one of method steps a.), a1.), aa.), aa1.), b.), b1.), b2.), bb.), bb1.), bb2.) and bbb.), a result of the respective method step is stored in a cluster association database and/or wherein subsequent to at least one of method steps a.), a1.), aa.), aa1.), b.), b1.), b2.), bb.), bb1.), bb2.) and bbb.), a cluster association neural network is trained utilizing results from a respective method step.

26. The method as claimed in claim 25, wherein at least one of the cluster association database and the cluster association neural network is utilized when performing the method, said utilization comprising at least one of (i) the component data clusters and the subcomponent data clusters being identified utilizing the cluster association database and the cluster association neural network and (ii) at least one of the component type designation or component ID information designation and the subcomponent type designation or in each case one subcomponent ID information designation is assigned utilizing at least one of the cluster association database and the cluster association neural network.

27. The method as claimed in one of the preceding claim 18, wherein at least one of the digital twin and the cluster association database is formed as at least one of a relational database, a NoSQL database and a knowledge graph database.

28. A method for creating a digital twin of an installation or device, a first data source from the following data sources being present: automation engineering data related to at least one of automation and an automation plan of the installation or device or parts thereof, mechanical computer-aided design data at least one of (i) related to a mechanical and/or spatial plan of the device or installation or parts thereof and (ii) related to a mechanical and/or spatial design of the device or installation or parts thereof, electrical computer-aided data at least one of (i) related to an electrical plan and/or circuit diagram of the device or installation or parts thereof (ii) related to an electrical design and/or implemented circuit diagram of the device or installation or parts thereof, and robotics data related to at least one of a plan and a design of one or more robots of the device or installation, the first data source comprising data from at least two data categories, the method comprising: a.) identifying component data clusters within the first data source, the component data clusters being associable with or associated with component types or component ID information related to the device or installation, b.) assigning a respective component type designation or a respective component ID information designation to at least one of the identified component data clusters; and c.) creating and storing the digital twin of the installation or device.

29. The method as claimed in claim 28, wherein subsequent to at least one of method step a.) and a1.), the method further comprising: b2.) identifying relationship information of the component data clusters identified in accordance method step a.) by evaluating at least one of the automation engineering data and additional information regarding these data, and/or identifying relationship information between subcomponent data clusters identified in accordance with method step a1.) by evaluating at least one of the data from the automation engineering data and the additional information regarding these data; and wherein during method step c.), the digital twin is additionally created utilizing identified relationship information; and wherein the automation engineering data are each replaced by the first data source.

30. The method as claimed in claim 28, wherein a second data source, different from the first data source, is selected from the following data sources: the automation engineering data related to at least one of automation and/or an automation plan of the installation or device or parts thereof, the MCAD data at least one of (i) related to a mechanical and/or spatial plan of the device or installation or parts thereof and related to a mechanical and/or spatial design of the device or installation or parts thereof, the ECAD data at least one of (i) related the electrical plan and/or circuit diagram of the device or installation or parts thereof and (ii) related to the electrical design and/or implemented circuit diagram of the device or installation or parts thereof, the robotics data in related to the plan and the design of one or more robots of the device or installation, and description data related to a plan and/or design of the device or installation or parts thereof; wherein the method further comprises, before the creation of the digital twin in accordance with step c.): aa.) identifying component data clusters within the second data source, the component data clusters being associable with or associated with component types or component ID information related to the installation or device; bb.) assigning a respective component type designation or a respective component ID information designation to at least one of the component data clusters identified in method step aa.); and bbb.) associating at least one of the component data clusters and subcomponent data clusters from the first data source identified in method step b.) with the component data clusters identified in method step bb.).

31. The method as claimed in claim 29, wherein a second data source, different from the first data source, is selected from the following data sources: the automation engineering data related to at least one of automation and/or an automation plan of the installation or device or parts thereof, the MCAD data at least one of (i) related to a mechanical and/or spatial plan of the device or installation or parts thereof and related to a mechanical and/or spatial design of the device or installation or parts thereof, the ECAD data at least one of (i) related the electrical plan and/or circuit diagram of the device or installation or parts thereof and (ii) related to the electrical design and/or implemented circuit diagram of the device or installation or parts thereof, the robotics data in related to the plan and the design of one or more robots of the device or installation, and description data related to a plan and/or design of the device or installation or parts thereof; wherein the method further comprises, before the creation of the digital twin in accordance with step c.): aa.) identifying component data clusters within the second data source, the component data clusters being associable with or associated with component types or component ID information related to the installation or device; bb.) assigning a respective component type designation or a respective component ID information designation to at least one of the component data clusters identified in method step aa.); and bbb.) associating at least one of the component data clusters and subcomponent data clusters from the first data source identified in method step b.) with the component data clusters identified in method step bb.).

32. The method as claimed in claim 30, wherein subsequent to method step aa.) and before method step bbb.), the method further comprises: aa1.) identifying identified subcomponent data clusters within the component data clusters, subcomponent types or subcomponent ID information related to the installation or device being associable with or being associated with the subcomponent data clusters; and bb1.) assigning a respective subcomponent type designation or a respective subcomponent ID information designation to at least one of the identified subcomponent data clusters.

33. A digital twin for a device or installation, the digital twin being created utilizing the method as claimed in claim 18.

34. A non-transitory computer-readable storage medium comprising at least one of a digital twin, a cluster association database and a cluster association neural network as claimed in claim 18.

35. The method as claimed in claim 22, wherein the digital twin is utilized to identify inconsistencies between the automation engineering data and the data from the second data source.

36. The method as claimed in claim 30, wherein the digital twin is utilized to identify inconsistencies between the data from the first data source and the data from the second data source.

37. The digital twin as claimed in 33, wherein the digital twin is configured to at least one of: create a digital twin of a changed device or installation; create a simulation of the device or installation or parts thereof and/or virtually commission the device or installation or parts thereof; and check whether the digital twin corresponds to original planning data of the device or installation.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0254] The present invention is described in more detail below by way of example with reference to the appended figures, in which:

[0255] FIG. 1 shows an exemplary schematic illustration of an assembly unit in accordance with an embodiment of the invention;

[0256] FIG. 2 shows a list of various engineering and/or planning data for the assembly unit of FIG. 1;

[0257] FIG. 3 shows a schematic sequence of the creation of a digital twin for the assembly unit of FIG. 1;

[0258] FIG. 4 shows a schematic sequence when creating a digital twin for the assembly unit of FIG. 1 using data from various engineering data sources;

[0259] FIG. 5 shows a depiction of data from various data categories of the automation engineering data for an exemplary description of a sequence for creating a digital twin for the assembly unit of FIG. 1;

[0260] FIG. 6 shows the result of a 1st clustering step in relation to the automation engineering data of FIG. 2;

[0261] FIG. 7 shows the result of a 2nd clustering step in relation to the automation engineering data of FIG. 2;

[0262] FIG. 8 shows the result of the 2nd clustering step of FIG. 7 with furthermore identified relationship associations between various components;

[0263] FIG. 9 shows an exemplary illustration of data from various data categories in relation to mechanical CAD data of the assembly unit of FIG. 1;

[0264] FIG. 10 shows the result of applying a 1st and 2nd clustering step and a relationship association to the mechanical CAD data of FIG. 9;

[0265] FIG. 11 shows a schematic graphical illustration of a digital twin of the assembly unit based on the data and information ascertained in the exemplary method steps in accordance with the invention; and

[0266] FIG. 12 is a flowchart of the method in accordance with the invention.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

[0267] FIG. 1 shows an assembly station 100 or assembly unit 100 having a first transport station 110 and a second transport station 120. Here, the first transport station 110 comprises a first robot unit 115 and the second transport station 120 comprises a second robot unit 125. The assembly station 100 furthermore comprises an assembly platform 130.

[0268] FIG. 2 shows a list of engineering data 150 for the assembly station 100 illustrated in FIG. 1. The engineering data 150 in this case comprise automation engineering data 200, mechanical CAD data (MCAD) 400, electrical CAD data (ECAD) 152, robotics data 154 and data in standard document formats 156.

[0269] In this case, the automation engineering data 200 comprise data that are required or used in the course of automating the assembly station 100, for example, using appropriate controllers or control units (for example, one or more programmable logic controllers).

[0270] Such data are, for example, a variables list of the variables used in the course of the control of the assembly unit 100 within such a control operation. The automation engineering data 200 furthermore comprise function modules and data modules used for control or the code of a corresponding control program for controlling the assembly unit via an appropriate controller or appropriate programmable logic controller.

[0271] The automation engineering data 200 also comprise a list of user-defined data formats (user-defined types (“UDTs”) that have been created or set up in the course of creating the automation engineering data 150.

[0272] The automation engineering data 200 furthermore comprise a list of information regarding the hardware components used in the assembly station 100. This information may, for example, comprise component names, component ID information (for example, brand names, serial numbers, order numbers or the like), component type designations, component description information, a list of respectively used parameters and/or corresponding parameter limit values, geometric information regarding corresponding hardware components and/or additional, background or support information regarding the corresponding hardware components.

[0273] The MCAD data 400 comprise a parts list of the components of the assembly station 100, 3D information regarding the components of the assembly station 100 and regarding the assembly station 100 itself. The MCAD data 400 furthermore comprise kinematic information in relation to individual components of the assembly station 100, assembly station 100 as a whole, and between various ones of the components of the assembly station 100. The MCAD data furthermore also comprise point clouds information in relation to individual components of the assembly station 100 and the assembly station 100 as a whole.

[0274] The ECAD data 152 comprise circuit diagrams of the assembly station 100 and its components, function plans, function diagrams, function lists and location information in relation to electrical modules and components of the components of the assembly station 100. The ECAD data furthermore comprise a parts list of used electrical and electronic components, a corresponding product identifier list and images of such components and corresponding circuits as are used in the context of the assembly station 100 and the components of the assembly station 100.

[0275] The robotics data 154 of the engineering data 150 for the assembly station 100 comprise signal lists of the robot units 115, 125 of the assembly station 100 and robot programs for the robot units 115, 125 of the assembly station 100.

[0276] The information contained in the engineering data 150 for the assembly station 100 in standard document formats 156 comprise PDF files, Excel files, Visio files, images and flowcharts containing information in relation to the assembly station 100 and its components. Such information may be, for example, function descriptions, operating instructions, parameter and other data lists, visual depictions and similar information.

[0277] FIG. 3 shows an exemplary schematic sequence for creating data for a digital twin 800 for the assembly station 100 in accordance with the present disclosure. This sequence is explained by way of example below with reference to the exemplary clustering of automation engineering data 200 for the assembly station 100. Here, the automation engineering data 200 also form one example of a first data source according to the present description.

[0278] The creation of the digital twin 800 of FIG. 3 begins with a first clustering step 610, in which type clusters within data selected for the clustering are identified from the automation engineering data 200 using a first clustering method. Here, respective data that belong to a particular component type are each assigned to an associated cluster. Such component types may be, for example, a robot, a line, an assembly station, a motor, a converter, a sensor or similar component types. This is explained in even more detail in connection with the following figures.

[0279] In a second assignment step 620, appropriate type descriptions are associated with the identified type clusters. This assignment may, for example, be performed manually by a user or semiautomatically or automatically based on stored corresponding information.

[0280] FIG. 3 furthermore shows a database 700 that comprises a type database 710 and an ID info database 720.

[0281] The results from the assignment step 620 are then stored accordingly in the type database 710 by associating the information regarding the identified type clusters, for example, inter alia, with the respective type descriptions within the database.

[0282] Next, in a second clustering step 630, ID information clusters within the type clusters found in the first clustering step 610 are identified. Here, within a type cluster, respective data that belong to a particular component or component entity are associated with a corresponding cluster. Such particular components or component entities may, for example, be characterized by a serial number or order number, or by an appropriate product name or manufacturer name. Here, various ID information clusters regarding various components may, for example, be present within one of the identified type clusters. The identified type cluster may furthermore also correspond to precisely one ID information cluster, i.e., there is exactly one component of a particular type.

[0283] Next, in a further assignment step 640, appropriate ID information is assigned to each of the identified ID information clusters. Such ID information may then, for example, even be the corresponding serial or order numbers, product names or manufacturer names already mentioned above.

[0284] This is followed by a relationship association step 650 in which relationships between various ones of the identified type clusters and/or ID information clusters are identified. Such relationships may be for example relationships such as “functionally associated”, “is part of”, “is associated”, “is connected to” or the like relationships. Such relationships may, for example, be ascertained based on the relationships of some of the data within the automation engineering data 200. Such relationships may thus, for example, be derived from call chains or orders of program modules, function modules, data modules or similar structures. Relationships may furthermore also be concluded from variable names or similar data.

[0285] The data stored in the type database 710 and the ID information database 720 are furthermore used to train an AI component 750 with a neural network 752. By way of example, the association of particular data with particular clusters and/or the association of particular designations with particular clusters and/or the data contained therein is used to train the neural network 752.

[0286] The trained neural network 752 of the AI component 750 may then, for example, also be used in the course of the clustering steps 610, 630 and the assignment steps 620, 640 illustrated in FIG. 3. Thus, for example, in the course of one of the clustering steps 610, 630, the source data may be supplied to the trained the neural network 752, which may then output a corresponding cluster structure or corresponding clusters or cluster designations associated with the individual data and thus accordingly supplement or possibly even replace clustering methods used in the respective clustering steps 610, 630.

[0287] In the same way, the AI component 750 may also be used for the type description assignment 620 or the ID information assignment step 640. The relationships identified in the relationship association step 650 may furthermore also be used to appropriately train the neural network 752, and the relationship association step 650 may thus also be supported later by an appropriately trained neural network 752.

[0288] FIG. 4 shows an exemplary schematic illustration of steps for creating a digital twin 800 in accordance with the present disclosure using data from various engineering data sources 200, 400, 152, 154, 156.

[0289] For this purpose, automation engineering data 200, as explained and elucidated in more detail by way of example in connection with FIG. 2, are subjected to one or more data selection and structuring steps 660, as are explained in more detail for example in the context of FIG. 3. Mechanical CAD data 400, as are likewise explained in more detail, for example, in connection with FIG. 2, are furthermore likewise subjected to one or more data selection and structuring steps 662, as have already been explained in more detail in connection with FIG. 3. In the same way, the electrical CAD data 152, the robotics data 154 and the standard document data 156, which are likewise explained in more detail in connection with FIG. 2, are each subjected to data selection and structuring steps 664, 666, 668, where these data selection and structuring steps 664, 666, 668, in this case each on their own or independently of one another, may again be designed and configured in accordance with the method explained in more detail with reference to FIG. 3.

[0290] In a further data comparison and fusion step 670, the data clusters respectively ascertained in the course of the data selection and structuring steps 660, 662, 664, 666, 668 are then associated with one another. This association is performed such that in each case those data clusters of the various data sources 200, 400, 152, 154, 156 that belong to the same component types, component type designations, component ID information and/or component ID information designations are each associated with one another.

[0291] This model comparison and fusion step 670 thus generates a consistent data model for the assembly station 100 beyond the limits of the various data sources 200, 400, 152, 154, 156 and is thus a good basis for creating a digital twin 800 in accordance with the present disclosure.

[0292] The method steps 610, 620, 630, 640, 650, already explained with reference to FIG. 3, are now intended to be explained in more detail by way of example with reference to FIGS. 5-8 using the example of exemplary automation engineering data 200 for the assembly station 100.

[0293] Similar clustering based on exemplary MCAD data 400 for the assembly station 100 is explained with reference to FIGS. 9-10, following which the model comparison and fusion step 670, already explained in more detail in connection with FIG. 4, is then explained in more detail by way of example with reference to FIG. 11 on the basis of the data clusters identified in relation to the clustering analysis of the automation engineering data 200 according to FIGS. 5-8 and the MCAD data 400 according to FIGS. 9-10.

[0294] FIG. 5 shows one exemplary example of automation engineering data 200 for the assembly station 100. In this example, the automation engineering data 200 comprise a variables list 210 containing the variables h to n, each of which is identified by a square symbol. The automation engineering data 200 furthermore comprise a function module list 220 containing function modules a to c, these being illustrated by triangular symbols. The automation engineering data 200 furthermore comprise a data module list 230 containing data modules d to g, each of which is illustrated by circular symbols. The automation engineering data 200 furthermore also comprise a UDT list, 240, a hardware information list 250 and a control program 260, these however not being used in the clustering exemplary embodiment illustrated in FIGS. 6-8.

[0295] FIG. 6 shows the result of a first clustering step for identifying clusters 310, 320, 250 that are each associated with component types of the assembly station 100. Such a clustering step may, for example, be configured as explained in more detail in the first clustering step in 610 the context of FIG. 3.

[0296] FIG. 6 furthermore also shows the result of a following type description assignment step for assigning appropriate component type designations 312, 322, 352 to the corresponding clusters 310, 320, 250. Such a type assignment step may, for example, be configured in accordance with the first type description assignment step 620 of FIG. 3.

[0297] For the first clustering step 610, the variables list 210, the function module list 220 and the data module list 230 are selected from the automation engineering data 200 of FIG. 5 in the form of a data selection 205 or first data source 205. Respectively associated variable names are then used for the clustering in relation to the variables h to n. Variables used in each case by the function modules or their associated names are used for the function modules a to c. Variables used by the data modules for the clustering or their associated names are likewise used for the data modules d to g.

[0298] The selected variable names used for the clustering are, for example, well-suited to the corresponding clustering because, when allocating variable names in the context of engineering, such as for the assembly station 100, the membership of variables to particular components, subcomponents or installation parts is usually jointly coded into the variable name. It is thus, for example, possible to conclude, from the match between particular parts of a variable name, as to a corresponding common feature when associating these variables with different components, component parts or subcomponents of the assembly station 100.

[0299] After selecting and performing an appropriate clustering method on the abovementioned data, for example, in accordance with the present description, the cluster image illustrated in FIG. 6 is then obtained, in which the variables i and h and the function module a are associated with a first cluster 310. The variables k, j, l, m, the function modules b and c and the data modules d and e are contained in a second cluster 320. Furthermore, the variable n and the data modules g and f are associated with a third cluster 250.

[0300] Next, in a further step, respectively appropriate component type designations 312, 322, 352 are assigned to the identified clusters 310, 320, 250. This component type designation assignment may, for example, be configured in accordance with the present description or else as in the component description assignment step 620 illustrated in connection with FIG. 3.

[0301] In the present example, this assignment of component type designations 312, 322, 352 may, for example, occur in a partially automated manner where, for example, for the data contained in the first data cluster 310, meta-information regarding these data or description information regarding these data is used and a search for matches in these data occurs. If this search unambiguously reveals a common feature, then this may, for example, be displayed to a user as a suggestion to be confirmed. The user may, for example, accept the suggestion, as a result of which this term is then assigned as component type designation 312, 322, 352 for the respective one of the clusters 310, 320, 250. In the case of various matches between the data, a corresponding selection may, for example, be displayed to a user, who then selects the suitable component type designation 312, 322, 352 for the respective one of the clusters 310, 320, 250.

[0302] This method may also run in a fully automated manner, according to which the system evaluates the found matches itself using an appropriate method and generates an appropriate component type designation 312, 322, 352 therefrom and associates it with the respective one of the clusters 310, 320, 250. This association may then for example be changed again subsequently by a user.

[0303] This assignment step may furthermore also occur completely manually by a user, for example, manually evaluating the meta-information or description information currently being displayed and forming an appropriate component type designation 312, 322, 352 for the respective one of the clusters 310, 320, 250 therefrom.

[0304] In the present example, a component type designation “assembly station” 312 has been ascertained through one of three ways mentioned above for the first cluster 310 and associated with this cluster 310. In functional terms, this means that the data contained in the cluster, the variable a and the function modules i and h, may be associated as a whole with the overall functionality of the assembly station 100.

[0305] In the same way, a cluster type designation “robot” 322 has been associated with the second cluster 320. In functional terms, the data contained in this second data cluster 320 may thus be associated with the functionality of the robot 115, 125 within the assembly station 100.

[0306] Again, in the same way, the type designation “transport” 352 has been associated with the third cluster 250. The functionality of the data contained in the second cluster 250 may thus be associated with transport stations one and two 110, 120 of the assembly station 100.

[0307] FIG. 7 shows the result after a second clustering step 630 for identifying component ID information clusters and a second assignment step 640 for assigning component ID information designations to the identified clusters according to the explanations in relation to FIG. 3 have been applied to the clusters illustrated in FIG. 6.

[0308] The result of the second clustering step 630 is in this case four clusters 310, 330, 340, 250. Here, the first cluster 310 corresponds to the first cluster 310 already identified in the first clustering step and the fourth cluster 250 corresponds to the third cluster 250 defined in the first step.

[0309] A conclusion may be drawn from this, for example, that the data associated with the type “assembly station” according to the first clustering step 610 can be associated with precisely one assembly station having specific ID information. In the same way, the data associated with the type “transport station” according to the first clustering step 610 in the corresponding data cluster 250 may be assigned to exactly one particular transport station having a particular transport station ID identifier. The second clustering step 610 has thus, in these two cases, not identified any new clusters, rather the second clustering 610 has not resulted in any change to the cluster structure here.

[0310] The case is different in relation to the second cluster 320, identified in the first clustering step 610, which is associated with the component type “robot”. Applying the second clustering step 630 has resulted here in the data contained in the type cluster 320 being divided into two component ID information clusters 330, 340, as is illustrated in FIG. 7. In this case, the variables l to m, the function module c and the data module e belong to a first of these clusters 330. The variables j to k, the function module b and the data module d are associated with a second of these clusters 340. From this, it is possible to drawn the conclusion, for example, that the data assigned to the component type “robot” may be associated with two different robot entities.

[0311] In accordance with the first description assignment step 620, appropriate component ID information designations 314, 334, 344, 354 may then also be assigned for the clusters identified in the second clustering step 630. Here, specific ID information 314 for the assembly station 100 may again be assigned to the first cluster 310 based on the description and meta-information assigned to the corresponding variables. A unique ID identifier of the first robot 115 of the assembly station 100 is then assigned to the second component ID information cluster 330, while a unique ID identifier of the second robot 125 of the assembly station 100 is assigned to the third component ID information cluster 340. In the same way, a unique ID identifier 352 for the first transport station 110 of the assembly station 100 has then been assigned to the fourth component ID information cluster 250.

[0312] FIG. 8 shows the result of a relationship association step 650 following the previous method steps 610, 620, 630, 640, as has been explained in more detail with reference to FIG. 3. This relationship association step 650 has been applied to the corresponding clustering result of FIG. 7. In order to identify relationship associations between the clusters 310, 330, 340, 250 illustrated in FIG. 7, a call structure or call chain, can taken from the function module list 220, has been evaluated in an automated manner for the function modules a to c. As an alternative, this evaluation may also occur in a partially automated manner in accordance with the present disclosure, or manually.

[0313] The result of this evaluation is illustrated in FIG. 8. Here, dot-and-dash arrows between the function modules a and c and also a and b illustrate that the function module c is called by the function module a and the function module b is likewise called by the function module a. The function module a is assigned to the cluster 310 for the assembly station 100 having the assembly station identifier 314, the function module b is assigned to the second robot 125 having the second robot identifier 344, and the function module c is assigned to the first robot 115 having the first robot ID identifier 334. It is then possible to conclude from the call association explained above that the data cluster 330 assigned to the first robot 115 is functionally associated with the data cluster 310 associated with the assembly station 100, this being illustrated by a dashed arrow 336 in FIG. 8. In the same way, the data cluster 340 associated with the second robot 125 is likewise associated with the data cluster 310 associated with the assembly station 100, this again being illustrated by a dashed arrow 346 in FIG. 8.

[0314] FIGS. 9 and 10 explain, by way of example, an application of method steps 610, 620, 630, 640, 650, which have already been explained in more detail with reference to FIG. 3, to mechanical CAD data 400 associated with the assembly station 100. Here, the basic sequence of method steps 610, 620, 630, 640, 650 corresponds to the sequence explained above with respect to FIGS. 5 to 8 in relation to a corresponding clustering of the automation engineering data 200 for the assembly station 100.

[0315] FIG. 9 shows MCAD data 400 for the assembly station 100. These MCAD data 400 comprise a list of 3D information 410 regarding components of the assembly station 100, these being illustrated by trapezoids having the letters s and t in FIG. 9. The MCAD data 400 furthermore comprise a parts list 420 that contains information regarding four individual components of the assembly station 100, these being symbolized by hexagons having the letters o to r. The MCAD data 400 furthermore comprise a list containing kinematic information 430 and a list containing points cloud information 440. For the clustering steps explained below, the 3D information list 410 and the parts list 420 have been selected, this being illustrated by a selection 405 illustrated in the form of a dotted rectangle in FIG. 9.

[0316] FIG. 10 shows the result of the clustering of the MCAD data according to the selection 405, illustrated in FIG. 9, from the entire set of MCAD data 400. The clustering result, illustrated in FIG. 10, for the MCAD data 405 corresponds to the clustering result, illustrated in FIG. 8, for the corresponding automation engineering data 205. Here, FIG. 10 shows the result of the clustering after the method steps 610, 620, 630, 640, 650 explained with reference to FIG. 3 have been applied to the MCAD data 405. Here, these method steps are applied in the same way as how these steps are applied as explained in relation to the automation engineering data 200 in connection with FIGS. 5 to 8.

[0317] FIG. 10 therefore shows the result following a sequence of a first clustering step 610 by component types, a first designation step of the clusters identified in the process with component type designations 620, a second clustering step 630 by component ID information and a following second designation step 640 of designating the clusters identified in the process with component ID information designations. This is then followed again, as a last step, by a relationship association step 650 for identifying relationships between the ID information clusters identified in the second clustering step 630.

[0318] This then results, as illustrated in FIG. 10, in a first cluster 530 that is associated with the first robot 115 of the assembly station 100, and to which ID information for this first robot 534 has therefore been assigned. This first robot cluster 530 comprises the parts “o” according to the parts list 420 of the MCAD data 400 and the 3D information “s”. A second cluster 540 is assigned to the second robot 125 of the assembly station 100. The cluster 540 therefore has an associated ID identifier of the second robot 544 as component ID information designation 544. This second robot cluster 540 comprises information regarding component “q” from the parts list 420 of the MCAD data 400 and the 3D information “t”.

[0319] FIG. 10 furthermore shows a cluster 550 assigned to the first transport station 110, with which an ID identifier of this first transport station 554 is associated as component ID information designation. This cluster contains the information “p” from the parts list 420 of the MCAD data 400. FIG. 10 furthermore shows a cluster 560 assigned to the second transport station 120, with which the ID identifier of this second transport station 564 is associated as component ID information designation. This cluster contains the information “r” from the parts list 420 of the MCAD data 400.

[0320] Analyzing, for example, information, contained in the parts list 420 of the MCAD data 400, regarding the components o to r has furthermore made it possible to ascertain the information that the first robot 115 is part of the transport station 110 and the second robot 125 is part of the transport station 120. These relationships were likewise associated with the respective clusters 530, 540, 550, 560 for said components 110, 115, 120, 125 by virtue of a corresponding “part of” relationship having been associated with the cluster 530 for the first robot 115 and the cluster 550 for the first transport station 110, this being symbolized in FIG. 10 by a dashed arrow 536 between these clusters 530, 550. A corresponding “part of” relationship is accordingly associated with the cluster 540 for the second robot 125 and the cluster 560 for the second transport station 120, this being symbolized in FIG. 10 by a dashed arrow 546 between both clusters.

[0321] FIG. 11 shows the result of applying a model comparison and fusion step 670, as has been illustrated in more detail for example in connection with the explanations in relation to FIG. 4. In this case, FIG. 11, in a left-hand part, shows a simplified illustration of the clustering result in relation to the automation engineering data 205, as illustrated in FIG. 8. Furthermore, a right-hand part in FIG. 11 shows a simplified illustration of the clustering result in relation to the MCAD data 405, as has been illustrated in FIG. 10.

[0322] In the course of the model comparison and fusion step 670, all identified component ID information designations 314, 334, 344, 354, 534, 544, 554, 564 were then collected and in each case appropriate symbols for corresponding components 100, 110, 115, 120, 125 were associated with these designations. This symbolic illustration of components for the assembly station 100, the first transport station 110, the first robot 115 of the first transport station 110 and the second transport station 120 with its second robot 125 is illustrated in a central excerpt in FIG. 11.

[0323] FIG. 11 furthermore illustrates, in each case by way of arrows between the respective clusters 330, 310, 340, 350 of the automation engineering data 205 and the respective clusters 530, 540, 550, 560 of the MCAD data 405, an association of the corresponding clusters with these installation parts 100, 110, 115, 120, 125 symbolized by rectangles in the central part of FIG. 11.

[0324] The information identified in this case and illustrated in FIG. 11 is then fixed in the form of an appropriate database to create a corresponding digital twin 800 for the assembly station 100. Here, both the associations of the individual data from the automation engineering data 205 and MCAD data 405 with the various clusters, their respective component type designations and their component ID information designations, along with the relationships between the various clusters and with the components of the assembly station 100, are accordingly recorded and stored in the digital twin 800.

[0325] Furthermore, this digital twin 800 also stores corresponding links between the individual data in the digital twin 800 regarding the original automation engineering source data 205 and MCAD source data 405, via which a connection and thus also access to the original data source is enabled. It is thus also possible to access all of the information stored there. By way of example, it is advantageously suitable to store this digital twin 800 in a knowledge graph database format or else a comparable NoSQL database format. Here, in FIG. 11, the illustration of the result of the model comparison and fusion step 670 may be considered to be a possible illustration of the digital twin 800 of the assembly station 100.

[0326] Based on this digital twin 800, it is then, for example, furthermore possible to create a simulation for the assembly station 100 or parts thereof. For this purpose, the component ID information designations 314, 334, 344, 354, 534, 544, 554, 564 identified in the course of creating the digital twin 800 may, for example, be used to appropriately select simulations corresponding to the corresponding component therefor, for example, from a database collection of such simulations. These simulations may then furthermore be parameterized, configured and linked to one another using the corresponding associated data from the associated data clusters. It is thereby possible to create a simulation of the assembly station 100 based on the created digital twin 100. The creation of this simulation may in turn also be considered to be creation of a digital twin in the context of the present disclosure. The simulation thus created is also a further possible embodiment of a digital twin in the context of the present disclosure.

[0327] FIG. 11 furthermore illustrates the database 700 containing the type database 710 and the ID information database 720 that have already been explained in the course of the description of FIG. 3. FIG. 11 also shows the AI component 750 with the neural network 752, which has likewise already been explained in more detail in the course of the description of FIG. 3. Corresponding connecting arrows in FIG. 11 symbolize that the result and the information are stored in the digital twin 800 in the course of the type database 710 and the ID information database 720, and these data are also used to train the neural network 752 in the AI component 750, as already explained in connection with FIG. 3.

[0328] FIG. 12 is flowchart of the method for creating a digital twin 800 of an installation or device 100, where a first data source containing automation engineering data 205 related to automation and/or an automation plan of the installation or device 100 or parts thereof is present, and the automation engineering data 205 comprises data from at least two data categories.

[0329] The method comprises a.) identifying component data clusters 310, 320, 330, 340, 250 within the automation engineering data 205, as indicated in step 1210. In accordance with the invention, the component data clusters 310, 320, 330, 340, 250 are able to be associated or associated with component types or component ID information related to the installation or device 100.

[0330] Next, b.) a respective component type designation 312, 322, 352 or a respective component ID information designation 314, 334, 344, 354 is assigned to at least one of the identified component data clusters 310, 320, 330, 340, 250, as indicated in step 1220.

[0331] Next, c.) the digital twin 800 of the installation or device 100 is created and stored, as indicated in step 1230.

[0332] Thus, while there have been shown, described and pointed out fundamental novel features of the invention as applied to a preferred embodiment thereof, it will be understood that various omissions and substitutions and changes in the form and details of the methods described and the devices illustrated, and in their operation, may be made by those skilled in the art without departing from the spirit of the invention. For example, it is expressly intended that all combinations of those elements and/or method steps which perform substantially the same function in substantially the same way to achieve the same results are within the scope of the invention. Moreover, it should be recognized that structures and/or elements and/or method steps shown and/or described in connection with any disclosed form or embodiment of the invention may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. It is the intention, therefore, to be limited only as indicated by the scope of the claims appended hereto.