Method and device for monitoring a filling and/or closing installation and/or post-processing installation

1 . A method for monitoring a filling and/or closing and/or post-processing installation, wherein an image of a transport, infeed and/or outfeed region of the filling and/or closing and/or post-processing installation is taken using a camera system and, wherein on the basis of the image, by use of an artificial intelligence model (AI model) that is trained to detect primary packaging means in the transport, infeed and/or outfeed region and to classify detected primary packaging means, it is determined in which image positions primary packaging means are present, and detected primary packaging means are assigned to a class, wherein the camera system is arranged above the transport, infeed and/or outfeed region, offset from the transport, infeed and/or outfeed region in such a manner that a primary air supply to the transport, infeed and/or outfeed region is not disturbed by the camera system, an optical axis of the camera system being inclined with respect to a vertical axis.

2 . The method according to claim 1 , wherein a determined disturbance is handled using a manipulator, the manipulator being movable by means of a machine controller, by means of a decentralized manipulator controller and/or by means of a manually operable controller for the purpose of handling the determined disturbance.

3 . The method according to claim 1 , wherein the method for monitoring the filling and/or closing and/or post-processing installation is for the pharmaceutical industry.

4 . The method according to claim 1 , wherein it is specified the method comprises to specify on the basis of the classification how the determined disturbance is handled and/or specified to specify on the basis of a prioritization whether—and if so, when—the determined disturbance is handled.

5 . The method according to claim 1 , wherein an output of the AI model is evaluated by use of a rule-based algorithm for the purpose of determining disturbances, and a determined disturbance is classified and/or prioritised prioritized.

6 . The device according to claim 1 , wherein the computing unit is configured to evaluate an output of the AI model by use of a rule-based algorithm for the purpose of determining disturbances, and to classify and/or prioritize a determined disturbance by use of the rule-based algorithm.

7 . The method according to claim 1 , wherein primary packaging means are classified on the basis of their type, and/or primary packaging means of a type are classified on the basis of their orientation.

8 . The method according to claim 1 , wherein an output of the AI model is evaluated by use of a rule-based algorithm for the purpose of determining disturbances.

9 . The method according to claim 1 , wherein a position of a determined disturbance in at least one of the infeed region, the transport region and the outfield region is identified.

10 . The method according to claim 1 , wherein the infeed region having a transport means and/or sorting means, and/or at least one of the infeed region, the transport region and the outfield region at which primary packaging means are provided or deposited in an unordered manner or ordered in a matrix, is monitored.

11 . A device for monitoring a filling and/or closing installation and/or post-processing installation, comprising a camera system configured to take an image of a transport, infeed and/or outfeed region of the filling and/or closing installation and/or post-processing installation, and a computing unit comprising an artificial intelligence model (AI model) that is trained to detect primary packaging means in the transport, infeed and/or outfeed region and to classify detected primary packaging means, the computing unit being configured to determine on the basis of the image, by use of the AI model, in which image positions primary packaging means are present, and to assign detected primary packaging means to a class, wherein an optical axis of the camera system is inclined with respect to a vertical axis, such that the camera system can be arranged above the transport, infeed and/or outfeed region, offset from the monitored transport, infeed and/or outfeed region in such a manner that a primary air supply to the transport, infeed and/or outfeed region is not disturbed.

12 . The device according to claim 11 , wherein a manipulator is provided, which is configured to handle a determined disturbance by means of a central machine controller, a decentralised manipulator controller and/or by means of a manually operable controller.

13 . The device according to claim 11 , wherein the device for monitoring the filling and/or closing installation and/or post-processing installation is for the pharmaceutical industry.

14 . The device according to claim 11 , wherein the computing unit is configured to specify, on the basis of a classification, how the determined disturbance is to be handled, and/or to specify, on the basis of a, prioritization whether, and if so, when, the determined disturbance is to be handled.

15 . The device according to claim 11 , further comprising a memory unit that is configured to electronically log information related to the detected disturbance.

16 . The device according to claim 11 , wherein that the AI model is trained to classify primary packaging means on the basis of their type, and/or primary packaging means of a type on the basis of their orientation.

17 . The device according to claim 11 , wherein the computing unit is configured to evaluate an output of the AI model by use of a rule-based algorithm for the purpose of determining disturbances.

18 . A filling and/or closing installation and/or post-processing installation, the installation comprising a transport, infeed and/or outfeed region and a device for monitoring, the device comprising a camera system configured to take an image of said transport, infeed and/or outfeed region of the filling and/or closing installation and/or post-processing installation, and a computing unit comprising an artificial intelligence model (AI model) that is trained to detect primary packaging means in the transport, infeed and/or outfeed region and to classify detected primary packaging means, the computing unit being configured to determine on the basis of the image, by use of the AI model, in which image positions primary packaging means are present, and to assign detected primary packaging means to a class,

the filling and/or closing installation and/or post-processing installation comprising an isolator housing in which the transport, infeed and/or outfeed region is arranged.

19 . The filling and/or closing installation and/or post-processing installation according to claim 18 ,

wherein a manipulator is provided, which is configured to handle a determined disturbance by means of a central machine controller, a decentralised manipulator controller and/or by means of a manually operable controller.

20 . A tangible, non-transitory computer-readable medium comprising program instructions that are executable by one or more processors such that one or more processors are configured to determine, on the basis of an image of an infeed of a filling and/or closing and/or post-processing installation, by use of an artificial intelligence model (AI model) that is trained to detect primary packaging means in the infeed and to classify detected primary packaging means, in which image positions primary packaging means are present, and to assign detected primary packaging means to a class;

the filling and/or closing installation and/or post-processing installation comprising an isolator housing in which the transport, infeed and/or outfeed region is arranged.

21 . The tangible, non-transitory computer-readable medium according to claim 20 , wherein the program instructions are executable by the one or more processors such that one or more processors are further configured to determine, on the basis of an output of the AI model, by use of a rule-based algorithm, whether there is a disturbance present.