Method for monitoring a turnout of a railway track installation

Abstract

The invention relates, inter alia, to a device for determining a classification model (KM, KM') for a switch (W) of a railway track system, which classification model is capable of determining a fault of the switch (W) based on measured values ​​measured during a switch revolution. According to the invention, it is provided that a reference revolution data set is determined for a plurality of switch revolutions, each of which relates to at least two physical measured variables measured during the respective switch revolution, and that the classification model (KM, KM') is determined based on the plurality of reference revolution data sets.

Description

Technical Field

[0001] The invention relates to a method and a device which enable particularly reliable monitoring of switches of a railway track system or provide a basis therefor, in particular in the form of a classification model. Background Art

[0002] A method for monitoring a switch of a railway track system is known from Korean patent document KR101823067 B1. In the previously known method, for a switch that is deemed to be operational or to be fault-free, the current consumption of the switch drive of the switch is recorded and a corresponding reference value is stored. If it is determined in a subsequent switch operation that the current measured value does not correlate with the reference measured value, a corresponding fault signal is generated, which indicates a fault of the switch.

[0003] Document US 2018 0154 913 A1 describes a computer-implemented method for notifying a user of the presence of a fault in an electromechanical system in a railway track infrastructure. The method includes receiving electrical usage data (the electrical usage data indicating the value of an electrical usage parameter, which is associated with the electromechanical system) and receiving temperature data, the temperature data showing the current temperature of the electromechanical system. In addition, based on a predetermined relationship between the electrical usage parameter and the temperature, it is determined whether the value of the electrical usage parameter indicates a fault in the electromechanical system. If this is the case, a warning is issued to indicate the presence of a fault. Summary of the invention

[0004] Among other things, the invention is based on the object of providing a method for determining a classification model which allows particularly reliable monitoring of switches in a railway track system.

[0005] To solve this problem, according to the invention a method is provided having the features of claim 1. Advantageous embodiments of the method are specified in the dependent claims.

[0006] According to the invention, provision is then made to determine a reference revolution data set for a plurality of switch revolutions, each of which relates to at least two physical measurement variables measured during the respective switch revolution, and to determine a classification model based on the plurality of reference revolution data sets.

[0007] The essential advantage of the method according to the invention is that, in contrast to previously known methods, switch monitoring is not carried out on the basis of a single physical measured variable (in this case the current), but on the basis of at least two or more measured variables, thereby forming an extended classification model and enabling particularly reliable fault detection.

[0008] It is considered to be advantageous to take into account or to determine the classification model based only on those reference revolution data sets whose associated switch revolutions are considered to be fault-free.

[0009] Preferably, for each switch revolution of the switch, an at least two-dimensional characteristic vector associated with a predefined vector space is created as a reference revolution data set, at least two vector components of which relate to at least two physical measurement variables measured during the switch revolution.

[0010] Preferably, the feature vectors are used to define a spatial segment in the vector space, which forms a classification model and in order to form a fault signal a check can be performed as to whether the feature vectors generated for subsequent switch rotations lie outside this spatial segment to a predetermined extent after completion of the classification model.

[0011] Advantageously, the classification model is determined at least in addition on the basis of reference rotation data sets, which relate to a predetermined number of turnout rotations after the initial installation of the turnout or to a predetermined time interval after the initial installation of the turnout. In other words, these reference rotation data sets created after the initial installation define an operational turnout with a predominant probability and form positive examples for an operational turnout.

[0012] Alternatively or additionally, provision can be made in an advantageous manner that the classification model is determined at least also on the basis of reference slew data sets which relate to a predetermined number of turnout turns after maintenance of the turnout or to a predetermined time interval after maintenance of the turnout. That is, these reference slew data sets created after maintenance define an operational turnout with a predominant probability and form positive examples for an operational turnout.

[0013] Alternatively or additionally, it can be advantageously provided that the classification model is determined at least also on the basis of reference rotation data sets which relate to a predetermined number of turnout rotations after maintenance of the turnout or to a predetermined time interval after maintenance of the turnout. That is, these reference rotation data sets created after maintenance define an operational turnout with a predominant probability and form positive examples for an operational turnout.

[0014] Advantageously, the first classification model is determined on the basis of reference revolution data sets, which relate to a predetermined number of turnout revolutions after initial installation of the turnout or to a predetermined time interval after initial installation of the turnout. Subsequently, the first classification model can be modified in an advantageous manner on the basis of reference revolution data sets, which relate to a predetermined number of turnout revolutions after first maintenance or first repair of the turnout or to a predetermined time interval after first maintenance or first repair of the turnout, to form a second classification model.

[0015] It is particularly advantageous if, after each repair or maintenance, an existing classification model is modified to form an updated classification model based on a reference rotation data set which relates to a predetermined number of switch rotations after the respective repair or maintenance of the switch or to a predetermined time interval after the respective repair or maintenance of the switch.

[0016] Preferably, the reference slewing data set also specifies in each case the slewing time of the switch as at least one of the measured physical measured variables. The slewing time of the switch is a particularly suitable measured variable for detecting a fault.

[0017] Particularly preferably, the classification model is determined using or based on a one-class support vector machine method.

[0018] When forming the second and / or updated classification model, a warning signal can be generated in an advantageous manner for a reference swivel data set that is outside the switch state range defined as permissible switch states by the respective previous classification model. In the presence of the warning signal, a check of the measurement and / or a check of the switch function can be carried out.

[0019] Furthermore, the invention relates to a method for determining a fault in a turnout in a railway track device. With regard to this method, it is provided according to the invention that during or after a turnout turn of the turnout a turnout data set is created, the turnout data set relating to at least two physical measurement variables measured during the turnout turnout; the turnout data set is compared with a classification model, which has been determined according to the method as described above for the same at least two measurement variables; and in the event that the turnout data set is outside a turnout state range defined as permissible turnout states by the classification model, a fault signal is generated indicating a faulty behavior of the turnout. The last-mentioned method according to the invention is therefore based on the use of a classification model based on at least two physical measurement variables and can therefore be performed particularly reliably; in this regard, reference is made to the above embodiments in conjunction with the method for determining the classification model, which embodiments apply accordingly here.

[0020] The invention further relates to a device for determining a classification model for a switch of a railway track system, which classification model enables the determination of a fault of the switch. With regard to such a device, it is provided according to the invention that the device is designed for this purpose to determine the classification model based on a plurality of reference slew data sets, which each relate to at least two physical measurement variables measured during a respective slew of the switch. With regard to the advantages of the device according to the invention, reference is made to the above embodiments in conjunction with the method according to the invention for determining a classification model, since these embodiments apply accordingly here.

[0021] Furthermore, the invention relates to a device for determining a fault in a switch of a railway track system. To this end, it is provided according to the invention that the device is constructed to create a swivel data set during or after a switch swivel of the switch, the swivel data set relating to at least two physical measurement variables measured during the swivel of the switch; the swivel data set is compared with a classification model, which has been determined based on a plurality of reference swivel data sets; and in the event that the swivel data set is outside a switch state range defined as permissible switch states by the classification model, a fault signal indicating a faulty behavior of the switch is generated. With regard to the last-mentioned advantages of the device according to the invention, reference is made to the above embodiments in conjunction with the method according to the invention for determining a fault in a switch of a railway track system, which apply accordingly here.

[0022] Advantageously, the device comprises a computing device and a memory, in which a computer program product is stored, which, when executed by the computing device, causes the computing device to carry out one or all of the methods described above.

[0023] Furthermore, the present invention relates to a computer program product which, when executed by a computing device, causes the computing device to perform one or all of the methods described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be described in more detail below based on embodiments; for example:

[0025] Figure 1 A first embodiment of the method according to the present invention is shown according to a flow chart.

[0026] Figure 2 A second embodiment of the method according to the present invention is shown according to a flow chart.

[0027] Figure 3 The block diagram shows an embodiment of the apparatus for determining a classification model according to the present invention.

[0028] Figure 4 A second embodiment of a device for determining a classification model is shown according to a block diagram.

[0029] Figure 5 An embodiment of the method for monitoring a switch of a railway track system according to the present invention is shown according to a flow chart.

[0030] Figure 6 A first embodiment of a device for determining a fault of a switch in a railway track system is shown according to a block diagram, and

[0031] Figure 7 A second exemplary embodiment of a device for determining a fault in a switch of a railway track system is shown with the aid of a block diagram.

[0032] For purposes of clarity, the same reference numbers are used throughout the drawings for the same or similar components. DETAILED DESCRIPTION

[0033] Figure 1 An exemplary embodiment of a method for determining a classification model KM is shown with reference to a flow chart, which method makes it possible to determine a fault in a switch W of a railway track system on the basis of measured values ​​measured during a switch rotation.

[0034] Within the scope of method step 110 , it is monitored whether a certain start signal S for starting the method or for starting the classification model KM is present. If this is the case, a subsequent acquisition process 120 for acquiring a reference swivel data set is started.

[0035] Within the scope of the acquisition process 120, a monitoring step 121 for detecting and monitoring the respective next switch rotation is first started. If the start of a new switch rotation is detected in method step 121, then in the subsequent method step 122, at least two physical measurement variables are respectively acquired for the respective switch rotation in a measurement-technical manner. The physical measurement variables can be, for example, the current consumption or the maximum current of the electric drive motor of the respective switch W or the switch rotation time of the switch W. Alternatively or additionally, further physical measurement variables can also be taken into account, such as the maximum electrical power consumption of the switch W and / or a possible phase shift between the current and the voltage at the drive motor of the switch W.

[0036] In the subsequent method step 123, a reference swivel data set is determined for each respective switch swivel, which reference swivel data set relates to at least two physical measurement variables. It is assumed below by way of example that a two-dimensional or multi-dimensional feature vector is created as the reference swivel data set, the vector components of which relate to the physical measurement variables measured during the respective switch swivel.

[0037] Figure 1In the method step 123, the characteristic vector formed is denoted by the reference symbol Mi, wherein the subscript i denotes the i-th switch rotation after the start signal S is present. Thus, the characteristic vector M1 will denote the first characteristic vector after the start signal S is present, and the characteristic vector Mn will denote the n-th characteristic vector after the start signal S is present.

[0038] For example, if two physical measurement variables, such as current consumption and switch rotation time, are measured, the characteristic vector at the i-th switch rotation after input of the start signal S will be a two-dimensional vector, which is represented, for example, as follows:

[0039] Mi=(I,T)

[0040] Wherein, I represents the current during the i-th turnout rotation, and T represents the turn duration during the i-th turnout rotation.

[0041] In the subsequent method step 124, it is checked whether enough switch revolutions have been detected after the input of the start signal S or whether a predetermined minimum number of revolutions has been reached. For example, in method step 124, it can be checked whether a number n=10 of switch revolutions have been detected. If this is the case, the measured characteristic vectors M1, ..., M10 are output in method step 124. If the number n=10 of switch revolutions has not yet been reached, the monitoring of the switch revolutions is continued in method step 121 until a predetermined number of switch revolutions has been reached.

[0042] Instead of a predetermined number of switch turns, it is also possible to check in method step 124 whether a predetermined time interval T has elapsed after input of the start signal S. If this is the case, method step 130 is continued, otherwise method step 121 is continued with recording of the next characteristic vector in each case.

[0043] After the acquisition process 120 is completed, a classification model KM is generated in a subsequent method step 130 based on the generated feature vectors M1, ..., Mn. It is considered particularly advantageous to consider or determine the classification model KM based on a one-class support vector machine method (One-Class-Support-Vektor-Machine-Verfahren). In this regard, reference is made to known literature, in which the generation of a classification model based on a one-class support vector machine method is described in detail, for example:

[0044] - "Support Vector Method for Novelty Detection", Bernhard Robert Williamson, Alex Smola, John Shawe-Taylor, John Platt, Advances in Neural Information Processing Systems 12, June 2000, pp. 582-588, MIT Press, and

[0045] - "Estimating the Support of a High-Dimensional Distribution", Bernhard John C. Platt, John C. Shawe-Taylor, Alex J. Smola, and Robert C. Williamson, Neural Computation archive, Vol. 13, No. 7, July 2001, pp. 1443–1471, MIT Press, Cambridge, MA, USA.

[0046] In short, according to Figure 1 The classification model KM in the method is created on the basis of characteristic vectors or reference turn data sets which relate to a predetermined number of turnout turns after the presence of the start signal S or to turnout turns which are carried out within a predetermined time interval after the start signal S occurs.

[0047] If the start signal S is generated after the switch W is reinstalled or after maintenance or repair of the switch W, it can be assumed with a predominant probability that the feature vector M or the corresponding reference rotation data set characterizes an operational or fault-free switch W, and a classification model can be formed from this, which is "trained" for the recognition of fault-free switch rotations. Figure 1 The method is based only on positive examples involving turnout rotations without faults; negative examples for turnouts with faults are not necessary for teaching or training the classification model KM.

[0048] In accordance with Figure 1 In the embodiment of the invention, the classification model KM is generated based on a type of support vector machine method; alternatively, other methods can of course be used, by means of which the classification model KM can be created only from positive examples, i.e. only from reference slewing data sets that are considered “fault-free”. In this regard, for example, the methods described in the following literature references can be mentioned:

[0049] - "A Review of Novelty Detection", Marco A. Pimentel, David A. Clifton, Lei Clifton, Lionel Tarassenko, Signal Processing, Vol. 99, June 2014, pp. 215-249, Elsevier,

[0050] - "A Survey of Recent Trends in One Class Classification", Shehroz S. Khan, Michael G. Madden, Artificial Intelligence and Cognitive Science, pp. 188-197, 2009, Springer, and

[0051] - “Review of Novelty Detection Methods”, Dubravko Miljkovic, 33rd MIPRO International Conference, May 2010, IEEE

[0052] Figure 2 A method for determining a classification model KM' is shown, which is created based on an already existing classification model KM by updating or modifying the existing classification model KM:

[0053] After the start signal S is present and a reference swivel data set is subsequently acquired in an acquisition process 120 (see also Figure 1 In the embodiment of the present invention, the existing classification model KM is modified in the modification method 131 based on the newly generated feature vectors M1, ..., Mn. This modification is particularly easy to implement by integrating the newly generated feature vectors M1, ..., Mn into the existing classification model KM, thereby generating a modified or new classification model KM'.

[0054] It is also possible to consider the feature vectors that have been considered to form the existing classification model KM together with the newly generated feature vectors M1, . . . , Mn to form a modified or new classification model KM′.

[0055] In other aspects, the above combination Figure 1 The implementation scheme is correspondingly applicable to Figure 2 method.

[0056] Figure 3An embodiment of a device 200 for determining a classification model KM is shown. The device 200 comprises a computing device 210 and a memory 220.

[0057] A computer program product CPP is stored in the memory 220, which includes a control program module SPM, a software module SM120 and a software module SM130 for generating a classification model KM. The software modules SM120 and SM130 are controlled by the control program module SPM.

[0058] Once the control program module SPM receives the corresponding start signal S, the software module SM120 executes the above combined Figure 1 and Figure 2 The acquisition process 120 described comprises method steps 121 to 124 for generating a reference swivel data set or a characteristic vector M.

[0059] The software module SM130 (in a manner controlled by the control program module SPM) uses the reference slewing data set or the corresponding feature vector M of the software module SM120 to form a classification model KM according to method step 130, as already described above in conjunction with Figure 1 and Figure 2 As explained.

[0060] Figure 4 An embodiment of the device 300 is shown, which is not only suitable for generating a classification model KM, but also for modifying an already existing classification model KM and generating a modified classification model KM'. For this purpose, the device 300 has an additional software module SM131, which is able to form an updated or modified classification model KM' based on the previously generated classification model KM and on the newly created feature vector M, as described above in conjunction with Figure 2 The embodiment or corresponding modification method 131 is described.

[0061] Figure 5 An embodiment of a method for determining a fault of a switch W of a railway track system is shown according to a flow chart. Within the scope of method step 140, each switch rotation of the switch W is monitored and a corresponding rotation data set is preferably generated in the form of a feature vector M. In an analysis step 150, it is checked whether the corresponding rotation data set characterizes a fault-free switch rotation according to a predefined classification model KM. If it is determined that the rotation data set is outside the switch state range defined by the classification model KM as permissible switch states, a fault signal SF is generated.

[0062] The classification model KM can be, for example, already based on Figure 1 within the scope of the method or in accordance with Figure 2 Generated within the scope of the method.

[0063] Figure 6 An embodiment of a device 400 for determining a fault of a switch W of a railway track system is shown. The device 400 comprises a computing device 210 and a memory 220. The memory 220 stores a computer program product CPP having a control program module SPM, a software module SM140, a software module SM150 and a classification model KM.

[0064] If the control program module SPM determines that a new switch rotation has occurred, a software module SM140 generates a rotation data set or a characteristic vector M which characterizes the corresponding switch rotation as a function of at least two physical measured variables.

[0065] Subsequently, the software module SM150 checks whether the acquired slewing data set or characteristic vector M lies outside the range of switch states defined as additional switch states by the classification model KM. If this is the case, a fault signal SF is generated.

[0066] Software module SM140 preferably performs as its combination Figure 5 The method steps 140 have already been described. The software module SM150 preferably performs the Figure 5 The analysis step 150 has already been described.

[0067] Figure 7 A further embodiment of a device 500 for determining a fault of a switch W of a railway track system is shown. Figure 7 In the device, in addition to software modules SM140 and SM150, there are also software modules SM120, SM130 and SM131, which are suitable for generating a classification model KM and for modifying or updating an existing classification model KM to form an updated classification model KM'. Regarding software modules SM120, SM130 and SM131, refer to the above combined Figure 3 and Figure 4 , which apply accordingly here.

[0068] In accordance with Figure 7 In the exemplary embodiment of , the device 500 can therefore not only detect faults based on the swivel data set or the newly measured feature vectors and possibly generate a fault signal SF, but can also generate a classification model KM or form a modified classification model KM′.

[0069] The control program module SPM is preferably designed to trigger the generation of the classification model KM by means of the software modules SM120 and SM130 respectively, if the classification model KM has not been previously generated, when the start signal S is present. Preferably, it is necessary to regenerate the classification model after the first commissioning of the switch W.

[0070] If a previously generated classification model KM already exists, the control program module SPM, preferably the software module SM131 , is activated when the start signal S is applied in order to update the existing classification model KM to form an updated classification model KM′. The updating of the respective existing classification model preferably takes place after each maintenance or repair.

[0071] Preferably, the formation of the first classification model and the formation of the updated classification model are each performed based on a predetermined number of switch turns after input of the start signal S or within a predetermined time interval after input of the start signal S. Preferably, the start signal S is generated after reinstalling the switch W and after maintenance and / or repair of the switch W and is input into the control program module SPM.

[0072] Although the present invention has been illustrated and described in detail by means of preferred embodiments, the present invention is not limited to the disclosed examples and other variations may be derived therefrom by those skilled in the art without departing from the scope of protection of the present invention.

[0073] Reference numerals list

[0074] 110 Methods and Steps

[0075] 120 Collection process

[0076] 121 Monitoring Steps

[0077] 122 Methods and Steps

[0078] 123 Methods and Steps

[0079] 124 Methods and Steps

[0080] 130 Methods and Steps

[0081] 131 Modification method

[0082] 140 Methods and Steps

[0083] 150 Analysis steps

[0084] 200 Installations

[0085] 210 Computing Device

[0086] 220 Memory

[0087] 300 Devices

[0088] 400 Devices

[0089] 500 Devices

[0090] CPP Computer Program Product

[0091] KM classification model

[0092] KM' classification model

[0093] M1 eigenvector

[0094] M eigenvector

[0095] Mi eigenvector

[0096] Mn eigenvector

[0097] S Start signal

[0098] SF fault signal

[0099] SM120 Software Module

[0100] SM130 Software Module

[0101] SM131 Software Module

[0102] SM140 Software Module

[0103] SM150 Software Module

[0104] SPM Control Program Module

[0105] W Turnout

Claims

1. A method for determining a classification model (KM, KM') for a turnout (W) of a railway track system, said classification model being capable of determining a fault of the turnout (W) on the basis of measured values ​​measured during a turnout rotation, wherein: For a plurality of switch revolutions, a reference revolution data set is respectively determined, which reference revolution data set respectively relates to at least two physical measurement variables measured during the respective switch revolution, and determining the classification model (KM, KM') based on a plurality of reference swivel data sets, It is characterized in that - for each switch revolution of the switch (W), creating as a reference revolution data set an at least two-dimensional characteristic vector (M) associated with a predetermined vector space, at least two vector components of which relate to at least two physical measurement variables measured during the switch revolution, and - using the characteristic vector (M) to define a spatial segment in a vector space, wherein the spatial segment forms a classification model (KM, KM') and the following check can be performed for forming a fault signal (SF): whether the characteristic vector (M) generated for a subsequent switch rotation after completion of the classification model (KM, KM') is located outside the spatial segment to a predetermined extent.

2. The method according to claim 1, It is characterized in that The classification model (KM, KM′) is determined taking into account or based only on those reference turn data sets whose associated turnout turns are considered to be fault-free.

3. The method according to claim 1 or 2, It is characterized in that The classification model (KM, KM') is determined at least on the basis of a reference revolution data set which relates to a predetermined number of turnout revolutions after initial installation of the turnout (W) or to a predetermined time interval after initial installation of the turnout (W).

4. The method according to claim 1 or 2, It is characterized in that The classification model (KM, KM') is determined at least on the basis of a reference revolution data set which relates to a predefined number of switch revolutions after maintenance of the switch (W) or to a predefined time interval after maintenance of the switch (W).

5. The method according to claim 1 or 2, It is characterized in that The classification model (KM, KM') is determined at least on the basis of a reference revolution data set which relates to a predefined number of switch revolutions after maintenance of the switch (W) or to a predefined time interval after maintenance of the switch (W).

6. The method according to claim 1 or 2, It is characterized in that a first classification model (KM) is determined on the basis of a reference revolution data set which relates to a predetermined number of turnout revolutions after initial installation of the turnout (W) or to a predetermined time interval after initial installation of the turnout (W), and - Modifying the first classification model (KM) to form a second classification model (KM') based on the following reference rotation data set, which relates to a predetermined number of switch rotations after the first maintenance or first repair of the switch (W) or to a predetermined time interval after the first maintenance or first repair of the switch (W).

7. The method according to claim 1 or 2, It is characterized in that - after each repair or maintenance, modifying the existing classification model (KM) to form an updated classification model (KM') based on a reference rotation data set, which relates to a predetermined number of turnout rotations after the respective repair or maintenance of the turnout (W) or to a predetermined time interval after the respective repair or maintenance of the turnout (W).

8. The method according to claim 1 or 2, It is characterized in that The reference swivel data set in each case specifies the swivel duration of the switch (W) at least as one of the measured physical measurement variables.

9. The method according to claim 1 or 2, It is characterized in that The classification model (KM, KM') is determined by considering or based on a one-class support vector machine method.

10. A method for determining a fault in a switch (W) of a railway track installation, It is characterized in that - during or after a switch rotation of the switch (W), a rotation data set is created, which relates to at least two physical measurement variables measured during the switch rotation, - comparing the swivel data set with a classification model (KM, KM') which has been determined for the same at least two measured variables according to the method according to any of the preceding claims, and - generating a fault signal (SF) indicating a faulty behavior of the switch (W) if the slewing data set lies outside a switch state range defined as permissible switch states by the classification model (KM, KM').

11. A device (200, 300, 400, 500) for determining a classification model (KM, KM') for a turnout (W) of a railway track system, the classification model being capable of determining a fault of the turnout (W), wherein: The device (200, 300, 400, 500) is designed to determine the classification model (KM, KM') based on a plurality of reference revolution data sets, each of which relates to at least two physical measurement variables measured during a respective turnout revolution, characterized in that the device (200, 300, 400, 500) is designed to: - for each switch revolution of the switch (W), creating as a reference revolution data set an at least two-dimensional characteristic vector (M) associated with a predetermined vector space, at least two vector components of which relate to at least two physical measurement variables measured during the switch revolution, and - using the characteristic vector (M) to define a spatial segment in a vector space, wherein the spatial segment forms the classification model (KM, KM') and the following check can be performed for forming a fault signal (SF): after completion of the classification model (KM, KM'), the characteristic vector (M) generated for a subsequent switch rotation is located outside the spatial segment to a predetermined extent.

12. A device (200, 300, 400, 500) for determining a fault of a switch (W) of a railway track equipment, It is characterized in that The device (200, 300, 400, 500) is designed to create a swivel data set during or after a switch swivel of the switch (W), the swivel data set involving at least two physical measurement variables measured during the switch swivel, the swivel data set being compared with a classification model (KM, KM') which has been determined based on a plurality of reference swivel data sets, and to generate a fault signal (SF) indicating a faulty behavior of the switch (W) if the swivel data set is outside a switch state range defined as permissible switch states by the classification model (KM, KM').

13. The device (200, 300, 400, 500) according to claim 11 or 12, It is characterized in that The device (200, 300, 400, 500) comprises a computing device (210) and a memory (220), in which a computer program product (CPP) is stored, which, when executed by the computing device (210), causes the computing device to carry out the method according to any one of claims 1 to 10.

14. A computer program product (CPP), It is characterized in that The computer program product (CPP) is suitable for causing a computing device (210) to carry out a method according to any one of claims 1 to 10 when the computer program product (CPP) is executed by the computing device.

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