WO2023004802A1

WO2023004802A1 - Method and device for automatically generating model of industrial process system

Info

Publication number: WO2023004802A1
Application number: PCT/CN2021/109857
Authority: WO
Inventors: 李朝春; 白新; 王冬; 傅玲
Original assignee: 西门子（中国）有限公司
Priority date: 2021-07-30
Filing date: 2021-07-30
Publication date: 2023-02-02

Abstract

Provided are a method and device for automatically generating a model of an industrial process system, and a computer storage medium. The method comprises: constructing an ontology related to simulation of an industrial process system according to a hierarchical structure of the industrial process system; acquiring design information and operation information of the industrial process system; instantiating the ontology according to the design information and operation information as well as a template library; generating model information on the basis of the instantiated ontology; and transmitting the model information to a modeling and simulation engine so that the modeling and simulation engine automatically generates a model of the industrial process system according to the model information. According to the method for automatically generating a model of an industrial process system, by using a unified process production ontology to describe various industrial process systems, engineers only need to know some field knowledge and design process data to automatically generate the model, and can easily manage the simulation model.

Description

Method and apparatus for automatically generating a model of an industrial process system

technical field

The present disclosure relates to the field of simulation, and more particularly, to methods, apparatus, computing devices, computer readable storage media, and computer program products for automatically generating models of industrial process systems.

Background technique

As the digitalization of continuous industrial process system manufacturing such as thermal power plants and chemical plants continues to accelerate, the demand for simulation continues to increase to tap bottlenecks in various manufacturing plants in pursuit of higher productivity and lower costs. Modeling continuous processes has not been an easy task until now. Manually generating a process model with numerous mass flow designs and equipment parameter settings would consume a lot of effort by the engineer. In addition to this, the accuracy of the model depends heavily on the modeling skills of the engineers in the commercial simulation platform and the domain expertise in the manufacturing process.

With the advent of the Industry 4.0 era, a comprehensive description of various processes is required, which requires a well-structured information system to represent processes (including continuous processes). However, there are still obstacles in the development of such systems. First, the information set to be processed is massive and heterogeneous. The detailed description of the continuous process involves various information generated throughout the life cycle, including design information (flow chart), operation information (real-time data set). Additionally, information from different sources often has different formats. Not to mention that these information sets may belong to different domains (eg design domain and simulation domain respectively, thermal power plant domain and chemical plant domain respectively, etc.). This will largely hinder information sharing between entities. Second, the real-time datasets produced by the represented processes have been increasing, but are underutilized and underutilized. Thirdly, information sources are highly dispersed and thus not fully utilized and mined.

Currently, modeling of industrial process systems is still done by experienced engineers by manually configuring the model, setting parameters in commercial continuous process simulation software (eg, Gproms, Flomatser, etc.). However, the modeling and simulation work was done on the local computer without communicating with other data management platforms as the data backbone.

Therefore, there is a need for an improved solution for generating models of industrial process systems.

Contents of the invention

Traditionally, generating a model of an industrial process system will consume a lot of effort by engineers, and the accuracy of the model largely depends on the engineers' modeling skills in commercial simulation platforms and domain expertise in manufacturing processes.

A first embodiment of the present disclosure proposes a method for automatically generating a model of an industrial process system, the method comprising:

constructing an ontology related to the simulation of the industrial process system according to the hierarchical structure of the industrial process system;

obtaining design information and operational information for the industrial process system;

instantiating the ontology according to the design information and the operation information and a template library;

Generate model information based on the instantiated ontology;

The model information is communicated to the modeling and simulation engine for automatic generation of a model of the industrial process system by the modeling and simulation engine from the model information.

In this embodiment, by using a unified process production ontology to describe various industrial process systems, engineers only need to understand some site knowledge and design process data to automatically generate models and easily manage simulation models.

The second embodiment of the present disclosure proposes an apparatus for automatically generating a model of an industrial process system, the apparatus comprising:

a building unit configured to build an ontology related to the simulation of the industrial process system according to the hierarchical structure of the industrial process system;

an acquisition unit configured to acquire design information and operation information of the industrial process system;

an instantiation unit configured to instantiate the ontology according to the design information, the operation information, and a template library;

a generating unit configured to generate model information based on the instantiated ontology;

A model unit configured to transmit the model information to a modeling and simulation engine for automatic generation of a model of the industrial process system by the modeling and simulation engine based on the model information.

A third embodiment of the present disclosure proposes a computing device, which includes: a processor; and a memory for storing computer-executable instructions that, when executed, cause the processor to perform the first embodiment. Methods.

A fourth embodiment of the present disclosure proposes a computer-readable storage medium having computer-executable instructions stored thereon, and the computer-executable instructions are used to execute the method of the first embodiment.

A fifth embodiment of the present disclosure proposes a computer program product that is tangibly stored on a computer-readable storage medium and includes computer-executable instructions that, when executed, cause at least one processing The device executes the method of the first embodiment.

Description of drawings

The features, advantages and other aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings, which show several embodiments of the present disclosure by way of illustration and not limitation. In the attached picture:

FIG. 1 illustrates a hierarchical structure of an exemplary industrial process system in which embodiments of the present disclosure may be applied;

FIG. 2 shows a data relationship diagram for an ontology according to an embodiment of the present disclosure;

3 shows a flowchart of an exemplary method for automatically generating a model of an industrial process system according to an embodiment of the present disclosure;

FIG. 4 shows an exemplary apparatus for automatically generating a model of an industrial process system according to an embodiment of the present disclosure;

Figure 5 illustrates an exemplary computing device for automatically generating a model of an industrial process system according to an embodiment of the disclosure.

Detailed ways

Various exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. While the example methods, apparatus described below include software and/or firmware executing on hardware, among other components, it should be noted that these examples are illustrative only and should not be viewed as limiting. For example, it is contemplated that any or all hardware, software, and firmware components may be implemented exclusively in hardware, exclusively in software, or in any combination of hardware and software. Therefore, although exemplary methods and apparatuses have been described below, those skilled in the art will readily understand that the examples provided are not intended to limit the manner in which these methods and apparatuses are implemented.

Furthermore, the flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block in the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams, can be implemented using a dedicated hardware-based system that performs the specified functions or operations , or can be implemented using a combination of dedicated hardware and computer instructions.

The terms "including", "comprising" and similar terms used herein are open-ended terms, that is, "including/including but not limited to", which means that other contents may also be included. The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one further embodiment" and so on.

As mentioned earlier, traditionally, generating a model of an industrial process system would consume a lot of effort by the engineer, and the accuracy of the model largely depends on the engineer's modeling skills in commercial simulation platforms and domain expertise in the manufacturing process . Aiming at this defect, the present invention proposes a solution to automatically generate a model of an industrial process system based on an ontology.

FIG. 1 illustrates a hierarchical structure of an exemplary industrial process system 100 in which embodiments of the present disclosure may be applied. Industrial process system 100 may implement, for example, a continuous process. The industrial process system 100 may include a plurality of

hierarchies

101 , 102 , 103 , 104 organized from the bottom up. The level 101 represents the equipment level. For example, the industrial process system 100 includes a plurality of equipment E1-E8 at the equipment level 101, and the equipment E1-E8 may be, for example, heat exchangers, turbines, pumps, condensers and the like. The level 102 represents the unit level. For example, the industrial process system 100 includes a plurality of units U1-U8 at the unit level 102. The units U1-U8 may be composed of several devices. For example, the unit U4 includes devices E3 and E5. Hierarchy 103 represents the plant area level. For example, the industrial process system 100 includes multiple plant areas S1-S6 at the plant area level 103. Plant areas S1-S6 may be composed of several units. For example, plant area S5 includes units U1 and U4. The level 104 represents the park level. For example, the industrial process system 100 includes multiple parks P1-P5 at the park level 104. The parks P1-P5 may consist of several plant areas. For example, the park P4 includes plant areas S2, S4 and S5. It should be understood that Fig. 1 is only an illustration of the hierarchical structure of the industrial process system and not limiting. Generally, the equipment level is the basic element that constitutes an industrial process system, and various levels above the equipment level (eg, unit level, plant level, park level) can be regarded as the system level.

FIG. 2 illustrates a data relational graph 200 for an ontology according to an embodiment of the present disclosure. For example, an ontology related to the simulation of the industrial process system 100 can be constructed according to the hierarchical structure of the industrial process system 100 in FIG. 1 and based on the data relationships in FIG. 2 . Ontologies are shared conceptual, explicit, and formal descriptions. Application systems can use ontology to explicitly declare the knowledge they contain, which is very useful for semantic modeling. For example, for an industrial process system, an ontology may include abstract definitions of entities and relationships between entities (eg, subordination, connection, etc.). In other words, the abstract classes of entities in the ontology (e.g., simulation, model, system, subsystem, environment, energy flow , equipment, material flow, connections, parts, etc.), data attributes (for example, the parameters of each entity), relationship attributes (for example, the affiliation and connection relationship of each entity), etc., each entity can be referenced through associated keywords (As indicated by the arrow in Figure 2).

FIG. 3 shows a flowchart of an example method 300 for automatically generating a model of an industrial process system according to disclosed embodiments. The example method 300 may be applied to the example industrial process system 100 shown in FIG. 1 .

Referring to FIG. 3 , method 300 starts at step 301 . In step 301, an ontology related to the simulation of the industrial process system is constructed according to the hierarchical structure of the industrial process system. For example, according to the hierarchical structure of the industrial process system 100, a unified ontology for the industrial process system is generated based on the data relationship diagram 200 in FIG.

Next, the method 300 proceeds to step 302 . In step 302, design information and operational information of the industrial process system are obtained. The design information may represent information on physical and logical connection relationships related to structural elements (for example, equipment, etc.) of the industrial process system 100, information representing the setting content of each structural element, information representing control specifications, and information representing functional specifications. wait. For example, design information can be obtained from a Process Flow Diagram (PFD) or a Piping and Instrument Diagram (P&ID), or it can be added manually. The operational information may represent various process information to be generated in the industrial process system 100, for example, pressure information, temperature information, and the like. For example, operational information may be obtained from a configuration repository of industrial process system 100, or may be added manually.

Next, the method 300 proceeds to step 303 . In step 303, the ontology is instantiated according to design information, operation information and a template library. For example, the topology of the industrial process system can be obtained according to the design information, and the corresponding templates of each entity can be called from the template library to generate entity instances and connections between entities, and the data attributes of the entities can be instantiated according to the operation information. For example, for a device that is a heat exchanger, the heat exchanger template can be called from the template library, and the design information and operation information can be used to fill the heat exchanger template, and based on the topological connection between the heat exchanger and other devices, the The data properties of the connection are populated.

Next, the method 300 proceeds to step 304 . In step 304, model information is generated based on the instantiated ontology. For example, an instantiated ontology can be transformed into model information in a specific format for interacting with other data platforms.

Next, the method 300 proceeds to step 305 . In step 305, the model information is transmitted to the modeling and simulation engine so that the modeling and simulation engine automatically generates a model of the industrial process system according to the model information. For example, the generated model information can be transmitted or pushed to the modeling and simulation engine, and the modeling and simulation engine can be triggered to perform modeling, so that the modeling and simulation engine can extract the device model and System model, and automatically drag and drop corresponding equipment modules on the modeling and simulation platform to form system modules, thus completing the modeling of industrial process systems.

In some embodiments, step 304 may further include: based on the instantiated ontology, generating model information in a format readable by a modeling and simulation engine. For example, a format readable by a modeling and simulation engine, eg, XML, OWL, CSV, TXT, etc., may be generated via a data converter. For example, when the modeling and simulation engine has an XML data interface, model information in XML format can be generated so that the model information can be easily shared by the modeling and simulation engine or other platforms.

In some embodiments, method 300 may also optionally include step 306 before step 305 . In step 306, the model information is checked for sanity before being transmitted to the modeling and simulation engine. In this step, by performing a sanity check, it is possible to avoid providing incomplete model information to the modeling and simulation engine. In addition, if the model information fails the completeness check, additional information may be requested to fill in missing information (for example, data, model parameters) in the model information until the model information passes the completeness check.

In some embodiments, the generated model information may include device model information and system model information, and the system information is constructed based on the device model information. The equipment model information corresponds to the equipment level, and the system model information corresponds to the system level. As mentioned above, the system level is a layer above the equipment level, so the system model is composed of various equipment models.

In some embodiments, step 306 may further include: performing a completeness check on the device model information; and performing a completeness check on the system model information after the device model information passes the completeness check. Since the system model is formed based on the equipment model, it is first necessary to perform a completeness check on the equipment model information, and then perform a completeness check on the system model information. Similarly, if the device model information fails the completeness check, additional information can be requested to fill in the missing information (for example, data, model parameters) in the device model information until the device model information passes the completeness check; if the system model If the information fails the completeness check, additional information may be requested to fill in missing information (for example, model parameters) in the system model information until the system model information passes the completeness check.

In some embodiments, step 306 may further include: checking the completeness of the data and model parameters of the device model information, and checking the completeness of the model parameters of the system model information. For example, checking the data in the equipment model information may refer to checking whether the data attributes that the equipment should possess as an entity are missing, and checking the model parameters in the equipment model information may refer to checking whether the model parameters of the equipment used in the simulation process are missing (for example , whether a specific parameter is missing when the simulation is solved). Since the system model is composed of the equipment model, when the equipment model passes the completeness check, the data information in the system model information is also complete, but it is still necessary to check whether the model parameters of the system used in the simulation process are missing (for example, when solving the simulation is missing a specific parameter).

In some embodiments, step 306 may further include: checking whether the device model information includes a template that does not exist in the template library; if not, adding the template to the template library. In this step, the template library of the device can be automatically discovered and expanded.

In some embodiments, method 300 may also optionally include step 307 . In step 307, obtain simulation data generated by simulating the generated model by the modeling and simulation engine; obtain process data generated during operation of the industrial process system; and calibrate the model based on the simulation data and process data. Since the initially established simulation model may be obtained based on offline data, with the continuous operation of the industrial process system, some parameters or attributes have changed, the simulation model may no longer be suitable for the industrial process system, and the simulation model needs to be calibrated.

In some embodiments, step 307 may further include: comparing the simulation data with the process data to determine whether the model is acceptable; when it is determined that the model is unacceptable, using the process data to calibrate the model. For example, it is possible to compare whether there is a large error between the simulation data and the actually obtained process data, if the error is large, the simulation model is unacceptable, and use the process data to discover which data or model parameters in the model information need is corrected to calibrate the simulation model.

In some embodiments, step 307 may further include: calculating the key performance indicators of the model based on the simulation data and process data; determining whether the model is matched based on the key performance indicators; when determining that the model is not matching, using the process data Calibrate the model. For example, the key performance indicators of the model can be calculated, if it is determined that the key performance indicators have not met the original design requirements, then it is determined that the simulation model is not suitable, and the process data is used to discover which data or model parameters in the model information need to be modified. Correction to calibrate the simulation model.

In some embodiments, method 300 may further include: modifying model information based on the calibrated model. For example, based on the calibrated data or model parameters in the simulation model, the corresponding data or model parameters in the model information can be modified accordingly, and the calibrated model and the modified model information can be stored or updated, so that the next modeling Calibrated model and modified model information can be recalled directly.

According to the aforementioned method 300, it has the following advantages: modeling and simulation are based on a unified process ontology, which can adapt to different types of industrial processes; engineers only need to have a certain understanding of field knowledge and design process data, and can generate information about industrial process systems. simulation models; for different industrial process systems, modeling and simulation cases can be easily managed in a hierarchical manner (e.g., through stored model information and models) for case benchmarking without opening different models on the simulation platform Compare.

FIG. 4 illustrates an exemplary apparatus 400 for automatically generating a model of an industrial process system according to an embodiment of the disclosure.

Referring to FIG. 4 , the apparatus 400 includes a construction unit 401 , an acquisition unit 402 , an instantiation unit 403 , a generation unit 404 , and a model unit 405 . The construction unit 401 is configured to perform the process described above in relation to step 301 in the method 300, the acquisition unit 402 is configured to perform the process described in the above relation to step 302 in the method 300, and the instantiation unit 403 is configured to perform the process described above in relation to step 302 in the method 300. Regarding the process described in step 303 of the method 300, the generating unit 404 is configured to perform the process described above in relation to step 304 in the method 300, and the model unit 405 is configured to perform the process described in relation to step 305 in the method 300 above .

The device 400 may also optionally include a checking unit 406 and a calibration unit 407 . The checking unit 404 is configured to perform the process as described above with respect to step 306 in the method 300 , and the calibration unit 407 is configured to perform the process as described above with respect to step 307 in the method 300 .

FIG. 5 shows a block diagram of an exemplary computing device 500 for automatically generating a model of an industrial process system according to an embodiment of the disclosure. The computing device 500 includes a processor 501 and a memory 502 coupled with the processor 501 . The memory 502 is used to store computer-executable instructions, and when the computer-executable instructions are executed, the processor 501 executes the methods in the above embodiments (for example, any one or more steps of the aforementioned method 300).

In addition, alternatively, the above method can be implemented by a computer-readable storage medium. A computer-readable storage medium carries computer-readable program instructions for implementing various embodiments of the present disclosure. A computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, such as a printer with instructions stored thereon A hole card or a raised structure in a groove, and any suitable combination of the above. As used herein, computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.

Accordingly, in another embodiment, the present disclosure provides a computer-readable storage medium having computer-executable instructions stored thereon for performing various implementations of the present disclosure. method in the example.

In another embodiment, the present disclosure provides a computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions that, when executed, cause At least one processor executes the methods in various embodiments of the present disclosure.

In general, the various example embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, microprocessor or other computing device. When aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it is to be understood that the blocks, devices, systems, techniques, or methods described herein may serve as non-limiting Examples are implemented in hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof.

The computer-readable program instructions or computer program products used to execute various embodiments of the present disclosure can also be stored in the cloud, and when called, the user can access the program stored on the cloud for execution through the mobile Internet, fixed network or other networks. The computer-readable program instructions of an embodiment of the present disclosure implement the technical solutions disclosed in accordance with various embodiments of the present disclosure.

While embodiments of the present disclosure have been described with reference to several specific embodiments, it is to be understood that the embodiments of the present disclosure are not limited to the specific embodiments disclosed. Embodiments of the present disclosure are intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the claims is accorded the broadest interpretation to encompass all such modifications and equivalent structures and functions.

Claims

A method for automatically generating a model of an industrial process system, characterized in that the method comprises:

constructing an ontology related to the simulation of the industrial process system according to the hierarchical structure of the industrial process system;

obtaining design information and operational information for the industrial process system;

instantiating the ontology according to the design information and the operation information and a template library;

Generate model information based on the instantiated ontology;

The model information is communicated to a modeling and simulation engine for automatic generation of a model of the industrial process system by the modeling and simulation engine from the model information.
The method according to claim 1, characterized in that the method comprises:

Based on the instantiated ontology, model information is generated in a format readable by the modeling and simulation engine.
The method according to claim 1, further comprising:

performing a sanity check on the model information prior to passing the model information to the modeling and simulation engine;

Model information that passes the sanity check is passed to the modeling and simulation engine.
The method according to claim 3, wherein the model information includes equipment model information and system model information, and the system model information is constructed based on the equipment model information.
The method according to claim 4, wherein checking the completeness of the model information comprises:

Perform a completeness check on the device model information;

After the device model information passes the integrity check, the system model information is checked for integrity.
The method according to claim 5, characterized in that,

Checking the completeness of the equipment model information includes checking the completeness of data and model parameters in the equipment model information;

Checking the integrity of the system model information includes checking the integrity of model parameters in the system model information.
The method according to claim 5, wherein the method further comprises:

Checking whether the device model information includes a template that does not exist in the template library;

If not present, the template is added to the template library.
The method according to claim 1, further comprising:

obtaining simulation data generated by the modeling and simulation engine simulating the generated model;

obtaining process data generated during operation of the industrial process system;

The model is calibrated based on the simulation data and the process data.
The method of claim 8, wherein calibrating the model based on the simulation data and the process data comprises:

comparing the simulated data to the process data to determine whether the model is acceptable;

When the model is determined to be unacceptable, the model is calibrated using the process data.
The method of claim 8, wherein calibrating the model based on the simulation data and the process data comprises:

calculating key performance indicators of the model based on the simulation data and the process data;

determining whether the model is a match based on the key performance indicator;

When the model is determined to be mismatched, the model is calibrated using the process data.
The method according to any one of claims 8 to 10, further comprising:

The model information is modified based on the calibrated model.
An apparatus for automatically generating a model of an industrial process system, characterized in that the apparatus comprises:

a building unit configured to build an ontology related to the simulation of the industrial process system according to the hierarchical structure of the industrial process system;

an acquisition unit configured to acquire design information and operation information of the industrial process system;

an instantiation unit configured to instantiate the ontology according to the design information, the operation information, and a template library;

a generating unit configured to generate model information based on the instantiated ontology;

A model unit configured to transmit the model information to a modeling and simulation engine for automatic generation of a model of the industrial process system by the modeling and simulation engine based on the model information.
The device according to claim 12, wherein the generating unit is further configured to:

Based on the instantiated ontology, model information is generated in a format readable by the modeling and simulation engine.
The method according to claim 12, wherein the device further comprises a checking unit configured to:

performing a sanity check on the model information prior to passing the model information to the modeling and simulation engine;

Model information that passes the sanity check is passed to the modeling and simulation engine.
The apparatus according to claim 14, wherein the model information includes equipment model information and system model information, and the system model information is constructed based on the equipment model information.
The device according to claim 15, wherein the checking unit is further configured to:

Perform a completeness check on the device model information;

After the device model information passes the integrity check, the system model information is checked for integrity.
The device according to claim 16, wherein the checking unit is further configured to:

Checking the completeness of the data and model parameters in the device model information;

Check the completeness of the model parameters in the system model information.
The device according to claim 16, wherein the checking unit is further configured to:

Checking whether the device model information includes a template that does not exist in the template library;

If not present, the template is added to the template library.
The device according to claim 12, wherein the device further comprises a calibration unit configured to:

obtaining simulation data generated by the modeling and simulation engine simulating the generated model;

obtaining process data generated during operation of the industrial process system;

The model is calibrated based on the simulation data and the process data.
The device according to claim 19, wherein the calibration unit is further configured as:

comparing the simulated data to the process data to determine whether the model is acceptable;

When the model is determined to be unacceptable, the model is calibrated using the process data.
The device according to claim 19, wherein the calibration unit is further configured to:

calculating key performance indicators of the model based on the simulation data and the process data;

determining whether the model is a match based on the key performance indicator;

When the model is determined to be mismatched, the model is calibrated using the process data.
Apparatus according to any one of claims 19 to 21 , wherein the model information is modified based on a calibrated model.
A computing device, wherein the computing device includes:

processor; and

A memory for storing computer-executable instructions which, when executed, cause the processor to perform the method according to any one of claims 1-11.
A computer-readable storage medium having computer-executable instructions stored thereon for performing the method according to any one of claims 1-11.
A computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions which, when executed, cause at least one processor to perform the any one of the methods described.