Patent Application
20220292136
Embodiments relate to technical systems and to a method and system for generating digital representation of asset information in a cloud computing environment.
Digital twins (DT) as a digital virtual representation of a physical industrial system have proven to be a key element in industrial digitalization. A physical industrial system may include a plurality of assets deployed. Each of the assets includes related information, referred herein as ‘asset information’. Generally, different types of digital twins are generated based on type of use cases required for the real-time industrial system. For example, the different types of digital twin include a structure twin, a service twin, operations twin, and so on. Typically, the digital twin for an industrial system is built as a central building block to serve multiple use cases of the industrial system. One of the major challenges while building the digital twin is the integration of asset information from various data sources. Conventionally, each data source may expose different data schemas, thereby making the asset information specific to the data sources using them. This shows that the asset information from one data source may not be compatible with the asset information of the other data source. Therefore, asset information distribution and integration to generate automatically a digital twin is a concern.
Further, an engineer handling the asset information stored in a specific data source may use a different tool than the other engineer handling the asset information stored in other data source. Hence, there is a large dependency on type of tools used to handle same asset information across different data sources.
Until now, two main strategies have been employed to build the digital twins. Firstly, for structured asset information, a standard approach referred as “classical” data integration approach using relational database systems (RDBMS) is used. However, such classical data approach is a tedious process due to the rigid schemas used in RDBMS that in turn require full upfront specification of the target schema. Further, such a classical data integration approach requires complex mappings between source and target schemata. Secondly, for unstructured asset information, human domain experts need to manually go through a large number of documents and collate information from them. This existing approach is time-consuming, and heavily dependent on the engineer expertise and experience in handling such unstructured asset information. This also amounts to human error while integrating such unstructured asset information and building a suitable digital twin.
In the light of the above, there is a need for a method and system for generating digital representation of asset information in a cloud computing environment.
The scope of the embodiments is defined solely by the appended claims and is not affected to any degree by the statements within this summary. The present embodiments may obviate one or more of the drawbacks or limitations in the related art.
Embodiments provide a method and system for generating digital representation of asset information in a cloud computing environment.
Embodiments provide a method for generating digital representation of asset information in a cloud computing environment. The method includes extracting asset information associated with one or more assets from a plurality of structured and unstructured data sources. The one or more assets are deployed in an industrial environment. The asset information includes asset design and maintenance information, asset configuration information, asset physical block information, test data set, asset alarms, and the like. In an embodiment, the asset includes servers, robots, switches, automation devices, programmable logic controllers (PLC)s, human machine interfaces (HMIs), input output modules, motors, valves, pumps, actuators, sensors, and other industrial equipment(s).
The method includes processing the extracted asset information based on a first predefined set of rules. The first predefined set of rules includes, among others, configuration and part supersede information, generic (e.g., a bill of material of an equipment forms a tree) and domain-specific (a turbine of make “A” must have exactly 8 top-level sub-structures called modules) structure requirements, but also requirements relevant to the intended use of the data (e.g., all serialized components must have measurement point information).
The method includes generating a digital representation of the processed asset information based on plurality of user devices. In an embodiment, the digital representation of the processed asset information may be a digital twin of the one or more assets. Specifically, the digital representation of the processed asset information may be a digital twin knowledge graph. In an exemplary embodiment, the digital representations are generated using the SPARQL/SPARUL data query and modification languages of the W3C stack and stored using the named graph feature of the W3C stack. Therefore, the digital representation generated may be virtually subdivided into graphs representing, for example, sources or various intermediate representations of a digital twin associated with the one or more asset(s). By keeping track of the digital representations, corresponding asset information, and also the dependencies between the digital representations, only affected parts of the digital representations are rebuilt when corresponding asset information is modified. Therefore, an amount of changes (made in runtime) required in case of partial updates to asset information is minimized.
The method further includes storing the generated representation of the processed asset information in a predefined file format in a database. The predefined file format is compatible with a format used by plurality of user devices.
Additionally, the method includes outputting the generated digital representation of the processed asset information on a user interface of the plurality of user devices.
In an embodiment, in extracting the asset information from the plurality of structured and unstructured data sources, the method includes receiving asset information from plurality of structured and unstructured data sources. Further, the method includes identifying relevant asset information among the received asset information based on content of the asset information. The content of the asset information includes, among others, engine serial numbers, module serial numbers, material numbers, and document identifiers. Furthermore, the method includes extracting the identified relevant asset information.
In an embodiment, in processing the extracted asset information based on the first predefined set of rules, the method includes determining whether the extracted asset information meets a predefined requirement based on execution of one or more tests on the asset information. The predefined requirement includes data structure requirement, content integrity requirement, receipt from valid data source authorized to store/access asset information and the like. The one or more tests may include queries that return a non-empty result whenever a specific rule is violated by a generated (intermediate) structure, with the query result indicating the sub-structure that violates the rule. Such rules may be implemented, among others, using the SPARQL or SHACL languages of the W3C Semantic Web stack. Further, the method includes processing the asset information that is determined to meet the predefined requirement. In case the asset information fails to meet the predefined requirement, the method includes either discarding the asset information, or generating a digital twin on a “best effort” basis while notifying the user of the user device about the issues detected in the underlying asset information.
In an embodiment, in processing the extracted asset information based on the first predefined set of rules, the method includes classifying the extracted asset information based on the content of the asset information and based on a second predefined set of rules. The second predefined set of rules includes rules that specify how certain modifications that may be embodied in a turbine affect its bill of material and equipment. Further, the method includes dynamically mapping the classified asset information with corresponding asset information extracted from other structured and unstructured data sources.
In an embodiment, in generating the digital representation of the processed asset information based on the type of user devices, the method includes generating a common data model based on the dynamically mapped asset information. The common data model corresponds to a common file format compatible with the format used by the plurality of user devices. Further, the method includes determining type of user devices intended to receive the digital representation of the processed asset information. The type of user devices includes application-based user devices, for example, electrical engineering application, mechanical engineering application, automation engineering application, service management application and the like. The method includes generating the digital representation of the processed asset information based on the generated common data model and based on the determined type of user devices.
In an embodiment, in generating the digital representation of the processed asset information based on the type of user devices, the method includes generating a graphical representation of the processed asset information based on the type of user devices.
In an embodiment, in generating the digital representation of the processed asset information based on the type of user devices, the method includes classifying the generated common data model into sub-common data models based on the classified asset information. The sub-common data models include, among others, a bill-of-material (BOM) model, a measurement data model (including measurement points and measurement readings), a modifications data model, and others. Further, the method includes generating one or more graphical representations of the processed asset information. Each of the one or more graphical representations corresponds to each of the sub-common data model.
In an embodiment, in processing the extracted asset information based on the first predefined set of rules, the method includes determining one or more modifications made to the extracted asset information based on pre-stored asset information. The one or more modifications includes any change in the asset information. For example, addition of any information, deletion of any information, update or change to any parameter of the asset. Any parameter of the asset may include configuration parameter, device specific parameter, asset ID, version number, network information, and the like.
In an embodiment, in generating the digital representation of the processed asset information based on the type of user devices, the method includes generating a part of the representation of the processed asset information based on the determined one or more modifications made to the asset information. The part of representation corresponds to the modified part of the asset information.
In an embodiment, the method includes performing one or more validity tests on the generated digital representation of the asset information. The one or more validity tests includes for example, checking whether serial numbers are unique for a given material across the fleet, or validating that the bill of material forms a proper tree structure with unique identifiers for all nodes. Also, the one or more validity tests may include test that the structure of the tree may not be deeper that a number n of levels, that no outdated materials are used in the most recent structure, that all used materials are known to the fleet-wide backbone systems (e.g., SAP), and more. Further, the method includes modifying the generated digital representation of the asset information based on the validation results. The validation results include a success or failure state of the digital representation. A success state indicates that the digital representation contains no error logs or faults. A failure state indicates that the digital representation includes error logs or faults. In case if the validation results indicate failure state, then a notification/alert message is displayed in the user interface of the user devices indicating the failure state of the digital representation.
Embodiments provide a cloud computing system for generating digital representation of asset information in a cloud computing environment. The cloud computing system may include one or more processors and a memory coupled to the one or more processors. The memory includes a digital representation management module stored in the form of machine-readable instructions and executable by the one or more processors. The digital representation management module is configured for performing the method described above.
Embodiments provide a cloud computing environment. The cloud computing environment including a cloud computing system, an industrial environment including one or more assets capable of communicating asset information associated with the one or more assets to the cloud computing system. The cloud computing environment further includes one or more structured and unstructured data sources capable of storing information associated with the one or more assets in one or more data format. The cloud computing environment further includes at least one user device communicatively coupled to the cloud computing system and the industrial environment via the network.
Embodiments provide a computer-program product having machine-readable instructions stored therein, that when executed by one or more processor(s), cause the one or more processor(s) to perform method steps as described above.
Embodiments are further described hereinafter with reference to illustrated embodiments shown in the accompanying drawings.
Various embodiments are described with reference to the drawings, wherein like reference numerals are used to refer the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide thorough understanding of one or more embodiments. It may be evident that such embodiments may be practiced without these specific details.
The cloud computing system 102 is connected to one or more asset(s) 108A-N in the industrial environment 106 via a network 104 (e.g., Internet). The one or more asset(s) 108A-N may include servers, robots, switches, automation devices, programmable logic controllers (PLC)s, human machine interfaces (HMIs), input output modules, motors, valves, pumps, actuators, sensors, gas turbines, and other industrial equipment(s). The cloud computing system 102 may be a public cloud, a private cloud, and/or a hybrid cloud configured to provide dedicated cloud services to its users. Although
Further, the cloud computing system 102 is also connected to user devices 122A-N via the network 104. The user devices 122A-N may access the cloud computing system 102 for automatically generating digital representations of the asset information. In an embodiment, the user devices 122A-N includes an engineering system capable of running an industrial automation application. The user devices 122A-N may be a laptop computer, desktop computer, tablet computer, smartphone, and the like. The user devices 122A-N may access cloud applications (such as enabling users to generate digital representations of the asset information based on user requirement) via a web browser. Further, the users are provided a quick option to download the digital representations from cloud platform 110 to directly into their Simulation Software running in the user devices 122A-N. Further, the user devices 122A-N may install a plug-in for accessing digital representations of asset information on the cloud computing system 102 via different simulation software running on the user devices 122A-N.
The cloud computing system 102 includes a cloud platform 110, a digital representation management module 112, a server 114 including hardware resources and an operating system (OS), a network interface 116, one or more structured and unstructured data sources 120A-N, and application program interfaces (APIs) 118. The network interface 116 provides communication between the cloud computing system 102, the industrial environment 106, and the user device(s) 122A-N. The cloud interface (not shown in
The one or more structured and unstructured data sources 120A-N is configured for storing information associated with the one or more assets 108A-N in one or more data format. The one or more structured and unstructured data sources 120A-N is, for example, a structured query language (SQL) data store or a not only SQL (NoSQL) data store. The one or more structured and unstructured data sources 120A-N is configured as cloud-based database implemented in the cloud computing environment 100, where computing resources are delivered as a service over the cloud platform 110. The one or more structured and unstructured data sources 120A-N, according to an embodiment, is a location on a file system directly accessible by the digital representation management system 112. In an embodiment, the one or more structured and unstructured data sources 120A-N may be external data sources each having different schema and different file formats. The one or more structured and unstructured data sources 120A-N is configured to store asset information, asset parameters, digital representations associated with the asset information, error logs, validation results, abnormalities associated with the asset 122A-N, common data models, sub-data models, behavior trends, and the like. The one or more structured and unstructured data sources 120A-N also maintains versions of the digital representations.
The processor(s) 202, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor unit, microcontroller, complex instruction set computing microprocessor unit, reduced instruction set computing microprocessor unit, very long instruction word microprocessor unit, explicitly parallel instruction computing microprocessor unit, graphics processing unit, digital signal processing unit, or any other type of processing circuit. The processor(s) 202 may also include embedded controllers, such as generic or programmable logic devices or arrays, application specific integrated circuits, single-chip computers, and the like.
The memory 204 may be non-transitory volatile memory and non-volatile memory. The memory 204 may be coupled for communication with the processor(s) 202, such as being a computer-readable storage medium. The processor(s) 202 may execute machine-readable instructions and/or source code stored in the memory 204. A variety of machine-readable instructions may be stored in and accessed from the memory 204. The memory 204 may include any suitable elements for storing data and machine-readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. In an embodiment, the memory 204 includes a digital representation management module 112 stored in the form of machine-readable instructions on any of the above-mentioned storage media and may be in communication with and executed by the processor(s) 202.
When executed by the processor(s) 202, the digital representation management module 112 causes the processor(s) 202 to generate digital representations of asset information in a cloud computing environment 100. In an embodiment, the digital representation management module 112 causes the processor(s) 202 to extract asset information associated with one or more assets 122A-N from plurality of structured and unstructured external data sources 120A-N. The one or more assets 122A-N are deployed in an industrial environment 106. The asset information includes asset design and maintenance information, asset configuration information, asset physical block information, test data set, asset alarms and the like. In an embodiment, the asset information from the plurality of structured and unstructured external data sources 120A-N is extracted by receiving asset information from plurality of structured and unstructured external data sources 120A-N and identifying relevant asset information among the received asset information based on content of the asset information. Further, the identified relevant asset information is extracted.
Further, the digital representation management module 112 causes the processor(s) 202 to process the extracted asset information based on a first predefined set of rules. The first predefined set of rules includes, among others, configuration and part supersede information, generic (e.g., a bill of material of an equipment forms a tree) and domain-specific (a turbine of make “A” must have exactly 8 top-level sub-structures called modules) structure requirements, but also requirements relevant to the intended use of the data (e.g., all serialized components must have measurement point information).
In an embodiment, in processing the extracted asset information based on the first predefined set of rules, the digital representation management module 112 causes the processor(s) 202 to determine whether the extracted asset information meets a predefined requirement based on execution of one or more tests on the asset information. The predefined requirement includes data structure requirement, content integrity requirement, receipt from valid data source authorized to store/access asset information and the like. The one or more tests may include queries that return a non-empty result whenever a specific rule is violated by a generated (intermediate) structure, with the query result indicating the sub-structure that violates the rule. Such rules may be implemented, among others, using the SPARQL or SHACL languages of the W3C Semantic Web stack. Further, the digital representation management module 112 causes the processor(s) 202 to process the asset information that is determined to meet the predefined requirement. If the asset information fails to meet the predefined requirement, then the asset information is discarding from the one or more structured and unstructured data sources 120A-N.
In an embodiment, in processing the extracted asset information based on the first predefined set of rules, the digital representation management module 112 causes the processor(s) 202 to classify the extracted asset information based on the content of the asset information and based on a second predefined set of rules. The content of the asset information includes, among others, engine serial numbers, module serial numbers, material numbers, and document identifiers. The second predefined set of rules includes rules that specify how certain modifications that may be embodied in a turbine affect its bill of material and equipment. In an embodiment, the second predefined set of rules includes rules to determine whether a component should be replaced on the next maintenance due to fleet-based models, or rules that confirm configuration changes against configuration management data. Further, the digital representation management module 112 causes the processor(s) 202 to dynamically map the classified asset information with corresponding asset information extracted from other external structured and unstructured data sources 120A-N. In an embodiment, knowledge graph technology is used for dynamically mapping the classified asset information with corresponding asset information extracted from other external structured and unstructured data sources 120A-N. Knowledge graphs provide “schema on read” capabilities. Due to this capability, the cloud computing system 102 efficiently handles asset information extraction and integration in the context of heterogeneous and potentially changing data source (such as data sources 120A-N) schemas. Further, Knowledge graphs expose different schemas for the same underlying knowledge graph (that is the digital representation), thereby meeting the needs of different consumer systems. Despite this flexibility, Knowledge Graphs (at least if expressed using the RDF formalism) has formalized and standardized syntax and semantics. This makes the chosen approach largely independent of concrete tools and allows for an easy extension/integration with other systems or twins.
In an embodiment, in processing the extracted asset information based on the first predefined set of rules, the digital representation management module 112 causes the processor(s) 202 to determine one or more modifications made to the extracted asset information based on pre-stored asset information. The one or more modifications includes any change in the asset information. For example, addition of any information, deletion of any information, update or change to any parameter of the asset. Any parameter of the asset may include configuration parameter, device specific parameter, asset ID, version number, network information and the like.
Further, the digital representation management module 112 causes the processor(s) 202 to generate a digital representation of the processed asset information based on plurality of user devices. In an embodiment, in generating a digital representation of the processed asset information based on plurality of user devices, the digital representation management module 112 causes the processor(s) 202 to generate a common data model based on the dynamically mapped asset information. The common data model corresponds to a common file format compatible with the format used by plurality of user devices 122A-N. Further, the digital representation management module 112 causes the processor(s) 202 to determine type of user devices 122A-N intended to receive the digital representation of the processed asset information and generate the digital representation of the processed asset information based on the generated common data model and based on the determined type of user devices 122A-N.
In an embodiment, in generating the digital representation of the processed asset information based on the type of user devices 122A-N, the digital representation management module 112 causes the processor(s) 202 to generate a graphical representation of the processed asset information based on the type of user devices 122A-N.
In an embodiment, in generating the digital representation of the processed asset information based on the type of user devices 122A-N, the digital representation management module 112 causes the processor(s) 202 to classify the generated common data model into sub-common data models based on the classified asset information and generate one or more graphical representations of the processed asset information. Each of the one or more graphical representations corresponds to each of the sub-common data model.
In an embodiment, in generating the digital representation of the processed asset information based on the type of user devices 122A-N, the digital representation management module 112 causes the processor(s) 202 to generate a part of the representation of the processed asset information based on the determined one or more modifications made to the asset information. The part of representation corresponds to the modified part of the asset information. In an exemplary embodiment, modifications are performed within the digital representation as part of the SPARQL/SPARUL queries.
Furthermore, the digital representation management module 112 causes the processor(s) 202 to store the generated representation of the processed asset information in a predefined file format in a database of the cloud computing system 102. The predefined file format is compatible with a format used by the plurality of user devices 122A-N.
Additionally, the digital representation management module 112 causes the processor(s) 202 to output the generated digital representation of the processed asset information on a user interface of the plurality of user devices 122A-N.
Moreover, the digital representation management module 112 causes the processor(s) 202 to perform one or more validity tests on the generated digital representation of the asset information and modify the generated digital representation of the asset information based on the validation results.
The storage unit 206 is configured for storing the asset data blocks associated with one or more asset(s).
The communication interface 208 is configured for establishing communication sessions between the one or more user device 122A-N and the cloud computing system 102. The communication interface 208 allows the one or more engineering applications running on the user devices 122A-N to import/export digital representations into the cloud computing system 102. In an embodiment, the communication interface 208 interacts with the interface at the user devices 122A-N for allowing the engineers to access the digital representations of the asset information and perform one or more actions on the digital representations stored in the cloud computing system 102.
The input-output unit 210 may include input devices a keypad, touch-sensitive display, camera (such as a camera receiving gesture-based inputs), etc. capable of receiving one or more input signals, such as user commands to process asset data. Also, the input-output unit 210 may be a display unit for displaying a graphical user interface that visualizes the asset data and also displays the status information associated with each set of actions performed on the asset data. The set of actions may include data entry, data modification or data display. The bus 212 acts as interconnect between the processor 202, the memory 204, the storage unit 206 and the input-output unit 210.
The hardware depicted in
For simplicity and clarity, the full structure and operation of all data processing systems suitable for use with the present disclosure is not being depicted or described herein. Instead, only so much of a cloud computing system 102 as is unique to the present disclosure or necessary for an understanding of the present disclosure is depicted and described. The remainder of the construction and operation of the cloud computing system 102 may conform to any of the various current implementation and practices known in the art.
The data extraction module 302 is configured for extracting asset information associated with one or more assets 122A-N from plurality of structured and unstructured external data sources 12-A-N. The one or more assets 122A-N are deployed in an industrial environment 106. The asset information includes asset design and maintenance information, asset configuration information, asset physical block information, test data set, asset alarms and the like. The asset information may be extracted using graph queries (e.g., SPARQL or another graph query language).
Specifically, the data extraction module 302 is configured for receiving asset information from plurality of structured and unstructured external data sources 120A-N. Further, the data extraction module 302 is configured for identifying relevant asset information among the received asset information based on content of the asset information. The content of the asset information includes engine serial numbers, module serial numbers, material numbers, and document identifiers. Furthermore, the data extraction module 302 is configured for extracting the identified relevant asset information. The identified relevant asset information is then provided to the data processing module 304.
The data processing module 304 is configured for processing the extracted asset information based on a first predefined set of rules.
The first predefined set of rules includes, among others, configuration and part supersede information, generic (e.g., a bill of material of an equipment forms a tree) and domain-specific (a turbine of make “A” must have exactly 8 top-level sub-structures called modules) structure requirements, but also requirements relevant to the intended use of the data (e.g., all serialized components must have measurement point information).
Specifically, the data processing module 304 is configured to classify the extracted asset information based on the content of the asset information and based on a second predefined set of rules. The second predefined set of rules includes rules that specify how certain modifications that may be embodied in a turbine affect its bill of material and equipment. In an embodiment, the second predefined set of rules includes rules to determine whether a component should be replaced on the next maintenance due to fleet-based models, or rules that confirm configuration changes against configuration management data. Further, the data processing module 304 is configured for dynamically mapping the classified asset information with corresponding asset information extracted from other external structured and unstructured data sources 120A-N. For example, the mapping is performed in order to build a look up table. Such look up table includes mapped asset information for each of the one or more assets 122A-N. For example, an asset A has asset information such as asset ID, asset type, asset configuration information, asset communication information, asset faults, asset current status and the like. Hence, the look up includes each of the above information of the asset mapped to asset A. Such processed asset information and/or the lookup table is then provided to the digital representation generation module 206.
Further, the data processing module 304 is configured for determining one or more modifications made to the extracted asset information based on pre-stored asset information. The one or more modifications includes any change in the asset information. For example, addition of any information, deletion of any information, update or change to any parameter of the asset. Any parameter of the asset may include configuration parameter, device specific parameter, asset ID, version number, network information and the like.
The digital representation generation module 206 is configured for generating a digital representation of the processed asset information based on plurality of user devices. The digital representation of the processed asset information includes a digital twin model of the asset, such as assets 108A-N. In an exemplary embodiment, the digital representation of the processed asset information is a knowledge graph based digital twin model virtually representing the physical asset, such as assets 108A-N deployed in the industrial environment 106. Specifically, upon receiving the processed asset information, the digital representation generation module 206 is configured for generating a common data model based on the dynamically mapped asset information. The common data model corresponds to a common file format compatible with the format used by plurality of user devices 122A-N. Further, the digital representation generation module 206 is configured for determining type of user devices 122A-N intended to receive the digital representation of the processed asset information. Additionally, the digital representation generation module 206 is configured for generating the digital representation of the processed asset information based on the generated common data model and based on the determined type of user devices 122A-N. In an exemplary embodiment, the digital representation generation module 206 is configured for generating a graphical representation of the processed asset information based on the type of user devices 122A-N.
Further, the digital representation generation module 206 is configured for classifying the generated common data model into sub-common data models based on the classified asset information. Further, the digital representation generation module 206 is configured for generating one or more graphical representations of the processed asset information. Each of the one or more graphical representations corresponds to each of the sub-common data model.
Furthermore, the digital representation generation module 206 is configured for generating a part of the representation of the processed asset information based on the determined one or more modifications made to the asset information. The part of representation corresponds to the modified part of the asset information.
The database 308 is configured for storing the generated representation of the processed asset information in a predefined file format. The predefined file format is compatible with a format used by the plurality of user devices 122A-N.
The output module 310 is configured for outputting the generated digital representation of the processed asset information on a user interface of the plurality of user devices 122A-N. In an embodiment, the generated digital representation of the processed asset information is displayed/rendered to the users of the user devices 122A-N using the W3C stack. This stack provides standard semantics for operators, and dedicated components (inference engines) that may be used to add additional information associated with the asset 108A-N. For example, using inference, additional, specific information (e.g., type labels) may be added on nodes based on user-defined rules. This helps domain experts get a better understanding of the asset information outputted on the user devices 122A-N, without having them to specify this information manually on each information item.
The data validation module 312 is configured for determining whether the extracted asset information meets a predefined requirement based on execution of one or more tests on the asset information. The predefined requirement includes data structure requirement, content integrity requirement, receipt from valid data source authorized to store/access asset information and the like. The one or more tests may include queries that return a non-empty result whenever a specific rule is violated by a generated (intermediate) structure, with the query result indicating the sub-structure that violates the rule. Such rules may be implemented, among others, using the SPARQL or SHACL languages of the W3C Semantic Web stack. Further, the data validation module 312 is configured for send the asset information to the data processing module 304 if the asset information is determined to meet the predefined requirement. If the asset information fails to meet the predefined requirement, then the data validation module 312 is configured for discarding the asset information. In an embodiment, RDF-based technology stack is used to perform automated data quality validation and scripted exports in a plurality of export formats. In another example, data validation is implemented using a graph query language, or some related formalism (e.g., SHACL).
Further, the data validation module 312 is configured for performing one or more validity tests on the generated digital representation of the asset information. The one or more validity tests includes for example, checking whether serial numbers are unique for a given material across the fleet, or validating that the bill of material forms a proper tree structure with unique identifiers for all nodes. Also, the one or more validity tests may include test that the structure of the tree may not be deeper that a number n of levels, that no outdated materials are used in the most recent structure, that all used materials are known to the fleet-wide backbone systems (e.g., SAP), and more. Further, the data validation module 312 is configured for modifying the generated digital representation of the asset information based on the validation results. The validation results include a success or failure state of the digital representation. A success state indicates that the digital representation contains no error logs or faults. A failure state indicates that the digital representation includes error logs or faults. In case if the validation results indicate failure state, then a notification/alert message is displayed in the user interface of the user devices indicating the failure state of the digital representation.
Specifically, the data validation module 312 analyzes results of validation of the asset information. If the results of validation are successful, the digital representations generated are outputted via the output module 310. If in case the validation results are unsuccessful, error log files associated with the digital representation are generated and displayed on a user interface of the user device 122A-N.
Embodiments may take a form of a computer program product including program modules accessible from computer-usable or computer-readable medium storing program code for use by or in connection with one or more computers, processors, or instruction execution system. For the purpose of this description, a computer-usable or computer-readable medium may be any apparatus that may contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium may be electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation mediums in and of themselves as signal carriers are not included in the definition of physical computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, random access memory (RAM), a read only memory (ROM), a rigid magnetic disk and optical disk such as compact disk read-only memory (CD-ROM), compact disk read/write, and DVD. Both processors and program code for implementing each aspect of the technology may be centralized or distributed (or a combination thereof) as known to those skilled in the art.
It is to be understood that the elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present embodiments. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent, and that such new combinations are to be understood as forming a part of the present specification.
While the present embodiments have been described above by reference to various embodiments, it may be understood that many changes and modifications may be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.
| Number | Date | Country | Kind |
|---|---|---|---|
| A 2019 00508 | Aug 2019 | RO | national |
This present patent document is a § 371 nationalization of PCT Application Serial Number PCT/EP2019/080528, filed Nov. 7, 2019, designating the United States, which is hereby incorporated in its entirety by reference. This patent document also claims the benefit of A 2019 00508 filed on Aug. 21, 2019, which is also hereby incorporated in its entirety by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2019/080528 | 11/7/2019 | WO |
