A method for incorporating key metrics of virtual objects in software components

Abstract

This invention relates to a computer-implemented method for merging at least one key metric of a virtual object (OBJ), the method comprising, for a predefined configuration of the virtual object (OBJ), performing the following steps: a) receiving a description of at least one key metric of the virtual object; b) receiving an attribute set (ATT) of the virtual object; c) receiving an indexed data model (DM) for the virtual object; d) receiving a rule set (RUL) to transform the attributes of the virtual object (OBJ) into the indexed data model (DM); e) applying the rule set (RUL) to transform the attributes into the indexed data model (DM); f) in an index (IND), transforming the indexed data model (DM) into a directed acyclic graph; and g) merging the key metrics in a software component (SCO) different from the index (IND) based on an extension of the directed acyclic graph.

Description

Technical Field

[0001] This invention relates to the field of Computer-Aided Engineering (CAE), and more specifically, to the field of Computer-Aided Design (CAD). The invention relates to a computer-based method for consolidating key metrics of a virtual object (particularly a complex object) composed of multiple object components. Background Technology

[0002] Key performance indicators (KI) are measurement characteristics that enable strategic decision-making. Several types of KI exist, including:

[0003] - Key sustainability indicators (KSIs), such as carbon footprint and impact on rare resources;

[0004] - Key Quality Indicators (KQIs), such as the defect rate during the production of an object part;

[0005] Key performance indicators (KPIs) include, for example, the weight of an object (also referred to as mass below), its inertia matrix, its center of gravity (these parameters are often referred to as “weight and balance”), and, depending on the object, its range, interruption distance, air permeability coefficient, minimum runway length for takeoff or landing, etc.

[0006] Over the decades, the number of components that make up a complex object has expanded dramatically in digital models, from thousands to millions of components, making the merging of key metrics extremely complex.

[0007] Objects can be described using a global structure that includes: an engineering bill of materials (reflecting how the object is designed), a manufacturing bill of materials (focusing on the components needed to manufacture the object), a list of processes during the object's use, and a bill of materials at the end of the object's lifecycle. The manufacturing bill of materials describes the object based on its sub-assemblies. For example, a manufacturing bill of materials consists of an item-by-item list of components of a structure shown in tabular form on a diagram. Therefore, the bill of materials can be represented by a hierarchical tree structure with a root entity and subtrees with child (leaf) nodes that have parent nodes. A key metric for merging objects is an investigation of this global bill of materials (from leaves to root). Bottom-up calculation of metrics is performed through rollup operations (or simply "rollup"). Rollup functions are defined as the way metrics are calculated from the bottom up. Complex decision-making and merging processes are applied at each level of the tree.

[0008] Some key metrics are relatively easy to merge: for example, merging the quality of a virtual object involves adding the quality of all its components. Therefore, the aggregation mainly involves adding values, which is less CPU-intensive. However, the difficulty lies in the various subcontractors that may be involved during the manufacturing and engineering processes. This means that the data needed to merge key metrics (data from text documents, spreadsheets, ERP software, or CAD software) (even data that doesn't consume much CPU) is stored in several systems and may not be up-to-date. Each of these systems can be considered a "silo" in the sense that each is isolated from the others. Operations within these silos can affect the efficient merging of key metrics.

[0009] Other key metrics relate to the object's inertia matrix. To merge the inertia matrices, the moment of inertia and product of inertia of the blades must first be calculated. Then, relative to the three-axis system of the instantiated object, the inertia matrix is ​​a 3x3 matrix containing those values. The inertia matrix is ​​then diagonalized. The eigenvalues ​​of the diagonalized inertia matrix are the principal moments of inertia of the object, and the three eigenvectors are the principal axes of inertia. It can be noted that diagonalizing the matrix is ​​CPU-intensive. Furthermore, once the inertia matrix has been calculated for a portion of the object (corresponding to the lowest child in the bill of materials), the inertia matrix of the parent portion of the object is calculated based on the moments of inertia of the child objects and the axes of inertia of the child objects. Therefore, very complex matrix calculations are performed when summing the inertia matrices from child to parent in the bill of materials. In that case, summarization is performed by implementing the parallel axis theorem (also known as the Huygens-Steiner theorem). In that case, the summarization function is specified by the Huygens-Steiner theorem.

[0010] Therefore, it is unacceptable for critical metrics involving millions of components to be combined over several hours. In particular, the precise measurement of a cruise ship's weight and balance (a highly complex operation compared to other critical metrics) is estimated in practice with a high degree of uncertainty during the ship's design phase. Currently, manufacturers cannot determine the exact mass of a ship during the design phase or even during the manufacturing phase. Once the ship is built, it is launched into the water, and its mass is derived from the volume of displaced water. If buoyancy correction is required, the ship is weighed using additional equipment. It is important to note that a cruise ship's weight and balance are crucial because they indicate how the ship navigates through waves.

[0011] In the automotive industry, numerous key performance indicators (KPIs) are monitored during the vehicle design process. For example, up to 350 KPIs may be monitored for racing cars. In practice, a large number of KPIs are analyzed during project reviews, and some KPIs may take precedence over others. KPI consolidation is a fully integrated decision-making process; therefore, this procedure must be executed whenever strategic decisions must be made.

[0012] Another constraint in the automotive industry is the sheer number of possible vehicle configurations. To date, automakers have consolidated key performance indicators (KPIs) for only one type of vehicle (i.e., the vehicle in the heaviest commercial line), without considering configuration diversity. Therefore, consolidation is performed on a single graph. Furthermore, even for those limited numbers of vehicles, consolidation takes hours, resulting in consolidated data that is not up-to-date. As vehicles comprise an increasing number of components, consolidating KPIs takes increasingly longer, regardless of different configurations.

[0013] Key performance indicators such as quality are also crucial in aerospace, particularly for satellites. Agreements between launch operators / manufacturers and satellite operators / manufacturers often include specific provisions regarding satellite quality, ensuring that the satellite does not exceed a predefined mass. In fact, each kilogram of payload is significant in terms of propellant during the launch phase. In practice, launch operators / manufacturers may claim penalties of thousands of euros for a 1kg error on the satellite. Therefore, quality indicators must be rigorously monitored. For satellite operators / manufacturers, it would also be interesting to be able to quantify the tolerances calculated during the aggregation period (e.g., through the margin of error).

[0014] Therefore, there is a need for a method for merging key metrics of virtual objects that is scalable, manages different data sources from different vendors, and takes into account the configuration diversity of objects. Summary of the Invention

[0015] One object of the present invention is a computer-implemented method for merging at least one key metric of virtual objects, the method comprising performing the following steps for a predefined configuration of the virtual objects:

[0016] a) Receive a description of at least one key metric for the virtual object;

[0017] b) Receive the attribute set of the virtual object;

[0018] c) Receive the indexed data model for the virtual object;

[0019] d) Receive the rule set to transform the attributes of the virtual object into a data model for indexing;

[0020] e) Apply the rule set to transform the attributes into a data model for indexing;

[0021] f) In the indexing, the data model used for indexing is transformed into a directed acyclic graph.

[0022] g) In a software component different from the index, the key metrics are merged based on an extension of the directed acyclic graph.

[0023] In a preferred embodiment, the virtual object in the data model has the following characteristics:

[0024] - At least one aggregation feature, said aggregation feature representing the composition of a virtual object according to the bill of materials; and / or

[0025] - At least one facet feature, which represents the classification of the virtual object.

[0026] In a preferred embodiment, aggregate features and surface features are referenced in the index.

[0027] In a preferred embodiment, aggregation features and / or facet features are incrementally updated in the index.

[0028] In a preferred embodiment, step g) includes applying a summary to the expanded directed acyclic graph in the index.

[0029] In a preferred embodiment, step a) includes receiving a summary function of key metrics during the summarization process on the extended directed acyclic graph, the summary function representing the bottom-up merging of key metrics in the bill of materials of the virtual object.

[0030] In a preferred embodiment, step g) includes: receiving the tolerance value for each aggregated feature and calculating the statistical tolerance based on the Euclidean distance of the tolerance value.

[0031] In a preferred embodiment, the key metrics are the key performance indicators of the virtual object.

[0032] In a preferred embodiment, key performance indicators include the weight and balance of the object, particularly the weight of the virtual object and / or the center of gravity and / or the inertia matrix of the virtual object.

[0033] In a preferred embodiment, the directed acyclic graph is transmitted from the index to the software component via a client-server communication protocol.

[0034] In a preferred embodiment, the client-server protocol is the Hypertext Transfer Protocol.

[0035] In a preferred embodiment, the virtual object is a vehicle, particularly a ship.

[0036] The present invention also relates to a computer program product stored on a non-volatile computer-readable data storage medium, comprising computer-executable instructions for causing a computer system to perform the aforementioned methods.

[0037] The present invention also relates to a non-transitory computer-readable data storage medium comprising computer-executable instructions for causing a computer system to perform the aforementioned methods.

[0038] The present invention also relates to a computer system configured to implement the aforementioned method, the computer system comprising: at least one client device configured to process user requests to merge at least one key metric of virtual objects; and at least one index configured to implement at least the step of transforming a data model used for indexing into a directed acyclic graph; and a software component different from the index for merging the key metric based on an extension of the directed acyclic graph. Attached Figure Description

[0039] Additional features and advantages of the invention will become apparent from the following description taken in conjunction with the accompanying drawings, which illustrate:

[0040] - Figure 1 During the index construction phase of the method according to the invention;

[0041] - Figure 2 The data model used in the method according to the present invention;

[0042] - Figure 3 An example of key indicator information attached to an entity in a data model;

[0043] - Figure 4 An example of a set of rules that drives the transformation of engineering information into a data model; the example also includes transforming diverse information into a data model;

[0044] - Figure 5 Examples of rules that drive the transformation of manufacturing information into a data model; the example also includes transforming diverse information into a data model;

[0045] - Figure 6 An example of a bill of materials containing three objects;

[0046] - Figure 7 According to the data model of the present invention Figure 6 The image shown is an indexed diagram of the objects.

[0047] - Figure 8 A diagram illustrating the computer environment according to the present invention;

[0048] - Figure 9 An example of a summary of weight and balance results on a ship.

[0049] - Figure 10 A computer suitable for performing the method according to an exemplary embodiment of the present invention. Detailed Implementation

[0050] This invention relies on two main steps, which will be referred to as “building” and “running”. Figure 1 The main construction steps are illustrated, where four computational systems CSY are represented. Of course, the number of computational systems CSY is not limited. Each of the computational systems CSY can be:

[0051] - Includes the PLM (Product Lifecycle Management) system CSY1, which includes CAD (Computer-Aided Design) systems and can provide 3D modeling data for virtual objects and their diverse dictionaries;

[0052] - The ERP (Enterprise Resource Planning) system CSY2 can provide supply chain data;

[0053] -EIS (Enterprise Information System) CSY3;

[0054] - Multiple CSY4 sensors can be connected to networks such as the Internet to collect data and send it directly or indirectly to another entity.

[0055] The computing systems (CSY1, CSY2, CSY3, CSY4) are connected to the search engine's index IND. The index IND is stored on a machine or set of machines connected to the computing system CSY via a connection method (cable, fiber optic, or wireless (using one of the wireless communication protocols)). By the term "index," those skilled in the art may also refer to a "cachment" or "data lake," i.e., a software component that can be queried and includes raw and structured, semi-structured, or unstructured data.

[0056] Upstream of step a) in this method, specifically, the description of each key metric is provided by the superuser. In practice, the description of the key metrics should not be modified by any ordinary user. The description of the key metrics includes a summary function, i.e., the behavior of the key metrics during summarization on the extended directed acyclic graph. For one configuration, the summary function represents the way key metrics are merged from bottom to top in the bill of materials of the virtual configuration object OBJ.

[0057] The description of key metrics also includes:

[0058] - Name (e.g., noise, mass, inertia matrix);

[0059] - Amplitude (e.g., mass, temperature, pressure);

[0060] - Potentially, security regarding access (editable by either the superuser or the regular user);

[0061] - Potentially, its parent and child. In fact, key metrics can contain elements that are themselves key metrics;

[0062] - Potentially, supplementary information will be incorporated into the analysis set of key indicators;

[0063] -Potentially, icon;

[0064] In step b) of the method of the present invention, the attribute set ATT of the virtual object is received via an index. Attribute ATT is data provided to the index IND by any of the computing systems CSY. Therefore, attribute ATT characterizes the object according to the computing system CSY from which the object originates.

[0065] In step c) of the method, a single data model DM is provided. The data model represents the type of objects in the index IND, and the type of links that organize the objects in the index IND. All attributes ATT sent to the index IND are transformed according to the data model DM, which is achieved through… Figure 2 As shown.

[0066] The conversion includes:

[0067] 1. Interpret objects OBJ from the computer system CSY, and then classify them according to the data model DM.

[0068] 2. Create links between objects that potentially originate from the computer system CSY. It should be noted that these links do not exist between computer systems CSY. They only exist in the index IND. Therefore, an index is a software component that creates semi-structured data based on initially unstructured data.

[0069] A data model DM can be defined as "unknowable" in the sense that any attribute of an object can be transformed according to the data model, regardless of the format of the computing system CSY or the attribute ATT from which it originates.

[0070] In the data model DM, two elements characterize a virtual object: at least one aggregate feature AGG (which represents the composition of the virtual object OBJ according to the bill of materials), and / or at least one surface feature FAC (which represents the classification of the virtual object OBJ). For example, a mechanical part may have a material surface, a recycling surface, a supply surface, etc. More generally, the surface feature specifies the virtual object. The aggregate feature AGG defines the composition of the object according to the bill of materials. An example of composition is the instantiation of the object. By "instantiation," those skilled in the art refer to its position and orientation in the reference frame attached to the object. The aggregate feature AGG and the surface feature FAC are referenced in the index IND.

[0071] Figure 3 Examples of key metric information attached to entities in a data model are provided. For example:

[0072] - The key metric KI1 points to weight and has three dimensions: value, tolerance, and confidence level;

[0073] - The key metric KI2 points to the carbon footprint and has three dimensions: value, tolerance, and confidence level;

[0074] - The key metric KI3 points to the nominal power and has three dimensions: value, tolerance, and confidence level;

[0075] - The key metric KI4 points to the center of gravity and has three surfaces: value, tolerance, and confidence level.

[0076] Whenever an attribute is indexed, the tolerance value can be copied to the data model DM. In a preferred embodiment, the statistical tolerance can be calculated based on the Euclidean distance of the tolerance values. If we consider the parent and n children, and σ... X It is the common difference of the father, and x i This is the tolerance of the i-th child. The parent's tolerance is calculated using the following formula: Therefore, when consolidating key metrics, users also obtain the tolerance values ​​associated with the key metrics, allowing them to quantify the tolerances calculated during the aggregation period, for example, through error margins.

[0077] Step d) of the method of the present invention includes receiving a rule set RUL to convert the attributes of a virtual object OBJ into a data model DM for indexing, and step e) of the method of the present invention includes applying the rule set RUL to convert the attributes into the data model DM for indexing.

[0078] For example, Figure 4The rules driving the transformation of engineering information into the data model DM during the build phase are illustrated. A checkmark indicates that an attribute is indexed. A cross indicates that the attribute is not indexed. An arrow indicates attribute flattening: the attribute is copied to an indexed attribute and then removed from the index. Superusers make decisions about whether to index or aggregate attributes based on requested key metrics. Therefore, the rules are scenario-dependent. Attributes can be aggregated with facets for categorization.

[0079] For example, it's unnecessary to index the visual representation of a triangle or geometric structure of a mathematically defined object. Since diversity (configuration) cannot be applied, key metrics don't need to be indexed as objects. In the index, key metrics become attributes of objects in the product bill of materials. Objects in the diversity dictionary (also known as configurations) do not participate in the bill of materials and therefore do not need to be indexed. On the other hand, the options used to define configurations are indexed. When diversity is impossible, a flattening operation is performed. Elements that do not affect the bill of materials are not indexed.

[0080] Similarly, Figure 5 The rules that drive the transformation of manufacturing information into the data model during the build phase are shown.

[0081] Figure 6 The Bill of Materials (BOM) is shown, which contains three object parts of the aircraft (Object Part 1 OBJ1, Object Part 2 OBJ2, and Object Part 3 OBJ3). The object properties are converted for indexing in the Indexing IND, such as... Figure 7 As shown.

[0082] exist Figure 7 In the index IND, a directed acyclic graph representing the aircraft is created. This directed acyclic graph aggregates information from the following: a first computational system CSY1 (e.g., PLM system), a second computational system CSY2 (e.g., EIS), and a third computational system CSY3 (e.g., ERP). Information from different silos is structured within the index IND.

[0083] In the index IND, the attribute ATT of the three computing systems (CSY1, CSY2, CSY3) is converted into the data model DM used for indexing.

[0084] In the first computing system CSY1, the root object (the root object on which key metrics will be merged, i.e., "airplane"), a simplified representation of the 3D geometry of objects OBJ1, OBJ2, and OBJ3, is stored, along with:

[0085] - The attribute "Estimated weight" of the first object OBJ1 has a value of 37kg;

[0086] - The attribute "calculated weight" of the third object OBJ3 has a value of 31.2 kg and a confidence level of 30%.

[0087] - And a simplified representation of the 3D geometry of the three objects OBJ1, OBJ2 and OBJ3.

[0088] In the second computing system CSY2, the attribute "weight" of the second object OBJ2 is stored, with a value of 22kg.

[0089] In the third computing system CSY3, the attribute "measured weight" of the third object OBJ3 is stored, with a value of 33.2 kg and a confidence level of 80%.

[0090] The aforementioned attribute ATT is transformed into the data model DM used for indexing. Then, the root object "Airplane" aggregates the following items:

[0091] - First element C1, which has a CSY2 plane C5;

[0092] - The second element C2 has a CSY3 plane C4;

[0093] - Third element C3.

[0094] Then, once the attribute ATT has been transformed according to the data model DM used for indexing, the data model is transformed into a directed acyclic graph in the tree data structure, such as... Figure 7 As shown (step f of the method of the present invention).

[0095] Once the data model has been transformed into a directed acyclic graph (DAG) during the build phase, the run phase can be performed on the DAG. In a preferred embodiment, the aggregation feature AGG and / or the facet feature FAC are incrementally updated in the index IND, and the build phase does not need to be performed from scratch whenever the data changes. Therefore, the run phase (which includes merging key metrics) is based on an extension of the DAG, taking into account the configuration (which is a set of options from diversity) and has the latest data (step g of the method of the present invention). On each object of the extended graph, a specific merging method (i.e., a summary function) for the key metrics is invoked from bottom to top.

[0096] Figure 8 This is an illustration of a computer environment according to the present invention. The system includes at least one client device CD configured to handle user requests for at least one key metric of merging virtual objects. For example, a query user request may be made by a program manager of a 3D object during the engineering phase or the object manufacturing phase.

[0097] In a preferred embodiment, the step of transforming the initial data into the data model is implemented in the index IND (during the build phase), and the step of merging key indicators is implemented in a software component SCO (which may be referred to as the aggregation component), a different component from the index IND, (during the runtime phase). The directed acyclic graph (DAG) upon which the graph expansion depends is copied between the index IND and the software component SCO. The DAG is computed in the index IND and transmitted to the software component SCO via a streaming / non-streaming technology based on the Hypertext Transfer Protocol. The index and the software component SCO are operating in two different software layers.

[0098] refer to Figure 7 In the example, the index IND expands the aircraft tree (the root aircraft itself, and three children, with two second-level children), and the software component SCO summarizes the expanded tree. The tree expansion retrieves six elements (C1-C6). The weight information is summarized from the bottom to the top of the tree. The fifth element C5 has a weight of 22 kg. The fourth element C4 has a weight of 33.2 kg. The third element C3 has a weight of 37 kg. The first element C1 directly receives the same value as the fifth element C5, therefore the first element C1 has a weight of 22 kg. For the second element C2, information from both facets is compared. With an 80% confidence level greater than 30%, the second element C2 is considered to have a weight of 33.2 kg. Finally, the aircraft, aggregated with the first element C1, the second element C2, and the third element C3, is considered to have a weight of 22 + 33.2 + 37 = 92.2 kg.

[0099] Steps a) to f) (construction phase) of the method of the present invention can be implemented offline, and step g) (running phase) can be implemented online. The offline implementation of the construction phase saves a significant amount of computation time.

[0100] Figure 9 A screenshot shows the results of representing the weight and balance KPIs of a cruise ship using the method of the present invention. On the left side of the screen, the user can select the entire object or a sub-object by browsing the object tree. The user's selection can be highlighted in the center of the screen. On the right side of the screen, the combined results are displayed, specifically the weight, center of gravity, and inertia matrix.

[0101] The method of the present invention can be performed by a properly programmed general-purpose computer or computer system, which may include a computer network that stores a suitable program in non-volatile form on a computer-readable medium (e.g., a hard disk, a solid-state drive, or a CD-ROM) and uses its microprocessor and memory to execute the program.

[0102] Each of the aforementioned client device CD, computing system CSY, index IND, and software component SCO may be suitable for execution according to the reference. Figure 10 The computer CPT describes an exemplary embodiment of the method of the present invention. Figure 10 In this system, the computer CPT includes a central processing unit (CPU) P, which executes the method steps described above while running an executable program (i.e., a computer-readable instruction set), the executable program being stored in a memory device (e.g., RAM M1, ROM M2, or hard disk drive (HDD) M3, DVD / CD drive M4) or being stored remotely.

[0103] The claimed invention is not limited to the form of a computer-readable medium on which computer-readable instructions and / or data structures of the inventive process are stored. For example, instructions and files may be stored on a CD, DVD, flash memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk, or any other information processing device (e.g., a server or computer) with which a computer communicates. Programs and files may be stored on the same memory device or on different memory devices.

[0104] In addition, computer programs suitable for performing the methods of the present invention can be provided as utility applications, background daemons, or components of operating systems, or combinations thereof, which are executed in conjunction with a CPU P and an operating system (e.g., Microsoft Vista, Microsoft Windows 10, UNIX, Solaris, LINUX, Apple MAC-OS, and other systems known to those skilled in the art).

[0105] CPU P can be a Xenon processor from Intel or an Opteron processor from AMD, or other processor types (e.g., Freescale ColdFire, IMX, or ARM processors from Freescale). Alternatively, as those skilled in the art will recognize, the CPU can be a processor such as the Core 2 Duo from Intel, or it can be implemented on an FPGA, ASIC, PLD, or using discrete logic circuitry. Furthermore, the CPU can be implemented as multiple processors working together to execute computer-readable instructions of the inventive processes described above.

[0106] Figure 10The computer's CPT (Computer Graphics Port) also includes a network interface (NI) (e.g., an Intel Ethernet PRO network interface card from Intel Corporation, USA) for connecting to a network (e.g., a Local Area Network (LAN), Wide Area Network (WAN), Internet, etc.). The computer further includes a display controller (DC) (e.g., an NVIDIA GeForce GTX graphics adapter from NVIDIA Corporation, USA) for connecting to a monitor (DY) (e.g., a Hewlett Packard HPL2445w LCD monitor). A general-purpose I / O interface (IF) connects to a keyboard (KB) and pointing devices (PD) (e.g., a ball, mouse, touchpad, etc.). The monitor, keyboard, and pointing devices, together with the display controller and I / O interface, form a graphical user interface (GUI) for use by the user to input commands and for the computer to display key metrics results.

[0107] The disk controller DKC connects the HDD M3 and DVD / CD M4 to the communication bus CBS, which can be an ISA, EISA, VESA, PCI, or similar bus to interconnect all components of the computer.

[0108] For the sake of brevity, descriptions of the general characteristics and functions of displays, keyboards, pointing devices, display controllers, disk controllers, network interfaces, and I / O interfaces are omitted in this document, as these characteristics are known.

[0109] In an alternative embodiment, the computer CPT is replaced by a server and an end-user computer. The overall architecture of the server can be the same as that discussed above with reference to the computer CPT, except that the server may lack a display controller, monitor, keyboard, and / or pointing device. The end-user computer runs the front-end portion of the "running" infrastructure, including the user interface; the server runs the "building" infrastructure and the back-end portion of the "running" infrastructure. User actions on the user interface initiate queries, such as REST queries to web services provided by a server (e.g., an Apache server) that performs key metric aggregation algorithms.

[0110] A network (NW) can be a public network (e.g., the Internet), a private network (e.g., a LAN or WAN network), or any combination thereof, and may also include PSTN or ISDN subnets. A network (NW) can also be wired (e.g., Ethernet) or wireless (e.g., a cellular network including EDGE, 3G, and 4G wireless cellular systems). A wireless network can also be Wi-Fi, Bluetooth, or any other known form of wireless communication. Therefore, a network (NW) is merely exemplary and does not in any way limit the scope of current developments.

[0111] Any method steps described herein should be understood as representing modules, segments, or portions of code that include one or more executable instructions for implementing specific logical functions or steps in the process, and alternative implementations are included within the scope of exemplary embodiments of the invention.

Claims

1. A computer-implemented method for merging at least one key indicator of a virtual object (OBJ) composed of a plurality of object components, the method comprising, for a predefined configuration of the virtual object (OBJ), performing the following steps: a) receiving a description of at least one key indicator of the virtual object; b) receiving a set of attributes (ATT) of the virtual object; c) receiving an indexed data model (DM) for the virtual object; d) receiving a set of rules (RUL) to convert attributes of the virtual object (OBJ) into the indexed data model (DM); e) applying the set of rules (RUL) to convert the attributes into the indexed data model (DM); f) in an index (IND), transforming the indexed data model (DM) into a directed acyclic graph, g) in a software component (SCO) different from the index (IND), merging the key indicators based on an extension of the directed acyclic graph.

2. The method of claim 1, wherein, In the data model (DM), the virtual object is characterized by: - at least one aggregated feature (AGG) representing the composition of the virtual object (OBJ) according to a bill of materials; and / or - at least one facet feature (FAC) representing a classification of the virtual object (OBJ).

3. The method of claim 2, wherein, The aggregated feature (AGG) and the facet feature (FAC) are referenced in the index (IND).

4. The method of any one of claims 2 or 3, wherein, The aggregated feature (AGG) and / or the facet feature (FAC) are incrementally updated in the index (IND).

5. The method of any one of claims 1-3, wherein, Step g) comprises applying an aggregation on the extended directed acyclic graph in the index (IND).

6. The method of claim 5, wherein, Step a) comprises receiving an aggregation function of the key indicators during the aggregation on the extended directed acyclic graph, the aggregation function representing a way to merge the key indicators bottom-up in the bill of materials of the virtual object (OBJ).

7. The method of claim 1 or 2, wherein, Step g) comprises receiving a tolerance value for each aggregated feature (AGG) and computing a statistical tolerance based on a Euclidean distance of the tolerance values.

8. The method of claim 1 or 2, wherein, The key indicators are key performance indicators (KPIs) of the virtual object (OBJ).

9. The method of claim 8, wherein, The key performance indicators (KPIs) comprise a weight and a balance of the object.

10. The method of claim 1 or 2, wherein, The directed acyclic graph is transmitted from the index (IND) to the software component (SCO) via a client-server communication protocol.

11. The method of claim 10, wherein, The client-server communication protocol is a hypertext transfer protocol.

12. The method of claim 1 or 2, wherein, The virtual object (OBJ) is a vehicle.

13. A computer program product stored on a non-transitory computer-readable data storage medium, the computer program product comprising computer-executable instructions for causing a computer system to perform the computer-implemented method according to any one of claims 1-12.

14. A non-transitory computer-readable data storage medium (Ml, M2, M3, M4) containing computer-executable instructions for causing a computer (CPT) system to perform the method according to any one of claims 1 to 12.

15. A computer system configured to implement the method of any one of claims 1 to 12, the computer system comprising: at least one client device (CD) configured to handle a user request to consolidate the at least one key indicator of a virtual object; and at least one index (IND) configured to implement at least the step of transforming the data model (DM) for indexing into a directed acyclic graph; and a software component (SCO) different from the index (IND) for consolidating the key indicators based on an extension of the directed acyclic graph.

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