Method for generating a database for tracking compliance indicators of components and associated devices

Abstract

Method for generating a database for monitoring conformity indicators of components and associated devices. The method for generating a semantic database (B) for monitoring indicators determining whether a component is suitable for a specific use includes: - a step of obtaining an initial database associating with the component an initial identification data and a plurality of initial characteristics, - a step of converting the initial database into the semantic database (B), presenting a modified data structure and a set of rules each intended to be applied to a characteristic stored in the semantic database (B) to evaluate an indicator, the data structure and the set of rules of the semantic database (B) determining the evaluation of the indicators by a computer (15).The process includes an evaluation step, for the component, of the indicator, by applying a rule to the associated characteristics. Figure for the abbreviation: Figure 1.

Description

Title of the invention: Method for generating a database for tracking conformity indicators of components and associated devices FIELD OF INVENTION

[0001] The present invention relates to a method for generating a semantic database for tracking component conformity indicators.

[0002] The present invention also relates to a semantic database for tracking component conformity indicators, a computer program product and an information medium.

[0003] Conformity is taken here in the sense that measured or calculated indicators conform to predefined expectations for a particular use. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0004] The invention relates to the field of monitoring conformity indicators of mechanical, electronic or software components, for example for the manufacture, maintenance or reconditioning of mechanical, electronic or software devices.

[0005] In industry, it is necessary to know the characteristics of different components in order to be able to develop, manufacture, maintain or recondition different devices.

[0006] For this purpose, it is known to use tabloid-type databases containing certain information relating to components.

[0007] However, these databases are generally specific to each stakeholder in the manufacturing, maintenance, or reconditioning chain of the device. Thus, the user does not necessarily know where to look for the information.

[0008] In addition, with tabloid-type databases, searching for information can be long and tedious.

[0009] Moreover, given the proliferation of databases, a user does not necessarily know where to look for the information he needs.

[0010] Even assuming that the user is aware of all the databases, he is often faced with a problem of terminology since each database tends to use its respective terminology specific to the stakeholders for whom said database was established.

[0011] Even assuming further that the user knows all the terminologies, the same characteristic can have different values ​​or different meanings, also called semantics, in different databases depending on the source from which the characteristic originates.

[0012] Thus, even assuming that the user consults each database, he is then faced with a problem of consistency between the characteristics present in the different databases and does not have an objective indicator to determine which characteristic he should trust the most.

[0013] These difficulties then lead to risks of non-conformity of the manufactured devices, resulting from maintenance or reconditioning.

[0014] For example, to manufacture a device intended to be immersed in water, a user needs to know the pressure resistance of the different components to be used to form the device in order to choose those that will actually be used.

[0015] Thus, for some stakeholders, an order of magnitude of the pressure resistance of a component is sufficient, while a precise value is necessary for others.

[0016] In the example shown above, there is therefore a risk that one of the components will not withstand the required pressure. Summary of the invention

[0017] There is therefore a need to make the provision of information more reliable and to simplify access and use, for a user, regarding the conformity of use of components for the manufacture, maintenance or reconditioning of devices, in particular mechanical, electronic or software.

[0018] To this end, the invention relates to a method for generating a semantic database for tracking component conformity indicators, the components being intended for use for a specific purpose, the specific purpose being the manufacture, maintenance or reconditioning of mechanical, electronic or software devices comprising at least one of the components, with conformity indicators for the purpose determining whether the component is suitable or not for use for the specific purpose,

[0019] the process being implemented by a computer system comprising a computer and a memory, the process comprising an initialization phase including:

[0020] - a step of obtaining an initial database, the initial database presenting an initial structure, the initial structure being a tabular structure associating, with each component of a plurality of initial components, at least one initial identification data for the component and a plurality of initial characteristics of said component, at least one of the characteristics resulting from a measurement of a quantity characterizing the component, and

[0021] - a step of converting the initial database into the semantic database of Compliance indicator tracking data, referred to as a converted database, presents a modified data structure compared to the initial database. and a set of evaluation rules, each evaluation rule being intended to be applied to at least one feature stored in the converted database to evaluate an indicator of compliance with the use of the component associated with at least one feature,

[0022] the data structure and the set of evaluation rules for the converted database determining the implementation of the evaluation of conformity indicators by the calculator,

[0023] the process further comprising a use phase including:

[0024] - an evaluation step, for each component, of the conformity indicator to the use of the component, by application, to the associated characteristics, of at least one rule from the set of evaluation rules.

[0025] According to particular embodiments, the generation process according to the invention comprises one or more of the following characteristics, taken individually or according to all technically possible combinations.

[0026] - the use phase further includes:

[0027] - an alert step, during which an alert is issued to a user if at least one compliance indicator violates a predefined compliance criterion established from the assessment rules;

[0028] - the method further comprises an update phase including:

[0029] - a step of receiving at least one new feature relating to at least a component of the converted database,

[0030] - a step of updating the converted database, to obtain a database of updated data, including: • a sub-step of reassessment, by the calculator, of each compliance indicator associated with the component, by applying the evaluation rules to a set including the characteristics associated with the component in the converted database and the new characteristic, and • a sub-step of memorization, in the updated database of each reassessed compliance indicator,

[0031] - an alert step, during which an alert is issued to a user if at least one reassessed compliance indicator violates a predefined compliance criterion established from the assessment rules;

[0032] - an edition date is associated with each feature, the edition date being the date on which the characteristic was measured, determined or established,

[0033] the use phase including, in addition:

[0034] - a prediction step, for each component, of a risk of non-compliance with a predefined time horizon, based on the results of the evaluation rules and the edit date of each characteristic associated with the component, and

[0035] - a warning step, during which a warning is issued to of a user if at least one risk of non-compliance is greater than a predefined threshold;

[0036] - the data structure of the converted database is a semantic graph;

[0037] - each evaluation rule is specific to at least one component of the database converted data, at least one rule from the set of evaluation rules depending on the specific use of the associated component(s);

[0038] - at least one conformity indicator quantifies the confidence placed in one of the ca component characteristics to describe an aspect of the component;

[0039] - the method further comprises a phase of integrating a new component in the converted database, including: - a step involving the reception of identification data for the new component and specific new evaluation rule(s), - an integration step, within the modified structure of the converted database, of the identification data and the new evaluation rule(s), the integration including the establishment of a semantic link between the characteristic(s) of the converted database and the identification data of the new component, and - an evaluation step of the conformity indicator associated with the new component by applying new evaluation rule(s) to the characteristic(s) linked to the identification data;

[0040] - at least one of the components of the converted database is a joint sealing whose specific use is the manufacture or maintenance of a watertight enclosure intended to be submerged in water to a predetermined depth to prevent water infiltration into the enclosure despite overpressure induced by water on the exterior of the enclosure,

[0041] a characteristic of said component being a measured resistance to overpressure,

[0042] one of the evaluation rules being specific to calculating a difference between the resistance to the measured overpressure and a predefined value depending on the predetermined depth,

[0043] the conformity indicator obtained by application of said evaluation rule being a function of the calculated difference;

[0044] - another component of the converted database is the same sealing gasket for a separate specific use, the separate specific use being the manufacture or maintenance of an aircraft fuselage to prevent air leaks from an interior of the fuselage to an exterior where atmospheric pressure is lower,

[0045] a characteristic of said component being a measured resistance to depression,

[0046] one of the evaluation rules being specific to calculating a difference between the measured resistance to depression and a predefined value depending on a predefined maximum flight altitude of the aircraft, and

[0047] the conformity indicator obtained by applying said evaluation rule being a function of the calculated difference; and

[0048] - at least one of the components of the converted database is a capacitor whose specific use is the reconditioning of a switched-mode power supply,

[0049] the characteristics of said component including: a capacitance measurement of said component, a thermal dispersion of said component and / or one or more acceptable soldering technologies,

[0050] one of the evaluation rules being specific to the technical characteristic considered and to the context of use defined by a user,

[0051] the conformity indicator obtained by application of said rule.

[0052] The present invention also relates to a semantic database for tracking component conformity indicators, the components being intended for use for a specific purpose, the specific purpose being the manufacture, maintenance or reconditioning of mechanical or electronic devices comprising at least one of the components, with conformity indicators for the purpose determining whether the component is suitable or not for use for the specific purpose,

[0053] the database for monitoring compliance indicators being likely to be obtained by a generation process as described above.

[0054] The present invention also relates to a computer program product comprising software instructions which, when implemented by a computer, implement a process as described above.

[0055] The present invention also relates to an information carrier comprising program instructions forming a computer program, the computer program being loadable onto a data processing unit and implementing a process as described above when the computer program is implemented on the data processing unit.

[0056] In this description, the expression "specific to" means interchangeably "suitable for", "adapted to" or "configured for". Brief description of the drawings

[0057] The invention will be better understood upon reading the following description, made with reference to the accompanying drawings in which:

[0058] [Fig-1] [Fig.1] is a schematic representation of a computer system of generation of a semantic database,

[0059] [Fig.2] [Fig.2] is a schematic representation of the semantic base of data from the computer system generating the [Fig.1],

[0060] [Fig.3] [Fig.3] is a flowchart of a generation process implemented by the computer system for generating the [Fig. 1], and

[0061] [Fig.4] [Fig.4] is a detailed view of a self-correction phase of the process of which An organizational chart is shown in [Fig.3].

[0062] DETAILED DESCRIPTION OF PREFERRED EMBODIMENT SYSTEM DESCRIPTION

[0063] Figure 1 shows a generation 10 computer system, also called computer system 10. Computer system 10 includes a computer 15 and a memory 20.

[0064] The computer 15 is adapted to implement a method for generating a semantic database, which will be described later. The generation method is therefore a computer-implemented generation method.

[0065] The calculator 15 is an information system designed to manipulate and / or transform represented data.

[0066] The method is implemented in the form of one or more software programs, that is, in the form of a computer program, also called a computer program product. It is further capable of being stored on a computer-readable medium, not shown. A computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. For example, a readable medium is an optical disc, a magneto-optical disc, a ROM, a RAM, any type of non-volatile memory (for example, FLASH or NVRAM), or a magnetic card. A computer program comprising software instructions is then stored on the readable medium.

[0067] To implement the generation process, the computer 15 communicates with the memory 20 which is used to store data to be processed by the computer 15 and data processed by the computer 15. Objective of the generation process Description of the semantic database

[0068] The operation of system 10 is now described with reference to [Fig.2].

[0069] The generation process aims to obtain a semantic database B for tracking 30 component conformity indicators.

[0070] The components 30 are here real physical entities forming part of a real physical device, which is here a mechanical, electronic or software device. Such a device is schematically represented by a rectangle D in [Fig.2] and will be referred to as device D in the remainder of the description.

[0071] Specific examples of components 30 will be given later.

[0072] Components 30 are intended to be used for a specific use U.

[0073] This specific use U is a technical use chosen from the development, manufacture, maintenance or reconditioning of the device D.

[0074] The conformity indicators I for use U determine whether component 30 is suitable or not for use for the specific use U.

[0075] Each conformity indicator I is thus a measure of the suitability of component 30 for use in use U.

[0076] Reading the semantic database B by the computer system 10 gives access to a set of information.

[0077] The information set includes, for each component of a plurality of initial components, at least one initial identification data for the component and a plurality of initial characteristics C of said component, at least one of the characteristics C resulting from a measurement of a quantity characterizing the component.

[0078] The information set therefore includes measurements, also called observation points, measured by means of metrics or indicators.

[0079] Reading the semantic database B by the computer system 10 therefore gives access to measurements carried out for each component 30.

[0080] However, the semantic database B is not a simple database.

[0081] Reading the semantic database by the computer system 10 provides specific instructions for evaluating conformity indicator I for each component 30.

[0082] This stems from the fact that the semantic database B is characterized by a data structure and a set of evaluation rules Rs which determine the implementation of the evaluation of the conformity indicators I by the calculator 15.

[0083] In fact, to calculate the conformity indicators I, the computer system 10 applies to the characteristics C available in the data structure of the semantic database B and relating to the component 30 to be characterized at least one rule R of the set of evaluation rules Rs.

[0084] The content of the semantic database B is thus a command of the actions carried out by the computer system 10.

[0085] Through the semantic database B, the computer system 10 is thus able to obtain the conformity indicators I from previously carried out measurements.

[0086] Preferably, each characteristic is associated with several conformity indicators I, quantifying or qualifying the characteristic respectively according to one of the aspects described below.

[0087] By way of example, the conformity indicators I are divided into four types of aspects. The four types are as follows: - the intrinsic type which quantifies the confidence placed in the characteristic as such and in the source that produced this characteristic; - the contextual type which quantifies the confidence placed in the characteristic for the use of the associated component(s) in the specific use considered; - the representational type that quantifies the confidence placed in the clarity of obtaining information from the characteristic; and - the type of accessibility that quantifies the confidence placed in the availability of data for different users involved in the specific use of the component(s) associated with the feature.

[0088] These types are defined specifically for the device D under consideration or for the components 30 constituting it. Alternatively, these types depend on the recipient of the semantic database.

[0089] The intrinsic attribute is independent of the specific use of the component(s) associated with the characteristic.

[0090] By way of example, intrinsic type I indicators include: credibility, truthfulness, accuracy, objectivity and reputation.

[0091] Credibility is the degree to which a feature is credible. Credibility is calculated, for example, based on the feature in question, that is, its order of magnitude, its unit of measurement, or its recency, as explained below. Credibility is also calculated based on the source that produced the feature, with sources being, for example, pre-evaluated by experts.

[0092] Veracity defines the fact that information is true and correct with respect to the real object it describes. Thus, veracity depends on the source of the characteristic, for example, the entity that produced the characteristic in the case of a certificate and / or the way in which the characteristic was collected. For example, veracity depends on the measuring instrument in the case where the characteristic results from a measurement. The calculation of veracity is based on business definitions and rules. The result is binary: 0 = FALSE, 1 = TRUE.

[0093] Accuracy quantifies that the feature is certified error-free, defect-free, or how easily errors are identifiable, as well as the precision of the feature. Accuracy is a score between 0 and 1.

[0094] Accuracy is calculated, for example, according to the following equation:

[0095] Accuracy — —? l+ < / (w, wm)

[0096] Where: • œ is the value of the characteristic, • is the corresponding actual value, and • d(-.) is a distance measure such as a Euclidean distance or a distance from Hamming.

[0097] For example, if the characteristic to be qualified has a precision to within 103, then the accuracy is approximately equal to 0.997.

[0098] Objectivity, also called impartiality, quantifies the degree to which a characteristic is impartial or is affected by subjective criteria in its development. For example, objectivity is calculated using approaches based on belief theory (an extension of probability functions in which the measure of the universe is less than or equal to 1, which allows for the characterization of a decision-maker's subjectivity) or fuzzy logic.

[0099] Reputation quantifies an overall subjective evaluation of the quality of the characteristic over a period of time predefined by a user. Reputation is, for example, calculated by labeling similar to the Nutri-Score type labeling, also called energy score ("ABCD or E") or by a financial rating.

[0100] By way of example, contextual type indicators I include: added value, relevance, completeness, and freshness.

[0101] Added value quantifies whether the characteristic provides a competitive advantage in the specific use. Added value is calculated, for example, by functional analysis and value analysis (FAV) methods.

[0102] Relevance quantifies the importance of the feature for the specific use under consideration. In other words, relevance quantifies whether the feature is useful, applicable, and usable for the specific use. Relevance is, for example, calculated based on the operational design domain (ODD, defined during the specifications). It is therefore verified that all the data types necessary to cover the ODD are present in order to answer the questions posed by users. The calculated relevance is then, for example, a coverage rate.

[0103] Completeness quantifies whether the characteristic describes the component in depth. Completeness is preferably complementary to relevance. Completeness is, for example, calculated as the coverage of the data to answer the question posed across the entire context studied.

[0104] Freshness quantifies the age of the feature. In other words, freshness defines the length of time since the feature has been established.

[0105] For example, freshness is calculated according to the following equation: 101061 Freshness = max( 1 - o)' shelf life /

[0107] where: • rnax (.,. ) is the maximum function • The age of the characteristic is the difference in time (in number of days) between the insertion of the feature into the converted database and the last freshness assessment associated with that feature, • The retention period is a predefined value for the characteristic corresponding to the number of days during which the characteristic is considered up-to-date, and • 5 is a parameter adjustable by an expert and dependent on the characteristic.

[0108] The representational approach quantifies the ability to present and utilize the information contained in the characteristic, i.e., in a file, document, message, or graphical interface used to transmit the information. Thus, we consider the form in which the information is "formatted" or "layout," depending on whether we are interested in an application or a human.

[0109] By way of example, the representational type indicators I include: interpretability, comprehensibility, coherence of representation and conciseness of representation.

[0110] Interpretability quantifies the ability to make the user understand the meaning of what is shown to them. It is calculated through user feedback (surveys, problem reports or questions).

[0111] Comprehensibility quantifies the readability and clarity of the information in the feature. Similarly, comprehensibility is calculated through user feedback.

[0112] Representation consistency quantifies a deviation in the presentation of information in the feature relative to other similar features C or over time. Representation consistency is preferentially guaranteed by construction and by maintaining the domain taxonomy and ontology, i.e., the semantic database.

[0113] The conciseness of the representation quantifies whether the feature does not contain an excessive amount of unnecessary information and whether the data formats are optimal for performance and usability. Conciseness is calculated as a ratio between the useful information (as defined by the ODD and the ontology) and the file size.

[0114] By way of example, the accessibility type indicators I include accessibility and access security.

[0115] Accessibility quantifies how easily a feature can be identified, retrieved, and made available, both at a given moment and on a permanent basis, to different users. Accessibility also quantifies how quickly a user or users can access the feature. For example, accessibility is the ratio of the number of times information is accessed to all requests, weighted by the effort (time and energy) required to obtain the necessary information.

[0116] Access security quantifies how the feature is secured and by This is particularly important if the characteristic is only accessible to users authorized to view it (need and right to know). Access security is defined by the coverage of security rules and implemented in relation to the sensitivity of each piece of information.

[0117] It is clear that each conformity indicator is not necessarily evaluated for each characteristic. In such a case, either the indicator or indicators take a neutral value, such as a median value, or the corresponding conformity indicator or indicators are not calculated.

[0118] Similarly, each indicator is evaluated according to a given context.

[0119] Each conformity indicator I being, as previously stated, a measure of the suitability of component 30 for use in use U, the computer system 10 is a measurement system since the computer system 10 takes into account measurements, performs calculations on these measurements and obtains a new measurement.

[0120] The semantic database B is thus suitable for controlling a computer system 10 so that it obtains measurements, performs calculations and obtains one or more new measurements.

[0121] Regardless of the value to a user of the technical data contained, the semantic database B therefore presents an additional advantage over a simple database which would have the same data since it allows a measurement system to be ordered to carry out the desired measurement.

[0122] It should be emphasized here that without the semantic database B, the computer system 10 would not be able to accurately calculate the conformity indicators I and therefore would not be able to measure whether component 30 is suitable for the specific use or not.

[0123] In the following section, another advantage of this semantic database B is shown, namely its generic nature. To this end, examples will be given of the notions previously used, namely the specific use U, the device D, the components 30 and the conformity indicator I.

[0124] Examples of elements present in the semantic base or used to obtain the semantic database B

[0125] According to a first example, in the mechanical field, the specific use is the manufacture or maintenance of a sealed enclosure. In this example, one component is a sealing gasket, another component being a cover for the enclosure.

[0126] According to a second example, also in the mechanical field, the specific use is the manufacture or maintenance of an aircraft fuselage. In this second example, the components include: a sealing gasket, a fuselage sheet, and a nut.

[0127] According to a third example, in the field of electronics, the specific use is the reconditioning of a switched-mode power supply. In this third example, the components include: a protective fuse, a diode bridge, a capacitor, a transistor, a transformer, an inductor, and a control circuit.

[0128] According to a fourth example in the software field, the specific use is the operation of open-source software. The components include: software terms of use, operating licenses, dependencies on other software or technical infrastructure, documentation, and software source code

[0129] The conformity indicators I for use determine whether the component is suitable for its specific use. The conformity indicators I thus quantify or qualify the confidence that can be placed in a plurality of characteristics C describing each component. Therefore, if the confidence is too low, for reasons that will be detailed below, the component is considered non-compliant for its specific use.

[0130] Preferably, the semantic database B comprises components for different specific uses. Preferably, the same semantic database B simultaneously comprises components intended for mechanical uses, components intended for electronic uses, and components intended for software uses.

[0131] To generate the semantic database B, the memory 20 stores, for example, one or more initial databases 25. Each initial database 25 has an initial structure. The initial structure is a tabular structure associating, with each component 30 of a plurality of initial components, at least one initial identification data for each component and a plurality of initial characteristics C of said component 30, at least one of the characteristics C resulting from a measurement of a quantity characterizing the corresponding component.

[0132] The identification data for each component 30 is, for example, a component name, a reference to the component 30 intrinsic to the initial database 25, or a normalized reference to the component 30.

[0133] For example, if component 30 is a sealing gasket, the identification data is for example "gasket", "sealing gasket", or "gasket REF '7'".

[0134] For example, if component 30 is a resistor (in the component sense), the identification data is for example "resistor" or "RT100000025M".

[0135] Each initial characteristic quantifies or qualifies an aspect of the component.

[0136] For example, for mechanical components 30, one of the initial characteristics C quantifies a geometry of the component 30, a mass of the component 30, a volume of the component 30, a resistance of the component 30, a thermal capacity of the component 30 a drying time or a covering power.

[0137] For example, for electronic components 30, one of the initial characteristics C quantifies a resistance of the component 30, an inductance of the component 30, a capacitance of the component 30, a maximum current that the component 30 can withstand, a transformation ratio of the component 30, or even a memory storage capacity, a read speed or a computing capacity of a computer component. .

[0138] For example, for software components 30, one of the characteristics C quantifies, a memory space occupied by the software component 30, a version of the software, a license agreement.

[0139] In all the preceding examples, another initial characteristic is, for example, a certificate of validity of a component 30 for use in a predefined device D or an authorization to market a device D comprising said component 30 in a specific country.

[0140] It is thus clear that the identification data is preferably a string of characters. Each feature is either a document, such as a PDF document or a table, or a value, preferably a binary, an integer, or a real number.

[0141] Each conformity indicator I is preferably a binary, a relative integer or a real number.

[0142] Each evaluation rule R is, for example, a computer function comprising one or more formulas for calculating the associated conformity indicator(s) I. In what follows, it will be explained how such a semantic database B can be obtained.

[0143] It should be understood that the methodology which will be set out is generic and can take many different forms depending on the components 30 and the specific uses considered.

[0144] Also, a generic description will first be described with reference to [Fig.3] and then a few sufficiently different concrete examples will be used to understand how the semantic database B can be obtained in each case through the generation process which is the subject of the present invention. Description of the generation process according to the

[0145] With reference to [Fig.3], the generation process comprises five phases: an initialization phase 100, a usage phase 200, an update phase 300, an integration phase 400 and a correction phase 500 corresponding to a loop back to the initialization phase 100.

[0146] Each of these phases will be described successively in what follows. Examples of initialization phase 100

[0147] The initialization phase 100 aims to obtain an initial semantic database B.

[0148] The initialization phase 100 includes a obtaining step 110 and a conversion step 120.

[0149] During the retrieval step 110, the calculator 15 obtains the initial database or databases 25, for example from memory 20.

[0150] Then, during the conversion step 120, the calculator 15 converts the initial database or databases into the semantic database B for monitoring compliance indicators I, referred to as the converted database B. The converted database B has a modified data structure compared to the initial database or databases 25 and a set of evaluation rules. This modification is preferably carried out either automatically or semi-automatically. Alternatively, it can be carried out entirely manually.

[0151] The data structure and the set of evaluation rules of the converted database B determines the implementation of the evaluation of the conformity indicators I by the calculator 15.

[0152] For example, the converted database B is a semantic graph as schematically illustrated in [Fig. 2]. In other words, the converted database B comprises, for example, nodes and edges. Each node is a component or a feature associated with a component. Each edge is a semantic relation associating a component and an associated feature.

[0153] According to one embodiment, in the converted database B, at least one feature is linked to several components by different arcs.

[0154] This is achieved because the converted database B is a semantic graph. Indeed, with a tabloid database, each feature is associated with only one single component.

[0155] For example, if several components are tested and a single certificate of validity is issued for all of these components, the converted database B contains only one copy of this certificate, which forms a node of the semantic graph. An arc then connects this node to each component considered in this certificate.

[0156] Preferably, each component is characterized, in the converted database B, by the following elements: identification, classification, characteristic(s), application, use and possibly associations.

[0157] The identification is for example the identification data of the component present in the or one of the initial databases 25.

[0158] The classification defines the type of component corresponding to the identification. For example, when the identification of a component is "RT100000025M", the classification- The associated function is "resistive".

[0159] The characteristic(s) C are those present in the initial database(s) 25. The characteristics C are for example of two types: documentation and technical characteristics.

[0160] The documentation includes the set of files contained in the initial database(s) 25 relating to the components.

[0161] The documentation includes, for example, component datasheets, component descriptions and certificates.

[0162] The technical characteristics are quantified values ​​directly entered into the initial database(s) 25 or automatically extracted from the documentation during the conversion step 120.

[0163] For example, certain technical characteristics result from a measurement carried out prior to the implementation of the process and either have measured values ​​directly integrated into the initial database 25, or have values ​​compiled into a document such as a PDF file. In this second case, the values ​​of the technical characteristics are preferably extracted automatically from said documentation.

[0164] The applications define, for example, the domain or context of use of the component. For example, the applications indicate usage recommendations from the component manufacturer. In a particular example where the component is an industrial paint, the applications define, for example, recommended temperature or humidity values ​​for applying the paint.

[0165] The uses are derived from the initial database(s) 25 and define who the users of the components are, as well as the use cases. Here, the uses are associated with the roles of the users of device D. These roles can be of different kinds: a user of the device such as a buyer or an engineer; a business expert in charge of the production and maintenance of the data.

[0166] Associations characterize the links between components. For example, associations indicate whether two components are compatible, equivalent, derived from each other, or replacement components for each other.

[0167] Each evaluation rule is intended to be applied to at least one feature stored in the converted database B to evaluate a conformity indicator I to the use of the component associated with at least one feature.

[0168] The evaluation rules are, for example, predefined and include the equations and the ways in which each indicator I is calculated.

[0169] Each evaluation rule is optionally specific to at least one component of the converted database B. At least one rule in the set of evaluation rules depends on the specific use of the associated component(s). Examples of usage phases 200

[0170] The use phase 200 includes an evaluation step 210, an alert step 220 and preferably a prediction step 230 and a warning step 240.

[0171] During the evaluation step 210, the calculator 15 evaluates, for each component, the conformity indicators I for the use of the component, by applying, to the associated characteristics C, at least one rule from the set of evaluation rules.

[0172] Preferably each rule further includes a predefined conformity criterion associated with each conformity indicator I. If at least one conformity indicator I does not meet the predefined criterion then the associated component is indicated as non-compliant in the converted database B.

[0173] The use phase 200 then includes an alert step 220, during which the control unit 15 issues an alert to a user if at least one conformity indicator I violates the predefined conformity criterion. Indeed, in such a situation, the component is not conforming to the specific use, and the alert step allows the user to be informed of this.

[0174] The alert is, for example, data available and made available to the user through different means depending on the use case and user experience, such as a visual alert (pop-up message, icon), a summary dashboard, a global or local map, or any type of signal likely to alert the user.

[0175] In the example where the characteristic is a certificate, to be issued annually, certifying that a component withstands a stress such as mechanical stress, if the certificate is more than one year old, then the conformity indicator I associated with said components does not meet the predefined conformity criterion. Thus, during alert step 220, an alert is issued to the user to warn them that the component is not compliant because the certificate was issued more than one year ago.

[0176] Optionally, an edit date in the source and / or an update date in the converted database B is associated with each characteristic in the converted database B. The edit date is the date on which the characteristic was measured, determined, or established. The update date is the date on which the characteristic was recorded in the converted database B on which the indicators are calculated.

[0177] As an optional addition, the usage phase 200 further includes a prediction step 230, for each component, of a risk of non-compliance over a predefined time horizon, based on the results of the evaluation rules and the date of issue of each characteristic associated with the component.

[0178] The predefined time horizon depends advantageously on the characteristic considered and / or the component considered and preferably on the context of use.

[0179] For example, in the case of a certificate to be issued annually, during prediction step 230, the calculator 15 predicts a risk of non-compliance at a predefined time horizon of the certificate if the anniversary date of the certificate expires before the predefined time horizon.

[0180] For example, the risk of non-compliance has a binary value. Alternatively, the risk of non-compliance has an integer or real value. The value of the risk of non-compliance depends on the predefined time horizon, the feature's issue date, and the evaluation rules.

[0181] According to this optional supplement, the use phase 200 further includes a warning step 240, during which the computer 15 issues a warning to a user if at least one risk of non-compliance is greater than a predefined threshold.

[0182] At the end of the usage phase 200, the converted database is functional and usable. For example, it can be queried by the user to verify the compliance indicators I associated with each component. Examples of update phase 300

[0183] The update phase 300 includes a reception step 310, an update step 320 and an alert step 330.

[0184] During update phase 300, at least one new feature concerning at least one component of the converted database B is added to said converted database B.

[0185] The update phase 300 includes a step 310 of receiving at least one new feature relating to at least one component of the converted database B.

[0186] Each new characteristic received is, for example, a certificate, a measurement, or an authorization to market a component.

[0187] The update phase 300 further includes a step 320 of refreshing the converted database B, to obtain an updated database.

[0188] The update step 320 includes a re-evaluation sub-step 321, during which the calculator 15 re-evaluates each conformity indicator I associated with said component, by applying the evaluation rules to a set comprising the characteristics C associated with said component in the converted database B and the new characteristic C. The update step 320 is also called the data update step.

[0189] Preferably, during the re-evaluation sub-step 321, only the conformity indicators I associated with the new characteristic are evaluated.

[0190] Alternatively, the converted database B identifies other features C dependent (directly or indirectly on) the new feature and The compliance indicators I associated with these characteristics C are also reassessed.

[0191] By way of example, if the new characteristic is an authorization to market the component only if a measurement of said component is below a threshold, then, during the re-evaluation sub-step 321, the calculator 15 re-evaluates the conformity indicators I of the measurement characteristic.

[0192] The update step 320 further includes a substep 322 of storing, in the updated database, each re-evaluated compliance indicator I.

[0193] The update phase 300 further includes an alert step 330, during which the computer 15 issues an alert to a user if at least one re-evaluated conformity indicator I violates a predefined conformity criterion established from the evaluation rules.

[0194] The alert issued during alert step 330 of update phase 300 is preferentially similar to the alert issued during alert step 220 of use phase 200. Examples of integration phase 400

[0195] The integration phase 400 includes a reception step 410, an integration step 420, an evaluation step 430 and an alert step 440.

[0196] The integration phase 400 concerns the integration of a new component into the converted database B.

[0197] The new component is, for example, a new item for manufacturing, maintaining or reconditioning, a new device D or an alternative to a component already included in the converted database B.

[0198] For example, the new component is theoretically identical to an existing component but comes from a competing supplier or is intended for a specific use distinct from that intended for the first component.

[0199] The integration phase 400 includes a step 410 of receiving identification data for the new component and preferably new specific evaluation rule(s).

[0200] The integration phase 400 further includes an integration step 420, which integrates the identification data and the new evaluation rule(s) into the modified structure of the converted database B. The integration step 420 includes establishing a semantic link between the characteristic(s) of the converted database B and the identification data of the new component.

[0201] In particular, if certain features C already present in the converted database B contain information associated with the new component, said features C are linked to the identification data of the new component by a respective semantic link.

[0202] For example, if a feature of the converted database B is a marketing authorization for a plurality of components including the new component, said feature is linked to the identification data of the new component.

[0203] The integration phase 400 further includes an evaluation step 430, during which the calculator 15 evaluates the conformity indicator(s) I associated with the new component, by applying new evaluation rule(s) to the characteristic(s) which is / are related to the identification data of the new component.

[0204] Preferably, the integration phase 400 further includes an alert step 440, during which the computer 15 issues the aforementioned alert to the user if a conformity indicator I does not meet a predefined criterion.

[0205] According to one embodiment, the update 300 and integration 400 phases are implemented simultaneously. Thus, during the acceptance steps 310 and 410, the identification data for a new component and new characteristic(s) related to that component are received. The re-evaluation sub-steps 321 and the evaluation step 430 are then implemented simultaneously and include the evaluation of the conformity indicators I of the characteristic(s) C related to the new component. Examples of a correction phase 500

[0206] During the correction phase 500, the characteristics whose indicators imply non-conformities (for example, below a desired threshold) make it possible to identify the corrections to be made to the input data.

[0207] The correction phase 500 includes a self-correction step 510 and, where appropriate, a correction recommendation step 520.

[0208] Alternatively or as an optional addition, the method further includes an automatic correction phase 500, during which the converted database B makes corrections or recommendations for correction in the identification, classification, characteristic(s), application, use, and possibly the respective associations of each component of the converted database. The automatic correction phase 500 is preferably implemented in parallel with the evaluation phase 200, the update phase 300, and the integration phase 400.

[0209] The automatic correction phase 500 includes an autocorrection step 510, during which errors, such as typographical errors or missing information, are automatically detected and corrected, for example from a dictionary and an automatic corrector included in the converted database B.

[0210] With reference to [Fig.4], the self-correction step 510 comprises a substep 512 error detection, during which the automatic corrector detects an error in one or more words by comparing the word with the words included in the dictionary.

[0211] The self-correction step 510 then includes a substep 514 of generating candidates for correction, during which the automatic corrector determines, for example among the words in the dictionary, those which are similar to the word containing an error, also called the erroneous word.

[0212] The self-correction step 510 then includes a substep 516 of ranking the generated candidates, during which the automatic corrector calculates, for each candidate, a distance from the erroneous word. The distance is, for example, the Levenshtein distance, also called the minimum editing distance. This distance quantifies the minimum number of operations required to transform the erroneous word into the candidate. The operations considered preferably include insertion, deletion, substitution, and transposition.

[0213] The self-correction step 510 then includes a comparison substep 518 in which the calculator 15 compares the distance between the candidate having said lowest distance and the erroneous word, to a predefined threshold.

[0214] If the distance is less than or equal to the predefined threshold, the self-correction step 510 includes a replacement substep 519 in which the calculator 15 automatically replaces the erroneous word with said candidate.

[0215] Otherwise, the self-correction phase 500 includes a correction recommendation step 520. Preferably, the correction recommendation phase 520 is based on word embedding techniques.

[0216] The correction recommendation step 520 includes an initialization substep 522 in which the calculator 15 associates, with each word in the dictionary, a vector of initial numerical values.

[0217] Then, the correction recommendation step 520 includes a learning substep 524 in which the computer 15 adjusts the numerical values ​​of each vector so that the vectors associated with two words regularly occurring in the same sentence and / or in the same context have substantially similar values. For this purpose, the CBOW or Skip-Gram process is used, for example. This then provides a semantic representation of the words based on their occurrences in the converted database B.

[0218] The correction recommendation step 520 includes a selection substep 526 in which the calculator 15 selects, as new candidates, words whose Levenshtein distance from the erroneous word is less than a predefined threshold.

[0219] Then the correction recommendation step 520 includes a proposal substep 528 during which the calculator 15 sends the user a list of new candidates.

[0220] Finally, the correction recommendation step 520 includes an implementation substep 529 in which the calculator 15 receives an instruction for a new candidate selected by the user from the list of new candidates. The implementation substep 529 includes replacing the erroneous word with the new candidate selected by the user.

[0221] The correction step 500 preferably also includes the transmission, to the initial databases, of the identified errors and the corrections made or recommendations as well as the validations made by the users.

[0222] According to a first variant, the process only includes the initialization phase 100 and the utilization phase 200.

[0223] According to a second variant, the process further comprises only the update phase 300 or the integration phase 400.

[0224] According to a third variant, compatible with the first and second variants, the process further includes the correction phase 500.

[0225] According to a fourth variant, compatible with the first, second and third variants, the use phase 200 does not include the prediction 230 and warning 240 steps.

[0226] Specific examples of application of the generation process

[0227] Examples of application of the process will now be described for specific uses.

[0228] According to a first application example of the method, at least one of the components of the converted database B is a sealing gasket whose specific use is the manufacture or maintenance of a watertight enclosure. The watertight enclosure is designed to be immersed in water to a predetermined depth to prevent water from entering the enclosure despite overpressure induced by water on the exterior of the enclosure.

[0229] In this first example of application of the process, a characteristic of said component is a resistance measured to overpressure.

[0230] One of the evaluation rules is specific to calculating a difference between the measured overpressure resistance and a predefined value depending on the predetermined depth. The conformity indicator I resulting from the application of this rule is a function of said difference.

[0231] Thus, if the difference is less than a predefined safety threshold, the associated conformity indicator I takes a low value and during the alert step 220, 330 or 440, the alert is issued to the user to indicate that the use of the seal is not compliant for the manufacture or maintenance of the watertight housing.

[0232] Still according to this first example of application of the process, the measured resistance has was measured by one of the operators in the production room and another characteristic is a pressure resistance measurement certificate, issued by a certified metrology institute.

[0233] In such a case, the conformity indicators I of credibility and veracity will take higher values ​​for the characteristic from the certified metrology institute than for the resistance measured by the operator.

[0234] According to the first application example of the method, another component of the converted database B is the same seal for a separate specific use. This separate specific use is the manufacture or maintenance of an aircraft fuselage to prevent air leaks from the interior of the fuselage to an exterior where the atmospheric pressure is lower. A characteristic of this component is its measured resistance to negative pressure.

[0235] Similar to the previous example, one of the evaluation rules is specific to calculating a difference between the measured resistance to low pressure and a predefined value depending on a predefined maximum flight altitude of the aircraft. The conformity indicator I obtained by applying said evaluation rule is a function of the calculated difference.

[0236] It is clear that the conformity indicators I evaluated, during evaluation steps 210, 430 or during re-evaluation substep 321, are not necessarily the same for the component intended for use in the watertight housing and for that intended for use in the fuselage of an aircraft, although these components are theoretically the same components.

[0237] According to a second application example of the method, at least one of the components of the converted database B is a capacitor whose specific use is the reconditioning of a switched-mode power supply. The characteristics C of said component include a thermal dissipation of said component and / or one or more acceptable soldering technologies.

[0238] One of the evaluation rules is specific to the requirements (constraints) of the device such as its conditions of use, the integration environment of the device and / or the operating conditions, and is based on the indicators defined previously for the given context.

[0239] According to a third application example of the method, at least one of the components of the converted database B is source code whose specific use is the operation of open-source software. A characteristic of said component is a version of the software. One of the evaluation rules assesses the correspondence between the source code and the software version, for example, using an identifier present in the source code that is incremented with each version.

[0240] The conformity indicator I obtained by applying said rule is representative of a correspondence between the version and the source code, such as a binary value equal to "1" if the source code matches the version, or equal to "0" otherwise.

[0241] Thus, the converted database B makes it possible to concentrate all the information and characteristics C associated with each component throughout its cycle, from the development of a device D, to the production, maintenance or reconditioning of said device D, through its marketing.

[0242] In addition, the conformity indicator evaluation technique I allows for the automatic evaluation of the confidence that can be placed in each characteristic and therefore in the use of the component.

[0243] The modified data structure of the converted database B allows for greater agility in the links between features C and components. Indeed, when the converted database B is a semantic graph, the connections, or arcs, make it possible to give meaning to each link between the components and the features C. Furthermore, the semantic graph structure makes it possible to infer knowledge from existing connections between these elements. This notably allows for the prediction of future nonconformities.

[0244] Thus, the method, the semantic database, the computer program product and the information medium according to the invention make it possible to simplify and make more reliable the provision of information to a user concerning the conformity of use of components for the manufacture, maintenance or reconditioning of devices, in particular mechanical, electronic or software.

Claims

Claims

1. Method for generating a semantic base (B) of data for tracking conformity indicators (I) of components (30), the components being intended to be used for a specific use, the specific use being the manufacture, maintenance or reconditioning of mechanical, electronic or software devices (D) comprising at least one of the components, conformity indicators (I) for the use determining whether or not the component is suitable for use for the specific use, the method being implemented by a computer system comprising a computer (15) and a memory (20), the method comprising an initialization phase (100) comprising: - a step (110) of obtaining an initial database, the initial database having an initial structure, the initial structure being a tabular structure associating, with each component of a plurality of initial components, at least one initial identification data item of the component and a plurality of initial characteristics of said component, at least one of the characteristics (C) resulting from a measurement of a quantity characterizing the component, and - a step (120) of converting the initial database into the semantic database (B) of compliance indicator tracking data (I), called the converted database (B), having a data structure modified with respect to the initial database and a set of evaluation rules, each evaluation rule being intended to be applied to at least one characteristic (C) stored in the converted database (B) to evaluate a compliance indicator (I) with the use of the component associated with the at least one characteristic (C), the data structure and the set of evaluation rules of the converted database (B) determining the implementation of the evaluation of the compliance indicators (I) by the calculator (15), the method further comprising a use phase (200) comprising: - a step (210) of evaluating, for each component, the indicator of conformity (I) to the use of the component, by application, to the associated characteristics (C), of at least one rule from the set of evaluation rules.

2. The method of claim 1, wherein the use phase (200) further includes: - an alert step (220), during which an alert is issued to a user if at least one compliance indicator (I) violates a predefined compliance criterion established from the evaluation rules.

3. Method according to claim 1 or 2, further comprising an update phase (300) comprising: - a step of receiving (310) at least one new characteristic (C) relating to at least one component of the converted database (B), - a step of updating (320) the converted database (B), to obtain an updated database, comprising: • a sub-step of re-evaluation (321), by the calculator (15), of each conformity indicator (I) associated with said component, by applying the evaluation rules to a set comprising the characteristics (C) associated with said component in the converted database (B) and the new characteristic (C), and • a storage sub-step (322), in the updated database of each re-evaluated conformity indicator (I), - an alert step (330), during which an alert is issued to a user if at least one re-evaluated compliance indicator (I) violates a predefined compliance criterion established from the evaluation rules.

4. A method according to any one of claims 1 to 3, wherein an edition date is associated with each characteristic (C), the edition date being the date on which the characteristic (C) was measured, determined or established, the use phase (200) further comprising: - a prediction step (230), for each component, of a risk of non-compliance at a predefined time horizon, from the results of the evaluation rules and the date editing each characteristic (C) associated with the component, and - a warning step (240), during which a warning is issued to a user if at least one risk of non-compliance is greater than a predefined threshold.

5. A method according to any one of claims 1 to 4, wherein the data structure of the converted database (B) is a semantic graph.

6. A method according to any one of claims 1 to 5, wherein each evaluation rule is specific to at least one component of the converted database, at least one rule of the set of evaluation rules depending on the specific use of the associated component(s).

7. Method according to any one of claims 1 to 6, in which at least one conformity indicator (I) quantifies the confidence given to one of the characteristics (C) of the component to describe an aspect of the component.

8. Method according to any one of claims 1 to 7, further comprising a phase (400) of integrating a new component into the converted database (B), comprising: - a step (410) of receiving identification data of the new component and new specific evaluation rule(s), - a step (420) of integrating, into the modified structure of the converted database (B) the identification data, and the new evaluation rule(s), the integration comprising the establishment of a semantic link between characteristic(s) (C) of the converted database and the identification data of the new component, and - a step (430) of evaluating the conformity indicator (I) associated with the new component by applying new evaluation rule(s) to the characteristic(s) (C) linked to the identification data.

9. A method according to any preceding claim, in wherein at least one of the components of the converted database (B) is a seal whose specific use is the manufacture or maintenance of a waterproof housing intended to be immersed in water to a predetermined depth to prevent water infiltration into the housing despite overpressure induced by the water on an exterior of the housing, a characteristic (C) of said component being a measured resistance to overpressure, one of the evaluation rules being capable of calculating a difference between the measured resistance to overpressure and a predefined value depending on the predetermined depth, the conformity indicator (I) obtained by application of said evaluation rule being a function of the calculated difference.

10. Method according to the preceding claim, in which another component of the converted database (B) is the same seal for a distinct specific use, the distinct specific use being the manufacture or maintenance of an aircraft fuselage to prevent air leaks from an interior of the fuselage to an exterior where the atmospheric pressure is lower, a characteristic (C) of said component being a measured resistance to depression, one of the evaluation rules being capable of calculating a difference between the measured resistance to depression and a predefined value depending on a predefined maximum flight altitude of the aircraft, and the conformity indicator (I) obtained by application of said evaluation rule being a function of the calculated difference.

11. Method according to any one of the preceding claims, in which at least one of the components of the converted database (B) is a capacitor whose specific use is the reconditioning of a switching power supply, the characteristics (C) of said component comprising: a measurement of capacitance of said component, a thermal dispersion of said component and / or one or more acceptable soldering technologies, one of the evaluation rules being specific to the technical characteristic considered and to the context of use defined by a user, the conformity indicator (I) obtained by application of said rule.

12. A method according to any preceding claim, in wherein at least one of the components of the converted database (B) is a source code whose specific use is the exploitation of Open Source software, a characteristic (C) of said component being a version of the software, one of the evaluation rules evaluating the correspondence between the source code and the version of the software, the conformity indicator (I) obtained by application of said rule being representative of a correspondence between the version and the source code.

13. Semantic database for tracking compliance indicators (I) of components (30), the components being intended to be used for a specific use, the specific use being the manufacture, maintenance or reconditioning of mechanical or electronic devices (D) comprising at least one of the components, compliance indicators (I) for the use determining whether or not the component is suitable for use for the specific use, the database for tracking compliance indicators (I) being obtainable by a generation method according to any one of the preceding claims.

14. A computer program product comprising software instructions which, when implemented by a computer, implement a method according to any one of claims 1 to 12.

15. Information medium comprising program instructions forming a computer program, the computer program being loadable onto a data processing unit and implementing a method according to any one of claims 1 to 12 when the computer program is implemented on the data processing unit.

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