Metamodel-based digital representation method for quantity value data

Abstract

The present invention relates to the technical field of digital transformation in metrology, and provides a metamodel-based digital representation method for quantity value data. The method comprises: constructing a metamodel for digitally representing a unit of measurement; on the basis of the metamodel for digitally representing a unit of measurement, performing a schema transformation on raw quantity value data to obtain digitally represented quantity value data; and on the basis of the metamodel for digitally representing a unit of measurement, verifying the digitally represented quantity value data to obtain a quantity value data verification result, and completing a digital representation of the quantity value data. The present invention addresses the machine-readability, machine-interpretability, and traceability issues relating to digital representations of quantity value data.

Description

A Meta-Model-Based Method for Digital Representation of Quantitative Data Technical Field

[0001] This specification relates to the field of metrological digital transformation technology, and in particular to a method for digital representation of measurement data based on a meta-model. Background Technology

[0002] The digital transformation of the industrial sector has created an urgent need for the digitalization of metrology. Metrology, as one of the foundations of manufacturing, needs to provide technical support for Industry 4.0, the factory of the future, and smart manufacturing. The International System of Units (SI), as the core of metrology, is also crucial in its digital representation. Machine interpretability and traceability are critical issues in the digital representation of units of measurement. Interpretability requires machines to correctly understand the meaning of units in order to exchange and integrate data between different systems; traceability requires that the definition of a unit can be traced back to its authoritative definition and source, ensuring the consistency and comparability of units.

[0003] To address the issue of digitizing units of measurement, numerous organizations and institutions have conducted research on this topic. Current methods for digitizing units of measurement primarily offer two solutions: one is a machine-readable unit identifier scheme based on international standards, such as ISO 80000 and IEC TS 62720; the other is a unit ontology representation scheme based on semantic web technology, such as QUDT, QUDV, OM, and vim:ontology. Solution one (unit identifiers) only achieves the minimum required machine readability. As the complexity of systems across various fields continues to increase, simple machine readability is no longer sufficient to meet the requirements of data exchange and integration between different systems; it fails to achieve machine interpretability or machine traceability. Solution two (unit ontology), while addressing the semantic expression of units to some extent, cannot accurately interpret the meaning of units, including the standardization of different unit representations and the consistency of different representations of the same unit.

[0004] Therefore, there are many difficulties in solving the problems of machine interpretability and traceability in unit digital representation methods using existing methods. Summary of the Invention

[0005] To address the aforementioned shortcomings in existing technologies, this invention provides a method for digital representation of quantity data based on a meta-model, which solves the problems of machine readability, machine interpretability, and traceability in the digital representation of quantity data.

[0006] To achieve the aforementioned objectives, the present invention employs the following technical solution: a method for digital representation of quantity data based on a meta-model, comprising:

[0007] S1: Constructing a digital representation meta-model of the unit system;

[0008] S2: Based on the aforementioned digital representation meta-model of the unit system, the original quantity data is transformed to obtain digitally represented quantity data;

[0009] S3: Based on the digital representation meta-model of the unit system, the digitally represented quantity data is verified to obtain the quantity data verification result, thus completing the digital representation of the quantity data.

[0010] (1) Ontology-based unit digitization schemes have various serialization forms, such as RDF, RDFS, and OWL. The XML format used in this invention has good universality and can be read and processed by different systems. Ontology-based unit digitization schemes have various structures, and different organizations define different unit systems, which lack consistency. This invention is developed based on authoritative standards and specifications in the field of metrology, and has universality and consistency. Although ontology-based unit digitization schemes provide relatively rich semantic information, they cannot be understood by computers and still require hard coding to utilize semantic information. The metadata model of this invention achieves machine interpretability and does not require different systems to hard-code to implement the function.

[0011] (2) The unit digitization solution based on unit identifiers relies on traditional string matching technology and cannot detect the same SI unit expression form of different non-SI units, while the metadata model defined in this invention can detect it; the unit digitization solution based on unit identifiers cannot determine different expression formats of the same unit, while the metadata model defined in this invention can identify them, for example, derive the non-SI expression form and SI unit expression form of the unit; the unit digitization solution based on unit identifiers only contains the basic name of the unit and the corresponding identifier string, lacking metadata description, while the metadata model proposed in this invention provides a metadata description framework, which describes the unit information more comprehensively according to the requirements of the SI manual;

[0012] (3) A single unit identifier does not contain the original definition data of the unit of measurement, which does not comply with the principle of traceability in metrology. This invention achieves the characteristic of machine traceability by tracing the unit definition back to the SI reference point.

[0013] Furthermore, the unit system digital representation meta-model includes:

[0014] The type definition module is used to compare and represent the basic concepts of the original quantity data to obtain comparison data; and to check the type definition and expression specifications of the declaration verification data to obtain type check data.

[0015] A machine-interpretable module is used to perform pattern transformation and standardization on the comparison data to obtain transformed data; and to perform strict pattern transformation and regular expression validation on the type check data to obtain validated data.

[0016] The machine traceability module is used to perform unit checks and traceability on the converted data to obtain traceable data; and to perform unit definition comparison and traceability capability checks on the verification data to obtain the verification result of the quantity data.

[0017] The header file declaration module is used to generate a file based on the declaration information to obtain a digitally represented quantity value data; and to perform version, syntax, and format verification on the digitally represented quantity value data based on the declaration information to obtain declaration verification data.

[0018] Furthermore, the type definition module includes:

[0019] The Real Quantity submodule is used to describe a single real quantity value and its related attributes using information including the measured value, unit, optional label, timestamp, and measurement uncertainty, to obtain comparative data representing the real quantity value; and to check the declaration verification data of the real quantity value to obtain type check data of the real quantity value.

[0020] The constant quantum module is used to obtain comparative data representing the numerical values ​​and related characteristics of constant quantities by utilizing values, units, optional labels, timestamps, and the uncertainty and distribution information of specified constant quantities; it also checks the declaration verification data of constant quantities to obtain type check data of constant quantities.

[0021] The Complex Quantity submodule is used to represent complex quantity values ​​in either Cartesian or polar coordinates using real, imaginary, and unit information, and to obtain comparison data of complex quantity values; it also checks the declaration verification data of complex quantity values ​​to obtain type check data of complex quantity values.

[0022] The Real Quantity List submodule is used to represent independent measurements and multivariate vector quantities by utilizing multiple real quantity submodule elements, optional labels of the list, timestamps, units, and global measurement uncertainty declarations to obtain comparative data for the real quantity value list; it also checks the declaration verification data of the real quantity value list to obtain type check data for the real quantity value list.

[0023] The Real Quantity XML List submodule is used to separate list data by spaces and obtain comparison data for a list of real quantity values ​​based on XML type; it also checks the declaration validation data of the list of real quantity values ​​based on XML type to obtain type check data for the list of real quantity values ​​based on XML type.

[0024] The Complex Quantity List submodule is used to process a set of multiple complex quantity values ​​using multiple complex quantity submodule elements, list labels, timestamps, units, and uncertainty declarations to obtain comparison data for the complex quantity value list; and to check the declaration verification data of the complex quantity value list to obtain type check data for the complex quantity value list.

[0025] The hybrid quantum module is used to add measurement values ​​in SI units and non-SI units to a machine-readable format using real numbers, complex numbers, various lists, and constant types to obtain comparative data of hybrid values; it also checks the declaration verification data of hybrid values ​​to obtain type check data of hybrid values.

[0026] The expanded uncertainty submodule is used to define various parameters of expanded measurement uncertainty using uncertainty values, coverage factors, coverage probabilities, and optional distribution information, and obtain comparative data representing the structure of expanded measurement uncertainty; it also checks the declaration verification data of expanded uncertainty to obtain type check data of expanded uncertainty.

[0027] The included interval submodule is used to describe the range and probabilistic characteristics of measurement uncertainty using information including standard uncertainty, interval minimum, interval maximum, and included probability, and to obtain comparative data representing a probabilistically symmetric included interval structure; it also checks the declared verification data of included intervals to obtain type check data of included intervals.

[0028] The covariance matrix submodule is used to describe the covariance relationship between multidimensional measurements using multiple column elements containing covariance values ​​and their units, and to obtain comparative data of the covariance matrix; it also checks the declaration verification data of the covariance matrix to obtain type check data of the covariance matrix.

[0029] The Elliptical Region submodule is used to describe the uncertainty range of multivariate measurements by defining the hyperellipsoidal inclusion region using the covariance matrix, inclusion factor, inclusion probability, and optional distribution information. It declares the uncertainties of complex values, lists of real values, and lists of complex values, and obtains comparative data representing the structure of the hyperellipsoidal inclusion region representing the uncertainty of multivariate quantities. It also checks the declaration verification data of the elliptical region to obtain the type check data of the elliptical region.

[0030] The rectangular region submodule is used to describe the uncertainty of multivariate measurements through a hyperrectangular containing region using the covariance matrix, inclusion factor, inclusion probability, and optional distribution information. It declares the uncertainty of complex values, lists of real values, and lists of complex values, and obtains comparative data of the hyperrectangular containing region structure representing the uncertainty of multivariate quantities. It also checks the declaration verification data of the rectangular region to obtain the type check data of the rectangular region.

[0031] Furthermore, the machine-interpretable module includes:

[0032] The strict mode definition unit is used to convert comparison data into strict mode data and type check data into strict mode data.

[0033] The regular expression pattern definition unit is used to standardize the expression of the strict pattern transformation data based on regular expressions to obtain the transformation data; and to perform regular expression validation on the strict pattern check data to obtain the validation data.

[0034] Furthermore, the machine traceability module includes:

[0035] An enumeration type definition unit is used to provide standardized identification information for units of measurement and prefixes, and to obtain traceability information from the units in the converted data to their authoritative definitions; the unit definition is compared with the verification data to obtain the unit definition comparison results;

[0036] The metadata description unit is used to define the relationship between elements and types based on the traceability information, construct the data processing flow and logic, and obtain traceability data; based on the unit definition comparison results, perform traceability capability checks and obtain quantity data verification results.

[0037] Further, S2 includes:

[0038] By comparing and representing the basic concepts of the original quantitative data, comparative data is obtained.

[0039] The comparison data is subjected to pattern transformation and standardized representation to obtain transformed data;

[0040] The converted data is checked for units and traced to obtain traceable data;

[0041] Based on the declaration information, the traceability data is processed to generate a file, resulting in a digitally represented quantity data.

[0042] Further, S3 includes:

[0043] Based on the declaration information, the digitally represented quantity data is checked for version, syntax, and format to obtain declaration verification data;

[0044] The declaration verification data is checked for type definition and expression specifications to obtain type-checked data;

[0045] The type of check data is subjected to strict pattern conversion and regular expression validation to obtain the validation data.

[0046] The unit definition is compared and traceability is checked on the verification data to obtain the verification result of the quantity data.

[0047] The beneficial effects of this invention are as follows: A method for digital representation of measurement data based on a meta-model can realize unambiguous, automated, full-process and intelligent data exchange and integration of measurement data information between different systems, while meeting the requirements of measurement standards and information technology standards and specifications, and has significant advantages of high accuracy, machine interpretability and traceability. Attached Figure Description

[0048] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0049] Figure 1 is an exemplary flowchart of a method for digital representation of quantity data based on a meta-model, according to some embodiments of this specification. Detailed Implementation

[0050] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0051] Example

[0052] Figure 1 is an exemplary flowchart of a method for digital representation of quantity data based on a metamodel, according to some embodiments of this specification. As shown in Figure 1, the process includes the following steps. In some embodiments, the process may be executed by a processor.

[0053] S1: Construct a digital representation meta-model of the unit system.

[0054] The unit system digital representation metamodel is a metamodel used for the digital representation and verification of quantity data.

[0055] In some embodiments, the unit system digital representation metamodel may include a type definition module, a machine-interpretable module, a machine-traceable module, and a header file declaration module.

[0056] The type definition module is used to compare and represent the basic concepts of the original quantity data to obtain comparison data; and to check the type definition and expression specifications of the declaration verification data to obtain type check data.

[0057] Raw quantity data refers to quantity data to be transformed. For example, raw quantity data can include paper-based data, non-digital data, and data that does not meet the requirements of machine interpretability and traceability. The types of raw quantity data can include real quantities, constants, complex quantities, lists of real quantities, XML lists of real quantities, lists of complex quantities, mixed quantities, expanded uncertainties, coverage intervals, covariance matrices, elliptical regions, and rectangular regions, etc.

[0058] Comparative data refers to the raw numerical data obtained by comparing basic concepts. For example, comparative data can include comparative data of real quantities, comparative data of the numerical values ​​and related characteristics of constant quantities, comparative data of complex quantities, comparative data of lists of real quantities, comparative data of lists of real quantities based on XML type, comparative data of lists of complex quantities, comparative data of mixed quantities, comparative data representing structures of expanded measurement uncertainty, comparative data representing probabilistic symmetric regions, comparative data of covariance matrices, comparative data of hyperellipsoidal regions representing uncertainties of multivariable quantities, and comparative data of hyperrectangular regions representing uncertainties of multivariable quantities, etc.

[0059] Declaration verification data reflects the results of syntax and format checks on digitally represented quantity data. For example, declaration verification data can include declaration verification data for real quantities, constant quantities, complex quantities, lists of real quantities, XML-based lists of real quantities, lists of complex quantities, mixed quantities, expanded uncertainties, intervals, covariance matrices, elliptical regions, and rectangular regions, etc.

[0060] Type check data reflects the type definition and expression specification checks of declared verification data. For example, type check data can include type check data for real values, constant values, complex values, lists of real values, lists of real values ​​based on XML types, lists of complex values, mixed values, expanded uncertainties, data containing intervals, covariance matrices, elliptical regions, and rectangular regions.

[0061] In some embodiments, the type definition module may include a real quantity submodule, a constant quantum module, a complex quantity submodule, a real quantity list submodule, a real quantity XML list submodule, a complex quantity list submodule, a mixed quantum module, an extended uncertainty submodule, a submodule containing an interval, a covariance matrix submodule, an elliptical region submodule, and a rectangular region submodule.

[0062] The Real Quantity submodule is used to describe a single real quantity value and its related attributes using information including the measured value, unit, optional label, timestamp, and measurement uncertainty, to obtain comparative data representing the real quantity value; and to check the declaration verification data of the real quantity value to obtain type check data of the real quantity value.

[0063] In some embodiments, the metadata description model defined by the real quantity submodule may include five fields, two of which are mandatory: value and unit; and three optional fields: label, timestamp, and measurement uncertainty. The two mandatory fields indicate the two essential components of a complete quantity value: the numerical value and the unit. Among the optional fields, the label describes the name and meaning of the quantity value, such as "the temperature measured by this sensor," the timestamp indicates the specific time when the quantity data was generated, and the measurement uncertainty indicates the uncertainty information determined after uncertainty evaluation.

[0064] In some embodiments, the real quantity submodule can be used to represent metadata for a real quantity value, including the measurement value, unit, optional label and timestamp, and information related to measurement uncertainty, to accurately describe a single real quantity value and its related attributes. For the quantity data "T = 306.32K, U = 0.061K", it can be represented as:

[0065] This measurement data comprises two inseparable atomic parts: the measured value and the measurement uncertainty. The description of the measured value includes the measured value and its unit; the value is 306.32, represented as a floating-point number in Kelvin, and identified by the identifier "\kelvin". Similarly, the description of the measurement uncertainty includes the value and its unit; the value is 0.061, represented as a floating-point number, and its unit should be consistent with the measured value, in Kelvin, identified by the identifier "\kelvin". This ensures the complete transmission of all information about the measured quantity when exchanging real-valued data.

[0066] The constant quantum module is used to obtain comparative data representing the numerical values ​​and related characteristics of constant quantities by utilizing values, units, optional labels, timestamps, and the uncertainty and distribution information of specified constant quantities; it also checks the declaration verification data of constant quantities to obtain type check data of constant quantities.

[0067] In some embodiments, the metadata description model defined in the constant quantum module may include 6 fields, 2 of which are required fields for value and unit, and 4 of which are optional fields for label, timestamp, uncertainty value and probability distribution type.

[0068] In some embodiments, constant quantum modules can be used to represent constants, including structures for fundamental physical and mathematical constants. Besides containing values, units, optional labels, and timestamps, they can also specify the uncertainty and distribution information of the constants. This is suitable for defining constants with specific properties in a data model, ensuring accurate representation of their numerical values ​​and related characteristics when processing data related to physical or mathematical constants. For example, the digital representation of "Euler's constant e" is:

[0069] The representation of the Euler constant should follow the specifications defined in the "Constant" submodule, including fields for label, value, unit, uncertainty, and probability distribution type. The label specifies the name and meaning of the constant; the value represents the specific numerical part; the unit field indicates the SI unit identifier used for the constant; the uncertainty field specifies the numerical part of the uncertainty, implicitly including the same unit as the constant; and the probability distribution information indicates the probability distribution method used to calculate the uncertainty of the constant.

[0070] The Complex Quantity submodule is used to represent complex quantity values ​​in either Cartesian or polar coordinates using the real part, imaginary part, and unit information, and to obtain comparison data of complex quantity values; it also checks the declaration verification data of complex quantity values ​​to obtain type check data of complex quantity values.

[0071] In some embodiments, a complex quantity value can be represented as follows:

[0072] Its representation consists of three parts: the real part, the imaginary part, and the unit. The real and imaginary parts are represented using numeric types, and the unit indicates the SI unit used for the complex quantity value.

[0073] In some embodiments, the complex quantity submodule can be used to define the structure of complex quantities in the metadata model, allowing complex quantity values ​​to be represented in Cartesian or polar coordinates, including information such as the corresponding real part, imaginary part (or amplitude, phase) and unit, and can also represent measurement uncertainty, for processing complex quantity values, and play a role in complex data processing (such as signal processing, circuit analysis, etc.).

[0074] The Real Quantity List submodule is used to represent independent measurements and multivariate vector quantities by utilizing multiple real quantity submodule elements, optional labels of the list, timestamps, units, and global measurement uncertainty declarations to obtain comparative data for the real quantity value list; and to check the declaration verification data of the real quantity value list to obtain type check data for the real quantity value list.

[0075] In some embodiments, the metadata description model defined in the real quantity list submodule may include four fields: one required field is a data structure defined by one or more real quantity submodules, represented in list form; and three optional fields include a list of labels or timestamps, a list of univariate uncertainties, and a list of multivariate uncertainties.

[0076] In some embodiments, the real quantity list submodule is used to represent metadata elements of a list of real quantity values. It can represent independent measurements or multivariate vector quantities, can contain multiple real quantity submodule elements, and provides optional labels, timestamps, units, and global measurement uncertainty declarations for the list. It can also represent multivariate uncertainties and is suitable for batch processing of multiple related real quantity values, such as in the application of batch processing and analysis of experimental data.

[0077] The Real Quantity XML List submodule is used to separate list data by spaces and obtain comparison data for a list of real quantity values ​​based on XML type; it also checks the declaration validation data of the list of real quantity values ​​based on XML type to obtain type check data for the list of real quantity values ​​based on XML type.

[0078] In some embodiments, the metadata description model defined in the real quantity XML list submodule may include four fields: one required field is a list of values ​​represented by an XML built-in list, and three optional fields are a list of labels or timestamps, a list of univariate uncertainties, and a list of multivariate uncertainties.

[0079] In some embodiments, the Real Quantity XML List submodule can be used to represent metadata elements of a list of real quantity values ​​based on XML type. It functions similarly to the Real Quantity List submodule, but the data storage method is different. List data is separated by spaces, which can save memory and is suitable for the storage and processing of large datasets.

[0080] The Complex Quantity List submodule is used to process a set of multiple complex quantity values ​​using multiple complex quantity submodule elements, list labels, timestamps, units, and uncertainty declarations, to obtain comparison data for the complex quantity value list; and to check the declaration verification data of the complex quantity value list to obtain type check data for the complex quantity value list.

[0081] In some embodiments, the metadata description model defined in the complex quantity list submodule may include four fields: one required field is a data structure defined by one or more complex quantity submodules, represented in list form; and three optional fields include a list of labels or timestamps, a list of univariate uncertainties, and a list of multivariate uncertainties.

[0082] In some embodiments, the complex quantity list submodule can be used as a metadata element to represent a list of complex quantity values, representing a set of complex quantity values. It contains multiple complex quantity submodule elements, as well as related list attributes (such as labels, timestamps, units, uncertainty declarations, etc.), and can be used to process collections of multiple complex quantity values, particularly in scenarios involving batch processing of complex data.

[0083] The hybrid quantum module is used to add measurement values ​​in SI units and non-SI units to a machine-readable format using real numbers, complex numbers, various lists, and constants to obtain comparative data of hybrid values; it also checks the declaration verification data of hybrid values ​​to obtain type check data of hybrid values.

[0084] In some embodiments, the hybrid quantum module can add measurement values ​​in different units to a machine-readable format, including real numbers, complex numbers, various lists, and constant types. This provides a flexible way to handle combinations of values ​​that may involve multiple units, adapting to measurement data representations of different unit systems or special needs, and playing a role in scenarios that require handling measurement values ​​in mixed units.

[0085] The expanded uncertainty submodule is used to define various parameters of expanded measurement uncertainty using uncertainty values, coverage factors, coverage probabilities, and optional distribution information, and to obtain comparative data representing the structure of expanded measurement uncertainty; it also checks the declaration verification data of expanded uncertainty to obtain type check data of expanded uncertainty.

[0086] In some embodiments, the extended uncertainty submodule can be used to represent the structure of extended measurement uncertainty, which is a way of representing measurement uncertainty. It includes uncertainty value, coverage factor, coverage probability and optional distribution information, and explicitly defines the parameters of extended measurement uncertainty. It is used when accurately representing measurement uncertainty and helps to improve the reliability and interpretability of measurement results.

[0087] The included interval submodule is used to describe the range and probabilistic characteristics of measurement uncertainty using information including standard uncertainty, interval minimum, interval maximum, and included probability, and to obtain comparative data representing a probabilistically symmetric included interval structure; it also checks the declared verification data of included intervals to obtain type check data of included intervals.

[0088] In some embodiments, the included interval submodule can be used to represent a probabilistically symmetric included interval structure, which is also a representation of measurement uncertainty. It includes information such as standard uncertainty, interval minimum, interval maximum, and coverage probability, thus describing in detail the range and probabilistic characteristics of the measurement uncertainty, and is used when dealing with measurement uncertainties represented by intervals.

[0089] The covariance matrix submodule is used to describe the covariance relationship between multidimensional measurements using multiple column elements containing covariance values ​​and their units, and to obtain comparative data of the covariance matrix; it also checks the declaration verification data of the covariance matrix to obtain type check data of the covariance matrix.

[0090] In some embodiments, the covariance matrix submodule can be used to represent covariance matrix elements of multidimensional uncertainty claims in a metadata model. By including multiple column elements (each containing a covariance value and its unit) to describe the covariance relationship between multidimensional measurement values, it is used when dealing with multiple related measurements and the need to consider their covariance, thus facilitating a more comprehensive assessment of the uncertainty of measurement results.

[0091] The elliptical region submodule is used to describe the uncertainty range of multivariate measurements by defining the hyperellipsoidal inclusion region using the covariance matrix, inclusion factor, inclusion probability, and optional distribution information. It declares the uncertainties of complex values, lists of real values, and lists of complex values, and obtains comparative data representing the structure of the hyperellipsoidal inclusion region representing the uncertainty of multivariate quantities. It also checks the declaration verification data of the elliptical region to obtain the type check data of the elliptical region.

[0092] In some embodiments, the elliptical region submodule can be used to represent the structure of the hyperellipsoidal containment region of uncertainty of a multivariate quantity, for uncertainty declarations of complex quantities and lists of real or complex quantity values, including covariance matrix, containment factor, containment probability and optional distribution information, describing the uncertainty range of a multivariate measurement quantity by defining the hyperellipsoidal containment region, and used when processing uncertainty representations of multivariate measurement data.

[0093] The rectangular region submodule is used to describe the uncertainty of multivariate measurements through a hyperrectangular containing region using the covariance matrix, inclusion factor, inclusion probability, and optional distribution information. It declares the uncertainty of complex values, lists of real values, and lists of complex values, and obtains comparative data of the hyperrectangular containing region structure representing the uncertainty of multivariate quantities. It also checks the declaration verification data of the rectangular region to obtain the type check data of the rectangular region.

[0094] In some embodiments, the rectangular region submodule can be used to represent the structure of a hyperrectangular containing region of uncertainty for a multivariate quantity, and is also used for uncertainty declarations of complex quantities and lists of real or complex quantities. Similar to the elliptical region submodule, it contains relevant information such as the covariance matrix, and describes the uncertainty of multivariate measurements through a hyperrectangular containing region, providing another way to represent the uncertainty of multivariate measurement data.

[0095] A machine-interpretable module is used to perform pattern conversion and standardization on the comparison data to obtain converted data; and to perform strict pattern conversion and regular expression validation on the type check data to obtain validation data.

[0096] Transformed data is comparative data that is presented in a standardized manner.

[0097] Validation data reflects the results of strict mode conversion and regular expression validation for type checking data.

[0098] In some embodiments, the machine-interpretable module may include a strict pattern definition unit and a regular pattern definition unit.

[0099] The strict mode definition unit is used to convert comparison data into strict mode data and type check data into strict mode data.

[0100] In some embodiments, the original data is a string type, which cannot be fully formatted. Therefore, the simple string type is converted to a strict type with regular expression restrictions, that is, the original string type is converted to a strict type with regular expression restrictions.<xs:restriction base="xs:string"> " converted to "<xs:restriction base="xs:string"><xs:patternvalue=pattern> ".

[0101] The regular expression pattern definition unit is used to standardize the expression of the strict pattern transformation data based on regular expressions to obtain the transformation data; and to perform regular expression validation on the strict pattern check data to obtain the validation data.

[0102] In some embodiments, the use of strict unit definition units and regular pattern definition units ensures that units used in XML documents conform to predefined SI unit rules, guaranteeing the consistency and accuracy of unit representation and facilitating correct parsing and manipulation in applications involving unit processing (such as metrological data exchange, scientific computing, etc.). It helps prevent misunderstandings or calculation errors caused by incorrect unit formats, improving the reliability and interoperability of data processing.

[0103] The machine traceability module is used to perform unit checks and traceability on the converted data to obtain traceable data; and to perform unit definition comparison and traceability capability checks on the verification data to obtain the verification result of the quantity data.

[0104] Traceability data is converted data that can be traced back to the SI Digital Reference Point (SIRP).

[0105] The verification results of the measurement data reflect whether the verification data has the ability to be traced back to SIRP.

[0106] In some embodiments, the machine traceability module may include an enumeration type definition unit and a metadata description unit.

[0107] An enumeration type definition unit is used to provide standardized identification information for units of measurement and prefixes, and to obtain traceability information from the units in the converted data to their authoritative definitions; the unit definition is compared with the verification data to obtain the unit definition comparison result.

[0108] In some embodiments, the enumerated type definition unit defines the complex types of all legal SI units and prefixes and their associated metadata frameworks, such as the unit "metre," whose type is defined as "".<xs:element name="metre"type="si:metreType" / > The invention defines the element type and its corresponding attributes for units, enabling unit reuse within the framework of this invention.

[0109] Traceability information is information that reflects the units in the transformed data to their authoritative definitions.

[0110] The unit definition comparison result reflects the comparison between the units in the verification data and the unit definitions in the enumerated type definition unit.

[0111] Metadata description units are used to define the relationships between elements and types based on the traceability information, construct data processing flows and logic, and obtain traceability data; based on the comparison results of the unit definitions, traceability capability checks are performed to obtain quantitative data verification results. For example, for the unit "ampere", it should include information such as unit name, symbol, identifier, and unit type, with its unit name being "unitName=ampere", symbol being "symbol=A", identifier being "dsiId=\ampere", and unit type being "type=SI base unit", etc.

[0112] The header file declaration module is used to generate a file based on the declaration information to obtain a digitally represented quantity value data; and to perform version, syntax, and format checks on the digitally represented quantity value data based on the declaration information to obtain declaration verification data.

[0113] Declaration information includes information about version and namespace details. For example, declaration information may include version declarations and namespace declarations.

[0114] A version declaration specifies the XML version, character encoding format (UTF-8), and version number of the metamodel, facilitating version management and identifying schema evolution. For example, when the declaration is "xs:schema version="2.1.0"" (using metamodel version 2.1.0), files using other version numbers will not pass validation.

[0115] A namespace declaration defines a prefix for the namespace, declaring the target namespace to which the elements and types defined in the metamodel belong. It distinguishes metamodel patterns from different sources, ensuring that the definition of the metamodel follows standard syntax and semantics, avoiding conflicts in element and type names, and guaranteeing the uniqueness and consistency of elements across different namespaces. For example, when the namespace is declared as "targetNamespace="https: / / www.nim.ac.cn / si"" (a metamodel published by the National Institute of Metrology), using frameworks published by other organizations or institutions will not pass validation.

[0116] The digitally represented quantity data is a file of digitally represented quantity data in a complete, universally formatted, version-compliant format.

[0117] In some embodiments, the enumeration type definition unit provides standardized identification information for units of measurement and prefixes, including their definitions and conversion relationships, thereby enabling traceability from units in the data to their authoritative definitions. The metadata description unit defines the relationships between elements and types; this relational information helps in constructing data processing flows and logic. During data exchange and integration, the system can use this relational information to determine how to correctly combine and process different types of data, ensuring data consistency throughout the system. Furthermore, for complex data structures, the metadata description provides guidance for understanding and processing these structures, avoiding errors during data operations and promoting data standardization and interoperability.

[0118] S2: Based on the digital representation meta-model of the unit system, the original quantity data is transformed to obtain the digitally represented quantity data.

[0119] In some embodiments, the processor may implement S2 based on the following steps: comparing and representing the basic concepts of the original quantity data to obtain comparison data; performing pattern conversion and standardization on the comparison data to obtain converted data; performing unit checking and tracing on the converted data to obtain traceable data; and generating a file from the traceable data based on the declaration information to obtain digitally represented quantity data.

[0120] In some embodiments, the processor can input the initial quantity data, such as "measured value is 20 m kg s⁻²," into the unit-based digital representation meta-model, and use the type definition module to confirm the category of the concepts contained in the input and determine the method of representation. "Measured value" indicates that the information is a quantity value and should be represented using the "value + unit" representation mode; "135 m kg s⁻²" indicates that the value is 135 and the unit is m kg s⁻², serving as comparative data for the initial quantity data.

[0121] In some embodiments, the processor may construct a strict mode field from the strict mode definition unit based on comparison data, such as in XML as " <strictunit> <value>"135<\value><\unit>m kg s-2<\unit><\strictUnit>" serves as strict mode check data. Regular expression pattern definition units are used to standardize the expression of the strict mode check data. For example, the field "<\unit>m kg s-2<\unit>" can be converted to the rule-compliant format "<\unit>\metre\kilogram\second\tothe{-2}<\unit>", resulting in the converted data.

[0122] In some embodiments, the processor can use enumerated type definition units to provide standardized identification information for units of measurement and prefixes. For example, "\metre\kilogram\second\tothe{-2}" can be compared to obtain a derived unit, which is "Newton", and can be represented as "\newton". This provides traceability information from the units in the conversion data to their authoritative definitions. Using metadata description units, based on the traceability information, the relationships between elements and types are defined, and data processing flows and logic are constructed. By tracing units to SIRP, machine traceability of units of measurement can be achieved. For example, after tracing, "\newton" can obtain its persistent identifier "https: / / si-digital-framework.org / SI / units / newton" as traceability data.

[0123] In some embodiments, the processor may utilize the header declaration module to, based on the declaration information, include "mkg s -2 "Unit conversion"<newton\> "Form, to obtain" <value>The information "135<\value><\unit>\newton<\unit>" is represented as a numerical value.

[0124] S3: Based on the digital representation meta-model of the unit system, the digitally represented quantity data is verified to obtain the quantity data verification result, thus completing the digital representation of the quantity data.

[0125] In some embodiments, the processor may implement S3 based on the following steps: based on the declaration information, perform version, syntax and format checks on the digitally represented quantity data to obtain declaration verification data; perform type definition and expression specification checks on the declaration verification data to obtain type check data; perform strict mode conversion and regular expression verification on the type check data to obtain verification data; and perform unit definition comparison and traceability checks on the verification data to obtain the quantity data verification result.

[0126] In some embodiments, the processor can utilize the header declaration module to perform syntax and format checks on the numerically represented quantity data. For example, it can check whether the input conforms to the metamodel version and namespace based on the version declaration (P001) and namespace declaration (P002) in the metamodel. Taking XML as an example, if the input prefix is ​​"si:unit", it conforms to the declaration; if it is "unit", it indicates that the namespace declaration is missing, and an error message should be returned as declaration verification data.

[0127] In some embodiments, the processor may utilize a type definition module to perform strict pattern conversion and regular expression validation on the type-checked data, for example, " <real>"Should include at least" <value>"and" <unit>"If either of the two fields is missing, the format is incorrect, an error message is returned, and the validation data is obtained."

[0128] In some embodiments, the processor can utilize a machine traceability module to perform unit definition comparison and traceability capability checks on the verification data. For example, it can... <unit>The content in the field is converted to " <strictunit>"Field pairs" <strictunit>The content "\newton" in the definition is checked, in the form of "\prefix? \unit\exponent?". If it does not conform to the rule, an error message is output. The check result is compared with metadata. For example, "\newton" will be identified as the exported unit "Newton". If the match fails in this step, an error message is output; if it succeeds, its traceability is further checked. Based on the "Newton" information, the metadata of SIRP stored in the enumeration type definition unit is checked to see if it can be traced. If not, an error message is output; if it can, the verification success information and the metadata generated in the process are output, and the value data verification result is obtained.

[0129] In some embodiments, the unit system digital representation meta-model can be used for the system's generation function. Paper or PDF versions of documents containing measurement data information can be input into the system or the system function can be called via API. The system calls the metadata model, and after identification and comparison, the metadata model returns a machine-interpretable and traceable XML format digital unit representation to the system. The system can then present the digital representation of the measurement data information on the front end or return it to the user via API. For the system's verification function, the user needs to upload the measurement data information to be verified to the system. The system calls the metadata model for comparison and verification, and after verification, the verification result is returned to the user through the user's upload channel.

[0130] In some embodiments of this specification, a method for digital representation of measurement data based on a meta-model can realize unambiguous, automated, end-to-end, and intelligent data exchange and integration of measurement data information between different systems, while meeting the requirements of measurement standards and information technology standards, and has significant advantages such as high accuracy, machine interpretability, and traceability.

[0131] (1) Ontology-based unit digitization schemes have various serialization forms, such as RDF, RDFS, and OWL. The XML format used in this invention has good universality and can be read and processed by different systems. Ontology-based unit digitization schemes have various structures, and different organizations define different unit systems, which lack consistency. This invention is developed based on authoritative standards and specifications in the field of metrology, and has universality and consistency. Although ontology-based unit digitization schemes provide relatively rich semantic information, they cannot be understood by computers and still require hard coding to utilize semantic information. The metadata model of this invention achieves machine interpretability and does not require different systems to hard-code to implement the function.

[0132] (2) The unit digitization solution based on unit identifiers relies on traditional string matching technology and cannot detect the same SI unit expression form of different non-SI units, while the metadata model defined in this invention can detect it; the unit digitization solution based on unit identifiers cannot determine different expression formats of the same unit, while the metadata model defined in this invention can identify them, for example, derive the non-SI expression form and SI unit expression form of the unit; the unit digitization solution based on unit identifiers only contains the basic name of the unit and the corresponding identifier string, lacking metadata description, while the metadata model proposed in this invention provides a metadata description framework, which describes the unit information more comprehensively according to the requirements of the SI manual;

[0133] (3) A single unit identifier does not contain the original definition data of the unit of measurement, which does not comply with the principle of traceability in metrology. This invention achieves the characteristic of machine traceability by tracing the unit definition back to the SI reference point.< / strictunit> < / strictunit> < / unit> < / unit> < / value> < / real> < / value> < / value> < / strictunit>

Claims

1. A method for digital representation of quantity data based on a meta-model, characterized in that, include: Constructing a digital representation meta-model of the unit system; Based on the aforementioned unit system digital expression meta-model, the original quantity data is transformed to obtain digitally represented quantity data; Based on the aforementioned unit system digital representation meta-model, the digitally represented quantity data is verified to obtain the quantity data verification results, thus completing the digital representation of the quantity data.

2. The method for digital representation of quantity data based on a meta-model according to claim 1, characterized in that, The unit system digital expression meta-model includes: The type definition module is used to compare and represent the basic concepts of the original quantity data to obtain comparison data; and to check the type definition and expression specifications of the declaration verification data to obtain type check data. A machine-interpretable module is used to perform pattern transformation and standardization on the comparison data to obtain transformed data; and to perform strict pattern transformation and regular expression validation on the type check data to obtain validated data. The machine traceability module is used to perform unit checks and traceability on the converted data to obtain traceable data; and to perform unit definition comparison and traceability capability checks on the verification data to obtain the verification result of the quantity data. The header file declaration module is used to generate a file based on the declaration information to obtain a digitally represented quantity value data; and to perform version, syntax, and format verification on the digitally represented quantity value data based on the declaration information to obtain declaration verification data.

3. The method for digital representation of quantity data based on a meta-model according to claim 2, characterized in that, The type definition module includes: The Real Quantity submodule is used to describe a single real quantity value and its related attributes using information including the measured value, unit, optional label, timestamp, and measurement uncertainty, to obtain comparative data representing the real quantity value; and to check the declaration verification data of the real quantity value to obtain type check data of the real quantity value. The constant quantum module is used to obtain comparative data representing the numerical values ​​and related characteristics of constant quantities by utilizing values, units, optional labels, timestamps, and the uncertainty and distribution information of specified constant quantities; it also checks the declaration verification data of constant quantities to obtain type check data of constant quantities. The Complex Quantity submodule is used to represent complex quantity values ​​in either Cartesian or polar coordinates using real, imaginary, and unit information, and to obtain comparison data of complex quantity values; it also checks the declaration verification data of complex quantity values ​​to obtain type check data of complex quantity values. The Real Quantity List submodule is used to represent independent measurements and multivariate vector quantities by utilizing multiple real quantity submodule elements, optional labels of the list, timestamps, units, and global measurement uncertainty declarations to obtain comparative data for the real quantity value list; it also checks the declaration verification data of the real quantity value list to obtain type check data for the real quantity value list. The Real Quantity XML List submodule is used to separate list data by spaces and obtain comparison data for a list of real quantity values ​​based on XML type; it also checks the declaration validation data of the list of real quantity values ​​based on XML type to obtain type check data for the list of real quantity values ​​based on XML type. The Complex Quantity List submodule is used to process a set of multiple complex quantity values ​​using multiple complex quantity submodule elements, list labels, timestamps, units, and uncertainty declarations to obtain comparison data for the complex quantity value list; and to check the declaration verification data of the complex quantity value list to obtain type check data for the complex quantity value list. The hybrid quantum module is used to add measurement values ​​in SI units and non-SI units to a machine-readable format using real numbers, complex numbers, various lists, and constant types to obtain comparative data of hybrid values; it also checks the declaration verification data of hybrid values ​​to obtain type check data of hybrid values. The expanded uncertainty submodule is used to define various parameters of expanded measurement uncertainty using uncertainty values, coverage factors, coverage probabilities, and optional distribution information, and obtain comparative data representing the structure of expanded measurement uncertainty; it also checks the declaration verification data of expanded uncertainty to obtain type check data of expanded uncertainty. The included interval submodule is used to describe the range and probabilistic characteristics of measurement uncertainty using information including standard uncertainty, interval minimum, interval maximum, and included probability, and to obtain comparative data representing a probabilistically symmetric included interval structure; it also checks the declared verification data of included intervals to obtain type check data of included intervals. The covariance matrix submodule is used to describe the covariance relationship between multidimensional measurements using multiple column elements containing covariance values ​​and their units, and to obtain comparative data of the covariance matrix; it also checks the declaration verification data of the covariance matrix to obtain type check data of the covariance matrix. The Elliptical Region submodule is used to describe the uncertainty range of multivariate measurements by defining the hyperellipsoidal inclusion region using the covariance matrix, inclusion factor, inclusion probability, and optional distribution information. It declares the uncertainties of complex values, lists of real values, and lists of complex values, and obtains comparative data representing the structure of the hyperellipsoidal inclusion region representing the uncertainty of multivariate quantities. It also checks the declaration verification data of the elliptical region to obtain the type check data of the elliptical region. The rectangular region submodule is used to describe the uncertainty of multivariate measurements through a hyperrectangular containing region using the covariance matrix, inclusion factor, inclusion probability, and optional distribution information. It declares the uncertainty of complex values, lists of real values, and lists of complex values, and obtains comparative data of the hyperrectangular containing region structure representing the uncertainty of multivariate quantities. It also checks the declaration verification data of the rectangular region to obtain the type check data of the rectangular region.

4. The method for digital representation of quantity data based on a meta-model according to claim 2, characterized in that, The machine-interpretable module includes: The strict mode definition unit is used to convert comparison data into strict mode data and type check data into strict mode data. The regular expression pattern definition unit is used to standardize the expression of the strict pattern transformation data based on regular expressions to obtain the transformation data; and to perform regular expression validation on the strict pattern check data to obtain the validation data.

5. The method for digital representation of quantity data based on a meta-model according to claim 2, characterized in that, The machine traceability module includes: An enumeration type definition unit is used to provide standardized identification information for units of measurement and prefixes, and to obtain traceability information from the units in the converted data to their authoritative definitions; the unit definition is compared with the verification data to obtain the unit definition comparison results; The metadata description unit is used to define the relationship between elements and types based on the traceability information, construct the data processing flow and logic, and obtain traceability data; based on the unit definition comparison results, perform traceability capability checks and obtain quantity data verification results.

6. The method for digital representation of quantity data based on a meta-model according to claim 1, characterized in that, S2 includes: By comparing and representing the basic concepts of the original quantitative data, comparative data is obtained. The comparison data is subjected to pattern transformation and standardized representation to obtain transformed data; The converted data is checked for units and traced to obtain traceable data; Based on the declaration information, the traceability data is processed to generate a file, resulting in a digitally represented quantity data.

7. The method for digital representation of quantity data based on a meta-model according to claim 1, characterized in that, S3 includes: Based on the declaration information, the digitally represented quantity data is checked for version, syntax, and format to obtain declaration verification data; The declaration verification data is checked for type definition and expression specifications to obtain type-checked data; The type of check data is subjected to strict pattern conversion and regular expression validation to obtain the validation data. The unit definition is compared and traceability is checked on the verification data to obtain the verification result of the quantity data.

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