CN115222284A - Digital twin system maturity evaluation method based on maturity model - Google Patents
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Abstract
The invention discloses a maturity model-based digital twin system maturity evaluation method, which comprises the following steps of: determining a digital twin system maturity level; constructing a digital twin system maturity evaluation factor; constructing a complete digital twin system maturity model based on the digital twin system maturity level and the digital twin system maturity evaluation factor; and calculating the evaluation result of the maturity of the digital twin system by using the maturity model of the digital twin system. According to the invention, an effective digital twin system maturity evaluation system is formed by constructing a digital twin system maturity model, the maturity evaluation requirements of most digital twin systems are met, a digital twin system construction blueprint can be drawn for digital twin practitioners in various fields, a general and easy-to-use construction level evaluation method is provided for the existing digital twin system, and strategic guidelines are provided for further optimization of the digital twin system, so that the comprehensive popularization and deep application of the digital twin are promoted.
Description
Technical Field
The invention belongs to the field of maturity evaluation of a digital twin system, and particularly relates to a maturity model-based method for evaluating the maturity of the digital twin system.
Background
The digital twin is an effective means for realizing the digital transformation and intelligent upgrade of the industry, and has important significance for accelerating the development of the digital economy and promoting the integration of the digital economy and the entity economy. In recent years, the digital twin theory has been studied by hundreds of families for singing, and has begun to be practiced by falling to the ground in various fields of manufacturing, agriculture, aerospace, transportation, medical treatment, electric power, and the like. With continuous maturity of new generation information technology, the requirements of enterprise digital transformation and intelligent upgrading are continuously increased, and the infrastructure supporting realization of digital twins is continuously perfected, the application practice of the digital twins is about to enter a blowout period, but at present, a tool or a method for evaluating the construction level of a digital twins system (or engineering) and guiding the existing digital twins system to optimize and upgrade is lacked, and the high quality and sustainable development of the digital twins are difficult to support. Therefore, it is necessary to provide a method for evaluating the maturity of a digital twin system that can be applied to various fields and uses various objects as physical entities.
Disclosure of Invention
In order to objectively evaluate the construction level of the existing digital twin system and further optimize the existing digital twin system or compare the construction levels of a plurality of digital twin systems, the invention provides a maturity model-based digital twin system maturity evaluation method, which can be used for effectively evaluating the maturity of the digital twin system by facing various fields and digital twin systems taking various objects as physical entities.
In order to achieve the purpose, the invention adopts the following technical scheme:
a digital twin system maturity evaluation method based on a maturity model comprises the following steps:
step (1), setting a digital twin system maturity grade;
step (2), constructing a digital twin system maturity evaluation factor;
step (3), constructing a digital twin system maturity model based on the digital twin system maturity level and the digital twin system maturity evaluation factor;
and (4) evaluating the maturity of the digital twin system by using the digital twin system maturity model.
Further, the step (1) is based on setting of the maturity level of the digital twin system, and specifically comprises six levels of simulating real L0 with a virtual model, reflecting real L1 with a virtual model, controlling real L2 with a virtual model, pre-real L3 with a virtual model, excellent real L4 with a virtual model and real-real symbiosis L5 with a virtual model. Wherein:
expressed in dashed and solid L0: the physical entity can be described and depicted by a digital twin model;
expressed as the dashed-solid L1: the real-time state and the change process of the physical entity can be reproduced by utilizing the digital twin model;
expressed as virtual control real L2: the operation process of the physical entity can be indirectly controlled by utilizing the digital twin model;
expressed as dashed pre-solid L3: the state of the physical entity in the future can be predicted by utilizing a digital twin model;
expressed by a virtual superior-solid L4: the operation and maintenance process of a physical entity can be optimized by using a digital twin model;
the deficiency-excess symbiosis L5 represents: the physical entities and the digital twin model are able to co-evolve and evolve over a full life cycle.
Further, the establishment of the evaluation factor of the maturity of the digital twin system in the step (2) is composed of the relevant capabilities of five elements of a physical entity, a digital twin model, digital twin data, connection interaction and functional service, and specifically includes 19 relevant capabilities:
the physical entity dimension has three evaluation factors of control, sensing and supporting facilities;
the digital twin model dimension has five evaluation factors of integrity, standardization degree, flexibility, interface and updating;
the digital twin data dimension has four evaluation factors of richness, compatibility, accessibility and quality;
the connection interaction dimension has four evaluation factors of a connection and interaction mode, interaction time delay, a coverage object range and interaction quality;
the functional service dimension has three evaluation factors of diversity, integration level and flexibility.
Further, the digital twin system maturity model in the step (3) describes the incidence relation between the digital twin system maturity level and the evaluation factor maturity level, so as to reach the digital twin system maturity of a certain level, and have different requirements on the maturity levels of the digital twin system maturity evaluation factors; the digital twin systems under different digital twin system maturity levels have different functions and capabilities; for any digital twin system maturity evaluation factor, the evaluation factor has different functions and capabilities under different maturity grades; the functions and the capabilities corresponding to the maturity levels of the digital twin system are presented in a progressive relationship, and the functions and the capabilities of the high maturity level are based on the functions and the capabilities of the low maturity level; the functions and the capabilities corresponding to the maturity grades of the maturity evaluation factors of each digital twin system are presented in a progressive relationship, and the functions and the capabilities of the high maturity grade are based on the functions and the capabilities of the low maturity grade; the maturity grade of each digital twin system presents a progressive relation to the maturity grade of each digital twin system maturity evaluation factor, the maturity grade of each digital twin system maturity evaluation factor of a digital twin system with a high maturity grade is not lower than the maturity grade of each digital twin system maturity evaluation factor of a digital twin system with a low maturity grade.
Further, the specific steps of the step (4) are as follows:
(4.1) determining the maturity evaluation purpose of the digital twin system, such as comparing maturity levels of a plurality of digital twin systems, or optimizing the digital twin system based on the evaluation result;
(4.2) collecting relevant information of the digital twin system through modes of investigation, testing, questionnaire investigation, historical data statistics and the like according to relevant contents of the maturity grade of the maturity evaluation factor of the digital twin system, and grading the maturity of the evaluation factor of the digital twin system;
(4.3) grading the maturity of the digital twin system to be evaluated according to the requirement of the maturity grade of each digital twin system on the maturity evaluation factor maturity grade of each digital twin system;
(4.4) after qualitatively grading the maturity of the digital twin system, quantitatively evaluating the maturity of each dimensional element of the digital twin system according to the following formula:
wherein s is X Represents the maturity score of element X, X now Represents the highest level of the maturity of the digital twin system that all the evaluation factors of the element X can support together,
representing a numerical twin System maturity rating X now +1 the sum of the minimum maturity levels of the evaluation factors of element X, N represents the sum of the maturity levels of the evaluation factors of element X,
representing a numerical twin system maturity rating of X now The sum of the minimum maturity grades of the evaluation factors of the required element X.
(4.5) on the basis, the maturity of the digital twin system can be quantitatively evaluated according to the following formula:
wherein s is L Represents the numerical twin system maturity score, L now The highest level of the maturity of the digital twin system which can be supported by the maturity levels of all the evaluation factors of the five-dimensional elements,
representing a numerical twin system maturity level L now +1 sum of minimum maturity levels of evaluation factors of five-dimensional elementsM represents the sum of the maturity levels of the evaluation factors of the current five-dimensional elements,
representing a numerical twin system maturity level L now The sum of the minimum maturity grades of the evaluation factors of the required five-dimensional elements.
Has the beneficial effects that:
compared with the prior art, the method forms a set of effective and universal digital twin system maturity evaluation method by constructing the digital twin system maturity model, can qualitatively and quantitatively evaluate the existing digital twin system, further draws a digital twin system construction blueprint for digital twin practitioners in various fields, and provides strategic guidance for further optimization of the digital twin system, thereby promoting comprehensive popularization and deep application of the digital twin.
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FIG. 1 is a schematic flow chart of a maturity model-based digital twin system maturity evaluation method of the present invention.
Detailed Description
The present invention will be further described with reference to specific embodiments, it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and after reading the present invention, various modifications of equivalent forms of the present invention (including but not limited to adding corresponding weights to any scoring results, reconstructing scoring rules, reconstructing maturity rating contents, etc.) by those skilled in the art fall within the scope defined by the appended claims of the present application.
The invention relates to a maturity model-based digital twin system maturity evaluation method, which relates to four parts of a digital twin system maturity grade, a digital twin system maturity evaluation factor, a digital twin system maturity model and a digital twin system maturity model, forms an effective digital twin system maturity evaluation system, meets maturity evaluation requirements of most digital twin systems, can draw a digital twin system construction blueprint for digital twin practitioners in various fields, provides a general and easy-to-use construction level evaluation method for the existing digital twin system, and provides strategic guidelines for further optimization of the digital twin system, thereby promoting comprehensive popularization and deep application of digital twin.
According to an embodiment of the present invention, as shown in fig. 1, the following is implemented:
1 in fig. 1 represents a digital twin system maturity level, 2 in fig. 1 represents a digital twin system maturity evaluation factor, and 3 in fig. 1 represents a digital twin system maturity model;
step (1), analyzing the existing digital twin related theoretical research and application practice according to the existing digital twin theoretical research and the existing digital twin application case statistics, and mainly classifying the existing digital twin related theoretical research and application practice into the following categories according to the functions and the purposes of the existing digital twin related theoretical research and application practice:
(1) physical entity design verification and equivalent analysis based on digital twins;
(2) visually monitoring the operation process of a physical entity based on digital twins;
(3) remote operation and maintenance management and control of physical entities based on digital twins;
(4) digital twin-based diagnosis and prognosis;
(5) intelligent decision and optimization based on digital twins;
(6) and (3) carrying out full life cycle tracking, backtracking and management on the physical entity based on the digital twin. Through common analysis on various digital twin researches and applications, the physical entity, the digital twin model and the connection and interaction between the two form the 'minimum concept' of the digital twin system. On the basis of the minimum concept, setting a maturity level of the digital twin system, which comprises six levels including L0-L5, by taking the functional service which can be provided by the digital twin system as a main line according to the difference of the connection interaction mode and the automation degree, wherein each level is defined as follows: a virtual reality L0 represents that a physical entity can be described and depicted by a digital twin model; the real-time state and the change process of the physical entity can be reproduced by a digital twin model represented by a virtual reality L1; the virtual control real L2 represents the operation process of indirectly controlling a physical entity by utilizing a digital twin model; the virtual pre-real L3 represents the state of the physical entity which can be predicted for a period of time in the future by using a digital twin model; the virtual optimal reality L4 represents that the operation and maintenance process of the physical entity can be optimized by using a digital twin model; the virtual-real symbiosis L5 represents that the physical entity and the digital twin model can evolve and evolve together in the whole life cycle.
And (2) constructing the maturity evaluation factors of the digital twin system, and dividing the maturity level of each corresponding factor according to the evaluation content of each digital twin system maturity evaluation factor. It includes multidimensional elements such as physical entities, digital twinning models, digital twinning data, connectivity interactions and functional services. Wherein, the evaluation factors of the physical entity comprise control, sensing and supporting facilities. The evaluation content of the control is a control mode and whether the digital control can be realized or not; the sensed evaluation content is the type of data which can be sensed and whether the data quantity can meet the requirement; the evaluation content of the supporting facilities is whether the operation equipment, the storage equipment, the numerical control auxiliary tool, the network equipment and the related equipment for automatically inspecting, maintaining and reconstructing the physical entity are complete or not. Evaluation factors of the digital twin model comprise integrity, standardization degree, flexibility, interface and updating. The evaluation content of the integrity is whether a multi-dimensional model of geometry, physics, behavior and rules is available or not, and whether the physical entity can be vividly depicted in multiple aspects of appearance, internal structure, physical characteristics, behavior, relevant rules and the like or not; the evaluation content of the standardization degree is whether the format, the parameters, the interface and the description text are standardized or not, and whether the automatic analysis and the compatibility of the model can be realized or not; the flexibility evaluation content is whether the configuration, the assembly and the reconstruction can be carried out or not, and the form is not; the evaluation content of the interface is the model interface with the type and the number, and whether the interface can be reconfigured; and the updated evaluation content is whether all parameters and structures can be automatically updated or not and whether new structures and parameters can be intelligently generated or not. The evaluation factors of the digital twin data comprise richness, compatibility, accessibility and quality. The richness evaluation content is how many kinds and large-scale available data are possessed; the evaluation content of the compatibility is whether the data format is standardized and whether the data can be analyzed and compatible; the accessibility evaluation content is the channels from which data can be acquired and the available data can be accessed in any mode; the quality evaluation content includes whether data repetition, omission, ambiguity and error phenomena exist or not, and whether a data dynamic monitoring, verifying, evaluating and early warning mechanism exists or not. Evaluation factors of connection interaction include mode, delay, range and quality. The evaluation content of the mode is the mode in which the connection configuration can be carried out and the mode in which the interaction can be carried out; the evaluation content of the time delay is how long the interactive process can be completed; the evaluation content of the range is how many object elements can participate in the interactive process; the evaluation content of the quality is whether data repetition, missing, ambiguity and error phenomena exist or not, and whether a data dynamic monitoring, verifying, evaluating and early warning mechanism exists or not. The evaluation factors of the functional service include diversity, integration and flexibility. The diversified evaluation contents comprise various functions and services, such as perception, communication, visualization, remote control of physical entities, data processing, backtracking, prediction, diagnosis, evaluation, verification, intelligent decision and optimization, configuration, control and reconstruction of five-dimensional elements and the like; the evaluation content of the integration level is whether various functional services in the whole life cycle can be integrated under a compatible software environment; the flexible evaluation content is used for encapsulating services of various functions and realizing automatic configuration, request, matching, calling, combination, optimization and reconstruction.
And (3) constructing a digital twin system maturity model based on the digital twin system maturity level and the digital twin system maturity evaluation factor. The digital twin system maturity model describes the minimum requirements of the digital twin system maturity levels on the maturity levels of the 19 evaluation factors, so that the digital twin system maturity level can be indirectly calculated by determining the maturity levels of the 19 evaluation factors.
The digital twin system maturity model describes the incidence relation between the digital twin system maturity level and the evaluation factor maturity level, achieves the digital twin system maturity of a certain level, and has different requirements on the maturity levels of the digital twin system maturity evaluation factors; the digital twin systems under different digital twin system maturity levels have different functions and capabilities; for any digital twin system maturity evaluation factor, the evaluation factor has different functions and capabilities under different maturity levels; the functions and the capabilities corresponding to the maturity levels of the digital twin system are presented in a progressive relationship, and the functions and the capabilities of the high maturity level are based on the functions and the capabilities of the low maturity level; the functions and the capabilities corresponding to the maturity grades of the maturity evaluation factors of each digital twin system are presented in a progressive relationship, and the functions and the capabilities of the high maturity grades are based on the functions and the capabilities of the low maturity grades; the maturity grade of each digital twin system presents a progressive relation to the maturity grade of each digital twin system maturity evaluation factor, the maturity grade of each digital twin system maturity evaluation factor of a digital twin system with a high maturity grade is not lower than the maturity grade of each digital twin system maturity evaluation factor of a digital twin system with a low maturity grade.
And (4) evaluating the maturity of the digital twin system by using a digital twin system maturity model, wherein the specific steps are as follows:
(4.1) specifying the purpose of evaluating the maturity of the digital twin system, including comparing maturity levels of a plurality of digital twin systems, or optimizing the digital twin system based on the evaluation result;
(4.2) collecting related information of the digital twin system by means of investigation, testing, questionnaire investigation and historical data statistics according to related contents of the maturity level of the digital twin system maturity evaluation factor, and grading the maturity of the digital twin system evaluation factor;
(4.3) grading the maturity of the digital twin system to be evaluated according to the requirement of the maturity grade of each digital twin system on the maturity evaluation factor maturity grade of each digital twin system;
(4.4) after qualitatively grading the maturity of the digital twin system, quantitatively evaluating the maturity of each dimensional element of the digital twin system according to the following formula:
wherein s is X Represents the maturity score of element X, X now Represents the highest level of digital twin system maturity that all of the evaluation factors of element X can support together,
representing a numerical twin system maturity rating of X now +1 the sum of the minimum maturity levels of the evaluation factors of element X, N represents the sum of the maturity levels of the evaluation factors of element X,
representing a numerical twin system maturity rating of X now The sum of the minimum maturity grades of all evaluation factors of the required element X;
(4.5) on the basis, the maturity of the digital twin system can be quantitatively evaluated according to the following formula:
wherein s is L Represents the numerical twin system maturity score, L now The highest level of the maturity of the digital twin system which can be supported by the maturity levels of all the evaluation factors of the five-dimensional elements,
representing a numerical twin system maturity level L now +1 sum of minimum maturity levels of evaluation factors of five-dimensional elements, M represents sum of maturity levels of evaluation factors of five-dimensional elements,
representing a numerical twin system maturity level L now The sum of the minimum maturity grades of the evaluation factors of the required five-dimensional elements.
In conclusion, the invention discloses a maturity evaluation method of a digital twin system based on a maturity model, which comprises the use of a maturity grade of the digital twin system, a maturity evaluation factor of the digital twin system, the maturity model of the digital twin system and the maturity model of the digital twin system, so that an effective maturity evaluation system of the digital twin system is formed, the maturity evaluation requirements of most digital twin systems are met, a construction blueprint of the digital twin system can be drawn for digital twin system practitioners in various fields, a general and easy-to-use construction level evaluation method is provided for the existing digital twin system, and a guideline strategy is provided for further optimization of the digital twin system, thereby promoting the comprehensive popularization and deep application of the digital twin.
Those matters not described in detail in the present specification are well known in the art to which the skilled person pertains.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (5)
1. A digital twin system maturity evaluation method based on a maturity model is characterized by comprising the following steps: the method is characterized in that a digital twin system maturity model is constructed for a digital twin system or engineering composed of five dimensions of a physical entity, a digital twin model, digital twin data, connection interaction and functional service, and the digital twin system is evaluated for maturity based on the digital twin system maturity model, and specifically comprises the following steps:
step (1), setting a maturity grade of a digital twin system;
step (2), constructing a digital twin system maturity evaluation factor;
step (3), constructing a digital twin system maturity model based on the digital twin system maturity level and the digital twin system maturity evaluation factor;
and (4) evaluating the maturity of the digital twin system by using the digital twin system maturity model.
2. The maturity model-based digital twin system maturity evaluation method of claim 1, wherein the step (1) specifically comprises six levels of virtual true L0, virtual true L1, virtual true L2, virtual true L3, virtual good and true L4 and virtual true symbiosis L5; wherein:
expressed as a dashed solid L0: the physical entity can be described and depicted by a digital twin model;
expressed as the dashed-solid L1: the real-time state and the change process of the physical entity can be reproduced by utilizing a digital twin model;
expressed as virtual control real L2: the operation process of the physical entity can be indirectly controlled by utilizing the digital twin model;
expressed as dashed pre-solid L3: the state of the physical entity in the future can be predicted by utilizing a digital twin model;
expressed as virtual, superior and solid L4: the operation and maintenance process of a physical entity can be optimized by using a digital twin model;
the deficiency-excess symbiosis L5 represents: the physical entities and the digital twin model are able to co-evolve and evolve over a full life cycle.
3. The maturity model-based digital twin system maturity evaluation method of claim 1, wherein the digital twin system maturity evaluation factor in step (2) is composed of the correlation capabilities of five-dimensional elements of physical entity, digital twin model, digital twin data, connection interaction and functional service, specifically including the following 19 correlation capabilities:
the physical entity dimension has three evaluation factors of control, sensing and supporting facilities;
the digital twin model dimension has five evaluation factors of integrity, standardization degree, flexibility, interface and updating;
the digital twin data dimension has four evaluation factors of richness, compatibility, accessibility and quality;
the connection interaction dimension comprises four evaluation factors of a connection and interaction mode, interaction time delay, a coverage object range and interaction quality;
the functional service dimension has three evaluation factors of diversity, integration level and flexibility.
4. The maturity model-based digital twin system maturity evaluation method of claim 3, wherein the digital twin system maturity model of step (3) describes an association relationship between a digital twin system maturity level and an evaluation factor maturity level, and the maturity level of a certain level of the digital twin system is reached, and different requirements are provided for the maturity levels of the digital twin system maturity evaluation factors; the digital twin systems under different digital twin system maturity levels have different functions and capabilities; for any digital twin system maturity evaluation factor, the evaluation factor has different functions and capabilities under different maturity levels; the functions and the capabilities corresponding to the maturity levels of the digital twin system are in a progressive relationship, and the functions and the capabilities of the high maturity level are based on the functions and the capabilities of the low maturity level; the functions and the capabilities corresponding to the maturity grades of the maturity evaluation factors of each digital twin system are presented in a progressive relationship, and the functions and the capabilities of the high maturity grade are based on the functions and the capabilities of the low maturity grade; the maturity grade of each digital twin system presents a progressive relation to the maturity grade of each digital twin system maturity evaluation factor, the maturity grade of each digital twin system maturity evaluation factor of a digital twin system with a high maturity grade is not lower than the maturity grade of each digital twin system maturity evaluation factor of a digital twin system with a low maturity grade.
5. The maturity model-based digital twin system maturity evaluation method of claim 4, wherein the specific steps of step (4) are as follows:
(4.1) defining the maturity evaluation purpose of the digital twin system, including comparing maturity levels of a plurality of digital twin system applications, or optimizing the digital twin system based on the evaluation result;
(4.2) collecting related information of the digital twin system by means of investigation, testing, questionnaire investigation and historical data statistics according to related contents of the maturity level of the digital twin system maturity evaluation factor, and grading the maturity of the digital twin system evaluation factor;
(4.3) grading the maturity of the digital twin system to be evaluated according to the requirement of the maturity grade of each digital twin system on the maturity evaluation factor maturity grade of each digital twin system;
and (4.4) after qualitatively grading the maturity of the digital twin system, further quantitatively evaluating the maturity of each dimensional element of the digital twin system according to the following formula:
wherein s is X Represents the maturity score of element X, X now Represents the highest level of digital twin system maturity that all of the evaluation factors of element X can support together,
representing a numerical twin System maturity rating X now +1 the sum of the minimum maturity levels of the evaluation factors of element X, N represents the sum of the maturity levels of the evaluation factors of element X,
representing a numerical twin System maturity rating X now The sum of the minimum maturity grades of all evaluation factors of the required element X;
(4.5) quantitatively evaluating the maturity of the digital twin system on the basis of the following formula:
wherein s is L Represents the numerical twin system maturity score, L now The highest level of the maturity of the digital twin system which can be supported by the maturity levels of all the evaluation factors of the five-dimensional elements,
representing a numerical twin system maturity level L now +1 the sum of the minimum maturity levels of the evaluation factors of the five-dimensional elements, M represents the sum of the maturity levels of the evaluation factors of the current five-dimensional elements,
representing a numerical twin system maturity level L now The sum of the minimum maturity grades of the evaluation factors of the required five-dimensional elements.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115759876A (en) * | 2022-12-14 | 2023-03-07 | 简易治慧(上海)智能科技发展有限公司 | Digital twin geometric model maturity evaluation method and device and storage medium |
| CN116432323A (en) * | 2023-06-14 | 2023-07-14 | 南京航空航天大学 | Aircraft structure digital twin credibility assessment method based on Bayesian network |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115759876A (en) * | 2022-12-14 | 2023-03-07 | 简易治慧(上海)智能科技发展有限公司 | Digital twin geometric model maturity evaluation method and device and storage medium |
| CN115759876B (en) * | 2022-12-14 | 2023-09-29 | 简易治慧(上海)智能科技发展有限公司 | Digital twin geometric model maturity assessment method, device and storage medium |
| CN116432323A (en) * | 2023-06-14 | 2023-07-14 | 南京航空航天大学 | Aircraft structure digital twin credibility assessment method based on Bayesian network |
| CN116432323B (en) * | 2023-06-14 | 2023-09-29 | 南京航空航天大学 | Aircraft structure digital twin credibility assessment method based on Bayesian network |
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