Digital twinborn grade evaluation and quantitative evaluation method

Abstract

The invention provides a digital twinning grade evaluation and quantitative evaluation method, which relates to the technical field of digital twinning, and comprises the following steps: after receiving a digital twinning system delivered by a supplier, judging whether the digital twinning system accords with basic definition conditions of digital twinning; if the digital twinning system meets the basic definition condition of digital twinning, judging whether the digital twinning system meets the corresponding judgment condition or not by taking the development trend of digital twinning as guidance, and grading the digital twinning system; and scoring the corresponding quantitative indexes item by item according to the divided grades, carrying out weighted summation on scoring results in combination with the determined weighting coefficient, and calculating the credibility score of the digital twin system. According to the digital twinborn system grade evaluation and credibility evaluation method suitable for different application scenes, the problem that the system development level cannot be comprehensively reflected by single scoring is avoided in a mode of combining qualitative grading and quantitative scoring.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and in particular to a digital twin grading and quantitative evaluation method. Background Art

[0002] With the rapid development of digital twin technology, more and more companies are beginning to pay attention to how to effectively use digital twins to improve production efficiency and product quality. However, there is currently a lack of a widely accepted general evaluation method to measure the capabilities and development level of digital twin systems. In addition, the credibility of digital twin systems has also become an important factor hindering their widespread application. Therefore, it is particularly necessary to develop a method that can both grade digital twin systems and scientifically evaluate their credibility. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the first objective of this application is to propose a digital twin rating and quantitative evaluation method.

[0005] The second purpose of this application is to propose a digital twin rating and quantitative evaluation device.

[0006] The third objective of the present application is to provide an electronic device.

[0007] A fourth objective of the present application is to provide a computer-readable storage medium.

[0008] A fifth object of the present application is to provide a computer program product.

[0009] To achieve the above objectives, the first embodiment of the present application proposes a digital twin rating and quantitative evaluation method, including:

[0010] After receiving the digital twin system delivered by the supplier, determine whether the digital twin system meets the basic definition conditions of digital twin, including whether it has a physical entity, a corresponding digital twin, and an automatic flow of data from the physical entity to the digital twin model;

[0011] If the digital twin system meets the basic definition conditions of the digital twin, the development trend of the digital twin is used as a guide to determine whether the digital twin system meets the corresponding judgment conditions, and the digital twin system is graded;

[0012] The corresponding quantitative indicators are scored item by item according to the divided levels, and the scoring results are weighted and summed in combination with the determined weighting coefficients to calculate the credibility score of the digital twin system.

[0013] Optionally, the digital twin system is divided into levels I to V, and the determination conditions for each level include:

[0014] Level I digital twin: primary form, with basic functions of digital twin, emphasizing basic authenticity and dynamic real-time, including the following conditions:

[0015] 1) Whether the geometric modeling and behavioral modeling of production factors are available;

[0016] 2) Whether the functional structure is basically consistent with the physical entity;

[0017] 3) Whether there is real-time data mapping from physical entities to virtual models;

[0018] Level II digital twin: emphasizes the transformation from qualitative description to quantitative description, quantitatively characterizes the changes in the appearance of the object entity, and keeps consistent with the physical entity from the dimension of data. On the basis of level I digital twin, the following conditions are added:

[0019] 4) Whether multiple sensors are available to collect key data and whether the sensor data is reliable;

[0020] 5) Whether the digital twin can reflect the dynamic characteristics of the physical entity and whether the total output is credible;

[0021] Level III digital twin: It is required to reflect the physical mechanism characteristics of the object, be able to perform principle analysis of complex physical systems, and emphasize the mechanism interpretability of the digital twin model. On the basis of Level II digital twin, the following conditions are added:

[0022] 6) Whether it contains physical models and data models that reflect key performance;

[0023] 7) Whether the key node indicators within the system can be analyzed for consistency and quantification;

[0024] Level IV digital twin: has the ability to evolve with the physical system, emphasizing the model's follow-up evolution performance during the system life cycle. Based on Level III digital twin, the following conditions are added:

[0025] 8) Whether the virtual model has the ability to follow the real-time evolution of the physical entity;

[0026] 9) During the operation phase of the digital twin system, the dynamic consistency of each key output continues to meet the indicators;

[0027] 10) The data generated by digital twins can be used for diagnosis, prediction, optimization and decision-making in actual production;

[0028] Level V digital twins: have strong consistency and trustworthiness of the model, form a closed loop with the physical entity throughout the entire life cycle, and add the following conditions to level IV digital twins:

[0029] 11) The digital twin system remains consistent with the physical entity throughout its life cycle;

[0030] 12) The digital twin system is trustworthy enough to directly operate and control the physical system.

[0031] Optionally, scoring the corresponding quantitative indicators item by item according to the divided levels includes:

[0032] The corresponding quantitative indicators are scored item by item through the scoring formulas of conditions 1-12, where the scoring formulas of conditions 1-12 are:

[0033] Scoring formula for condition 1:

[0034]

[0035] Among them, S1 represents the degree of completeness of geometric modeling and behavioral modeling of the digital twin system in condition 1, and the total score is 1. DNG Num is the number of common geometric models required by the user. SNG is the number of common geometric models provided in the digital twin system, Num DIG Num is the number of important geometric models required by the user. SIG Num is the number of important geometric models provided in the digital twin system. DB is the number of behavior models required by the user, Num SB The number of behavioral models provided for the digital twin system; ω 111 ,ω 112 They are general geometry model weight and important geometry model weight respectively;

[0036] Scoring formula for condition 2:

[0037]

[0038] Among them, S2 is the score reflecting the similarity between the digital twin and the physical entity in terms of functional structure, Sc i is the structural score of the i-th model, n is the number of models that need to be scored, Sf j is the function score of the jth model, m is the number of models that need to be scored; ω 21 ,ω 22 They are the structural and functional model weights of the digital twin model, respectively;

[0039] Scoring formula for condition 3:

[0040]

[0041] Among them, S3 reflects the real-time data mapping effect of the physical entity to the digital twin model, Sb i It is a Boolean value, indicating whether the i-th state quantity that the digital twin needs to interact with in real time is available. i Indicates the network transmission performance of the state quantity, Sp i Score the representation and logic of the state in the digital twin, Pb j It is a Boolean value, indicating whether the j-th process data that the digital twin needs to interact with in real time is available. j To reflect the accuracy of process data, Pp j For the display effect of the process data, Qb k Qa is a Boolean value, indicating whether the k-th quality data that the digital twin needs to interact with in real time is available. k To reflect the accuracy of the quality data, Qd k For the data delay, Qu k Indicates the availability of the quality data, ω 31 ,ω 32 ,ω 33 The items are weighted by the user according to their importance to the data type;

[0042] Scoring formula for condition 4:

[0043]

[0044] Among them, S4 is the credibility of the sensor data used to collect key parameters, n is the number of key data that need to be collected by the sensor, and Ss i is the stability of the i-th key sensor data, Sp i is the accuracy of sensor data, Sl i is the linearity of the sensor data, Sr i is the anti-interference ability of the sensor, So i are other factors that affect the reliability of the sensor, ω 41 ,ω 42 ,ω 43 ,ω 44 ,ω 45 The corresponding weights of sensor data stability, accuracy, linearity, anti-interference performance and other factors on credibility;

[0045] Scoring formula for condition 5:

[0046]

[0047] Among them, S5 reflects the overall credibility of the output of the geometric model and behavioral model of the digital twin model, f1, f2, f3, and f4 represent the difference analysis methods used for different output data types, corresponding to the output data types of random data, deterministic data, periodic time series data, and non-periodic time series data, respectively, and ω 51 ,ω 52 ,ω 53 ,ω 54 is the importance weight of each data type, u i 、u j 、u k 、u l are the normalization coefficients corresponding to the difference analysis methods used for different types of data, so that the total output credibility of each model satisfies uf∈[0,1], Od is the output of the digital twin model, and Ot is the theoretical measurement result of the corresponding data;

[0048] Scoring formula for condition 6:

[0049]

[0050] Among them, S6 represents the completeness of the key physical model and data model in terms of quantity, Num DFm Indicates the number of physical models and data models required by the user, Num SFm Indicates the number of physical models and data models provided by the digital twin system;

[0051] The scoring formula for condition 7 is:

[0052]

[0053] Among them, S7 reflects the output credibility of the physical model and data model in the digital twin, which is expressed as the weighted sum of the credibility data scores of each key node in each physical model in the digital twin, f(Od ij ,Ot ij ) represents the numerical representation obtained after differential analysis of the jth performance parameter of the i-th physical model, u ij is the normalization parameter, ω ij is the weight of the performance parameter in the model, where m is the number of parameters that need to be analyzed for credibility in the i-th physical model, Fm i is a Boolean value, 0 means that the physical model does not exist, 1 means that it exists; n is the number of physical models, ω i is the weight value of the physical model in all physical models, where

[0054] The scoring formula for condition 8 is:

[0055]

[0056] Among them, S8 reflects the strength of the digital twin model's ability to follow the real-time evolution of the physical entity, n is the number of performance parameters that need to have real-time evolution capabilities, and Ar i is a Boolean value, indicating whether the i-th performance parameter of the required digital twin model has the ability to evolve in real time;

[0057] The scoring formula for condition 9 is:

[0058]

[0059] Among them, S9 is used to evaluate the dynamic credibility of the model during the evolution process. The performance parameters considered in S9 are selected from S7. The meanings of the parameters in S9 and S7 are the same. The difference is that S7 performs offline calculations based on the collected historical data, while the calculation of S9 runs through the entire life cycle of the digital twin and needs to be embedded in the digital twin system as part of the running program.

[0060] The scoring formula for condition 10 is:

[0061]

[0062] Among them, S 10 Fp reflects the strength of the extended functions such as analysis and prediction of the digital twin system. i Fp is a Boolean value, indicating whether the digital twin system has a certain required extended function. i Rate the technical experts' confidence in the feature;

[0063] The scoring formula for condition 11 is:

[0064]

[0065] Among them, S 11 Reflects the consistency of various performance parameters of the system with the physical entity throughout the system's life cycle, S 11 The evaluation method used is the same as S9, but includes more comprehensive scoring parameters;

[0066] The scoring formula for condition 12 is:

[0067]

[0068] Among them, S 12 Reflects the completeness of the physical system’s open control interface to the digital twin. i is a Boolean value indicating whether the i-th control interface is open, ω i is a weight parameter, reflecting the importance of the i-th control interface, where

[0069] Optionally, the credibility score calculation formula for different levels of digital twin systems is:

[0070] The calculation formula for the credibility score of the Level I digital twin system is:

[0071] S I =ω1·S1+ω2·S2+ω3·S3

[0072] Among them, ω1, ω2, and ω3 correspond to the weights of S1, S2, and S3 respectively, where ω1+ω2+ω3=1;

[0073]

[0074] Among them, ω1~ω5 correspond to the weights of S1~S5 respectively.

[0075]

[0076] Among them, ω1~ω7 correspond to the weights of S1~S7 respectively.

[0077]

[0078] Since the calculation of S9 includes S7, i≠7, and ω1~ω9 correspond to S1~S 10 The weight of

[0079]

[0080] Among them, due to S 11 The calculation includes S7 and S9, so i≠7,9, ω1~ω 12 Corresponding to S1~S 12 The weight of

[0081] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a digital twin rating and quantitative evaluation device, including:

[0082] A judgment module is used to judge whether the digital twin system meets the basic definition conditions of the digital twin after receiving the digital twin system delivered by the supplier, including whether it has a physical entity, a corresponding digital twin, and an automatic flow of data from the physical entity to the digital twin model;

[0083] A level classification module, which is used to determine whether the digital twin system meets the corresponding judgment conditions and classify the digital twin system if the digital twin system meets the basic definition conditions of the digital twin;

[0084] The scoring module is used to score the corresponding quantitative indicators item by item according to the divided levels, and to perform weighted summation on the scoring results in combination with the determined weighting coefficients to calculate the credibility score of the digital twin system.

[0085] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0086] The memory stores computer-executable instructions;

[0087] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0088] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0089] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, which implements any method in the first aspect when executed by a processor.

[0090] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0091] Through the method of comprehensive qualitative and quantitative evaluation, the target digital twin system is first graded, and then quantitatively scored within the established level. The level is divided according to the development trend of digital twins. The level reflects the development level of the target digital twin system. Compared with most of the current horizontal evaluation methods that use software and hardware capability indicators as evaluation factors, it has the advantage of guiding the system to iteratively update; the intra-level scoring is based on the credibility and availability of the system, reflecting the user's confidence in the decision to apply the target digital twin system to actual production. The lower the level of the target system, the fewer quantitative indicators are required. This evaluation method is more suitable for evaluating the digital twin systems currently deployed by enterprises. In addition, the method of qualitative rating first and then quantitative scoring avoids the limitation that a single indicator cannot highlight the advantages and disadvantages of the target system. It has important guiding significance for users to write indicator requirements before implementing digital twins and quantitatively accept them one by one after implementation.

[0092] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0094] Figure 1 A framework diagram of a digital twin rating and quantitative evaluation method provided in an embodiment of the present application;

[0095] Figure 2 A flowchart of a digital twin rating and quantitative evaluation method provided in an embodiment of the present application;

[0096] Figure 3 A schematic diagram of the structure of a digital twin rating and quantitative evaluation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0097] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0098] In view of the technical problems raised by the prior art, the present application embodiment provides a digital twin rating and quantitative evaluation method. Figure 1 and 2 They are respectively a framework diagram and a flow chart of a digital twin rating and quantitative evaluation method provided in an embodiment of the present application.

[0099] The digital twin rating and quantitative evaluation method provided in the embodiment of the present application is not only used to guide the project acceptance after the digital twin is implemented, to determine whether the system functions are complete, whether the system quality is qualified, whether it meets the design requirements, whether it has advantages compared with similar products, and whether its capabilities have been improved compared with historical versions, etc. It is also used as a reference standard for users to write requirements before project implementation. Even for advanced digital twin systems, the evaluation runs through the entire life cycle of the digital twin system, and performance evaluation is carried out periodically or in real time to achieve system adaptation and self-update, so that the digital twin system can evolve and age with the physical entity.

[0100] like Figure 2 As shown, the method comprises the following steps:

[0101] Step 101, after receiving the digital twin system delivered by the supplier, determine whether the digital twin system meets the basic definition conditions of the digital twin.

[0102] It should be noted that if Figure 1As shown in the figure, before deploying digital twins, enterprise users need to write a project requirement document containing all quantitative indicators based on the expected evaluation level and corresponding evaluation indicators, combined with the available hardware and software conditions (including hardware such as multi-source sensors, servers, high-speed networks, equipment documentation for modeling, and related professional and technical personnel, etc.). The digital twin supplier builds the digital twin system based on the requirement document, tests and improves the system based on the evaluation indicators, and then delivers it to the enterprise user.

[0103] After receiving the digital twin system from the supplier, the enterprise user conducts an evaluation of the target digital twin system based on the evaluation system indicators. First, the basic conditions are determined to confirm whether it meets the basic conditions defined by the digital twin. In order to make the evaluation process more widely applicable, the basic conditions for determining the digital twin in the embodiment of the present application are: whether there is a physical entity, a corresponding digital twin, and a data flow from the physical entity to the digital twin model automatically.

[0104] It should be noted that in some reviews of existing solutions, digital shadows and digital twins are divided according to whether there is an automatic data flow from the digital twin to the corresponding physical entity in the reverse direction. Considering that in most industrial manufacturing systems, direct operation or modification of equipment requires strict verification and review, and in published papers, direct manipulation from digital twins to physical entities is rarely mentioned, therefore, the digital twins mentioned in the embodiments of this application generally include digital shadows and digital twins, and the digital twins mentioned later only require an automatic data flow from the physical entity to the digital twin model.

[0105] Step 102: If the digital twin system meets the basic definition conditions of the digital twin, the development trend of the digital twin is used as a guide to determine whether the digital twin system meets the corresponding judgment conditions, and the digital twin system is graded.

[0106] In the embodiment of the present application, if the digital twin system meets the basic definition conditions of the digital twin, that is, after being determined to be a digital twin, a team of technical experts with a high level of relevant knowledge in the enterprise will carry out subjective level classification based on whether the corresponding judgment conditions are met. It should be noted that due to the large differences in evaluations between technicians due to individual differences in habits and knowledge levels, subjective grading is not the final evaluation result. The final result needs to be determined based on the score within the level. If the score is too low, the target digital twin system will be downgraded.

[0107] In the embodiment of the present application, the levels of the digital twin system are divided into level I to level V, and the high level includes the conditions of the low level. Level I includes conditions 1 to 3, level II includes conditions 1 to 5, level III includes conditions 1 to 7, level IV includes conditions 1 to 10, and level V includes conditions 1 to 12. The digital twin level determination is shown in Table 1.

[0108]

[0109] Specifically, Level I digital twins are in primary form, have the basic functions of digital twins, emphasize basic authenticity and dynamic real-time, and include three conditions:

[0110] 1) Whether the geometric modeling and behavioral modeling of production factors are available. Geometric modeling is mainly based on the geometric modeling of equipment. Digital twins need to establish three-dimensional geometric models for related equipment to achieve visualization. The granularity of each device's geometric model is determined according to its importance in the scene. Considering the difficulty of behavioral modeling such as model evolution and degradation, the behavioral modeling defined here only involves the dynamic behavior of the corresponding device model during the real-time mapping process of the digital twin. In addition, users can also import the operator's character geometric model and behavioral model into the digital twin scene as needed.

[0111] 2) Whether the function and structure are basically consistent with the physical entity. As a digital mirror of a physical entity, the digital twin must be basically consistent with the physical entity in function and structure to achieve the twin effect. The consistency of structural functions is determined according to the needs of the project, such as whether the system, subsystem and unit module are consistent in appearance and behavior, and whether the twin model of the equipment is consistent with the function of the corresponding physical equipment.

[0112] 3) Whether there is real-time data mapping from physical entities to virtual models. The main difference between digital twins and traditional simulations is that digital twins can use the current data of physical entities as input in real time. Even low-level digital twins must be able to receive information obtained by online sensor measurements and monitoring in real time, including equipment status data (equipment operation status information, process parameter information, fault information, etc.), production process data and quality data (such as location information of key parts, physical deformation data, etc.), and realize real-time mapping of key states of digital twins. In the Level I digital twin system, the key state changes of complex physical systems are intuitively presented through high-resolution and low-latency three-dimensional display technology, which can solve the problem of personnel not being able to discover equipment emergencies in a timely manner during actual production, and greatly shorten the equipment fault location and troubleshooting cycle.

[0113] Level II digital twins emphasize the transition from qualitative description to quantitative description, quantitatively characterizing the changes in the appearance of the object entity, and maintaining consistency with the physical entity from the data dimension. Level II digital twins require the three conditions of level I digital twins and two more conditions:

[0114] 4) Whether it has multiple sensors to collect key data and whether the sensor data is credible. This level of digital twin has the ability to map the dynamic performance of physical entities in real time, so it is necessary to distribute corresponding sensors in the key parts of the physical object, and the measurement accuracy of the sensors must meet the requirements. Sensor installation errors or excessive noise will produce low-quality digital twin input data, which will directly affect the credibility of the results presented by the digital twin.

[0115] 5) Whether the digital twin reflects the dynamic characteristics of the physical entity and the total output is credible. The digital twin system includes static models and dynamic models. Static models are mostly used to build environmental scenarios. In order to reduce hardware pressure, it is usually not necessary to arrange sensors and physical entities for real-time mapping. The dynamic model controls the behavior of the model accordingly based on the information sent back by the sensor. It is mostly used in key parts. It is necessary to consider whether the communication delay meets the requirements and whether the actions are consistent. Comparing the deviation of the total output of the physical entity and the digital twin model is the simplest and most effective way to evaluate the credibility of the digital twin. If the root mean square error or correlation coefficient of the end position of the robot arm and the corresponding digital twin output in the same time period meets the index requirements, it can basically reflect that the joints and connecting rods of the robot arm are consistent with the physical entity.

[0116] Level III digital twins are required to reflect the physical mechanism characteristics of objects, be able to perform principle analysis of complex physical systems, and emphasize the mechanism interpretability of digital twin models. On the basis of Level II digital twins, the following conditions are added:

[0117] 6) Whether it contains physical models and data models that reflect key performance. Geometric models and behavioral models focus on keeping the appearance consistent with the physical entity, while physical models reflect the internal laws of physical entities from the principles of dynamics, kinematics, thermodynamics, etc. For example, during the grasping process of the robot, the dynamic model can reflect the force distribution of each mechanical link. These parameters are difficult to obtain directly from sensors, but can be solved through a dynamic model constructed from information such as material structure parameters, posture, and the mass of the grasped object. Since physical modeling requires developers to have strong professional knowledge, modeling is difficult and takes a long time. It is usually not necessary to perform physical modeling on all modules. Only key links with high interpretability requirements need to be physically modeled. When physical modeling conditions are not available but data is sufficient, data modeling can be used as an alternative.

[0118] 7) Whether the key node indicators within the system can be analyzed for consistency and quantification. Since the digital twin system in Level II does not have a physical model, its credibility is relatively simple to judge. The digital twin at this level completes the physical modeling of the key parts of the object, which can reflect the operating rules within the system from a mechanism point of view, and output and reflect the key characteristics of the system in real time and accurately. In order to simulate and predict the relevant states or parameter changes of physical entities and manage their health through digital twins, it is necessary not only to ensure that the total output of the system is credible, but also to ensure that the credibility of the key process nodes of each physical model within the system meets the requirements, so as to ensure that the analysis results of the digital twins can be reliably used for actual production analysis and decision-making.

[0119] Level IV digital twins have the ability to evolve with the physical system, emphasizing the model's ability to evolve over the system life cycle. Based on Level III digital twins, the following conditions are added:

[0120] 8) Whether the virtual model has the ability to follow the real-time evolution of the physical entity. This level of digital twin has a rich data acquisition device, which can obtain the slowly changing data of the key parts of the physical entity, and dynamically modify the model parameters through algorithms based on historical data and real-time data, so as to achieve the "symbiosis and growth" of the model and the corresponding physical entity, and maintain consistency throughout the life cycle of the system.

[0121] 9) During the operation phase of the digital twin system, the dynamic consistency of each key output continues to meet the indicators. The digital twin at this level must not only meet the consistency requirements of each key node output at the initial state of the digital twin system, but also meet the dynamic consistency throughout the life cycle of the model. In other words, the consistency assessment continues over time, and after the physical system evolves or ages, the corresponding digital twin system still meets the consistency indicator requirements.

[0122] 10) The data generated by digital twins can be used for diagnosis, prediction, optimization and decision-making in actual production. That is, digital twins contain relevant rule models, which can continuously self-update and self-evolve based on historical data learning rules, so as to have the ability to predict, analyze and optimize physical entities. Digital twins can accurately reflect the various state characteristics of physical entities. By simulating and analyzing digital twins, the results can be directly involved in decision-making. For example, for the strain distribution of the robot arm, as time goes by, the deformation of the connecting rod causes the strain at a certain position to slowly increase. Through the simulation analysis of the digital twin, it is predicted that the robot arm will break in the near future. Since the consistency score obtained by the analysis of indicator 9) previously meets the credibility threshold, the digital twin system is credible enough, and the decision maker directly decides to replace the robot arm based on this analysis result. Level IV digital twins can meet the needs of most companies.

[0123] Level V digital twins have strong consistency and trustworthiness of the model, forming a closed loop with the physical entity throughout the entire life cycle. Based on Level IV digital twins, the following conditions are added:

[0124] 11) The digital twin system is consistent with the physical entity throughout its life cycle. This level of digital twin has the ability to evolve synchronously with the physical entity, and its flexibility, scalability and intelligence are significantly improved. The sensors throughout the system can automatically generate models based on changes on site, update the digital twin system, and perform consistency analysis and corrections. Users can directly perform analysis based on the digital twin system according to customized needs.

[0125] 12) The digital twin system is sufficiently credible that the digital twin can directly operate and control the physical system. This level of digital twins has a very high degree of credibility. Decision makers believe that the analysis results based on digital twins are highly scientific and safe, and open the interface for the digital twin to flow to the physical entity. Operators with permission can directly operate, modify and debug the physical object through the digital twin. A two-way closed loop is formed between the physical entity and the virtual digital twin.

[0126] It is understandable that the number and requirements of the above five levels of digital twin indicators increase step by step, which is consistent with the logic of enterprise product evaluation. Enterprises can refer to the level determination chart shown in Table 1, and scientifically set the target level in combination with their own hardware conditions and project expectations. During acceptance, the enterprise judges whether the target digital twin system has the ability to meet the specified conditions and whether it has reached the expected level. In addition, the change in level reflects the direction of iterative updates of the digital twin system. The difference in capabilities between levels can effectively guide enterprises to upgrade and transform the digital twin system in a targeted manner after hardware upgrades.

[0127] Step 103, scoring the corresponding quantitative indicators item by item according to the divided levels, and weighted summing the scoring results in combination with the determined weighting coefficients to calculate the credibility score of the digital twin system.

[0128] It is understandable that the level of digital twins is qualitatively evaluated by technical experts against hard conditions. Due to differences in individual subjective evaluations, it is difficult to have universal persuasiveness. Therefore, for the graded digital twin system, it is necessary to quantify and score according to the corresponding indicators to refine the credibility of the digital twin system within the evaluation level. Further confirmation of the evaluation level through quantitative scoring within the level is an effective means to increase its scientificity and rationality. When the additional indicator score value relative to the previous level within the rating does not meet the standard, the rating is downgraded accordingly to reduce the random deviation caused by qualitative judgment.

[0129] The following is a quantitative calculation formula for the scores of each indicator and a calculation method for the credibility score of each level of digital twins. Unless otherwise specified, the values ​​of the indicators mentioned in the formula that need to be scored are in the range of [0,1]. The closer the value is to 1, the better the performance is, and vice versa.

[0130] The quantitative calculation formula for the scores of conditions 1 to 12 is as follows:

[0131] Scoring formula for condition 1:

[0132]

[0133] Among them, S1 represents the degree of completeness of geometric modeling and behavioral modeling of the digital twin system in condition 1, and the total score is 1. DNG Num is the number of common geometric models required by the user. SNG is the number of common geometric models provided in the digital twin system, Num DIG Num is the number of important geometric models required by the user. SIG Num is the number of important geometric models provided in the digital twin system. DB is the number of behavior models required by the user, Num SB The number of behavioral models provided for the digital twin system; ω 111 ,ω 112 are the general geometric model weight and the important geometric model weight respectively, where ω 112 Value greater than ω 111 , and ω 111 +ω 112 =1;ω 11 ,ω 12 are the geometric model weight and the behavioral model weight respectively. Considering that digital twins have higher requirements for dynamic performance, ω is usually set 12 Value greater than ω 11 , and ω 11 +ω 12 =1.

[0134] Scoring formula for condition 2:

[0135]

[0136] Among them, S2 is the score reflecting the similarity between the digital twin and the physical entity in terms of functional structure. i Score the structure of the i-th model, and score it according to the refinement and accuracy of the model structure. n is the number of models that need to be scored, which are listed by the user according to project needs. jScore the function of the jth model, such as the indicator light status change of the power model and the gripping mapping function of the robot model, and evaluate the model according to its functional completeness. m is the number of models that need to be scored. 21 ,ω 22 are the weights of the digital twin model structure and function model, respectively. Their values ​​are set according to the relative importance of function and structure. Generally, they can be set to the same. 21 +ω 22 =1.

[0137] Scoring formula for condition 3:

[0138]

[0139] Among them, S3 reflects the real-time data mapping effect of the physical entity to the digital twin model, and comprehensively scores according to the interactive status data, process data and quality data. i It is a Boolean value, indicating whether the i-th state quantity that the digital twin needs to interact with in real time is available. i Indicates the network transmission performance of the state quantity, whether there is packet loss, whether the delay meets the system's requirements for timeliness, Sp i Score the representation and logic of the state in the digital twin. For example, fault status should usually be expressed in a prominent manner, and the fault status of key parts cannot be displayed on separate pages. Different faults should be differentiated in terms of their importance.

[0140] Pb j It is a Boolean value, indicating whether the j-th process data that the digital twin needs to interact with in real time is available. j To reflect the accuracy of process data, such as whether there are over-inspections or missed inspections in the number of products being produced and the number of inventory obtained by sensors, Pp j For the presentation effect of process data, for example, for inventory data, the effect of displaying a section of data is better than the single data at the current moment, and whether the time scale is more suitable in days or months, etc.

[0141] Qb k Qa is a Boolean value, indicating whether the k-th quality data that the digital twin needs to interact with in real time is available. k To reflect the correctness of the quality data, such as the joint angle data of the robot arm, the distance data collected by the ultrasonic sensor to reflect the deformation of the robot arm, Qd k Qd is the data delay. Since quality data is usually used to reflect changes in key parts, the real-time requirement is higher than that of status data and process data. It is necessary to consider the deterioration effect of data transmission delay on the system. kThe closer the value is to 1, the more severe the delay. k Indicates the availability of the quality data, such as whether the acquired joint angle data is used in the digital twin system, whether it is displayed in the form of a numerical pop-up window or dynamically drives the rotation of the robotic arm model.

[0142] ω 31 ,ω 32 ,ω 33 The user assigns weights based on the importance of the project to the data type, where ω 31 +ω 32 +ω 33 =1.

[0143] Scoring formula for condition 4:

[0144]

[0145] Among them, S4 reflects the credibility of sensor data used to collect key parameters. The data can come from a single sensor or the result of multiple sensor fusion. When considering credibility, the system has reached Level II. At this time, users use digital twins as an important auxiliary means of production monitoring and analysis. Sensors are key nodes for mapping physical entities to digital twins. The credibility of their output data has a direct impact on the simulation and analysis of digital twins. Among them, n is the number of key data that need to be collected by sensors, and Ss is i is the stability of the i-th key sensor data. The stability is determined by analyzing the repeated measurement results within a certain time range under the same background to determine whether the measurement results are consistent, whether there is zero drift, etc.; Sp i The accuracy of sensor data is obtained by comparing it with standard instruments. Different accuracy analysis methods can be selected according to the data type collected in different scenarios, such as random data or time series data; Sl i Sr is the linearity of the sensor data. For scenarios where dynamic data needs to be measured, it is necessary to consider whether the sensor has nonlinear errors. i For the anti-interference ability of the sensor, when the environment where the sensor is located is unstable, it is necessary to measure the anti-interference performance of the sensor under changes in temperature, humidity and electromagnetic environment; i Other factors that affect the reliability of sensors, such as the expected sensor life and failure rate through historical data analysis, and the performance degradation of sensors due to material wear in special environments. The closer the value is to 1, the greater the impact of other factors, and the lower the reliability of sensor data. 41 ,ω 42 ,ω 43 ,ω 44 ,ω 45They correspond to the weights of sensor data stability, accuracy, linearity, anti-interference performance and other factors on credibility, and their sum is 1. The weight assignment is given according to the actual situation on site. For example, in a relatively harsh environment, ω 44 and ω 45 The weight is relatively large, and for scenarios requiring precise measurement, the environment is usually stable, but the requirements for data accuracy, stability and linearity are relatively high.

[0146] Scoring formula for condition 5:

[0147]

[0148] Among them, S5 reflects the overall credibility of the output of the geometric model and behavioral model of the digital twin model. f1, f2, f3, and f4 represent the difference analysis methods used for different output data types

[40] , corresponding to the output data types: random data, deterministic data, periodic time series data, and non-periodic time series data. For random data, the t test, ks test, and chi-square test can be used. 2 For deterministic data, relative error, mean square error, etc. can be used; for periodic time series, frequency domain analysis methods such as maximum entropy spectrum analysis and wavelet analysis can be used; for non-periodic time series, correlation coefficients, Theil inequality coefficients, etc. can be used for evaluation. For the same data type in the same digital twin system, the same analysis method is usually used to reduce volatility. 51 ,ω 52 ,ω 53 ,ω 54 is the importance weight of each data type, u i 、u j 、u k 、u l They are the normalization coefficients corresponding to the difference analysis methods used for different types of data, so that the total output credibility of each model satisfies uf∈[0,1]. Od is the output of the digital twin model, and Ot is the theoretical measurement result of the corresponding data, which is generally the collection result of the sensor.

[0149] Scoring formula for condition 6:

[0150]

[0151] Among them, S6 represents the completeness of the key physical model and data model in terms of quantity, Num DFm Indicates the number of physical models and data models required by the user, Num SFm Indicates the number of physical models and data models provided by the digital twin system.

[0152] The scoring formula for condition 7 is:

[0153]

[0154] Among them, S7 reflects the output credibility of the physical model and data model in the digital twin, which is expressed as the weighted sum of the credibility data scores of each key node in each physical model in the digital twin. The physical model is used to reflect and analyze the mechanism of the physical entity, such as kinematic model, dynamic model, electromagnetic model, control model, etc. ij ,Ot ij ) represents the numerical representation obtained after differential analysis of the jth performance parameter of the i-th physical model, u ij is the normalization parameter, ω ij is the weight of the performance parameter in the model, where m is the number of parameters that need to be analyzed for credibility in the i-th physical model, Fm i is a Boolean value, 0 means that the physical model does not exist, 1 means that it exists; n is the number of physical models, ω i is the weight value of the physical model in all physical models, where

[0155] The scoring formula for condition 8 is:

[0156]

[0157] Among them, S8 reflects the strength of the digital twin model's ability to follow the real-time evolution of the physical entity, n is the number of performance parameters that need to have real-time evolution capabilities, and Ar i It is a Boolean quantity, indicating whether the i-th performance parameter of the required digital twin model has the ability to evolve in real time.

[0158] The scoring formula for condition 9 is:

[0159]

[0160] Among them, S9 is used to evaluate the dynamic credibility of the model during the evolution process. The calculation method is similar to S7, but it is relatively lighter. For the digital twin system, from an economic point of view, it is not necessary to consider the aging evolution of all components. Only the components that will have a significant impact on the system need to be considered. For example, in precision manufacturing, the manipulator that realizes the dynamic mapping of the model through the joint angle data collected by the sensor needs to continuously carry out credibility calculations to ensure that the deviation between the output of the model and the terminal output after the connecting rod is deformed is always within the credibility deviation threshold. The digital twin compares the virtual and real deviation results with the set threshold in real time throughout the life cycle of the system to determine whether the evolution is established, and dynamically adjusts the model parameters to realize the real-time evolution of the digital twin system. The performance parameters considered in S9 are selected from S7. The parameters in S9 and S7 have the same meaning. The difference is that S7 can perform offline calculations through the collected historical data, while the calculation of S9 runs through the entire life cycle of the digital twin and needs to be embedded in the digital twin system as part of the running program. For example, the credibility of 1,000 historical data points up to the current moment is calculated every hour, and the credibility of the digital twin system is continuously evaluated through the change curve of the S9 value.

[0161] The scoring formula for condition 10 is:

[0162]

[0163] Among them, S 10 It reflects the strength of the extended functions such as analysis and prediction of the digital twin system. i Fp is a Boolean value, indicating whether the digital twin system has a certain required extended function. i Technical experts rate the credibility of this function. The advantages of digital twins are not only reflected in the ability to trace back historical data and reflect the current status in real time, but also in the ability to perform online predictive analysis through digital twin models, which is more accurate than traditional simulation methods. Since the dynamic consistency of digital twins at this level has reached the set threshold, the output of digital twins is sufficiently credible, and users fully recognize that the health management of physical systems can be achieved through online simulation analysis and future state prediction in digital twins, such as production scheduling optimization of production lines, prediction of fault evolution trends of process node robots, and traceability of defective products.

[0164] The scoring formula for condition 11 is:

[0165]

[0166] Among them, S 11Reflects the consistency of various performance parameters of the system with the physical entity throughout the life cycle of the system. Compared with S9, which focuses on some key indicators, the hardware performance of digital twins that have reached level V, such as sensor accuracy, communication bandwidth, and computer speed, has been greatly improved. The system can simultaneously calculate more evolution parameters in real time, and the deviation between the digital twin model and the physical entity is further reduced. The credibility standard is also higher, and the digital twin system maintains high consistency with the physical entity throughout the life cycle. S 11 The evaluation method used is the same as S9, but includes more comprehensive scoring parameters. Considering that the digital twin system at this stage has a huge amount of performance parameters, and the various functions that the digital twin can have are already perfect, the system pays more attention to user experience. This score can also be scored by experts and users based on individual experience.

[0167] The scoring formula for condition 12 is:

[0168]

[0169] Among them, S 12 Reflects the completeness of the physical system’s open control interface to the digital twin. i is a Boolean value indicating whether the i-th control interface is open, ω i is a weight parameter, reflecting the importance of the i-th control interface, where At this point, the digital twin and the physical entity coexist and grow together, and the digital twin system has been fully tested and verified, and is sufficiently safe, trustworthy, and reliable, gaining the full trust of users. Users open the control interface of the physical system to the digital twin system, and users achieve real-time behavior mapping of the physical entity through the operation of the digital twin, thus realizing the information closed loop between the physical system and the digital twin.

[0170] In the embodiment of the present application, according to the level classification rules obtained from Table 1, the following is a calculation method for the credibility score corresponding to each level. The digital twin system rated at a higher level needs to use more indicators for comprehensive scoring.

[0171] The calculation formula for the credibility score of the Level I digital twin system is:

[0172] S I =ω1·S1+ω2·S2+ω3·S3

[0173] Among them, ω1, ω2, and ω3 correspond to the weights of S1, S2, and S3 respectively, where ω1+ω2+ω3=1;

[0174]

[0175] Among them, ω1~ω5 correspond to the weights of S1~S5 respectively.

[0176]

[0177] Among them, ω1~ω7 correspond to the weights of S1~S7 respectively.

[0178]

[0179] Since the calculation of S9 includes S7, i≠7, and ω1~ω9 correspond to S1~S 10 The weight of

[0180]

[0181] Among them, due to S 11 The calculation includes S7 and S9, so i≠7,9, ω1~ω 12 Corresponding to S1~s 12 The weight of

[0182] It can be understood that the above gives a relatively general credibility scoring method. Users score each item according to the digital twin level assessed by technical experts, and then refer to the corresponding conditions that should be met within the level, combined with the indicators that the project itself needs to consider, and set weights according to the company's preference for the importance of each condition and indicator. Finally, the weighted sum is obtained to obtain the credibility score value of the corresponding level. Considering that different industries and projects consider different types and quantities of parameters and different levels of emphasis on various indicators, users need to select and solidify according to their own system characteristics, and use the same set of weight parameters throughout the life cycle of the digital twin system to serve the comparison between iterative versions of the digital twin system.

[0183] In addition, the scores of each detailed parameter in each sub-indicator are scored by the company's technical experts. In order to reduce the scoring deviation caused by differences in knowledge level and personal preferences of the technical expert team, the scoring standards of specific indicators should be standardized before formulating project requirements. For example, in a project requirement, the Si that reflects the network transmission performance of status data i For scoring, please refer to the example of indicator scoring standards shown in Table 2.

[0184] Table 2

[0185]

[0186] In order to implement the above embodiments, the present application also proposes a digital twin grading and quantitative evaluation device. Figure 3 A schematic diagram of the structure of a digital twin rating and quantitative evaluation device provided in an embodiment of the present application. Figure 3As shown, the device comprises:

[0187] A judgment module is used to judge whether the digital twin system meets the basic definition conditions of digital twins after receiving the digital twin system delivered by the supplier, including whether it has a physical entity, a corresponding digital twin, and an automatic flow of data from the physical entity to the digital twin model;

[0188] The level classification module is used to determine whether the digital twin system meets the corresponding judgment conditions and classify the digital twin system if the digital twin system meets the basic definition conditions of the digital twin;

[0189] The scoring module is used to score the corresponding quantitative indicators item by item according to the divided levels, and to perform weighted summation of the scoring results in combination with the determined weighting coefficients to calculate the credibility score of the digital twin system.

[0190] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0191] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0192] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0193] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0194] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0195] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0196] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0197] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0198] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0199] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0200] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0201] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0202] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0203] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

[0204] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0205] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A digital twin rating and quantitative evaluation method, characterized in that: The following steps are involved: After receiving the digital twin system delivered by the supplier, determine whether the digital twin system meets the basic definition conditions of digital twin, including whether it has a physical entity, a corresponding digital twin, and an automatic flow of data from the physical entity to the digital twin model; If the digital twin system meets the basic definition conditions of the digital twin, the development trend of the digital twin is used as a guide to determine whether the digital twin system meets the corresponding judgment conditions, and the digital twin system is graded; The corresponding quantitative indicators are scored item by item according to the divided levels, and the scoring results are weighted and summed in combination with the determined weighting coefficients to calculate the credibility score of the digital twin system.

2. The method according to claim 1, characterized in that The digital twin system is divided into levels I to V, and the criteria for determining each level include: Level I digital twin: primary form, with basic functions of digital twin, emphasizing basic authenticity and dynamic real-time, including the following conditions: 1) Whether the geometric modeling and behavioral modeling of production factors are available; 2) Whether the functional structure is basically consistent with the physical entity; 3) Whether there is real-time data mapping from physical entities to virtual models; Level II digital twin: emphasizes the transformation from qualitative description to quantitative description, quantitatively characterizes the changes in the appearance of the object entity, and keeps consistent with the physical entity from the dimension of data. On the basis of level I digital twin, the following conditions are added: 4) Whether multiple sensors are available to collect key data and whether the sensor data is reliable; 5) Whether the digital twin can reflect the dynamic characteristics of the physical entity and whether the total output is credible; Level III digital twin: It is required to reflect the physical mechanism characteristics of the object, be able to perform principle analysis of complex physical systems, and emphasize the mechanism interpretability of the digital twin model. On the basis of Level II digital twin, the following conditions are added: 6) Whether it contains physical models and data models that reflect key performance; 7) Whether the key node indicators within the system can be analyzed for consistency and quantification; Level IV digital twin: has the ability to evolve with the physical system, emphasizing the model's follow-up evolution performance during the system life cycle. Based on Level III digital twin, the following conditions are added: 8) Whether the virtual model has the ability to follow the real-time evolution of the physical entity; 9) During the operation phase of the digital twin system, the dynamic consistency of each key output continues to meet the indicators; 10) The data generated by digital twins can be used for diagnosis, prediction, optimization and decision-making in actual production; Level V digital twins: have strong consistency and trustworthiness of the model, form a closed loop with the physical entity throughout the entire life cycle, and add the following conditions to level IV digital twins: 11) The digital twin system remains consistent with the physical entity throughout its life cycle; 12) The digital twin system is trustworthy enough to directly operate and control the physical system.

3. The method according to claim 2, characterized in that The corresponding quantitative indicators are scored item by item according to the divided levels, including: The corresponding quantitative indicators are scored item by item through the scoring formulas of conditions 1-12, where the scoring formulas of conditions 1-12 are: Scoring formula for condition 1: Among them, S1 represents the degree of completeness of geometric modeling and behavioral modeling of the digital twin system in condition 1, and the total score is 1. DNG Num is the number of common geometric models required by the user. SNG is the number of common geometric models provided in the digital twin system, Num DIG Num is the number of important geometric models required by the user. SIG Num is the number of important geometric models provided in the digital twin system. DB is the number of behavior models required by the user, Num SB The number of behavioral models provided for the digital twin system; ω 111 ,ω 112 They are general geometry model weight and important geometry model weight respectively; Scoring formula for condition 2: Among them, S2 is the score reflecting the similarity between the digital twin and the physical entity in terms of functional structure, Sc i is the structural score of the i-th model, n is the number of models that need to be scored, Sf j is the function score of the jth model, m is the number of models that need to be scored; ω 21 ,ω 22 They are the structural and functional model weights of the digital twin model, respectively; Scoring formula for condition 3: Among them, S3 reflects the real-time data mapping effect of the physical entity to the digital twin model, Sb i It is a Boolean value, indicating whether the i-th state quantity that the digital twin needs to interact with in real time is available. i Indicates the network transmission performance of the state quantity, Sp i Score the representation and logic of the state in the digital twin, Pb j It is a Boolean value, indicating whether the j-th process data that the digital twin needs to interact with in real time is available. j To reflect the accuracy of process data, Pp j For the display effect of the process data, Qb k Qa is a Boolean value, indicating whether the k-th quality data that the digital twin needs to interact with in real time is available. k To reflect the accuracy of the quality data, Qd k For the data delay, Qu k Indicates the availability of the quality data, ω 31 ,ω 32 ,ω 33 The items are weighted by the user according to their importance to the data type; Scoring formula for condition 4: Among them, S4 is the credibility of the sensor data used to collect key parameters, n is the number of key data that need to be collected by the sensor, and Ss i is the stability of the i-th key sensor data, Sp i is the accuracy of sensor data, Sl i is the linearity of the sensor data, Sr i is the anti-interference ability of the sensor, So i are other factors that affect the reliability of the sensor, ω 41 ,ω 42 ,ω 43 ,ω 44 ,ω 45 The corresponding weights of sensor data stability, accuracy, linearity, anti-interference performance and other factors on credibility; Scoring formula for condition 5: Among them, S5 reflects the overall credibility of the output of the geometric model and behavioral model of the digital twin model, f1, f2, f3, and f4 represent the difference analysis methods used for different output data types, corresponding to the output data types of random data, deterministic data, periodic time series data, and non-periodic time series data, respectively, and ω 51 ,ω 52 ,ω 53 ,ω 54 is the importance weight of each data type, u i 、u j 、u k 、u l are the normalization coefficients corresponding to the difference analysis methods used for different types of data, so that the total output credibility of each model satisfies uf∈[0,1], Od is the output of the digital twin model, and Ot is the theoretical measurement result of the corresponding data; Scoring formula for condition 6: Among them, S6 represents the completeness of the key physical model and data model in terms of quantity, Num DFm Indicates the number of physical models and data models required by the user, Num SFm Indicates the number of physical models and data models provided by the digital twin system; The scoring formula for condition 7 is: Among them, S7 reflects the output credibility of the physical model and data model in the digital twin, which is expressed as the weighted sum of the credibility data scores of each key node in each physical model in the digital twin, f(Od ij ,Ot ij ) represents the numerical representation obtained after differential analysis of the jth performance parameter of the i-th physical model, u ij is the normalization parameter, ω ij is the weight of the performance parameter in the model, where m is the number of parameters that need to be analyzed for credibility in the i-th physical model, Fm i is a Boolean value, 0 means that the physical model does not exist, 1 means that it exists; n is the number of physical models, ω i is the weight value of the physical model in all physical models, where The scoring formula for condition 8 is: Among them, S8 reflects the strength of the digital twin model's ability to follow the real-time evolution of the physical entity, n is the number of performance parameters that need to have real-time evolution capabilities, and Ar i is a Boolean value, indicating whether the i-th performance parameter of the required digital twin model has the ability to evolve in real time; The scoring formula for condition 9 is: Among them, S9 is used to evaluate the dynamic credibility of the model during the evolution process. The performance parameters considered in S9 are selected from S7. The meanings of the parameters in S9 and S7 are the same. The difference is that S7 performs offline calculations based on the collected historical data, while the calculation of S9 runs through the entire life cycle of the digital twin and needs to be embedded in the digital twin system as part of the running program. The scoring formula for condition 10 is: Among them, S 10 Fp reflects the strength of the extended functions such as analysis and prediction of the digital twin system. i Fp is a Boolean value, indicating whether the digital twin system has a certain required extended function. i Rate the technical experts' confidence in the feature; The scoring formula for condition 11 is: Among them, S 11 Reflects the consistency of various performance parameters of the system with the physical entity throughout the system's life cycle, S 11 The evaluation method used is the same as S9, but includes more comprehensive scoring parameters; The scoring formula for condition 12 is: Among them, S 12 Reflects the completeness of the physical system’s open control interface to the digital twin. i is a Boolean value indicating whether the i-th control interface is open, ω i is a weight parameter, reflecting the importance of the i-th control interface, where 4. The method according to claim 3, characterized in that The calculation formula for the credibility score of digital twin systems at different levels is: The calculation formula for the credibility score of the Level I digital twin system is: S I =ω1·S1+ω2·S2+ω3·S3 Among them, ω1, ω2, and ω3 correspond to the weights of S1, S2, and S3 respectively, where ω1+ω2+ω3=1; Among them, ω1~ω5 correspond to the weights of S1~S5 respectively. Among them, ω1~ω7 correspond to the weights of S1~S7 respectively. Since the calculation of S9 includes S7, i≠7, and ω1~ω9 correspond to S1~S 10 The weight of Among them, due to S 11 The calculation includes S7 and S9, so i≠7,9, ω1~ω 12 Corresponding to S1~S 12 The weight of 5. A digital twin rating and quantitative evaluation device, characterized in that: include: A judgment module is used to judge whether the digital twin system meets the basic definition conditions of the digital twin after receiving the digital twin system delivered by the supplier, including whether it has a physical entity, a corresponding digital twin, and an automatic flow of data from the physical entity to the digital twin model; A level classification module, if the digital twin system meets the basic definition conditions of the digital twin, is used to determine whether the digital twin system meets the corresponding judgment conditions based on the development trend of the digital twin, and to classify the digital twin system; The scoring module is used to score the corresponding quantitative indicators item by item according to the divided levels, and to perform weighted summation on the scoring results in combination with the determined weighting coefficients to calculate the credibility score of the digital twin system.

6. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 4 when being executed by a processor.

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