Twin data (DD) is the driver of the digital twin. DD mainly includes physical entity PE data (D _P ), twin entity VE data ( _DV ), service (Ss) data data (D _S ), knowledge data (D _K ), and fusion derived data (D _F ). ¹

DD = (D _P , D _V , D _S , D _K , D _F ).

D _P mainly includes physical element attribute data reflecting physical entity PE specifications, functions, performance, relationships, etc., and dynamic process data reflecting physical entity PE operating conditions, real-time performance, environmental parameters, sudden disturbances, etc. System, data acquisition card, etc. to collect;

D _V mainly includes data related to virtual entity VE, such as data related to geometric models such as geometric dimensions, assembly relationships, and positions, data related to physical models such as material properties, loads, and features, and data related to behavior models such as driving factors, environmental disturbances, and operating mechanisms. Relevant data of rule models such as constraints, rules, and association relationships, as well as simulation data of process simulation, behavior simulation, process verification, evaluation, analysis, prediction, etc. based on the above models;

DS mainly includes FService -related data (such as algorithms, models, data processing methods, etc.) and _BService -related data (such as enterprise management data, production management data, product management data, market analysis data, etc.);

D _K  includes expert knowledge, industry standards, rule constraints, inferences, commonly used algorithm libraries and model libraries, etc.;

DF  is the _{derived data obtained after data conversion, preprocessing} , classification, association, integration, fusion, etc. of DP, DV _, DS , and DK. Historical statistical data, expert knowledge and other information data are obtained from cyber-physical fusion data, so as to reflect more comprehensive and accurate information, and realize information sharing and value-added.

Digital twin needs to be driven by data. The above description comes from Professor Tao Fei’s paper “Digital Twin Five-Dimensional Model and Ten Field Applications”. According to the above classification of data required by digital twin, in our actual At work, in the face of larger projects, data preparation in multiple fields can be carried out in parallel based on this. These data flow through the network system that connects the various components of the digital twin, realize the parallel operation of the digital twin and the physical entity, and provide customers with an available, accurate, and constantly updated integrated service.

References:

1 Digital twin five-dimensional model and application in ten fields Tao Fei, Liu Weiran et al. 2019 DOI: 10.13196/j.cims.2019.01.001