Sustainable digital twin system

A method for detecting modifications of physical assets in a facility, comprising:

• receiving data associated with a plurality of physical assets, wherein the plurality of physical assets represents a plurality of physical objects in the facility;
• based on the received data, train at least one machine learning engine from a plurality of machine learning engines in an integrated artificial intelligence framework;
• based on the training, classify the plurality of physical assets by the integrated artificial intelligence framework, wherein classification consists of scanning the plurality of physical assets and producing a plurality of point clouds consisting of a 2-dimensional (2D) matrix including a plurality of rows and a plurality of columns of a plurality of 3-dimensional (3D) vertices, wherein the 3D vertices consists of a plurality of photographic images associated with the plurality of physical assets in the facility;
• based on the classification, identify a plurality of objects from the plurality of 3D vertices, wherein the identification consists of a mapping of the plurality of objects and the plurality of 3D vertices;
• based on the identification, extract one or more dimensional meta data and an asset classification from the plurality of 3D vertices;
• based on the extracted one or more dimensional meta data and the asset classification, generate a plurality of partially classified 3D virtual models corresponding to the plurality of physical assets in the facility;
• based on the plurality of partially classified 3D virtual models, generating a plurality of virtual assets, wherein the plurality of virtual assets includes the plurality of partially classified 3D virtual models corresponding to the plurality of physical assets, and a plurality of attributes associated with each physical asset;
• upon detecting a modification of at least one virtual asset, analyzing the modification, wherein the modification is at least one of:
• a modification of at least one attribute associated with at least one virtual asset;
• and/or
• a modification of at least one data link from a plurality of data links between the plurality of virtual assets;
• and/or
• an addition of at least one new virtual asset corresponding to an addition of at least one new physical asset;
• and/or
• a deletion of at least one virtual asset corresponding to a deletion of at least one physical asset;
• based on the analysis, generating a plurality of recommendations including a corresponding modification of the at least one virtual asset;
• based on a plurality of modified virtual asset, validating a linked asset data corresponding to at least one modified virtual asset;
• based on the validation and the modification of the at least one virtual asset, updating the at least one virtual asset to reflect a corresponding modification;
• and
• displaying the plurality of virtual assets including the at least one modified virtual asset.

A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• receiving data associated with a plurality of physical assets, wherein the plurality of physical assets represents a plurality of physical objects in a facility;
• based on the received data, train at least one machine learning engine from a plurality of machine learning engines in an integrated artificial intelligence framework;
• based on the training, classify the plurality of physical assets by the integrated artificial intelligence framework, wherein classification consists of scanning the plurality of physical assets and producing a plurality of point clouds consisting of a 2-dimensional (2D) matrix including a plurality of rows and a plurality of columns of a plurality of 3-dimensional (3D) vertices, wherein the 3D vertices consists of a plurality of photographic images associated with the plurality of physical assets in the facility;
• based on the classification, identify a plurality of objects from the plurality of 3D vertices, wherein the identification consists of a mapping of the plurality of objects and the plurality of 3D vertices;
• based on the identification, extract one or more dimensional meta data and an asset classification from the plurality of 3D vertices;
• based on the extracted one or more dimensional meta data and the asset classification, generate a plurality of partially classified 3D virtual models corresponding to the plurality of physical assets in the facility;
• based on the plurality of partially classified 3D virtual models, generating a plurality of virtual assets, wherein the plurality of virtual assets includes the plurality of partially classified 3D virtual models corresponding to the plurality of physical assets, and a plurality of attributes associated with each physical asset;
• upon detecting a modification of at least one virtual asset, analyzing the modification, wherein the modification is at least one of:
• a modification of at least one attribute associated with at least one virtual asset;
• and/or
• a modification of at least one data link from a plurality of data links between the plurality of virtual assets;
• and/or
• an addition of at least one new virtual asset corresponding to an addition of at least one new physical asset;
• and/or
• a deletion of at least one virtual asset corresponding to a deletion of at least one physical asset;
• based on the analysis, generating a plurality of recommendations including a corresponding modification of the at least one virtual asset;
• based on a plurality of validation rules, validating a linked asset data corresponding to at least one modified virtual asset;
• based on the validation and the modification of the at least one virtual asset, updating the at least one virtual asset to reflect a corresponding modification;
• and
• displaying the plurality of virtual assets including the at least one modified virtual asset.