Knowledge graph responsive generation of model interconnections and new models for digital twin optimization of vessel emissions

A data processing system adapted for model generation for digital twin prediction of vessel emissions, the system comprising:

• a host computing platform comprising one or more computers, each with memory and one or processing units including one or more processing cores;
• a digital twin executing in the memory and simulating vessel emissions for the vessel based upon values sensed or estimated for elements of the vessel modeled within a model hierarchy;
• a knowledge graph of nodes stored in the memory, each one of the nodes of the knowledge graph encapsulating an identifier for a model representing a specific system or function of a vessel, different ones of the nodes specifying axes to related others of the nodes;
• and,
• a model generation module comprising computer program instructions enabled while executing in the memory of at least one of the processing units of the host computing platform to perform:
• applying a reasoner to the nodes to infer additional axes between selected ones of the nodes;
• and,
• generating the model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes, the data structure defining the model hierarchy for the vessel.

A computing device comprising a non-transitory computer readable storage medium having program instructions stored therein, the instructions being executable by at least one processing core of a processing unit to cause the processing unit to perform model generation method for digital twin prediction of vessel emissions by:

• loading a knowledge graph of nodes into memory of a host computer, each one of the nodes of the knowledge graph encapsulating an identifier for a model representing a specific system or function of a vessel, different ones of the nodes specifying axes to related others of the nodes;
• applying a reasoner to the nodes to infer additional axes between selected ones of the nodes;
• generating a model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes, the data structure defining the model hierarchy for the vessel;
• and,
• executing a digital twin in the memory of the host computer, the digital twin simulating vessel emissions for the vessel based upon values sensed or estimated for elements of the vessel modeled within the model hierarchy.