Mobile energy and data planning and optimization

A method for mobile energy and data planning and optimization in a computing device, comprising the steps of:

• collecting a plurality of data from a plurality of sensors associated with the computing device or other systems, wherein the data includes one or more of objectives and objective functions, performance metrics and traces, observability data, sensor data, and system states associated with the computing device or the other systems, and wherein the plurality of data is populated into a knowledge vector graph, a vector graph, or a combination thereof;
• training an artificial intelligence model using the plurality of data from the knowledge vector graph, the vector graph, or the combination thereof to optimize efficiency of one or more of data, energy, and network being used or generated by the computing device;
• producing a plurality of recommendations using the artificial intelligence model, wherein the plurality of recommendations cause the computing device to optimize efficiency in achieving the one or more objectives and objective functions while utilizing energy and processing data;
• and modifying a current state of the computing device into an updated state for future state progression or transition by implementing at least one of the plurality of recommendations produced by the artificial intelligence model.

Non-transitory, computer-readable storage media having computer-executable instructions embodied thereon that, when executed by one or more processors of a computing system for mobile energy and data planning and optimization in a computing device, cause the computing system to:

• collect a plurality of data from a plurality of sensors associated with the computing device or other systems, wherein the data includes one or more of objectives and objective functions, performance metrics and traces, observability data, sensor data, and system states associated with the computing device or the other systems, and wherein the plurality of data is populated into a knowledge vector graph, a vector graph, or a combination thereof;
• train an artificial intelligence model using the plurality of data from the knowledge vector graph, the vector graph, or the combination thereof to optimize efficiency of one or more of data, energy, and network being used or generated by the computing device;
• produce a plurality of recommendations using the artificial intelligence model, wherein the plurality of recommendations cause the computing device to optimize efficiency in achieving the one or more objectives and objective functions while utilizing energy and processing data;
• and modify a current state of the computing device into an updated state for future state progression or transition by implementing at least one of the plurality of recommendations produced by the artificial intelligence model.

A system for mobile energy and data planning and optimization in a computing device, comprising one or more computers with executable instructions that, when executed, cause the system to:

• collect a plurality of data from a plurality of sensors or systems associated with the computing device or other systems, wherein the data includes one or more of objectives and objective functions, performance metrics and traces, observability data, sensor data, and system states associated with the computing device or the other systems, and wherein the plurality of data is populated into a knowledge vector graph, a vector graph, or a combination thereof;
• train an artificial intelligence model using the plurality of data from the knowledge vector graph, the vector graph, or the combination thereof to optimize efficiency of one or more of data, energy, and network being used or generated by the computing device;
• produce a plurality of recommendations using the artificial intelligence model, wherein the plurality of recommendations cause the computing device to optimize efficiency in achieving the one or more objectives and objective functions while utilizing energy and processing data;
• and modify a current state of the computing device into an updated state for future state progression or transition by implementing at least one of the plurality of recommendations produced by the artificial intelligence model.