Workspace actor selection systems and methods

One or more non-transitory computing device-readable storage media storing instructions executable by one or more computing devices to perform a method comprising:

• determining, by convolution neural network deep learning performed on a video frame stream, a first activity performed by a first actor in a first activity space and a second activity performed by a second actor in a second activity space, the first activity and the second activity comprising a physical movement of a physical object;
• generating a virtual activity space comprising 3 or more dimensions, a virtual activity, and constraints and metrics related to the virtual activity;
• analyzing, by automated artificial intelligence analysis, a first overlay of the first activity in the first activity space of the first actor on the virtual activity in the virtual activity space in real time and a second overlay of the second activity in the second activity space of the second actor on the virtual activity in the virtual activity space in real time, wherein the automated artificial intelligence analysis includes utilizing machine learning back-end unit processes in the analysis;
• and
• forwarding feedback on results of the analyzing, wherein the results include an identification of a selection between the first actor and the second actor with regards to performance of the first activity and the second activity, including respective ability of the first actor and the second actor to comply with an objective of moving the physical object efficiently and effectively, from the first overlay and the second.

A system comprising:

• one or more engines, wherein the one or more engines include:
• one or more back-end units configured to:
• create a virtual activity space, the virtual activity comprising virtual information regarding a virtual activity, the virtual activity space comprising grid points corresponding to grid locations in a real world work space, the virtual activity space comprising three or more dimensions;
• access information regarding one or more stations, the accessed information including automated artificial intelligence analysis of performance of an activity by a first actor or a plurality of other actors, wherein the activity includes movement of a physical object by the first actor or the plurality of other actors;
• and
• correlate the information with the first actor or the plurality of other actors in the one or more stations, wherein the automated artificial intelligence analysis includes utilizing machine learning back-end unit processes to determine the information from one or more cycles, one or more processes, one or more actions, one or more sequences, one or more objects and one or more parameters by convolution neural network deep learning from one or more video frame streams;
• one or more analytics units configured to perform statistical analysis on the information including a real-time overlay of the activity in an activity space of the first actor and the activity in the activity space of the plurality of other actors on the virtual activity in the virtual activity space;
• and
• one or more front-end units configured to forward results of the analysis as feedback, wherein the results include identification of a second actor to replace the first actor with respect to performance of the activity, wherein the second actor is one of the plurality of other actors and the selection is based upon the second actor satisfying objectives including performing the movement of the physical object more effectively and efficiently than the first actor;
• and
• one or more data storage units, the one or more data storage units configured to store information for the one or more engines, including information associated with a first actor and corresponding sensed activity information within the activity space.