Time modular energy-industrial systems and their designs, architectures, operations, and methods of optimization

A time modular energy-industrial system comprising:

• a plurality of mobile modular energy sources configured to output energy to one or more locations;
• a mobile modular productive capacity configured to produce a product output, wherein the mobile modular productive capacity is operatively connected to at least one of the plurality of mobile modular energy sources;
• a network component connecting the plurality of mobile modular energy sources to the mobile modular productive capacity;
• and
• an artificial intelligence optimization component configured to:
• receive demand data associated with the product output from one or more data sources connected to the network component;
• generate a demand topography representing demand for the product output across at least one of a geospatial dimension, a temporal dimension, and a product dimension;
• predict a future state of the demand topography by applying a machine learning model to the demand topography;
• determine an optimized allocation of the plurality of mobile modular energy sources and the mobile modular productive capacity based on the predicted future state;
• and
• output allocation instructions that modify deployment of at least one of the plurality of mobile modular energy sources and the mobile modular productive capacity to meet the optimized allocation.

A method of optimizing a time modular energy-industrial system using artificial intelligence, the method comprising:

• receiving, by a computing system, operational data from a time modular energy-industrial system comprising a plurality of mobile modular energy sources and a mobile modular productive capacity connected by a network component;
• constructing, by the computing system, an n-dimensional demand topography from the operational data, wherein the n-dimensional demand topography represents at least demand, inventory performance, and return on investment across a plurality of time states;
• applying, by the computing system, a machine learning model to the n-dimensional demand topography to identify at least one trend in the demand topography across successive time states;
• generating, by the computing system, a predicted future state of the n-dimensional demand topography based on the identified trend;
• determining, by the computing system, a revised allocation of the plurality of mobile modular energy sources and the mobile modular productive capacity that optimizes at least one performance dimension based on the predicted future state;
• and
• outputting, by the computing system, instructions to modify at least one of the deployment, configuration, or operational assignment of the plurality of mobile modular energy sources and the mobile modular productive capacity in accordance with the revised allocation.

A system for predictive and corrective optimization of time modular energy-industrial inventories, the system comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• collecting state data from a time modular energy-industrial system comprising a plurality of mobile modular energy sources, a mobile modular productive capacity, and a network component connecting the mobile modular energy sources to the mobile modular productive capacity;
• representing the collected state data as an n-dimensional surface topography over a plurality of time states, wherein each dimension of the n-dimensional surface topography corresponds to at least one of a demand variable, an inventory performance variable, a return on investment variable, a geospatial variable, or a temporal variable;
• filtering the n-dimensional surface topography to identify maxima and minima chains constituting n-dimensional waveforms representing trends in the state data;
• predicting a future state of the n-dimensional surface topography based on the identified n-dimensional waveforms using at least one of a neural network model, a genetic algorithm, an evolutionary algorithm, a hidden Markov chain analysis, or a multivariate optimization algorithm;
• comparing the predicted future state to an observed subsequent state of the n-dimensional surface topography;
• correcting the prediction based on an error between the predicted future state and the observed subsequent state;
• and
• recursively repeating the collecting, representing, filtering, predicting, comparing, and correcting operations to continuously optimize the allocation of the plurality of mobile modular energy sources and the mobile modular productive capacity.