Ai-enabled intervention optimization for maintaining temperature range in vehicle cabin for refrigerated objects

A non-transitory computer-readable medium comprising memory with instructions encoded thereon and one or more processors that, when executing the instructions, are caused to perform operations, the instructions comprising instructions to:

• obtain, by a control unit mounted within a vehicle having a plurality of individually temperature-controlled regions, a set of signals, the control unit spanning at least a portion of each region of the plurality of individually temperature-controlled regions, the control unit configured to control a temperature of each of the plurality of individually temperature-controlled regions;
• input, by the control unit, the set of signals into a machine learning model;
• receive, by the control unit, as output from the machine learning model, for a given region of the plurality of individually temperature-controlled regions, a rate of change of temperature;
• determine, by the control unit, whether the rate of change of temperature will take a temperature of the given region out of a target range;
• and
• responsive to determining that the rate of change of temperature will take a temperature of the given region out of the target range, perform, by the control unit, an intervention on at least one hardware component within the vehicle.

A system comprising:

• memory with instructions encoded thereon;
• and
• one or more processors that, when executing the instructions, are caused to perform operations comprising:
• obtaining, by a control unit mounted within a vehicle having a plurality of individually temperature-controlled regions, a set of signals, the control unit spanning at least a portion of each region of the plurality of individually temperature-controlled regions, the control unit configured to control a temperature of each of the plurality of individually temperature-controlled regions;
• inputting, by the control unit, the set of signals into a machine learning model;
• receiving, by the control unit, as output from the machine learning model, for a given region of the plurality of individually temperature-controlled regions, a rate of change of temperature;
• determining, by the control unit, whether the rate of change of temperature will take a temperature of the given region out of a target range;
• and
• responsive to determining that the rate of change of temperature will take a temperature of the given region out of the target range, performing, by the control unit, an intervention on at least one hardware component within the vehicle.