Systems and methods for sensor monitoring and sensor-related calculations

A system for determining a monitored exposure threshold for a monitored good, comprising:

• a memory to store an exposure prediction neural network (NN);
• and
• one or more processors in communication with the memory, the one or more processors configured to:
• analyze a first one or more datasets using the exposure prediction NN to determine an average early warning time (AEWT), the AEWT being determined from evaluation datasets by averaging, across a plurality of exposure event datasets, differences between respective non-predictive alert times (NPATs) and ending times of portions of the exposure event datasets that caused the NN to predict the exposure events at a given accuracy wherein the AEWT is an average amount of time prior to the NPATs for dataset exposure events of one or more exposure event datasets of the first one or more datasets that the dataset exposure events can be predicted, and wherein the NN identifies the one or more exposure event datasets from among the one or more datasets with the given accuracy;
• determine, across the one or more exposure event datasets, an average exposure amount between the NPATs for the one or more exposure event datasets and corresponding times NPAT plus AEWT for the one or more exposure event datasets;
• and
• set the monitored exposure threshold for the monitored good based on the average exposure amount, wherein an exposure event for the monitored good is to be predicted prior to reaching the monitored exposure threshold using the exposure prediction NN.

A method for environmental monitoring and performing environmental-related calculations, comprising:

• receiving a plurality of temperature readings taken at a temperature-controlled area;
• applying a timestamp to each one of the plurality of temperature readings;
• determining, based on the plurality of temperature readings, a temperature of a monitored good stored within the temperature-controlled area at each timestamp;
• calculating, using the temperature of the monitored good at each timestamp, an exposure of the monitored good at each timestamp;
• determining an average amount of exposure between times of exposure events of a plurality of exposure event datasets and corresponding times of the exposure events plus an average early warning time (AEWT) associated with the plurality of exposure event datasets;
• setting the monitored exposure threshold using the average amount of exposure;
• predicting, based on the exposure of the monitored good at each timestamp, that the exposure of the monitored good will exceed the monitored exposure threshold;
• and
• sending an alert to a first user in response to the predicting that the exposure of the monitored good will exceed the monitored exposure threshold.