System for real-time overload detection using image processing analysis

A computer program product for real-time overload detection using image processing analysis, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to:

• periodically capture a resiliency status of a device;
• transform the resiliency status of the device into a first set of images, wherein transforming further comprises:
• comparing each performance parameter of the device to a corresponding performance threshold;
• and
• mapping each performance parameter to a color spectrum in a heat map, wherein performance parameters that exceed their corresponding performance thresholds are mapped to warm colors in the color spectrum according to a value of the performance parameters relative to the corresponding performance thresholds, and performance parameters that do not exceed their corresponding performance thresholds are mapped to cool colors in the color spectrum according to the value of the performance parameters relative to the corresponding performance thresholds;
• deploy, using a machine learning (ML) subsystem, a trained ML model on the first set of images of the device;
• determine, using the trained ML model, a change in the resiliency status of the device based on the first set of images to preemptively identify instances of device malfunction;
• generate a notification indicating the change in the resiliency status of the device;
• and
• transmit control signals configured to cause a first user input device to display the notification, wherein the notification comprises one or more actions configured to cause a change in the resiliency status of the device, wherein the change comprises at least turning off the device.

A method for real-time overload detection using image processing analysis, the method comprising:

• periodically capturing a resiliency status of a device;
• transforming the resiliency status of the device into a first set of images, wherein transforming further comprises:
• comparing each performance parameter of the device to a corresponding performance threshold;
• and
• mapping each performance parameter to a color spectrum in a heat map, wherein performance parameters that exceed their corresponding performance thresholds are mapped to warm colors in the color spectrum according to a value of the performance parameters relative to the corresponding performance thresholds, and performance parameters that do not exceed their corresponding performance thresholds are mapped to cool colors in the color spectrum according to the value of the performance parameters relative to the corresponding performance thresholds;
• deploying, using a machine learning (ML) subsystem, a trained ML model on the first set of images of the device;
• determining, using the trained ML model, a change in the resiliency status of the device based on the first set of images to preemptively identify instances of device malfunction;
• generating a notification indicating the change in the resiliency status of the device;
• and
• transmitting control signals configured to cause a first user input device to display the notification, wherein the notification comprises one or more actions configured to cause a change in the resiliency status of the device, wherein the change comprises at least turning off the device.