ML Image Analysis for Real-Time Device Overload Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing overload detection systems using image processing analysis are inefficient and lack real-time capabilities, failing to accurately predict device malfunctions based on performance telemetry.
Innovation Solution
A system utilizing machine learning (ML) models trained on device images to detect changes in resiliency status, generating notifications and control signals for user interfaces to manage device overload in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional image processing analysis is used for overload detection, then the system structure is simple, but the detection speed is slow and lacks real-time capabilities
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with machine learning models and neural networks. The system uses trained ML models to analyze device images and detect overload conditions, substituting conventional algorithmic approaches with intelligent systems that provide real-time detection capabilities while maintaining manageable system complexity through modular architecture.
2Measurement precision
If traditional performance monitoring methods are used, then the implementation is straightforward, but the accuracy in predicting device malfunctions is insufficient
Solution Approach 1:
The patent introduces trained machine learning models as intermediary components between device images and malfunction prediction. These ML models act as intelligent mediators that process visual data and translate it into accurate predictions of device resiliency status and potential malfunctions, significantly improving measurement precision while the modular system architecture keeps overall complexity manageable.
3Reliability
If continuous real-time image analysis is implemented, then the detection capability is improved, but the computational resource consumption increases
Solution Approach 1:
The patent applies preliminary action by training machine learning models offline before deployment. The models are pre-trained on extensive datasets to achieve high accuracy, then deployed as fixed computational structures for real-time inference. This approach enables reliable real-time detection while minimizing ongoing computational resource consumption, as the intensive training work is completed in advance.
4Measurement precision
If multiple device images are processed for comprehensive analysis, then the detection accuracy is improved, but the processing time increases
Solution Approach 1:
The patent replaces sequential mechanical processing of multiple images with parallel neural network inference. The trained ML models can process multiple device images simultaneously or in rapid succession, maintaining high detection accuracy for resiliency status while dramatically reducing processing time through optimized computational architectures and hardware acceleration.
Data Source
AI summary
Systems, computer program products, and methods are described herein for real-time overload detection using image processing analysis. The present disclosure is configured to receive a first set of images associated with a device, wherein the one or more images are associated with a resiliency status of the device; 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; and 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.