Real-Time IoT Predictive Model Tuning via Deviation Feedback
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Solution Overview
Problem
Current techniques for analyzing Internet of Things (IoT) data are limited to simple business rules and do not provide advanced analytics for real-time IoT data, failing to generate actionable insights.
Innovation Solution
A method and system that acquire real-time IoT data, build a predictive model using machine learning algorithms, predict future events, determine deviations, and tune the model based on these deviations to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple business rules are used for analyzing IoT data, then the system complexity is low, but the analytical depth and actionable insights are insufficient
Solution Approach 1:
The patent transforms the analysis approach by changing the parameter of data processing complexity. Instead of using simple business rules, it implements machine learning models with multiple parameters (input parameters, explanatory parameters, output parameters) that can capture complex patterns in IoT data, thereby generating actionable insights without excessive system complexity
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw IoT data and business decisions. These models act as mediators that process real-time streaming data, extract meaningful patterns, and generate predictions, bridging the gap between simple data collection and actionable insights
2Loss of information
If machine learning predictive models are built for real-time IoT data analysis, then actionable insights are generated, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models using historical IoT data before deployment. The models are trained offline to learn patterns and relationships, so that during real-time operation, they can make predictions with reduced computational complexity. This preliminary training phase separates the heavy computational work from the real-time processing requirements
Solution Approach 2:
The patent segments the data processing into distinct phases: data collection from IoT devices, real-time streaming data processing, model prediction generation, and result interpretation. This segmentation allows each component to be optimized independently, managing computational complexity across different stages of the analysis pipeline
3Loss of time
If real-time streaming data is processed continuously, then up-to-date predictions are provided, but the energy consumption and processing load increase
Solution Approach 1:
The patent implements periodic action by processing real-time IoT data at optimized intervals rather than continuously. The machine learning models evaluate incoming streaming data at strategic points, generating predictions when new information becomes available that could change the outcome. This periodic processing maintains prediction timeliness while reducing energy consumption compared to continuous processing
Solution Approach 2:
The patent applies partial action by processing only the most relevant portions of incoming IoT data streams. The machine learning models selectively evaluate data points based on their predictive value and change detection, rather than processing every single data point. This approach provides timely predictions with reduced processing load by focusing on critical information
Data Source
AI summary
This disclosure relates to method and system for analyzing IoT data in real-time and predicting future events. In one embodiment, the method may include acquiring the real-time IoT data corresponding to one or more IoT devices, and building a predictive model based on the real-time IoT data. The predictive model may include a machine learning algorithm that generates an output parameter representing a future event based on a set of input parameters derived from the real-time IoT data. The predictive model may be built by training the predictive model for one or more explanatory input parameters and an expected output parameter. The method may further include predicting the future event based on the real-time IoT data using the predictive model, determining a deviation between the future event and an actual event, and tuning the predictive model based on the deviation.