IoT Multi-Frequency Data Processing via Modular AI Segmentation
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Solution Overview
Problem
Traditional data processing architectures in IoT systems face challenges in managing and processing multi-frequency data in real-time due to sheer volume, data format diversity, and security concerns, leading to bottlenecks, single points of failure, and delayed decision-making across multiple cloud environments.
Innovation Solution
An interoperable digital architecture with an automated event detection module, modular AI module for parallel processing, data sorting module, and relational database for secure and efficient data management, enabling real-time processing and visualization of multi-frequency data across various IoT devices and sensors, and secure data transfer using automated signed URLs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional linear data processing architecture is used, then system simplicity is maintained, but processing speed and real-time capability deteriorate due to bottlenecks and sequential operations
Solution Approach 1:
The patent segments the monolithic data processing architecture into multiple independent processing containers, each handling specific data types or frequencies. This allows parallel processing of different data streams simultaneously, eliminating sequential bottlenecks while maintaining manageable complexity through modular design
Solution Approach 2:
The patent introduces a new dimensional approach by implementing multi-layer logic processing (edge computing layer, cloud computing layer, and data lake layer) operating in parallel across different computational dimensions. This enables real-time processing without requiring a single complex processing path
2Reliability
If single cloud environment is used, then data management simplicity is maintained, but system reliability and security deteriorate due to single point of failure
Solution Approach 1:
The patent segments data storage and processing across multiple cloud environments and geographic regions, creating redundant copies of critical data and processing capabilities. This distribution eliminates single points of failure while maintaining reliability through failover mechanisms
Solution Approach 2:
The patent implements dynamic parameter adjustment for data replication and synchronization across cloud environments, adapting redundancy levels and synchronization frequencies based on data criticality and system state to balance reliability with operational complexity
3Loss of information
If complex algorithms are applied for data analysis, then insight quality is improved, but processing time and computational resources deteriorate
Solution Approach 1:
The patent performs preliminary data filtering, aggregation, and preprocessing at the edge computing layer before data reaches cloud processing. This preliminary action reduces the volume and complexity of data requiring complex algorithms, enabling faster processing while preserving critical information
Solution Approach 2:
The patent applies complex algorithms selectively to only those data streams and parameters that require deep analysis, while using simpler processing for routine data. This partial application of complex algorithms reduces overall processing time while maintaining information quality where needed
4Productivity
If data is processed sequentially in linear architecture, then system simplicity is maintained, but decision-making speed deteriorates due to delays
Solution Approach 1:
The patent segments the linear processing chain into parallel processing streams that can operate simultaneously. Different data types (multi-frequency, time-series, event-driven) are routed to dedicated processing containers that execute in parallel, dramatically accelerating decision-making throughput
Solution Approach 2:
The patent implements dynamic data routing and processing path selection that adapts to real-time system conditions. Data flows through optimized paths based on priority, type, and current processing capacity, enabling flexible parallel processing that accelerates decision-making while managing complexity dynamically
Data Source
AI summary
A system comprising an interoperable digital architecture configured for real-time multi-frequency data collection, processing, and visualization of data from various IoT devices in real-time, an automated event detection module configured to detect events from the IoT devices in real-time and manage multi-frequency data, a modular AI module in communication with the automated event detection module, and configured to process the multi-frequency data in real-time through separate aggregated containers for increased computational efficiency, where the modular AI module is configured to parallelize AI processing, receive the multi-frequency data from the event detection module and processes it in parallel using a plurality of AI algorithms. A data sorting module configured to sort the processed multi-frequency data and send it to destinations across multiple cloud environments, and a relational database for segregation of raw multi-frequency data and processed multi-frequency data.