Digital Product Network Control Tower for Edge Query Processing
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Solution Overview
Problem
The proliferation of data from IoT sensors and other sources in value chain networks overwhelms traditional centralized data collection methods, leading to complexity and inefficiencies in data transmission and automated decision-making.
Innovation Solution
A method for processing queries in a distributed database using edge devices, which store queries on a dynamic ledger, generate approximate responses based on summary data, and transmit these responses, while also utilizing probability distribution models and neural networks to optimize data processing and reduce network overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If centralized data collection methods are used to gather data from IoT sensors and other sources in value chain networks, then comprehensive data coverage is achieved, but system complexity and transmission overhead increase significantly
Solution Approach 1:
The patent segments the centralized data collection architecture into distributed edge computing nodes deployed throughout the value chain network. Each edge node independently processes and analyzes data locally, eliminating the need for a single centralized collection point and reducing system complexity while maintaining comprehensive data coverage across all network participants.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing hierarchical data processing across multiple levels: edge devices process data locally, regional aggregators consolidate findings, and central systems perform high-level analysis. This multi-dimensional architecture reduces transmission overhead by filtering and preprocessing data at lower levels before sending only essential information upward.
2Measurement precision
If all raw data is transmitted to centralized systems for processing, then complete analysis capability is maintained, but network bandwidth consumption and transmission time increase
Solution Approach 1:
The patent extracts and processes critical data features at the edge computing level before transmission. Edge nodes perform local analytics, extract meaningful patterns, and send only processed insights to centralized systems, maintaining complete analysis capability while dramatically reducing network bandwidth consumption by eliminating transmission of redundant raw data.
Solution Approach 2:
The patent implements preliminary data processing and filtering at edge devices before data enters the network. Edge nodes perform initial analysis, validate data quality, and pre-process information locally, ensuring that only high-value, processed data consumes network bandwidth for transmission to centralized systems.
3Loss of time
If edge devices generate and store approximate responses based on summary data, then response time is improved, but data processing accuracy may be reduced
Solution Approach 1:
The patent implements partial processing by generating approximate responses from summary data at edge devices for time-critical operations. Edge nodes maintain both full raw data and processed summaries, providing rapid approximate answers when needed while preserving complete data for situations requiring high precision analysis, thus balancing response time and accuracy requirements.
Data Source
AI summary
A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control memory.