Smart Contract Configuration for Edge-Based Contingency Response
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Solution Overview
Problem
Existing data management systems in value chain networks are overwhelmed by the complexity and volume of data from IoT sensors and wearables, leading to inefficiencies in data transmission and centralized decision-making, limiting the ability to convert data into actionable insights.
Innovation Solution
A distributed database system utilizing edge devices with dynamic ledgers and probability distribution models to process queries efficiently, generating approximate responses based on summary data and statistical information, and optimizing data transmission through sharding algorithms and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transmitted through centralized systems in value chain networks, then data collection is comprehensive, but network overhead increases and processing efficiency decreases
Solution Approach 1:
The patent segments the centralized data management system into distributed edge devices deployed throughout the value chain network. Each edge device independently processes and analyzes data locally, eliminating the need to transmit all raw data through centralized systems. This segmentation reduces network overhead while maintaining comprehensive data collection capabilities across the network.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying edge devices at multiple locations throughout the value chain network rather than relying on a single centralized point. This dimensional change enables parallel processing across different network nodes, improving overall processing efficiency while reducing the burden on any single system and minimizing network traffic.
2Measurement precision
If all raw data is transmitted and processed centrally, then complete data insights are achieved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by performing data filtering, aggregation, and initial analysis at edge devices before data is transmitted to centralized systems. This pre-processing reduces the volume of data requiring centralized processing while maintaining insight accuracy, as edge devices prepare data in advance and only transmit relevant processed information.
Solution Approach 2:
The patent ensures continuous useful action by enabling edge devices to process and generate insights from data in real-time as it is collected, rather than waiting for centralized batch processing. This continuous local processing maintains data insight accuracy while dramatically reducing processing time, as insights are generated immediately at the source rather than after data accumulation and centralized analysis.
3Reliability
If centralized systems process all data, then decision-making is unified, but response time to contingencies increases
Solution Approach 1:
The patent segments decision-making authority by enabling edge devices to autonomously respond to contingencies using pre-configured smart contracts and decision rules. This segmentation allows local decisions to be made immediately at the network edge without waiting for centralized approval, improving response speed while maintaining reliability through consistent decision frameworks deployed across all edge devices.
Solution Approach 2:
The patent introduces smart contracts as intermediaries between edge devices and centralized systems. These smart contracts encode decision logic and contingencies, enabling edge devices to autonomously execute decisions based on pre-agreed rules without real-time centralized intervention. This intermediary layer maintains decision-making consistency through standardized contracts while enabling rapid local responses to contingencies.
4Loss of information
If data volume from IoT sensors and wearables increases, then data insights are enhanced, but system complexity increases
Solution Approach 1:
The patent extracts complex data processing and analysis functions from the centralized system and places them at edge devices. This extraction reduces system complexity by distributing computational burden across multiple independent nodes, each handling only local data processing. The centralized system retains only high-level coordination and aggregation functions, while edge devices independently manage local data volume and generate insights.
Solution Approach 2:
The patent enables edge devices to self-service by autonomously processing, filtering, and analyzing data locally without requiring complex centralized processing infrastructure. Each edge device independently manages its data stream, applying local intelligence and smart contracts to generate insights. This self-service approach reduces overall system complexity by eliminating the need for a highly complex centralized processing system capable of handling all raw data.
Data Source
AI summary
A system for managing future costs associated with a product includes a future requirement system programmed to estimate an amount of resources required for manufacturing, distributing, and selling the product at a future point in time. The system includes an adverse contingency system configured to identify adverse contingencies and calculate changes in costs associated with obtaining the amount of resources at the future point in time. The system includes a smart contract system programmed to autonomously configure and execute a smart futures contract based on the amount of resources required and on the changes in costs to manage the future costs associated with the product.