Smart Contract Orchestration With Edge Ledger Data Processing
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Solution Overview
Problem
Existing systems face challenges in managing vast amounts of data from distributed sensors and devices in value chain networks, leading to overwhelmed networks and limited centralized data collection due to bandwidth, storage, and processing limitations, which hinder effective automated decision-making.
Innovation Solution
Implementing a distributed database system with edge devices that utilize a dynamic ledger, such as a blockchain, to store queries and generate approximate responses based on summary data, leveraging probability distribution models and neural networks for efficient data processing and query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If centralized data collection is implemented to manage vast amounts of data from distributed sensors, then data availability for decision-making is improved, but network bandwidth and processing capacity are overwhelmed
Solution Approach 1:
The patent segments the centralized data collection architecture into distributed edge computing nodes. Each edge device processes and analyzes data locally, generating only summary results and insights rather than transmitting all raw sensor data to a central location. This segmentation reduces network bandwidth consumption while maintaining data availability for decision-making across the value chain network.
Solution Approach 2:
The patent introduces a hierarchical data processing dimension by implementing edge devices at intermediate levels between sensors and central systems. These edge nodes perform local aggregation and analysis, transforming the flat centralized architecture into a multi-dimensional structure where data processing occurs at multiple levels (sensor level, edge level, and central level), thereby reducing the data volume transmitted through the network.
2Loss of information
If all raw sensor data is transmitted to centralized systems for processing, then comprehensive data analysis is improved, but storage and processing capacity requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information and insights from raw sensor data at the edge devices. Instead of transmitting complete raw datasets, edge nodes process data locally and extract key findings, trends, and anomalies, transmitting only these condensed results to centralized systems. This extraction approach maintains data completeness for decision-making while dramatically reducing the quantity of data that needs to be stored and processed centrally.
Solution Approach 2:
The patent implements preliminary data processing and analysis at edge devices before data reaches centralized systems. Edge nodes perform initial filtering, aggregation, and analysis operations locally, preparing data in advance so that centralized systems receive pre-processed, high-value information rather than raw unprocessed data. This preliminary action reduces the storage and processing burden on centralized systems while maintaining comprehensive analytical capabilities.
3Loss of time
If real-time data processing is implemented across distributed devices, then decision-making speed is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent enables edge devices to perform self-service data processing by implementing local analytics and decision-making capabilities directly on edge nodes. Each edge device autonomously processes data from its local sensors, generates insights, and executes decisions without requiring constant communication with centralized systems. This self-service approach reduces decision-making time while keeping individual device complexity manageable through standardized edge computing frameworks.
Data Source
AI summary
An autonomous futures contract orchestration platform includes a set of processors programmed with a set of non-transitory computer-readable instructions. The instructions include receiving, from a data source, an indication associated with a product that relates to an entity that purchases or sells the product. The instructions include predicting a baseline cost of purchasing or selling the product at a future point in time based on the indication. The instructions include retrieving a futures cost, at a current point in time, of a futures contract for an obligation to the purchasing or selling the product for delivery or performance of the product at the future point in time. The instructions include executing a smart contract for the futures contract based on the baseline cost and the futures cost. The instructions include orchestrating the delivery or performance of the product at the future point in time.