Value Chain Contract Orchestration With Edge Query Summaries
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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, where queries are stored on a dynamic ledger, generating approximate responses based on summary data, and transmitting these responses, with the option to use a neural network for probability distribution modeling and query planning across edge devices.
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 complete data collection is achieved, but network overhead and complexity increase significantly
Solution Approach 1:
The patent segments the centralized data collection architecture into distributed edge devices that operate autonomously across the value chain network. Each edge device collects and processes data locally, eliminating the need for a single centralized collection point and reducing network overhead while maintaining comprehensive data coverage through distributed participation.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing hierarchical data representation with summary data and detailed data layers. Edge devices generate compact summary data that captures essential information patterns, allowing the system to maintain complete information coverage while transmitting and storing significantly reduced data volumes, thus reducing complexity.
2Reliability
If all raw data from distributed sensors is transmitted across the network for processing, then accurate decision-making is enabled, but network bandwidth and transmission efficiency deteriorate
Solution Approach 1:
The patent extracts essential information patterns from raw sensor data at the edge device level by generating summary data representations. This extraction process removes redundant and non-essential data elements before transmission, allowing accurate decision-making to be maintained through preserved key patterns while significantly reducing network bandwidth consumption.
Solution Approach 2:
The patent performs preliminary data processing and summary generation at edge devices before data leaves the local network segment. This preliminary action of creating condensed summary representations enables downstream systems to make accurate decisions based on pre-processed information, eliminating the need to transmit and process complete raw data sets across the entire network.
3Measurement precision
If detailed query processing is performed across all edge devices, then query accuracy is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent implements dynamic query processing that adapts based on the specific query requirements and available summary data. The system dynamically determines whether summary data suffices for accurate query resolution or whether detailed data retrieval is necessary, enabling flexible adjustment between processing speed and accuracy based on real-time conditions and query criticality.
Solution Approach 2:
The patent applies partial action by processing only the portion of data necessary to answer each query accurately. Instead of retrieving and processing all available data, the system selectively accesses summary data and detailed data based on query requirements, reducing processing time while maintaining sufficient accuracy for decision-making purposes.
Data Source
AI summary
An autonomous futures contract orchestration platform includes processors programmed with non-transitory computer-readable instructions to collectively execute receiving, from a data source, an indication associated with a product that relates to an entity that at least one of purchases or sells the product. The instructions include predicting a baseline cost of at least one 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 the product. The instructions include generating a risk threshold based on a specified risk tolerance of the entity indicating a difference between the baseline cost and the futures cost. The instructions include executing a smart contract for the futures contract based on the baseline cost, the futures cost, and the risk threshold.