Predictive Cyber-Physical Asset Tracking with Sensors and Smart Contracts
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Solution Overview
Problem
Current asset tracking systems rely heavily on human interaction, leading to data integration challenges, human error, and high operational risks, particularly in managing cyber-physical assets, where sensor monitoring is limited and costly, and contractual obligations are enforced by humans, introducing unknown variables.
Innovation Solution
A system and method for predictive cyber-physical resource management using a computing device with machine learning algorithms to process sensor data, update asset states, and apply smart contracts for decentralized decision-making, enabling autonomous sensor data monitoring and risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human operators are used to report and enforce the physical status of objects, then flexibility and adaptability are maintained, but human error and data integration challenges increase
Solution Approach 1:
The system enables cyber-physical assets to autonomously report their own status through embedded sensors and communication modules, eliminating the need for human operators to manually report physical status. The assets self-monitor parameters such as location, temperature, and operational state, and automatically transmit this data through the platform, thereby maintaining flexibility while eliminating human error in status reporting.
Solution Approach 2:
The patent replaces the mechanical system of human operators physically inspecting and reporting asset status with an automated sensor-based monitoring system. Sensors embedded in or attached to cyber-physical assets continuously monitor their state and communicate this data through the platform, substituting human mechanical observation and reporting with automated electronic sensing and data transmission.
2Reliability
If sensor monitoring is implemented for cyber-physical assets, then operational risk management is improved, but cost and barrier of entry increase
Solution Approach 1:
The platform is designed as a universal system that can monitor multiple types of cyber-physical assets simultaneously using standardized sensor interfaces and communication protocols. By creating a multi-functional platform that handles diverse assets through common infrastructure, the system achieves comprehensive operational risk management while distributing costs across a broad user base, reducing the barrier of entry for individual users.
Solution Approach 2:
The patent introduces a platform as an intermediary layer between sensors and users, which aggregates, processes, and presents sensor data in a unified manner. This intermediary absorbs the complexity and cost of data integration and processing, allowing users to access comprehensive monitoring capabilities without bearing the full burden of implementing and maintaining complex sensor networks themselves.
3Productivity
If decentralized decision-making with smart contracts is implemented, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The system uses smart contracts to pre-program decision-making logic and enforcement rules for contractual obligations related to asset status. Rather than requiring real-time human negotiation and decision-making, the smart contracts automatically execute predetermined actions based on sensor data, such as triggering alerts or enforcing penalties when thresholds are violated, thereby improving operational efficiency while containing complexity through automation of routine decisions.
Solution Approach 2:
The patent implements automated feedback loops where sensor data is continuously monitored against smart contract conditions, and decisions are automatically executed based on this feedback. The system closes the loop between monitoring and decision-making by having smart contracts automatically respond to sensor inputs, eliminating delays and improving efficiency while managing complexity through algorithmic rather than human decision processes.
Data Source
AI summary
A system and method for predictive cyber-physical resource management, including a business operating system, parameter evaluation engine, at least one cyber-physical asset, at least one crypt-ledger, a network, and the ability to represent data in Markov State Models and finite state machines.