Autonomous Machine Risk Relationship Platform for Future Operations
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Solution Overview
Problem
There is a need to establish and manage risk relationships for autonomous machines in a secure and efficient manner, as existing systems lack effective methods to assess and mitigate risks associated with these machines.
Innovation Solution
A system and method that involve a risk relationship data store, a device proximate to the autonomous machine with an interface and transmitter, and a back-end application computer server that associates request signals with specific machine operations and updates risk relationship records, facilitating secure and efficient risk management through a remote risk relationship platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a risk relationship management system is established for autonomous machines, then risk assessment capability is improved, but system complexity increases
Solution Approach 1:
The system is divided into multiple independent components: autonomous machines generate risk data, a communication network transmits data, a data store manages information, and a user interface presents risk relationships. This segmentation allows each component to be developed and maintained independently, reducing overall system complexity while maintaining comprehensive risk assessment capability.
Solution Approach 2:
A back-end application computer server acts as an intermediary between autonomous machines and the risk relationship platform. The server receives risk data from machines, processes it through the communication network, and manages data storage and retrieval. This intermediary layer simplifies the interaction between diverse components and standardizes data flow.
2Measurement precision
If detailed machine attributes are stored in the risk relationship data store, then measurement precision is improved, but data storage requirements increase
Solution Approach 1:
The system stores different types of attributes with varying levels of detail based on their specific requirements. Critical attributes such as machine identification, hardware features, and software versions are stored with high precision, while less critical attributes are stored with appropriate but reduced detail. This local quality approach ensures measurement precision where needed while optimizing data storage requirements.
3Loss of information
If real-time risk relationship updates are implemented, then information availability is improved, but processing time increases
Solution Approach 1:
The system implements periodic updates of risk relationship information rather than continuous real-time updates. The back-end application computer server processes and updates risk data at scheduled intervals, balancing information availability with processing efficiency. This periodic action ensures that stakeholders receive timely risk relationship information without the system being overwhelmed by constant update requirements.
Data Source
AI summary
A risk relationship data store contains electronic records that represent, for risk relationships with an enterprise: a risk relationship identifier, an autonomous machine identifier, at least one attribute value, and a resource amount. A device located proximate to an autonomous machine includes an interface for an entity associated with the autonomous machine and a transmitter to transmit a request signal generated via the interface. A back-end application computer server associates the request signal with a specific future operation of the autonomous machine and forwards information about the specific future operation to a remote risk relationship platform. The computer server then receives information about a potential risk relationship with another enterprise and transmits a risk relationship offer to the device located proximate to the autonomous machine. Responsive to an agreement signal received from the device located proximate to the autonomous machine, the risk relationship data store is updated.