Transport Event Severity Consensus Using Multi-Device Atypical Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack the capability to determine the cause of events such as accidents or transport failures without the need for on-site investigative personnel, and they cannot provide timely and accurate data on dangerous situations involving vehicles and external devices.
Innovation Solution
A method and system that enables vehicles to receive data from proximity devices, determine dangerous situations, obtain consensus from other devices and vehicles to validate these situations, and notify relevant devices accordingly, using a server to analyze primary and secondary data for event severity and send notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If on-site investigative personnel are used to determine event causes, then measurement precision of event causes is improved, but loss of time and loss of substance increase
Solution Approach 1:
The system performs preliminary data collection and analysis by gathering sensor data, device data, and environmental data before an event occurs. This pre-positioning of measurement capabilities allows immediate analysis when an event happens, eliminating the need for post-event on-site investigation while maintaining high measurement precision.
Solution Approach 2:
The patent introduces a server as an intermediary that receives and analyzes data from multiple sources (transports, devices, environmental sensors). This intermediary processes the data to determine event causes remotely, replacing the need for physical investigators while maintaining accurate cause determination through multi-source data correlation.
2Measurement precision
If on-site investigative personnel are used to determine event causes, then measurement precision of event causes is improved, but loss of substance increases
Solution Approach 1:
The system enables self-service by allowing transports and devices to automatically collect, transmit, and share their own operational data and sensor readings. This self-generated data serves as evidence for event cause determination, eliminating the need for external investigators to physically examine the scene and reducing resource consumption.
Solution Approach 2:
The server acts as an intermediary that aggregates data from multiple transports and devices, performing remote analysis to determine event causes. This eliminates the need for physical investigation resources while maintaining accurate cause determination through comprehensive data analysis from multiple perspectives.
3Reliability
If consensus validation from multiple devices is implemented, then reliability of dangerous situation determination is improved, but device complexity increases
Solution Approach 1:
The server provides a universal platform that handles consensus validation for all transports and devices in the network. Instead of each device implementing its own complex consensus logic, the server performs this function universally for all participants, reducing individual device complexity while maintaining high reliability through multi-source validation.
Solution Approach 2:
The server serves as an intermediary that coordinates the consensus process between multiple transports and devices. It collects data from various sources, performs validation, and reaches consensus on dangerous situations, thereby distributing the complexity to the server rather than requiring complex peer-to-peer consensus mechanisms among all devices.
Data Source
AI summary
An example operation includes one or more of determining, by a server, an event associated with a transport, receiving, by the server, atypical data related to the transport from a plurality of devices over various times prior to the event, analyzing, by the server, the atypical data, forming, by the server, a consensus based on the analyzed atypical data to determine a severity of the event, and determining, by the server, an action to take based on the severity.