Transport Environment Adjustment via Occupant Data Analysis
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Solution Overview
Problem
Existing transport systems lack automated adjustment capabilities to tailor environmental settings such as lighting, temperature, and sound to individual occupants' preferences, requiring manual adjustments by occupants or drivers.
Innovation Solution
A system utilizing a processor and memory to receive data about occupants, determine their destination and mode of travel, and adjust the transport environment accordingly, using blockchain technology for data storage and smart contracts to automate these adjustments based on occupant behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of environmental settings is provided, then occupants can customize their preferences, but the process requires time and effort from occupants or drivers
Solution Approach 1:
The system performs preliminary action by automatically adjusting environmental settings (lighting, temperature, sound, seat positions) before the occupant enters the transport or immediately upon entry. The processor receives data about the occupant from various sources, determines their preferences, and configures the environment in advance, eliminating the need for manual adjustment during travel.
Solution Approach 2:
The system implements self-service by autonomously managing environmental adjustments without requiring occupant or driver intervention. The processor continuously monitors occupant behavior data, analyzes preferences, and automatically modifies environmental parameters, allowing the system to serve itself rather than relying on human operation.
2Productivity
If automated adjustment based on occupant preferences is implemented, then time and effort are saved, but the system complexity increases
Solution Approach 1:
The system achieves universality by using a single processor to perform multiple functions: receiving data from various sources, analyzing occupant preferences, determining environmental settings, and controlling different environmental parameters (lighting, temperature, sound, seats). This multi-functional approach consolidates complexity into one central unit rather than requiring separate systems for each function.
Solution Approach 2:
The processor acts as an intermediary between various data sources (sensors, user profiles, external systems) and the environmental control systems. It receives and processes data from multiple sources, translates occupant preferences into specific environmental parameters, and coordinates adjustments across different subsystems, simplifying the overall system architecture through centralized mediation.
3Adaptability or versatility
If environmental settings are pre-configured for each occupant, then personalization is achieved, but data storage and processing requirements increase
Solution Approach 1:
The system applies local quality by tailoring environmental settings specifically to each occupant's local preferences and needs. The processor analyzes individual occupant data and configures parameters such as lighting intensity, temperature, sound levels, and seat positions according to each person's specific preferences, creating a personalized environment for each occupant rather than a uniform setting for all.
Solution Approach 2:
The system performs preliminary action by pre-configuring environmental settings based on stored occupant preference data before the occupant enters the transport. The processor retrieves pre-stored preference information, determines the appropriate environmental configuration, and prepares the setting in advance, reducing the need for real-time data processing and storage during travel.
Data Source
AI summary
An example operation may include one or more of receiving, by a device, from at least one data source, data related to an occupant of a transport, determining, by the device, a destination of the occupant based on the data related to the occupant, determining, by the device, a departure time of the occupant based on the destination, requesting, by the device, a transport at a location of the occupant at the departure time, and adjusting, by the device, an environment of the transport based on the data related to the occupant and the destination.