Vehicle Climate Controller Pattern Recognition
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Solution Overview
Problem
Automatic temperature control systems in vehicles face challenges in adapting to user preferences and environmental conditions, leading to inefficient performance and user dissatisfaction, as current systems lack effective monitoring and feedback mechanisms to optimize settings.
Innovation Solution
A system that includes a climate controller with manual and auto modes, storing sample vectors of operation settings and climate conditions, and a wireless communication system sending data packages to a remote server for pattern analysis, allowing for adaptive adjustments and user education based on user override commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic temperature control systems use sophisticated algorithms to respond to changing environmental conditions, then thermal comfort for occupants is improved, but device complexity increases and manufacturing cost rises
Solution Approach 1:
The system implements feedback by monitoring user override actions on climate controls and using this information to iteratively improve the automatic control algorithms. Sensors detect when users manually adjust settings, and this feedback data is fed back to refine the algorithms, allowing the system to learn and adapt to user preferences over time without requiring overly complex initial algorithms
Solution Approach 2:
The system performs preliminary action by collecting and analyzing user override data during the vehicle operation to proactively identify patterns and preferences. This preliminary data collection enables the system to prepare improved control strategies in advance, rather than relying solely on pre-programmed complex algorithms
2Adaptability or versatility
If automatic temperature control systems are designed to accommodate every potential combination of conditions, then adaptability is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The system applies dynamics by making the control algorithm adaptive and evolving rather than static and fixed. The algorithms dynamically adjust based on real user behavior data collected from override actions, allowing the system to adapt to new conditions and preferences without requiring comprehensive pre-programming for every possible scenario
Solution Approach 2:
The system collects preliminary data on user preferences and override patterns during normal operation. This preliminary action enables the system to build a knowledge base of user preferences that gradually improves adaptability without requiring the system to anticipate every possible condition in advance
3Loss of information
If user override actions are monitored and data is collected from fleet vehicles, then loss of information is reduced and user behavior patterns are identified, but device complexity and data management requirements increase
Solution Approach 1:
The system implements multi-functionality by using the same climate control hardware and user interface for both its primary temperature control function and for collecting preference data. The existing sensors, processors, and communication systems serve dual purposes: maintaining climate comfort and gathering user behavior information, eliminating the need for separate dedicated data collection hardware
Solution Approach 2:
The system performs self-service by automatically monitoring and recording user override actions without requiring separate manual data collection processes. The climate control system itself generates and stores the preference data during normal operation, and the data is automatically transmitted when vehicles connect, reducing the burden of active data management
Data Source
AI summary
Each vehicle in a fleet comprises a climate controller having an auto mode. The auto mode controls climate actuators in response to a model relating sensed climate conditions to respective settings for the actuators. The vehicle has a buffer memory periodically storing sample vectors comprised of actuator/operation settings and sensed climate conditions. A user interface in the vehicle is responsive to a user override commands to modify respective operation settings while in auto mode. Each vehicle has a wireless communication system for sending data packages to a remote server when the user generates the override. Each data package is comprised of a plurality of stored sample vectors and an identification of the override command A central database associated with the remote server receives the data packages from the fleet vehicles in order to identify patterns within the received vectors that are associated with any given override command