Vehicle Window Management via Dynamic Sensor Sampling
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Solution Overview
Problem
Vehicle operators often forget to close vehicle windows, leading to potential damage from precipitation.
Innovation Solution
A vehicle system that uses a computer communicatively coupled with sensors and a remote server to automatically close windows based on weather data, optimizing sensor sampling frequency to conserve resources and efficiently manage window movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors continuously monitor precipitation to prevent interior damage, then reliability of window management is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the sensor sampling frequency based on weather conditions. When precipitation is detected or weather forecasts indicate high precipitation probability, the sampling frequency increases to ensure reliable window management. When conditions are favorable, the frequency decreases to conserve energy, thus resolving the contradiction between reliability and energy consumption.
Solution Approach 2:
The system changes the operational parameters of the sensor based on environmental conditions. By adjusting the sampling frequency parameter according to weather data and actual precipitation detection, the system optimizes the balance between monitoring reliability and energy consumption, allowing the sensor to work more intensively only when necessary.
2Measurement precision
If sensor sampling frequency is increased to detect precipitation accurately, then measurement precision is improved, but resource consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic sampling of precipitation data at adjusted intervals. The sampling period dynamically changes based on weather conditions - shorter periods when precipitation is detected or forecasted, longer periods when conditions are stable. This periodic action maintains measurement precision when needed while significantly reducing average resource consumption.
Solution Approach 2:
The sampling frequency parameter is made dynamic rather than static. The system adjusts the sampling interval based on real-time weather data and sensor readings, increasing frequency only when precipitation detection accuracy is critical, thereby optimizing the trade-off between measurement precision and energy loss.
3Object-affected harmful factors
If windows are automatically closed based on weather data, then protection from precipitation is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by closing windows in advance based on weather forecast data before precipitation actually occurs. This proactive approach protects the vehicle interior from damage while using a relatively simple control logic that triggers window closure when forecasted precipitation probability exceeds a threshold, avoiding the need for complex real-time decision-making systems.
Solution Approach 2:
The system uses weather forecast data as an intermediary to trigger window closure actions. Rather than directly sensing precipitation and reacting, the system uses forecast information as a mediator to anticipate and prevent damage, simplifying the control architecture while improving protection effectiveness.
Data Source
AI summary
A vehicle computer is programmed to receive weather data from a remote server, to determine a frequency, based on the weather data, to obtain precipitation data from one or more vehicle sensors. The computer is further programmed to obtain the precipitation data, and to close one or more vehicle windows based at least in part on the precipitation data.