Vehicle Rainfall Sensor Network for Precipitation Index Estimation
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Solution Overview
Problem
Existing technologies face challenges in accurately estimating precipitation intensity due to unstable atmospheric conditions, leading to a need for improved methods to predict sudden and localized heavy rains for disaster prevention.
Innovation Solution
A precipitation index estimation apparatus that collects rainfall amount data from vehicles equipped with sensors and uses this data to estimate precipitation intensity within predetermined areas, incorporating operation mode data from windshield wipers and rainfall amount data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional weather observation methods are used, then the system is simple to operate, but the measurement precision of precipitation intensity is insufficient
Solution Approach 1:
The patent combines rainfall amount sensors from multiple vehicles into a distributed measurement network. By merging data from numerous mobile sensing units, the system achieves high-precision precipitation estimation without requiring a complex centralized observation infrastructure, thus improving measurement precision while maintaining operational simplicity.
Solution Approach 2:
Vehicles equipped with rainfall amount sensors automatically collect and transmit precipitation data without requiring dedicated weather observation equipment or personnel. The vehicles serve themselves as mobile observation posts, eliminating the need for complex stationary measurement systems while achieving accurate precipitation intensity estimation through aggregated data.
2Measurement precision
If data from multiple vehicles is collected, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The rainfall amount sensors in vehicles serve multiple functions: they act as both vehicle maintenance components and weather observation devices. This multi-functionality allows the system to collect precipitation data from multiple vehicles without requiring dedicated weather measurement equipment, thereby improving measurement precision while avoiding additional system complexity.
Solution Approach 2:
A server acts as an intermediary to collect, aggregate, and process rainfall data from multiple vehicles. This centralized mediator simplifies the complexity of direct peer-to-peer data exchange by providing a single point of data aggregation, enabling high-precision precipitation estimation through coordinated data collection without requiring complex distributed processing systems.
3Productivity
If real-time precipitation data is provided, then the productivity of disaster prevention is improved, but the loss of time for data processing increases
Solution Approach 1:
The system continuously collects and pre-processes rainfall data from vehicles in real-time, maintaining a ready pool of processed precipitation information. This preliminary action ensures that when disaster prevention decisions are needed, the data is already prepared and available immediately, improving response productivity without incurring additional processing delays at the moment of need.
Solution Approach 2:
The server continuously receives, processes, and provides feedback on precipitation data in real-time, creating a closed-loop system where information flows continuously from vehicles to the server and back to users. This ongoing feedback mechanism eliminates batch processing delays, enabling immediate disaster prevention responses based on current precipitation conditions while maintaining efficient data processing through continuous operation.
Data Source
AI summary
A precipitation index estimation apparatus includes a data collection unit and an estimation processing unit. The data collection unit is configured to collect rainfall amount data that is detected by a rainfall amount sensor in one or more vehicles positioned in a predetermined area within a predetermined period. The estimation processing unit is configured to estimate a precipitation index indicating an intensity of precipitation in the predetermined area within the predetermined period, based on the collected rainfall amount data.