Road Treatment Control System Using Salinity Data
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Solution Overview
Problem
Current road treatment systems for gritting vehicles lack efficiency in determining optimal amounts and types of treatment materials due to varying road conditions, leading to potential over- or under-treatment, which affects road user safety and environmental impact.
Innovation Solution
A control system for road treatment vehicles that uses salinity sensors and historical data to calculate a treatment material index, determining the degradation rate of treatment materials and adjusting deployment parameters such as amount, composition, and spread pattern based on real-time and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed amount of treatment material is deployed on all road sections, then the deployment process is simple and fast, but the treatment effectiveness varies due to different road characteristics and conditions
Solution Approach 1:
The system determines specific treatment parameters (amount, type, spread pattern) for each individual road section based on its characteristics such as road type, gradient, curvature, and historical salinity data. This local customization ensures optimal treatment effectiveness for each section while maintaining an efficient automated deployment process across the entire route.
Solution Approach 2:
The control system pre-determines treatment parameters for multiple road sections ahead of time using stored characteristic data and historical salinity information. This preliminary calculation allows the vehicle to deploy treatment material efficiently without real-time delays, while still achieving effective treatment tailored to each section's specific conditions.
2Measurement precision
If treatment material is deployed based on real-time salinity measurement only, then treatment accuracy is improved, but the system cannot account for road characteristics that affect material degradation
Solution Approach 1:
The system combines real-time salinity measurement data with stored historical salinity data and road characteristic data (road type, gradient, curvature) to comprehensively determine treatment parameters. This integration allows the system to account for both current road conditions and how road characteristics affect treatment material degradation over time.
Solution Approach 2:
The system uses historical salinity measurement data from previous treatments as feedback to understand how treatment material degrades on specific road sections. This feedback loop, combined with road characteristic data, enables the system to adaptively determine optimal treatment parameters that account for both measurement precision and road condition variability.
3Manufacturing precision
If the control system stores and processes detailed characteristic data for each road section, then treatment parameter determination is optimized, but the system complexity increases
Solution Approach 1:
Road characteristic data (road type, gradient, curvature) and historical salinity data are stored in advance in the control system's storage device. This preliminary data collection and organization enables efficient real-time processing during deployment, as the system only needs to retrieve and apply pre-stored characteristics rather than collecting and analyzing all data in real-time.
4Productivity
If the vehicle treats each road section with uniform treatment parameters, then the deployment process is efficient, but over-treatment or under-treatment occurs due to varying degradation rates
Solution Approach 1:
The system determines specific treatment parameters including amount, type, and spread pattern for each individual road section based on its characteristics and historical performance. This localized optimization prevents both over-treatment (wasting material) and under-treatment (insufficient effectiveness) while maintaining efficient automated deployment across the entire route.
Solution Approach 2:
The system dynamically adjusts treatment parameters (amount of material, type of material, spread pattern) based on determined degradation rates for different road sections. By changing these parameters to match specific road conditions and historical performance, the system optimizes material usage efficiency and prevents waste while maintaining deployment effectiveness.
Data Source
Figure 1a~1b
Figure 2a~2b
Figure 3a~3b
AI summary
Control system (10) for a road treatment vehicle (20), the control system configured to: receive, from a salinity sensor (22) associated with the road treatment vehicle, salinity data (16) comprising an indication of a salinity associated with a surface of a road section on which the road treatment vehicle is located; receive, from a storage unit (14) of the control system, historical data (18) comprising historical salinity data indicating the salinity associated with the surface of the road section on which the road treatment vehicle is located at a previous time; and determine, in dependence on receiving the salinity data and historical data, one or more road treatment parameters (15) for the road treatment material.