Predictive Fill Level Sensor for Waste Collection Optimization
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Solution Overview
Problem
Conventional systems for monitoring refuse container fill levels provide limited optimization for waste collection routes, as they rely on simple fill level measurements and cannot accurately predict when containers will reach specific fill levels, leading to inefficient resource allocation and unnecessary emptying or refilling.
Innovation Solution
A device and system that utilize level sensors to determine current fill levels, transmit data to a control center, and analyze fill rates to predict future fill levels, allowing for more precise optimization of collection schedules and resource allocation, including the use of historical data, seasonal, and environmental parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems use simple fill level measurements to determine when containers need emptying, then the system complexity is low, but the route planning optimization is limited and trucks may still make unnecessary trips
Solution Approach 1:
The system performs preliminary actions by calculating predicted fill levels and projecting when containers will reach full capacity. This allows the routing system to plan ahead and schedule collections only when necessary, avoiding unnecessary trips while maintaining simple sensor hardware.
Solution Approach 2:
The system dynamically adjusts routing decisions based on predicted fill levels rather than static thresholds. By continuously updating predictions based on current fill level and historical data, the system optimizes routes in real-time without requiring complex sensor systems.
2Loss of time
If the system queries fill levels from a central system to optimize route planning, then unnecessary stops can be avoided, but the system cannot accurately predict when containers will reach full capacity
Solution Approach 1:
The system implements feedback by continuously monitoring actual fill levels and using this data to refine predictions of future fill levels. This feedback loop allows the system to accurately predict when containers will reach full capacity, reducing unnecessary stops while improving prediction accuracy.
Solution Approach 2:
The system performs preliminary calculations to predict future fill levels based on current data and historical patterns. This allows the system to proactively identify which containers will need emptying and plan routes accordingly, avoiding unnecessary stops while maintaining accurate predictions.
3Device complexity
If the system only plans the next round of purging in advance with limited information, then the system complexity remains low, but the discharge optimization is limited
Solution Approach 1:
The system performs preliminary actions by calculating predicted fill levels and projecting future container status. This enables advanced planning of collection routes and timing, significantly improving discharge optimization while maintaining relatively simple system architecture.
Solution Approach 2:
The system dynamically optimizes discharge planning by continuously updating predictions based on current fill levels and historical data. This allows the system to adapt routing decisions in real-time, improving discharge efficiency without requiring complex system infrastructure.
Data Source
Figure 1~2
Figure 3~4
AI summary
The device (3) has a level sensor (4) which determines the current filling level of the material (6). A telecommunication unit transfers the determined filling level data to the control center. A filling rate analysis unit detects and/or determines the fill rate parameter, by which the filling rate analysis unit creates the predicted filling parameters. The filling parameters are transferred to the control unit through the communication unit. An independent claim is included for system for monitoring filling level of material treated to empty or to collection point in container.