IoT Road Cleaning Platform Using Video Prediction for Sweeper Control
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Solution Overview
Problem
Current methods for managing traffic road cleaning in smart cities lack efficient real-time monitoring and control mechanisms to maintain road cleanliness, leading to suboptimal cleaning operations and potential traffic safety issues.
Innovation Solution
An Internet of Things (IoT) system that utilizes a traffic management platform to capture road videos, extract target images, predict road cleanliness and traffic flow, and control road sweepers based on these predictions, enabling real-time monitoring and targeted cleaning operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional road cleaning management methods are used, then cleaning operations can be performed, but real-time monitoring and control are insufficient leading to suboptimal cleaning efficiency
Solution Approach 1:
The system implements real-time feedback by capturing road videos through imaging devices, extracting target images, predicting road cleanliness levels, and using this information to dynamically adjust road sweeper operations. The feedback loop ensures cleaning operations respond to actual road conditions rather than operating on fixed schedules, thereby improving cleaning efficiency while maintaining real-time monitoring capability.
Solution Approach 2:
The patent replaces traditional mechanical monitoring and manual assessment of road cleanliness with an automated vision-based system. Imaging devices capture videos, image processing algorithms extract and analyze target images, and prediction models determine cleanliness levels, substituting mechanical inspection methods with optical and computational approaches that enable real-time monitoring and optimize cleaning operations.
2Reliability
If road sweepers operate continuously to maintain cleanliness, then road cleanliness is improved, but energy consumption and resource waste increase
Solution Approach 1:
The system dynamically adjusts road sweeper operations based on real-time road cleanliness predictions and traffic flow data. Rather than operating continuously at fixed intensity, the sweepers adjust their operation levels according to actual road conditions, ensuring reliable cleanliness maintenance while minimizing energy consumption during periods when roads are already clean or traffic is low.
Solution Approach 2:
The patent changes operational parameters of road sweepers based on predicted road cleanliness levels and traffic flow. When cleanliness is high or traffic is low, operation intensity is reduced; when cleanliness deteriorates or traffic increases, operation intensity increases. This parameter adjustment strategy maintains road cleanliness reliability while optimizing energy consumption by avoiding unnecessary operations during stable conditions.
3Object-affected harmful factors
If cleaning operations are increased to address traffic safety issues, then road safety is improved, but social costs and resource allocation become less efficient
Solution Approach 1:
The system performs preliminary prediction of road cleanliness and traffic flow conditions before cleaning operations are activated. By predicting potential safety issues in advance and proactively adjusting cleaning operations accordingly, the system prevents harmful factors from developing rather than reacting after problems occur, thereby improving traffic safety while maintaining efficient resource allocation through targeted rather than reactive cleaning.
Solution Approach 2:
The system enables self-service cleaning management where the road cleaning system automatically monitors its own performance and adjusts operations based on predicted conditions. The imaging devices and prediction models continuously assess road states and traffic conditions, allowing the system to self-regulate cleaning intensity according to actual needs, thereby improving traffic safety through continuous monitoring while optimizing resource allocation efficiency by avoiding unnecessary external intervention.
Data Source
AI summary
The embodiments of the present disclosure provide methods and Internet of Things systems for managing traffic road cleaning in smart city. The method is executed by a traffic management platform, comprising: obtaining a road video captured by an imaging device on a road during a time period; extracting a target image from the road video and processing the target image through a prediction model to predict a road cleanliness; processing the road video in the time period and predicting a flow corresponding to the road; and controlling a road sweeper to clean the road based on the flow and the road cleanliness.