Methods for planning garbage cleaning route in smart cities and internet of things systems thereof
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Solution Overview
Problem
Current garbage cleaning methods in smart cities are inefficient, leading to repeated cleaning, manpower and resource wastage, and uneven cleanliness across urban roads.
Innovation Solution
An IoT system and method for planning garbage cleaning routes, which involves monitoring road conditions, identifying garbage accumulation points, and determining optimized cleaning routes based on real-time data and predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fixed route cleaning is used, then cleaning coverage is ensured, but resource wastage increases due to repeated cleaning
Solution Approach 1:
The cleaning route is transformed from a fixed static pattern to a dynamic adaptive route that changes based on real-time garbage accumulation data. Sensors continuously monitor garbage levels and the system dynamically adjusts the cleaning path to match actual needs, ensuring coverage where required while avoiding already-clean areas.
Solution Approach 2:
A feedback loop is established where sensors detect garbage accumulation, the system processes this information, and adjusts the cleaning route accordingly. The cleaning status is continuously monitored and fed back to the control system, which modifies the route in real-time to optimize resource usage while maintaining necessary cleaning coverage.
2Measurement precision
If comprehensive monitoring is implemented, then route planning accuracy improves, but system complexity increases
Solution Approach 1:
The monitoring system is divided into independent sensor nodes distributed along the cleaning path. Each sensor independently monitors local garbage conditions, and the data is aggregated by a central control system. This segmentation allows comprehensive monitoring without requiring a single complex monitoring unit, thereby improving measurement precision while managing system complexity through modular design.
3Productivity
If real-time route adjustment is made, then cleaning efficiency improves, but control complexity increases
Solution Approach 1:
The cleaning system is equipped with autonomous decision-making capability through embedded sensors and control algorithms. The system automatically detects garbage accumulation patterns, calculates optimal routes, and adjusts cleaning operations without external intervention. This self-service approach improves cleaning efficiency by enabling real-time adaptation while minimizing the need for complex external control infrastructure.
Data Source
AI summary
The embodiments of the present disclosure provide a method for planning a garbage cleaning route in a smart city and an Internet of Things (IoT) system. The method is implemented by the Internet of Things system for planning a garbage cleaning route in a smart city. The IoT system includes a user platform, a service platform, a management platform, a sensor network platform and an object platform. The method is performed by the management platform. The method includes obtaining monitoring information on at least one road in a road network area, and recognizing a garbage accumulation situation on the at least one road; determining at least one target garbage cleaning point based on the garbage accumulation situation; and determining a garbage cleaning route based on the at least one target garbage cleaning point.