IoT-Based Smart City Greening Platform for Vegetation Anomaly Prioritization
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Solution Overview
Problem
The challenge of coordinating urban greening efforts across multiple departments in smart cities to avoid improper arrangements that affect urban traffic and road management, while leveraging Internet of Things technology for effective greening management.
Innovation Solution
A method and system utilizing a management platform that integrates a sensor network to collect vegetation data, including species, climate, soil, and maintenance information, to determine vegetation anomalies and predict air quality based on traffic flow and population density, thereby establishing a greening processing priority.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple departments coordinate greening work manually, then communication and coordination between departments can be achieved, but the complexity of coordination increases and may affect urban traffic and road management
Solution Approach 1:
The patent merges multiple departmental functions into a unified IoT-based management platform that integrates vegetation monitoring, anomaly detection, and coordination management. This consolidates previously separate departmental operations into a single integrated system, reducing coordination complexity while maintaining multi-departmental adaptability.
Solution Approach 2:
The management platform performs multiple functions including data collection from sensors, anomaly detection, air quality prediction, and coordination management. This multi-functional approach allows a single system to handle various greening management tasks that previously required separate departmental systems, simplifying the overall coordination structure.
2Productivity
If traditional greening management methods are used without IoT technology, then existing processes can be maintained, but the efficiency of greening management and response time to vegetation issues are reduced
Solution Approach 1:
The system performs preliminary actions by continuously monitoring vegetation conditions through sensor networks and detecting anomalies before they become serious issues. The air quality prediction function also performs preliminary assessment of potential problems, allowing proactive intervention rather than reactive response, thereby improving efficiency and reducing response time.
Solution Approach 2:
The IoT system establishes continuous feedback loops where sensors monitor vegetation status, the platform analyzes data to detect anomalies, and maintenance actions are triggered based on detected issues. This automated feedback mechanism eliminates manual inspection delays and ensures rapid response to vegetation problems, significantly improving management efficiency.
3Measurement precision
If comprehensive vegetation data collection is implemented using sensor networks, then the accuracy of vegetation monitoring and air quality prediction improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex monitoring task into distinct functional modules: sensor data collection, vegetation anomaly detection, air quality prediction, and maintenance management. Each module handles specific aspects of data processing, which reduces the complexity of any single component while maintaining comprehensive monitoring capabilities and high measurement precision.
Data Source
AI summary
The embodiments provide a method of greening management in smart cities, a system, and a storage medium thereof. The method is executed by a management platform, comprising: obtaining, based on an object platform, vegetation data of a monitoring region corresponding to the object platform through a sensor network platform, the vegetation data including at least one of species information, and actual growth parameters; obtaining vegetation anomaly information of the monitoring region based on the vegetation data, the vegetation anomaly information including a vegetation anomaly position and a vegetation anomaly amount; obtaining a predicted air quality of the monitoring region according to the vegetation anomaly information of the monitoring region in combination with a traffic flow and a population density of the monitoring region; and determining a greening processing priority of the monitoring region based on the predicted air quality and the vegetation anomaly information of the monitoring region.