IoT Cleanliness Prediction for Smart City Communal Facilities
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Solution Overview
Problem
Current urban greening management relies heavily on manual recognition for road and wall cleaning, lacking intelligence and integration, which hinders efficient communal facilities management in smart cities.
Innovation Solution
A method and system for communal facilities management in smart cities using the Internet of Things, which involves obtaining and analyzing cleanliness information, weather, construction, and traffic data to automatically generate cleaning plans and instructions for communal facilities, utilizing a sensor network platform to execute these instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual recognition methods are used for road and wall cleaning, then implementation is simple, but cleaning efficiency is low and subjective influence is high
Solution Approach 1:
The patent replaces manual recognition and mechanical cleaning scheduling with an automated image recognition system and intelligent algorithm. The system captures images of communal facilities, uses AI algorithms to automatically assess cleanliness levels, and generates cleaning schedules based on objective data rather than manual judgment, thereby eliminating subjective influence and improving cleaning efficiency.
Solution Approach 2:
The system enables communal facilities to be automatically monitored and assessed without manual intervention. Image recognition technology automatically captures and analyzes facility conditions, the algorithm independently determines cleanliness levels, and the system self-generates cleaning schedules, allowing the management system to serve itself without continuous human input.
2Measurement precision
If automated image recognition and prediction algorithms are implemented, then objective cleanliness assessment is achieved, but system complexity increases
Solution Approach 1:
The patent replaces subjective manual assessment with automated image recognition technology and prediction algorithms. The system objectively captures facility images, uses AI to analyze cleanliness conditions, and predicts future ash deposit levels based on environmental factors, providing precise and unbiased measurements that eliminate human subjectivity.
Solution Approach 2:
The system introduces image recognition technology and prediction algorithms as intermediaries between the physical facility conditions and the cleaning decision-making process. These intermediaries objectively translate physical conditions into measurable data, providing a neutral and accurate basis for determining cleaning needs without direct human intervention.
3Productivity
If cleaning schedules are generated based on multiple factors (weather, construction, traffic), then cleaning optimization is improved, but data processing complexity increases
Solution Approach 1:
The patent creates a multi-functional integrated management platform that simultaneously processes diverse data types (weather information, construction data, traffic conditions) and performs multiple functions (image recognition, cleanliness assessment, prediction, schedule generation). This universal system handles various data sources through a unified algorithm framework, optimizing cleaning schedules while managing data processing complexity through integration.
Solution Approach 2:
The system merges multiple data processing functions into a single integrated algorithm. Instead of separately analyzing weather, construction, and traffic data, the system combines these factors into a unified prediction model that simultaneously processes all inputs to generate optimized cleaning schedules, reducing overall system complexity through consolidation.
4Loss of time
If manual cleaning scheduling is used, then resource allocation is simple, but time consumption and labor costs are high
Solution Approach 1:
The system performs preliminary actions by automatically generating cleaning schedules in advance based on predicted ash deposit levels and facility conditions. The algorithm forecasts future cleanliness states and proactively schedules cleaning tasks before they are needed, eliminating the need for time-consuming manual scheduling and enabling proactive resource allocation.
Solution Approach 2:
The patent replaces manual scheduling operations with automated algorithmic scheduling. The system independently analyzes facility conditions, predicts cleaning needs, and generates optimized schedules without human intervention, dramatically reducing time consumption and labor costs while managing complexity through automated decision-making processes.
Data Source
AI summary
The present disclosure provides a method for communal facilities management in a smart city based on an Internet of Things. The method includes: obtaining first cleanliness information of the communal facilities in a target area at a first time point and weather information, construction information, factory information, and traffic information of the target area during a target time period; determining second cleanliness information of the communal facilities at the second time point based on the first cleanliness information, the weather information, the construction information, the factory information, and the traffic information; determining, based on the second cleanliness information, target cleanliness information of the communal facilities at the second time point; determining the communal facilities as target communal facilities when the target cleanliness information of the communal facilities satisfies a preset condition, and determining cleaning instructions for cleaning the target communal facilities; and sending the cleaning instructions to an object platform.