Pollutant Early Warning via K-means Cluster Analysis
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Solution Overview
Problem
Conventional river inspection and monitoring methods are labor-intensive, costly, and unable to provide real-time, quantitative analysis and early warning for water pollution, relying on subjective image judgment and lacking automation.
Innovation Solution
A method and system using high-definition cameras to capture pollutant images, applying K-means cluster analysis to perform image recognition and pollutant area calculation, with a pollutant color gamut database and aberration threshold for real-time, automatic, and unattended pollutant early warning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual on-site inspections and drone inspections are used, then river inspection can be conducted, but the inspection duration is limited, cost is high, and only qualitative judgment is possible
Solution Approach 1:
The patent replaces manual mechanical inspection systems with an automated image processing system using K-means cluster analysis. The system automatically segments pollutant regions from water body images through algorithmic color space analysis, eliminating the need for manual on-site inspections and drone operations while achieving both quantitative measurement and continuous automation.
Solution Approach 2:
The system performs self-service by automatically acquiring water body images, processing them through K-means clustering to identify pollutant regions, calculating pollutant areas, and generating early warnings without human intervention. The entire workflow from image acquisition to pollutant identification and area calculation is automated, enabling the system to serve itself continuously.
2Productivity
If video system monitoring is used for key river sections, then long-term monitoring is possible, but it requires manual stationing and cannot provide quantitative analysis
Solution Approach 1:
The patent transforms qualitative visual information into quantitative data by analyzing color parameters in the RGB color space. Through K-means clustering, the system converts image pixel data into distinct color clusters, identifies pollutant-specific color ranges, and calculates precise pollutant areas, thereby recovering quantitative information that would otherwise be lost in subjective visual assessment.
Solution Approach 2:
The patent introduces image processing technology as an intermediary between video monitoring and quantitative analysis. The K-means cluster analysis acts as a mediator that bridges the gap between visual monitoring data and measurable pollutant characteristics, enabling automated extraction of quantitative information from video images without requiring manual intervention.
3Reliability
If conventional river inspection methods are used, then inspection can be performed, but labor and material resources are heavily consumed
Solution Approach 1:
The patent extracts the essential function of river inspection from labor-intensive field operations and isolates it into an automated image processing system. By taking out the core monitoring function and implementing it through algorithmic analysis of water body images, the system eliminates the need for continuous human presence and material resources while maintaining or improving early warning reliability through consistent automated assessment.
Data Source
AI summary
A method and a system for pollutant identifying and early warning based on cluster analysis include steps of: installing multiple high-definition cameras on a river or a sluice dam, capturing pollutant images in front of the sluice dam at certain intervals, and transmitting the pollutant images captured by the high-definition cameras to a computer; reading the pollutant images transmitted to the computer; extracting a main pollutant color in the pollutant images through cluster analysis; calculating a difference between the main pollutant color and RGB data in a pollutant color gamut database, and setting an aberration threshold to identify the pollutant color; performing scale conversion with two sets of common point image pixel coordinates and actual plane coordinates to calculate a current pollutant area; and judging whether a polluted area exceeds a critical value according to a preset pollutant area threshold, thereby realizing early warning.