Fluid Concentration Trajectory Mapping for Pollution Source Tracing
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Solution Overview
Problem
Conventional air quality monitoring methods rely heavily on human judgment and experience, leading to inaccuracies in estimating pollution sources and occurrence times due to the reliance on video playback of concentration distribution images.
Innovation Solution
A fluid quality tracing method and system that includes obtaining a number of pieces of fluid concentration distribution data of a detected region, wherein the pieces of fluid concentration distribution data corresponds to a number of detection time points respectively; generating a number of pieces of concentration grid data respectively; obtaining a number of pieces of fluid moving data of the detected region, wherein the pieces of fluid moving data correspond to the detection time points respectively; obtaining a number of estimated positions according to the pieces of fluid moving data and an initial position; and creating a fluid concentration trajectory using the concentration grid data, initial position, and estimated positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video playback method is used for monitoring air quality, then the operation simplicity is maintained, but the measurement precision of pollution source identification deteriorates
Solution Approach 1:
The patent segments the continuous video playback process into discrete time-point concentration distribution images. Each image represents a specific detection time point with grid-based concentration data. This segmentation allows systematic processing of pollution trajectory by analyzing individual time points rather than continuously playing back videos, improving identification accuracy while maintaining manageable complexity.
Solution Approach 2:
The patent creates a digital copy of the pollution concentration distribution in grid form, replacing the need for human operators to visually interpret video images. The concentration grid data serves as a structured copy that can be processed computationally to automatically generate pollution trajectories, eliminating reliance on individual judgment and experience.
2Productivity
If manual video playback analysis is used, then the device complexity is reduced, but the productivity of pollution source identification deteriorates
Solution Approach 1:
The patent replaces the mechanical process of manual video playback and visual analysis with an automated computational system. The processing device automatically generates concentration grid data from detection data, calculates estimated positions using fluid moving data, and constructs pollution trajectories through algorithmic processing. This substitution dramatically improves identification speed while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The patent performs preliminary processing of detection data into structured concentration grid data before trajectory analysis. By pre-organizing the concentration distribution into grids and pre-calculating estimated positions based on fluid moving data, the system prepares all necessary information in advance, enabling rapid pollution source identification without requiring manual analysis during the actual monitoring process.
3Measurement precision
If automated trajectory generation is implemented, then the measurement precision of pollution source identification is improved, but the device complexity increases
Solution Approach 1:
The patent adds a temporal dimension to the spatial concentration distribution by processing multiple detection time points. The system generates concentration grids at different time points and uses fluid moving data to connect these temporal snapshots into a four-dimensional pollution trajectory (x, y, z, time). This dimensional approach automatically provides accurate pollution occurrence time estimation while the systematic methodology keeps algorithmic complexity manageable.
Solution Approach 2:
The patent introduces concentration grid data as an intermediary between raw detection data and final trajectory analysis. The grid structure serves as a mediator that organizes concentration information in a standardized format, making it easier to process temporally and spatially. This intermediary layer simplifies the overall algorithmic complexity while enabling precise trajectory generation and occurrence time estimation.
Data Source
AI summary
A fluid quality tracing method includes obtaining pieces of fluid concentration distribution data of a detected region corresponding to detection time points respectively, generating pieces of concentration grid data respectively according to the pieces of fluid concentration distribution data, obtaining pieces of fluid moving data of the detected region corresponding to the detection time points respectively, obtaining estimated positions according to the fluid moving data and an initial position, and creating a fluid concentration trajectory according to the pieces of concentration grid data, the initial position and the estimated positions. The initial position and the estimated positions are located in the detected region. The fluid concentration trajectory includes line segments with terminals corresponding to the initial position and the estimated positions respectively, and the line segments indicate concentration representative values respectively.