Dust detection method based on image processing and related equipment thereof

1 . A dust detection method based on image processing, wherein the method comprises:

obtaining dust data in a current area, wherein the dust data comprises dust image data and dust concentration data;

processing the dust image data and the dust concentration data through a trained dust feature analysis model to obtain dust feature data in the current area; and

integrating and outputting the dust feature data through a visualization module to generate a visualized dust feature report,

wherein obtaining of the dust data in a current area comprises:

collecting dust in a current area over a preset time period through a preset concentration sensor to obtain corresponding dust concentration data; and

capturing images of dust in the current area over a preset time period through a preset optical camera to obtain corresponding dust image data;

wherein capturing images of dust in the current area over a preset time period through a preset optical camera to obtain corresponding dust image data comprises:

performing image enhancement preprocessing on captured first image data to obtain second image data; and

performing feature marking preprocessing on the second image data to obtain the corresponding dust image data, wherein the dust image data comprises corresponding dust features,

wherein processing of the dust image data and the dust concentration data through a trained dust feature analysis model to obtain dust feature data in the current area comprises:

performing similarity calculation between the dust concentration data and dust features corresponding to the dust image data to determine an overlapping portion between the dust image data and the dust concentration data; and

determining dust feature data in the current area based on the overlapping portion, wherein the dust feature data comprises current concentration, concentration trends, and corresponding early-warning schemes.

2 . The dust detection method based on image processing according to claim 1 , wherein before processing of the dust image data and the dust concentration data through a trained dust feature analysis model to obtain dust feature data in the current area, the method further comprises:

obtaining a dust feature analysis model to be trained and a training dust sample set, wherein the training dust sample set comprises dust concentration, dust images corresponding to the dust concentration, and dust concentration labels corresponding to the dust images; and

performing iterative training on the dust feature analysis model to be trained based on the dust concentration, the dust images corresponding to the dust concentration, and the dust concentration labels corresponding to the dust images, to obtain the trained dust feature analysis model upon completion of iterative training.

3 . The dust detection method based on image processing according to claim 2 , wherein the performing iterative training on the dust feature analysis model to be trained based on the dust concentration, the dust images corresponding to the dust concentration, and the dust concentration labels corresponding to the dust images, to obtain the trained dust feature analysis model upon completion of iterative training, comprises:

taking newly obtained dust images corresponding to the latest dust concentration as latest training samples to replace a current training set; and

performing iterative training on the dust feature analysis model to be trained through the latest training samples to obtain the trained dust feature analysis model.

4 . The dust detection method based on image processing according to claim 1 , wherein integrating and outputting the dust feature data through a visualization module to generate a visualized dust feature report comprises:

determining curve graphs of each feature data in hierarchical feature data based on the dust feature data;

obtaining trend graphs of each feature data based on the curve graphs; and

generating corresponding early-warning strategies according to the trend graphs and the curve graphs, and generating a visualization report for display.

5 . A dust detection apparatus based on image processing, wherein the apparatus comprises:

a preset optical camera, configured to capture images of dust in a current area over a preset time period to obtain dust image data;

a preset concentration sensor, configured to collect dust in the current area over a preset time period to obtain dust concentration data;

a processor, configured to process the dust image data and the dust concentration data through a trained dust feature analysis model to obtain dust feature data in the current area; and

a smart terminal, configured to integrate and output the dust feature data to generate a visualized dust feature report,

the preset optical camera is configured specifically to perform image enhancement preprocessing on first image data obtained from the capturing to obtain second image data, and to perform feature marking preprocessing on the second image data to obtain the corresponding dust image data, wherein the dust image data comprises corresponding dust features,

wherein the processor comprises:

a first sub-processor, configured to perform similarity calculation between dust features corresponding to the dust image data and the dust concentration data to determine an overlapping portion between the dust image data and the dust concentration data;

a second sub-processor, configured to determine dust feature data in the current area based on the overlapping portion, wherein the dust feature data comprises current concentration, concentration trends, and corresponding early-warning schemes.

6 . The dust detection apparatus based on image processing according to claim 5 ,

wherein the processor is further configured to acquire a dust feature analysis model to be trained and a training dust sample set, wherein the training dust sample set comprises dust concentration, dust images corresponding to the dust concentration, and dust concentration labels corresponding to the dust images;

the processor, is further configured to perform iterative training on the dust feature analysis model to be trained based on the dust concentration, the dust images corresponding to the dust concentration, and the dust concentration labels corresponding to the dust images, and obtain a trained dust feature analysis model upon completion of iterative training.

7 . The dust detection apparatus based on image processing according to claim 6 ,

the processor is specifically configured to take newly obtained dust images corresponding to the latest dust concentration as latest training samples to replace a current training set, and to perform iterative training on the dust feature analysis model to be trained through the latest training samples, thereby obtaining the trained dust feature analysis model.

8 . The dust detection apparatus based on image processing according to claim 5 , wherein

the smart terminal is specifically configured to determine curve graphs of each feature data in hierarchical feature data based on the dust feature data, to obtain trend graphs of each feature data based on the curve graphs, and to generate corresponding early-warning strategies according to the trend graphs and the curve graphs, and generate a visualization report for display.

9 . An electronic device, wherein the device comprises:

a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein when executing the computer program, the processor implements the steps in the dust detection method based on image processing according to claim 1 .