Heavy metal pollutant damage assessment method, device and system and storage medium

Abstract

The invention discloses a heavy metal pollutant damage assessment method, device and system, and a storage medium. The method comprises the following steps: S1, acquiring a historical surface water hyperspectral remote sensing image of a predetermined area; s2, obtaining surface sediment data according to the historical surface water hyperspectral remote sensing image; s3, training a BP neural network model according to the historical surface water hyperspectral remote sensing image and the surface sediment data to obtain a heavy metal inversion model; s4, inputting the surface water hyperspectral remote sensing image of the target area into the heavy metal inversion model to obtain pollution characteristic information of heavy metal; and S5, performing pollutant damage assessment according to the pollution characteristic information of the heavy metal. By adopting the technical scheme, the damage of the heavy metal pollutants in the surface water can be evaluated.

Description

Technical Field

[0001] The present invention belongs to the field of heavy metal pollution assessment, and in particular relates to a heavy metal pollutant damage assessment method and device, system, and storage medium. Background Art

[0002] Heavy metal pollution has the characteristics of toxicity, non-biodegradability, persistence and bioaccumulation, and can eventually enter the human body through the food chain, causing serious health hazards. It has become a hot topic of global concern. Surface water sediments are sinks for heavy metals, and can also release heavy metals into water bodies to form secondary pollution, causing more direct harm to aquatic systems and human health. Therefore, it is currently impossible to effectively assess the damage of heavy metal pollutants in surface water. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a heavy metal pollutant damage assessment method and device, system and storage medium based on spectral analysis.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A heavy metal pollutant damage assessment method, comprising:

[0006] Step S1, obtaining historical surface water hyperspectral remote sensing images of a predetermined area;

[0007] Step S2, obtaining surface sediment data based on historical surface water hyperspectral remote sensing images, wherein the surface sediment data includes heavy metal content;

[0008] Step S3, training a BP neural network model based on historical surface water hyperspectral remote sensing images and surface sediment data to obtain a heavy metal inversion model;

[0009] Step S4, inputting the surface water hyperspectral remote sensing image of the target area into the heavy metal inversion model to obtain the pollution characteristic information of heavy metals;

[0010] Step S5: conducting a pollutant damage assessment based on the heavy metal pollution characteristic information.

[0011] Preferably, step S2 comprises:

[0012] Performing spatial variation spectral analysis of the historical surface water hyperspectral remote sensing images at different scales to obtain spatial variation information of heavy metal content and regional contour maps at corresponding scales;

[0013] The pollution characteristic information of heavy metals in sediments is determined according to the spatial variation information of heavy metal content and the regional contour map; the pollution characteristic information includes: pollution range, pollution area and pollution degree.

[0014] Preferably, in step S5, the impact degree of surface water in the target area is weighted according to the pollution characteristic information of different heavy metals, and the environmental risk level of surface water in the target area is evaluated by single factor index method, Nemerow index method and comprehensive index method.

[0015] The present invention also provides a heavy metal pollutant damage assessment device, comprising:

[0016] An acquisition module, used to acquire historical surface water hyperspectral remote sensing images of a predetermined area;

[0017] An analysis module is used to obtain surface sediment data based on historical surface water hyperspectral remote sensing images, and the surface sediment data includes heavy metal content;

[0018] The training module is used to train the BP neural network model based on historical surface water hyperspectral remote sensing images and surface sediment data to obtain a heavy metal inversion model;

[0019] The inversion module is used to input the surface water hyperspectral remote sensing image of the target area into the heavy metal inversion model to obtain the pollution characteristic information of heavy metals;

[0020] An assessment module is used to perform pollutant damage assessment based on the pollution characteristic information of the heavy metals.

[0021] Preferably, the analysis module comprises:

[0022] An analysis unit is used to perform spatial variation spectral analysis of the historical surface water hyperspectral remote sensing image at different scales to obtain spatial variation information of heavy metal content and regional contour map at corresponding scales;

[0023] The determination unit is used to determine the pollution characteristic information of heavy metals in sediments according to the spatial variation information of the heavy metal content and the regional contour map; the pollution characteristic information includes: pollution range, pollution area and pollution degree.

[0024] Preferably, the assessment module weights the impact of different heavy metals on the surface water in the target area according to their pollution characteristics, and uses single factor index method, Nemerow index method and comprehensive index method to evaluate the environmental risk level of the surface water in the target area.

[0025] The present invention also provides a heavy metal pollutant damage assessment system, characterized in that it includes: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a heavy metal pollutant damage assessment method when executed by the processor.

[0026] The present invention also provides a storage medium, on which a computer program is stored, and when the computer program is run, an inversion method for heavy metal pollutant damage assessment is executed.

[0027] The present invention obtains surface sediment data based on historical surface water hyperspectral remote sensing images, and the surface sediment data includes heavy metal content; trains a BP neural network model based on historical surface water hyperspectral remote sensing images and surface sediment data to obtain a heavy metal inversion model; inputs the surface water hyperspectral remote sensing images of the target area into the heavy metal inversion model to obtain heavy metal pollution characteristic information; and performs pollutant damage assessment based on the heavy metal pollution characteristic information. The technical solution of the present invention can effectively assess the damage of heavy metal pollutants in surface water. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0029] Figure 1 The figure is a flow chart of a method for assessing damage caused by heavy metal pollutants according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] Embodiment 1:

[0033] like Figure 1 As shown, an embodiment of the present invention provides a heavy metal pollutant damage assessment method, comprising:

[0034] Step S1, obtaining historical surface water hyperspectral remote sensing images of a predetermined area;

[0035] Step S2, obtaining surface sediment data based on historical surface water hyperspectral remote sensing images, wherein the surface sediment data includes heavy metal content;

[0036] Step S3, training a BP neural network model based on historical surface water hyperspectral remote sensing images and surface sediment data to obtain a heavy metal inversion model;

[0037] Step S4, inputting the surface water hyperspectral remote sensing image of the target area into the heavy metal inversion model to obtain the pollution characteristic information of heavy metals;

[0038] Step S5: conducting a pollutant damage assessment based on the heavy metal pollution characteristic information.

[0039] As an implementation method of the embodiment of the present invention, before step S2, the method further includes: performing data cleaning on the historical surface water hyperspectral remote sensing image, specifically:

[0040] Screen out garbled data and offside data from historical surface water hyperspectral remote sensing images to obtain screening index data;

[0041] Performing vectorization operation on the screening index data to obtain a screening index vector set, and adding a time-space vector to each screening index vector in the screening index vector set to obtain a time-space vector set;

[0042] Performing feature clustering on the space-time vector set to obtain a cluster center data set;

[0043] Fill each empty value data in the screening index data according to the cluster center data set.

[0044] As an implementation of the embodiment of the present invention, step S2 includes:

[0045] The factor kriging analysis method and the collaborative regionalization model are used to perform spatial variation spectral analysis of different scales on the historical surface water hyperspectral remote sensing images to obtain the spatial variation information of heavy metal content and regional contour maps at the corresponding scales; wherein the collaborative regionalization model is a mathematical model constructed based on the variogram; the variogram includes: an autovariogram and a covariogram;

[0046] The pollution characteristic information of heavy metals in sediments is determined according to the spatial variation information of heavy metal content and the regional contour map; the pollution characteristic information includes: pollution range, pollution area and pollution degree.

[0047] As an implementation of the embodiment of the present invention, step S3 includes:

[0048] Based on historical surface water hyperspectral remote sensing images and surface sediment data, a heavy metal pollutant migration model under the action of seepage-concentration coupling field was obtained;

[0049] Based on the BP neural network model, the model is trained based on historical surface water hyperspectral remote sensing images and surface sediment data;

[0050] The trained BP neural network model is optimized according to the heavy metal pollutant migration model to obtain a heavy metal inversion model; wherein the heavy metal inversion model uses partial least squares regression analysis to invert the heavy metal content of surface water from hyperspectral remote sensing images.

[0051] As an implementation method of an embodiment of the present invention, in step S5, the impact degree of surface water in the target area is weighted according to the pollution characteristic information of different heavy metals, and the environmental risk level of surface water in the target area is evaluated using the single factor index method, the Nemerow index method and the comprehensive index method.

[0052] Furthermore, the present invention adopts the single factor index method, taking advantage of its simple calculation characteristics, to quantify the risks of a large number of detailed indicators; then the Nemerow index method is used to fully consider the average and maximum values ​​of the single factor index to reflect the environmental risks under the joint action of various detailed indicators; finally, the comprehensive index method is used to weight the indicators to avoid the Nemerow index from exaggerating the effects of high-concentration pollutants. The purpose is to combine the three methods to give full play to the advantages of various methods while avoiding the errors in the evaluation results caused by the limitations of the methods themselves.

[0053] Embodiment 2:

[0054] The present invention also provides a heavy metal pollutant damage assessment device, comprising:

[0055] An acquisition module, used to acquire historical surface water hyperspectral remote sensing images of a predetermined area;

[0056] An analysis module is used to obtain surface sediment data based on historical surface water hyperspectral remote sensing images, and the surface sediment data includes heavy metal content;

[0057] The training module is used to train the BP neural network model based on historical surface water hyperspectral remote sensing images and surface sediment data to obtain a heavy metal inversion model;

[0058] The inversion module is used to input the surface water hyperspectral remote sensing image of the target area into the heavy metal inversion model to obtain the pollution characteristic information of heavy metals;

[0059] An assessment module is used to perform pollutant damage assessment based on the pollution characteristic information of the heavy metals.

[0060] As an implementation of the embodiment of the present invention, the analysis module includes:

[0061] An analysis unit is used to perform spatial variation spectral analysis of the historical surface water hyperspectral remote sensing image at different scales to obtain spatial variation information of heavy metal content and regional contour map at corresponding scales;

[0062] The determination unit is used to determine the pollution characteristic information of heavy metals in sediments according to the spatial variation information of the heavy metal content and the regional contour map; the pollution characteristic information includes: pollution range, pollution area and pollution degree.

[0063] As an implementation method of an embodiment of the present invention, the evaluation module weights the impact degree of surface water in the target area according to the pollution characteristic information of different heavy metals, and uses the single factor index method, the Nemerow index method and the comprehensive index method to evaluate the environmental risk level of the surface water in the target area.

[0064] Embodiment 3:

[0065] The present invention also provides a heavy metal pollutant damage assessment system, characterized in that it includes: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a heavy metal pollutant damage assessment method when executed by the processor.

[0066] Embodiment 4:

[0067] The present invention also provides a storage medium, on which a computer program is stored, and when the computer program is run, an inversion method for heavy metal pollutant damage assessment is executed.

[0068] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for assessing damage caused by heavy metal pollutants, characterized in that: include: Step S1, obtaining historical surface water hyperspectral remote sensing images of a predetermined area; Step S2, obtaining surface sediment data based on historical surface water hyperspectral remote sensing images, wherein the surface sediment data includes heavy metal content; Step S3, training a BP neural network model based on historical surface water hyperspectral remote sensing images and surface sediment data to obtain a heavy metal inversion model; Step S4, inputting the surface water hyperspectral remote sensing image of the target area into the heavy metal inversion model to obtain the pollution characteristic information of heavy metals; Step S5: conducting a pollutant damage assessment based on the heavy metal pollution characteristic information.

2. The heavy metal pollutant damage assessment method according to claim 1, characterized in that: Step S2 includes: Performing spatial variation spectral analysis of the historical surface water hyperspectral remote sensing images at different scales to obtain spatial variation information of heavy metal content and regional contour maps at corresponding scales; The pollution characteristic information of heavy metals in sediments is determined according to the spatial variation information of heavy metal content and the regional contour map; the pollution characteristic information includes: pollution range, pollution area and pollution degree.

3. The heavy metal pollutant damage assessment method according to claim 2, characterized in that: In step S5, the impact degree of surface water in the target area is weighted according to the pollution characteristic information of different heavy metals, and the environmental risk level of surface water in the target area is evaluated by using single factor index method, Nemerow index method and comprehensive index method.

4. A heavy metal pollutant damage assessment device, characterized in that: include: An acquisition module, used to acquire historical surface water hyperspectral remote sensing images of a predetermined area; An analysis module is used to obtain surface sediment data based on historical surface water hyperspectral remote sensing images, and the surface sediment data includes heavy metal content; The training module is used to train the BP neural network model based on historical surface water hyperspectral remote sensing images and surface sediment data to obtain a heavy metal inversion model; The inversion module is used to input the surface water hyperspectral remote sensing image of the target area into the heavy metal inversion model to obtain the pollution characteristic information of heavy metals; An assessment module is used to perform pollutant damage assessment based on the pollution characteristic information of the heavy metals.

5. The heavy metal pollutant damage assessment device according to claim 4, characterized in that: The analysis modules include: An analysis unit is used to perform spatial variation spectral analysis of the historical surface water hyperspectral remote sensing image at different scales to obtain spatial variation information of heavy metal content and regional contour map at corresponding scales; The determination unit is used to determine the pollution characteristic information of heavy metals in sediments according to the spatial variation information of the heavy metal content and the regional contour map; the pollution characteristic information includes: pollution range, pollution area and pollution degree.

6. The heavy metal pollutant damage assessment device according to claim 5, characterized in that: The assessment module weights the impact of different heavy metals on the surface water in the target area according to their pollution characteristics, and uses the single factor index method, Nemerow index method and comprehensive index method to evaluate the environmental risk level of the surface water in the target area.

7. A heavy metal pollutant damage assessment system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the method for assessing damage caused by heavy metal pollutants as described in any one of claims 1 to 3 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when run, executes the heavy metal pollutant damage assessment inversion method as described in any one of claims 1 to 3.

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