Monitoring method and digital early warning platform based on soil and groundwater remediation control

Abstract

The present invention discloses a monitoring method and digital early warning platform based on soil and groundwater restoration and control, and relates to the field of soil and groundwater monitoring technology. The method comprises the following steps: S1, collecting groundwater parameters in the soil area, and pre-processing the groundwater parameters to obtain standard groundwater parameters; S2, constructing a watershed deposition function for the soil area according to the river distribution position in the soil area and the soil profile structure; S3, using the watershed deposition function of the soil area and the standard groundwater parameters to generate soil and groundwater restoration and control monitoring results. The present invention can realize real-time monitoring of the concentration of organic pollutants in soil and groundwater, improve the accuracy of monitoring, and provide a scientific basis for pollution control.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and groundwater monitoring, and in particular to a monitoring method and a digital early warning platform based on soil and groundwater remediation control. Background Art

[0002] With the acceleration of industrialization, soil and groundwater pollution problems are becoming increasingly serious, especially the emission and accumulation of organic pollutants, which pose a serious threat to the ecological environment and human health. Traditional monitoring methods use a combination of manual collection and equipment detection, which often have problems such as complex operation, long time consumption and insufficient accuracy, and are difficult to meet the urgent needs of current environmental monitoring. Therefore, it is particularly important to develop an efficient and accurate monitoring method and platform for soil and groundwater remediation and control. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a monitoring method and a digital early warning platform based on soil and groundwater remediation control.

[0004] The technical solution of the present invention is: a monitoring method based on soil and groundwater remediation control includes the following steps:

[0005] S1. Collect groundwater parameters in the soil area and pre-process the groundwater parameters to obtain standard groundwater parameters;

[0006] S2, constructing a watershed deposition function for the soil region based on the river distribution locations and soil profile structure within the soil region;

[0007] S3. Generate soil and groundwater restoration management and monitoring results using the watershed sedimentation function of the soil area and standard groundwater parameters.

[0008] Furthermore, S1 includes the following sub-steps:

[0009] S11. Collect the concentration of organic pollutants in groundwater in the soil area as groundwater parameters;

[0010] S12. Smoothing the organic pollutant concentration to generate a smoothed organic pollutant concentration as a standard groundwater parameter.

[0011] Furthermore, S2 includes the following sub-steps:

[0012] S21, obtaining a soil profile structure of a soil region, and calculating a morphological coupling model of each layer in the soil profile structure;

[0013] S22, calculating the morphological dependence between the smoothed organic pollutant concentration and each layer of the morphological coupling model;

[0014] S23, constructing a Bayesian network of the soil region by taking the smoothed organic pollutant concentrations as nodes and several morphological dependencies as directed edges;

[0015] S24. Generate diffusion paths of organic pollutants using Bayesian networks;

[0016] S25. Generate a deposition function for the soil region based on the distribution of rivers in the soil region and the diffusion paths of organic pollutants.

[0017] The beneficial effect of the above further scheme is: in the present invention, the soil profile is a layered structure in the vertical direction of the soil, usually including a debris layer, a peat layer, a leached layer, a ash bleaching layer, a sedimentation layer, a parent material layer and a parent rock layer, but it does not mean that all soils have all the above layers. The morphological coupling model of the layers that exist in the soil profile is calculated. The Bayesian network is a graphical probabilistic model that can simulate the concentration of pollutants and their morphological dependence through nodes and directed edges to obtain accurate diffusion paths of organic pollutants. The basin sedimentation function takes into account the complexity of the soil and groundwater profile, including the erosion and sedimentation of the river and the influence of different layers of the soil profile on the migration of pollutants, thereby improving the accuracy and scientificity of monitoring.

[0018] Furthermore, in S21, the morphological coupling model C of the i-th layer in the soil profile structure i The calculation formula is:

[0019] ;

[0020] In the formula, h i represents the vertical thickness of the i-th layer, d i represents the soil erodibility factor of the i-th layer, d i-1 represents the soil erodibility factor of the i-1th layer, d i+1 represents the soil erodibility factor of the i+1th layer, Tr(·) represents the rank operation of the matrix, b i-1 represents the gravel content of the i-1th layer, b i+1 represents the gravel content of the i+1th layer, l i represents the mean particle size of all soil particles contained in the i-th layer, and e represents the index.

[0021] The beneficial effect of the above further scheme is: in the present invention, the soil erodibility factor reflects the sensitivity of the soil to wind erosion and water erosion, and the sand content has a significant impact on the erodibility of the soil. Soil with a higher sand content usually has better air permeability and water permeability. Therefore, the erodibility factor and particle size are substituted into the morphological coupling model for analysis.

[0022] Furthermore, in S22, the morphological dependence relationship P between the smoothed organic pollutant concentration and the i-th layer morphological coupling model is i The expression is:

[0023] ;

[0024] Where I represents the number of layers in the soil profile structure, N represents the smoothed organic pollutant concentration, and n i represents the organic matter content of the i-th layer, e represents the index, C i Represents the morphological coupling model of the i-th layer in the soil profile structure.

[0025] Furthermore, in S25, the expression of the deposition function K of the soil area is:

[0026] ;

[0027] In the formula, δ h represents the weight between the hth river distribution location and diffusion path, v h represents the groundwater velocity at the hth river distribution location, O h represents the dissolved oxygen at the hth river distribution location, D h represents the biochemical oxygen demand of the hth river distribution location, T h represents the chemical oxygen demand of the hth river distribution location, c represents a constant, and H represents the total number of river distribution locations.

[0028] The beneficial effect of the above further solution is that in the present invention, the diffusion paths generated by the Bayesian network are distributed throughout the soil area and may completely or partially overlap with the river distribution positions. If the hth river distribution position is all diffusion paths, then δ h =2; if there is a partial diffusion path in the hth river distribution location, then δ h =1; if there is no diffusion path in the hth river distribution location, then δ h =1 / 2.

[0029] Furthermore, S3 includes the following sub-steps:

[0030] S31, inputting the watershed deposition function of the soil region and the standard groundwater parameters into the support vector machine to obtain the final organic pollutant concentration;

[0031] S32. When the final organic pollutant concentration exceeds the organic pollutant concentration threshold, a real-time reminder is given.

[0032] Furthermore, in S31, the expression of the kernel function V of the support vector machine is:

[0033] ;

[0034] Where N represents the smoothed organic pollutant concentration, K represents the deposition function of the soil area, a represents the first parameter of the Sigmoid kernel of the support vector machine, b represents the second parameter of the Sigmoid kernel of the support vector machine, and tanh(·) represents the hyperbolic tangent function.

[0035] a is a positive real parameter that controls the shape and slope of the Sigmoid kernel function, and b is a real parameter that determines the vertical position of the Sigmoid kernel function.

[0036] The beneficial effects of the present invention are as follows: the present invention does not rely solely on the concentration of organic pollutants collected by the equipment for soil and groundwater remediation monitoring, but combines the river distribution position and soil profile structure of the soil area to obtain the watershed deposition function, and corrects the organic pollutant concentration through the watershed deposition function to obtain accurate monitoring results; it can more accurately reflect the migration and transformation laws of pollutants in the soil and groundwater system; it can realize real-time monitoring of the concentration of organic pollutants in the soil and groundwater, improve the accuracy of monitoring, and provide a scientific basis for pollution control.

[0037] Based on the above method, the present invention also proposes a digital early warning platform based on soil groundwater remediation control, including a groundwater parameter processing unit, a watershed sedimentation function construction unit and a remediation control monitoring unit;

[0038] The groundwater parameter processing unit is used to collect groundwater parameters in the soil area and pre-process the groundwater parameters to obtain standard groundwater parameters;

[0039] The watershed sedimentation function construction unit is used to construct a watershed sedimentation function for a soil region according to the river distribution position and soil profile structure in the soil region;

[0040] The restoration management and monitoring unit is used to generate restoration management and monitoring results for soil and groundwater using the watershed deposition function of the soil area and standard groundwater parameters.

[0041] The beneficial effects of the present invention are as follows: the present invention can realize real-time monitoring of the concentration of organic pollutants in soil and groundwater, improve the accuracy of monitoring, and provide a scientific basis for pollution control. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of the monitoring method based on soil and groundwater remediation control;

[0043] Figure 2 This is a structural diagram of the digital early warning platform based on soil and groundwater remediation and control. DETAILED DESCRIPTION

[0044] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0045] like Figure 1 As shown, the present invention provides a monitoring method based on soil groundwater remediation management and control, comprising the following steps:

[0046] S1. Collect groundwater parameters in the soil area and pre-process the groundwater parameters to obtain standard groundwater parameters;

[0047] S2, constructing a watershed deposition function for the soil region based on the river distribution locations and soil profile structure within the soil region;

[0048] S3. Generate soil and groundwater restoration management and monitoring results using the watershed sedimentation function of the soil area and standard groundwater parameters.

[0049] In this embodiment of the present invention, S1 includes the following sub-steps:

[0050] S11. Collect the concentration of organic pollutants in groundwater in the soil area as groundwater parameters;

[0051] S12. Smoothing the organic pollutant concentration to generate a smoothed organic pollutant concentration as a standard groundwater parameter.

[0052] In this embodiment of the present invention, S2 includes the following sub-steps:

[0053] S21, obtaining a soil profile structure of a soil region, and calculating a morphological coupling model of each layer in the soil profile structure;

[0054] S22, calculating the morphological dependence between the smoothed organic pollutant concentration and each layer of the morphological coupling model;

[0055] S23, constructing a Bayesian network of the soil region by taking the smoothed organic pollutant concentrations as nodes and several morphological dependencies as directed edges;

[0056] S24. Generate diffusion paths of organic pollutants using Bayesian networks;

[0057] S25. Generate a deposition function for the soil region based on the distribution of rivers in the soil region and the diffusion paths of organic pollutants.

[0058] In the present invention, the soil profile is a layered structure in the vertical direction of the soil, which usually includes a debris layer, a peat layer, a leached layer, a ash bleaching layer, a sedimentation layer, a parent material layer and a parent rock layer, etc., but it does not mean that all soils have all of the above layers. The morphological coupling model of the layers that exist in the soil profile is calculated. The Bayesian network is a graphical probabilistic model that can simulate the concentration of pollutants and their morphological dependencies through nodes and directed edges to obtain accurate diffusion paths of organic pollutants. The basin sedimentation function takes into account the complexity of the soil and groundwater profile, including river erosion, sedimentation, and the impact of different levels of the soil profile on pollutant migration, thereby improving the accuracy and scientificity of monitoring.

[0059] In the embodiment of the present invention, in S21, the morphological coupling model C of the i-th layer in the soil profile structure i The calculation formula is:

[0060] ;

[0061] In the formula, h i represents the vertical thickness of the i-th layer, d i represents the soil erodibility factor of the i-th layer, d i-1 represents the soil erodibility factor of the i-1th layer, d i+1 represents the soil erodibility factor of the i+1th layer, Tr(·) represents the rank operation of the matrix, b i-1 represents the gravel content of the i-1th layer, b i+1 represents the gravel content of the i+1th layer, l i represents the mean particle size of all soil particles contained in the i-th layer, and e represents the index.

[0062] In the present invention, the soil erodibility factor reflects the sensitivity of the soil to wind erosion and water erosion, and the sand content has a significant impact on the erodibility of the soil. Soil with a higher sand content usually has better air permeability and water permeability. Therefore, the erodibility factor and particle size are substituted into the morphological coupling model for analysis.

[0063] In the embodiment of the present invention, in S22, the morphological dependence relationship P between the smoothed organic pollutant concentration and the i-th layer morphological coupling model is i The expression is:

[0064] ;

[0065] Where I represents the number of layers in the soil profile structure, N represents the smoothed organic pollutant concentration, and n i represents the organic matter content of the i-th layer, e represents the index, C i Represents the morphological coupling model of the i-th layer in the soil profile structure.

[0066] In the embodiment of the present invention, in S25, the expression of the deposition function K of the soil area is:

[0067] ;

[0068] In the formula, δ h represents the weight between the hth river distribution location and diffusion path, v h represents the groundwater velocity at the hth river distribution location, O h represents the dissolved oxygen at the hth river distribution location, D h represents the biochemical oxygen demand of the hth river distribution location, T h represents the chemical oxygen demand of the hth river distribution location, c represents a constant, and H represents the total number of river distribution locations.

[0069] In the present invention, the diffusion paths generated by the Bayesian network are distributed throughout the soil area and may overlap completely or partially with the river distribution locations. If the hth river distribution location is all diffusion paths, then δ h =2; if there is a partial diffusion path in the hth river distribution location, then δ h =1; if there is no diffusion path in the hth river distribution location, then δ h =1 / 2.

[0070] In this embodiment of the present invention, S3 includes the following sub-steps:

[0071] S31, inputting the watershed deposition function of the soil region and the standard groundwater parameters into the support vector machine to obtain the final organic pollutant concentration;

[0072] S32. When the final organic pollutant concentration exceeds the organic pollutant concentration threshold, a real-time reminder is given.

[0073] In the embodiment of the present invention, in S31, the expression of the kernel function V of the support vector machine is:

[0074] ;

[0075] Where N represents the smoothed organic pollutant concentration, K represents the deposition function of the soil area, a represents the first parameter of the Sigmoid kernel of the support vector machine, b represents the second parameter of the Sigmoid kernel of the support vector machine, and tanh(·) represents the hyperbolic tangent function.

[0076] a is a positive real parameter that controls the shape and slope of the Sigmoid kernel function, and b is a real parameter that determines the vertical position of the Sigmoid kernel function.

[0077] Based on the above method, the present invention also proposes a digital early warning platform based on soil and groundwater remediation management and control, such as Figure 2 As shown, it includes groundwater parameter processing unit, basin sedimentation function construction unit and restoration management and monitoring unit;

[0078] The groundwater parameter processing unit is used to collect groundwater parameters in the soil area and pre-process the groundwater parameters to obtain standard groundwater parameters;

[0079] The watershed sedimentation function construction unit is used to construct a watershed sedimentation function for a soil region according to the river distribution position and soil profile structure in the soil region;

[0080] The restoration management and monitoring unit is used to generate restoration management and monitoring results for soil and groundwater using the watershed deposition function of the soil area and standard groundwater parameters.

[0081] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A monitoring method based on soil and groundwater remediation management and control, characterized in that: The following steps are involved: S1. Collect groundwater parameters in the soil area and pre-process the groundwater parameters to obtain standard groundwater parameters; S2, constructing a watershed deposition function for the soil region based on the river distribution locations and soil profile structure within the soil region; S3. Generate soil and groundwater restoration control monitoring results using the watershed sedimentation function of the soil area and standard groundwater parameters; The S2 comprises the following sub-steps: S21, obtaining a soil profile structure of a soil region, and calculating a morphological coupling model of each layer in the soil profile structure; S22, calculating the morphological dependence between the smoothed organic pollutant concentration and each layer of the morphological coupling model; S23, constructing a Bayesian network of the soil region by taking the smoothed organic pollutant concentrations as nodes and several morphological dependencies as directed edges; S24. Generate diffusion paths of organic pollutants using Bayesian networks; S25, generating a deposition function for the soil region according to the distribution locations of rivers in the soil region and the diffusion paths of organic pollutants; In S21, the morphological coupling model C of the i-th layer in the soil profile structure i The calculation formula is: ； In the formula, h i represents the vertical thickness of the i-th layer, d i represents the soil erodibility factor of the i-th layer, d i-1 represents the soil erodibility factor of the i-1th layer, d i+1 represents the soil erodibility factor of the i+1th layer, Tr(·) represents the rank operation of the matrix, b i-1 represents the gravel content of the i-1th layer, b i+1 represents the gravel content of the i+1th layer, l i represents the mean particle size of all soil particles contained in the i-th layer, and e represents the index; In S22, the morphological dependence relationship P between the smoothed organic pollutant concentration and the i-th layer morphological coupling model i The expression is: ； Where I represents the number of layers in the soil profile structure, N represents the smoothed organic pollutant concentration, and n i represents the organic matter content of the i-th layer, e represents the index, C i represents the morphological coupling model of the i-th layer in the soil profile structure; In S25, the expression of the deposition function K of the soil area is: ； In the formula, δ h represents the weight between the hth river distribution location and diffusion path, v h represents the groundwater velocity at the hth river distribution location, O h represents the dissolved oxygen at the hth river distribution location, D h represents the biochemical oxygen demand of the hth river distribution location, T h represents the chemical oxygen demand of the hth river distribution location, c represents a constant, and H represents the total number of river distribution locations; The S3 comprises the following sub-steps: S31, inputting the watershed deposition function of the soil region and the standard groundwater parameters into the support vector machine to obtain the final organic pollutant concentration; S32. When the final organic pollutant concentration exceeds the organic pollutant concentration threshold, a real-time reminder is given.

2. The monitoring method based on soil and groundwater remediation management and control according to claim 1 is characterized in that: The S1 comprises the following sub-steps: S11. Collect the concentration of organic pollutants in groundwater in the soil area as groundwater parameters; S12. Smoothing the organic pollutant concentration to generate a smoothed organic pollutant concentration as a standard groundwater parameter.

3. The monitoring method based on soil and groundwater remediation management and control according to claim 1 is characterized in that: In S31, the expression of the kernel function V of the support vector machine is: ； Where N represents the smoothed organic pollutant concentration, K represents the deposition function of the soil area, a represents the first parameter of the Sigmoid kernel of the support vector machine, b represents the second parameter of the Sigmoid kernel of the support vector machine, and tanh(·) represents the hyperbolic tangent function.

4. A digital early warning platform based on soil and groundwater remediation management and control, characterized in that: The digital early warning platform based on soil and groundwater remediation control is used to execute the monitoring method based on soil and groundwater remediation control as claimed in claim 1, and the platform includes a groundwater parameter processing unit, a watershed sedimentation function construction unit and a remediation control monitoring unit; The groundwater parameter processing unit is used to collect groundwater parameters in the soil area and pre-process the groundwater parameters to obtain standard groundwater parameters; The watershed deposition function construction unit is used to construct a watershed deposition function for the soil region according to the river distribution position and soil profile structure in the soil region; The restoration management and monitoring unit is used to generate restoration management and monitoring results of soil and groundwater by using the watershed deposition function of the soil area and standard groundwater parameters.

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