CN116167285A - Organic pollutant migration prediction method and device and electronic equipment - Google Patents
Organic pollutant migration prediction method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a method and a device for predicting migration of organic pollutants and electronic equipment, wherein the method comprises the following steps: basic characteristic data of a low-permeability pollution site to be predicted is obtained; performing feature derivation operation on the basic feature data, and extracting target derived feature data from the derived feature data obtained by the feature derivation operation; and carrying out pollutant concentration prediction on the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration. In the method for predicting the organic pollutant migration, the pollutant concentration of the low-permeability pollution site to be predicted is obtained by predicting the pollutant concentration of the target derivative characteristic data by adopting the pollutant concentration prediction model, and compared with the forward modeling method for the organic pollutant migration of the low-permeability site, the operation speed of the pollutant concentration prediction model is high, and the prediction speed and the prediction efficiency of the organic pollutant migration are improved.
Description
Technical Field
The invention relates to the technical field of pollutant migration, in particular to a method and a device for predicting organic pollutant migration and electronic equipment.
Background
Along with the continuous acceleration of economic development and industrialization and urban processes in China, a large number of chemical industry and pesticide production enterprises need to be moved or have been moved, and a large number of left sites have the problem of organic pollution, and soil layers of the sites basically contain low-permeability media such as clay (hereinafter, the sites are simply referred to as low-permeability sites).
The fast forward computation is the basis for high-precision hydrogeologic parameter inversion. Currently, the simulation prediction of the organic pollutant migration of the hypotonic site requires high-precision hydrogeologic parameters, and the timeliness of the forward modeling method of the organic pollutant migration of the hypotonic site (namely, the result of the organic pollutant migration of the hypotonic site obtained through forward modeling of the assumed hydrogeologic parameters—the organic pollutant concentration of the hypotonic site) is an important reason for restricting the inversion of the high-precision hydrogeologic parameters of the polluted site (namely, the inversion of the result of the organic pollutant migration of the hypotonic site obtained through forward modeling and the adjustment of the assumed hydrogeologic parameters).
Forward modeling of organic pollutant migration in hypotonic sites is generally based on finite element or finite difference numerical simulation methods, and it is critical to use a sufficiently fine space-time grid to describe the concentration gradient that causes the diffusion of the pollutant, so that forward modeling is slow and takes a long time to calculate. Even though there may be explicitly expressed forward formulas under certain assumption conditions, due to numerical integration and multi-layer cyclic operations, one forward calculation often takes tens of seconds or even longer, whereas forward calculation often needs to be invoked thousands of times, even more than millions of times, in high-precision hydrogeologic parameter inversion, which tends to result in a long total time for high-precision hydrogeologic parameter inversion.
In summary, the existing forward modeling method for the migration of the organic pollutants in the hypotonic site has the technical problems of long time consumption and low efficiency when the organic pollutant migration is predicted.
Disclosure of Invention
In view of the above, the present invention aims to provide a method, a device and an electronic device for predicting organic pollutant migration, so as to alleviate the technical problems of long time consumption and low efficiency of the existing forward modeling method for organic pollutant migration in a hypotonic site when the organic pollutant migration is predicted.
In a first aspect, an embodiment of the present invention provides a method for predicting migration of an organic pollutant, including:
basic characteristic data of a low-permeability pollution site to be predicted is obtained, wherein pollutants of the low-permeability pollution site to be predicted are organic pollutants;
performing feature derivation operation on the basic feature data, and extracting target derived feature data from derived feature data obtained by the feature derivation operation;
and carrying out pollutant concentration prediction on the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration.
Further, performing feature derivation operation on the basic feature data, including:
Performing square feature derivation operation on each piece of basic feature data to obtain first derived feature data;
performing difference calculation on each basic feature data and other basic feature data as well as each first derivative feature data, and performing difference calculation on each first derivative feature data and other first derivative feature data to obtain second derivative feature data;
summing each basic characteristic data with each other basic characteristic data and each first derivative characteristic data, and summing each first derivative characteristic data with each other first derivative characteristic data to obtain third derivative characteristic data;
multiplying each basic feature data with each other basic feature data and each first derivative feature data, and multiplying each first derivative feature data with each other first derivative feature data to obtain fourth derivative feature data;
dividing each basic feature data with each other basic feature data and each first derivative feature data, and dividing each first derivative feature data with each other first derivative feature data to obtain fifth derivative feature data;
And combining the basic feature data, the first derivative feature data, the second derivative feature data, the third derivative feature data, the fourth derivative feature data and the fifth derivative feature data to obtain derivative feature data.
Further, extracting target derivative feature data from derivative feature data obtained by the feature derivative operation includes:
extracting target derived feature data corresponding to the preset target features from the derived feature data according to the preset target features.
Further, the pollutant concentration prediction model includes: XGBoost model.
Further, the method further comprises:
obtaining a basic characteristic data sample of a hypotonic pollution site;
calculating the basic characteristic data samples by adopting a Dandy-salt formula to obtain a pollutant concentration true value corresponding to each group of basic characteristic data samples;
performing feature derivation operation on the basic feature data sample, and extracting a target derived feature data sample from the derived feature data sample obtained by the feature derivation operation;
and training an original pollutant concentration prediction model by adopting the target derived characteristic data sample and the corresponding pollutant concentration truth value to obtain the pollutant concentration prediction model.
Further, extracting a target derived feature data sample from the derived feature data samples obtained from the feature derivation operation, includes:
performing feature importance calculation on the derivative feature data sample by adopting LASSO regression to obtain the importance of each feature corresponding to the derivative feature data sample;
determining target features in the features corresponding to the derivative feature data samples according to the importance of the features corresponding to the derivative feature data samples, wherein the target features are features with importance larger than a preset threshold value in the features corresponding to the derivative feature data samples;
and extracting the target derivative characteristic data sample corresponding to the target characteristic from the derivative characteristic data sample according to the target characteristic.
Further, the basic features corresponding to the basic feature data samples include: the distance from the pollution source, the vertical distance from the pollution source, the pollution feather average load concentration in the permeation layer, the density of the hypotonic layer, the organic carbon distribution coefficient, the organic carbon fraction of the permeation layer, the organic carbon fraction of the hypotonic layer, the effective porosity of the permeation layer, the total porosity of the hypotonic layer, the seepage speed of the permeation layer, the pollutant migration speed of the permeation layer, the length of the pollution source in the x-axis direction, the transverse dispersion of the permeation layer, the effective molecular diffusion coefficient of the permeation layer, the occurrence of the self-pollution source, the pollution loading duration and the pollution source removal time;
Calculating the basic characteristic data sample by adopting a Dandy-salt formula, wherein the method comprises the following steps:
the Dandy-salt formula is used:
calculating the basic characteristic data samples to obtain pollutant concentration true values corresponding to each group of basic characteristic data samples, wherein c trans (x, z, t) represents the true value of the concentration of the pollutant at the moment t of the current spatial point (x, z), and x represents the distance from the pollution sourceFrom z represents the vertical distance of the spatial point from the source of pollution, t represents the span of time from the occurrence of the source of pollution to the moment of interest, t=t 0 +t 1 ，t 0 Indicating the occurrence of self-pollution source, the loading time of pollution, t 1 Indicating the time after the removal of the contamination source, C 0 Indicating the pollution plume average load concentration in the permeable layer above the low permeability layer, b indicating the source characteristics,
v represents the seepage velocity of the permeable layer, L represents the length of the pollution source in the x-axis direction, and D t Represents the transverse hydrodynamic dispersion coefficient of the permeable layer, D t ＝Vα t +D e ，α t Represents the lateral dispersion of the permeable layer, D e Representing the effective molecular diffusion coefficient of said permeation layer, < >>
Indicating the total porosity of the permeation layer,/o>
V c Indicating the rate of pollutant transport through the permeable layer,
r represents the blocking coefficient of the permeation layer, and->
ρ b Density of the permeation layer, K oc Organic carbon partition coefficient, f oc The organic carbon fraction of the permeable layer, the effective porosity of the n permeable layer, ζ represents the distance between the current integration point and the pollution source, γ represents an intermediate variable, +.>
n 'represents the total porosity of the hypotonic layer, R' represents the coefficient of retardation of the hypotonic layer,
ρ b 'represents the density of the hypotonic layer, f' oc The organic carbon fraction of the hypotonic layer, D represents the transverse dispersion coefficient of the hypotonic layer, D * ＝n′ (p) D o ，D 0 Represents the pollutant migration speed of the permeable layer, p represents the surface bending coefficient index, +.>
In a second aspect, an embodiment of the present invention further provides a device for predicting migration of an organic pollutant, including:
the device comprises an acquisition unit, a prediction unit and a prediction unit, wherein the acquisition unit is used for acquiring basic characteristic data of a low-permeability pollution site to be predicted, and pollutants of the low-permeability pollution site to be predicted are organic pollutants;
the feature deriving operation unit is used for performing feature deriving operation on the basic feature data and extracting target derived feature data from the derived feature data obtained by the feature deriving operation;
the pollutant concentration prediction unit is used for predicting the pollutant concentration of the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to any one of the first aspects when the processor executes the computer program.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing machine-executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of the first aspects.
In an embodiment of the present invention, there is provided a method for predicting migration of organic pollutants, including: basic characteristic data of a low-permeability pollution site to be predicted is obtained, wherein pollutants of the low-permeability pollution site to be predicted are organic pollutants; performing feature derivation operation on the basic feature data, and extracting target derived feature data from the derived feature data obtained by the feature derivation operation; and carrying out pollutant concentration prediction on the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration. According to the method for predicting the organic pollutant migration, disclosed by the invention, the pollutant concentration of the low-permeability pollution site to be predicted is obtained by predicting the pollutant concentration of the target derivative characteristic data by adopting the pollutant concentration prediction model, compared with the forward method for predicting the organic pollutant migration of the low-permeability site, the operation speed of the pollutant concentration prediction model is high, the prediction speed and the prediction efficiency of the organic pollutant migration are improved, and the technical problems of long time consumption and low efficiency when the conventional forward method for predicting the organic pollutant migration of the low-permeability site is used for predicting the organic pollutant migration are solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting migration of organic pollutants according to an embodiment of the present invention;
FIG. 2 is a flowchart of a feature derivation operation on basic feature data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a device for predicting migration of organic pollutants according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The existing forward modeling method for the migration of the organic pollutants in the hypotonic site is long in time consumption and low in efficiency when the migration of the organic pollutants is predicted.
Based on the method, in the method for predicting the organic pollutant migration, the pollutant concentration of the low-permeability pollutant site to be predicted is obtained by predicting the pollutant concentration of the target derivative characteristic data by adopting the pollutant concentration prediction model, and compared with a forward modeling method for the organic pollutant migration of the low-permeability site, the operation speed of the pollutant concentration prediction model is high, and the prediction speed and the prediction efficiency of the organic pollutant migration are improved.
For the convenience of understanding the present embodiment, a method for predicting migration of organic pollutants disclosed in the present embodiment will be described in detail.
Embodiment one:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method of predicting migration of organic contaminants, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
FIG. 1 is a flow chart of a method for predicting migration of organic pollutants according to an embodiment of the present invention, as shown in FIG. 1, the method comprising the steps of:
step S102, basic characteristic data of a low-permeability pollution site to be predicted is obtained, wherein pollutants of the low-permeability pollution site to be predicted are organic pollutants;
in the embodiment of the present invention, the to-be-predicted hypotonic pollution site may be any actual hypotonic pollution site, and basic feature data of the to-be-predicted hypotonic pollution site may be obtained, where the basic feature data includes: the distance from the pollution source, the vertical distance from the pollution source, the pollution feather average load concentration in the permeation layer, the density of the hypotonic layer, the organic carbon distribution coefficient, the organic carbon fraction of the permeation layer, the organic carbon fraction of the hypotonic layer, the effective porosity of the permeation layer, the total porosity of the hypotonic layer, the seepage speed of the permeation layer, the pollutant migration speed of the permeation layer, the length of the pollution source in the x-axis direction, the transverse dispersion of the permeation layer, the effective molecular diffusion coefficient of the permeation layer, the appearance of the self-pollution source, the pollution loading duration and the time after the pollution source is removed.
Step S104, performing feature derivation operation on the basic feature data, and extracting target derived feature data from the derived feature data obtained by the feature derivation operation;
This process is described in detail below and is not described in detail here.
And S106, carrying out pollutant concentration prediction on the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration.
The prediction result of the organic pollutant migration is that the organic pollutant moves from the space point position A to the current space point position B, and the concentration change value and the consumed time are long.
The calculation speed of the pollutant concentration of each low-permeability pollution site to be predicted is within 1 second.
In an embodiment of the present invention, there is provided a method for predicting migration of organic pollutants, including: basic characteristic data of a low-permeability pollution site to be predicted is obtained, wherein pollutants of the low-permeability pollution site to be predicted are organic pollutants; performing feature derivation operation on the basic feature data, and extracting target derived feature data from the derived feature data obtained by the feature derivation operation; and carrying out pollutant concentration prediction on the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration. According to the method for predicting the organic pollutant migration, disclosed by the invention, the pollutant concentration of the low-permeability pollution site to be predicted is obtained by predicting the pollutant concentration of the target derivative characteristic data by adopting the pollutant concentration prediction model, compared with the forward method for predicting the organic pollutant migration of the low-permeability site, the operation speed of the pollutant concentration prediction model is high, the prediction speed and the prediction efficiency of the organic pollutant migration are improved, and the technical problems of long time consumption and low efficiency when the conventional forward method for predicting the organic pollutant migration of the low-permeability site is used for predicting the organic pollutant migration are solved.
The above-mentioned brief description of the method for predicting migration of organic pollutants according to the present invention will make a detailed description of the specific matters involved therein.
In an alternative embodiment of the present invention, referring to fig. 2, the step S104 performs a feature deriving operation on the basic feature data, and specifically includes the following steps:
step S201, performing square feature derivation operation on each basic feature data to obtain first derived feature data;
for example: the basic characteristic data are: [ a ] 1 a 2 a 3 ... a 17 ]Then the first derivative feature data is: [ a ] 1 2 a 2 2 a 3 2 ... a 17 2 ]。
Step S202, carrying out difference calculation on each basic feature data and other basic feature data as well as each first derivative feature data, and carrying out difference calculation on each first derivative feature data and other first derivative feature data to obtain second derivative feature data;
as exemplified above, the second derivative characteristic data may be obtained as: [ a ] 1 -a 2 a 1 -a 3 a 1 -a 4 ... a 1 -a 17 2 a 2 -a 3 a 2 -a 4 a 2 -a 5 ... a 2 -a 25 2 ... a 1 2 -a2 2 a 1 2 -a 3 2 ... a 1 2 -a 17 2 ... a 16 2 -a 17 2 ]。
Step S203, carrying out summation calculation on each basic feature data and other basic feature data as well as each first derivative feature data, and carrying out summation calculation on each first derivative feature data and other first derivative feature data to obtain third derivative feature data;
Similarly, with reference to the description of step S202 described above, third derivative feature data can be obtained by summing calculation.
Step S204, performing multiplication calculation on each basic feature data and other basic feature data as well as each first derivative feature data, and performing multiplication calculation on each first derivative feature data and other first derivative feature data to obtain fourth derivative feature data;
similarly, with reference to the description of step S202 described above, fourth derivative feature data can be obtained by multiplication calculation.
Step S205, dividing each basic feature data with other basic feature data and each first derivative feature data, and dividing each first derivative feature data with other first derivative feature data to obtain fifth derivative feature data;
similarly, with reference to the description of step S202, the fifth derivative feature data can be obtained by division calculation.
Step S206, combining the basic feature data, the first derivative feature data, the second derivative feature data, the third derivative feature data, the fourth derivative feature data and the fifth derivative feature data to obtain derivative feature data.
In an alternative embodiment of the present invention, the method for extracting target derivative feature data from derivative feature data obtained by a feature derivative operation specifically includes the following steps:
and extracting target derivative feature data corresponding to the preset target feature from the derivative feature data according to the preset target feature.
The preset target features are important features (for the concentration of the pollutants) obtained by screening when training a pollutant concentration prediction model.
In an alternative embodiment of the invention, the contaminant concentration prediction model comprises: XGBoost model.
The following describes the training process of the pollutant concentration prediction model:
in an alternative embodiment of the invention, the method further comprises the steps of:
(1) Obtaining a basic characteristic data sample of a hypotonic pollution site;
specifically, basic characteristic data samples of the hypotonic pollution site are collected first, wherein basic characteristics corresponding to the basic characteristic data samples comprise: distance from the pollution source, vertical distance from the pollution source, above the low permeable layer, pollution plume average load concentration in the permeable layer, density of the low permeable layer, organic carbon partition coefficient, organic carbon fraction of the permeable layer, organic carbon fraction of the low permeable layer, effective porosity of the permeable layer, total porosity of the low permeable layer, seepage velocity of the permeable layer, pollutant transport velocity of the permeable layer, length of the pollution source in the x-axis direction, lateral dispersion of the permeable layer, effective molecular diffusion coefficient of the permeable layer, appearance of the pollution source, duration of pollution loading and time after pollution source removal are shown in the following table:
Pollution plume average load concentration C in the permeable layer from above the low permeable layer 0 To the effective molecular diffusion coefficient De of the permeation layer, the values are randomly generated with uniform distribution. Distance x, z and time t 0 、t 1 Each basic characteristic data sample is expressed as Deltax, deltaz, deltat between the respective minimum and maximum values 0 And Deltat 1 The values are taken at equal intervals.
(2) Calculating the basic characteristic data samples by adopting a Dandy-salt formula to obtain a pollutant concentration true value corresponding to each group of basic characteristic data samples;
specifically, the Dandy-salt formula is used:
calculating basic characteristic data samples to obtain a pollutant concentration true value corresponding to each group of basic characteristic data samples, wherein c trans (x, z, t) represents a true value of the concentration of the contaminant at the current spatial point (x, z) at time t, x represents the distance from the source of the contaminant, z represents the vertical distance of the spatial point from the source of the contaminant, t represents the span of time from the occurrence of the source of the contaminant to the moment of interest, t=t 0 +t 1 ，t 0 Indicating the occurrence of self-pollution source, the loading time of pollution, t 1 Indicating the time after the removal of the contamination source, C 0 Indicating the average load concentration of pollution feathers in the permeable layer above the low permeable layer, b indicating the source characteristics,
v represents the seepage velocity of the osmotic layer, L represents the length of the pollution source in the x-axis direction, and D t Represents the transverse hydrodynamic dispersion coefficient of the permeable layer, D t ＝Vα t +D e ，α t Represents the transverse dispersion of the permeable layer D e Indicating the effective molecular diffusion coefficient of the permeable layer, +.>
Indicating the total porosity of the permeation layer,
V c indicating the pollutant migration rate of the permeable layer, +.>
R represents the blocking coefficient of the permeation layer,
ρ b density of the permeation layer, K oc Organic carbon partition coefficient, f oc The organic carbon fraction of the permeable layer, the effective porosity of the n permeable layer, ζ represents the distance between the current integration point and the pollution source, γ represents an intermediate variable, +.>
n 'represents the total porosity of the hypotonic layer, R' represents the coefficient of retardation of the hypotonic layer,/L->
ρ b 'represents the density of the hypotonic layer, f' oc Organic carbon fraction of hypotonic layer, D represents transverse dispersion coefficient of hypotonic layer, D * ＝n′ (p) D o ，D 0 Represents the pollutant migration speed of the permeation layer, p represents the surface bending coefficient index, < >>
(3) Performing feature derivation operation on the basic feature data sample, and extracting a target derived feature data sample from the derived feature data sample obtained by the feature derivation operation;
specifically, performing square feature derivation operation on each basic feature data sample to obtain a first derived feature data sample;
performing difference calculation on each basic characteristic data sample and each other basic characteristic data sample and each first derivative characteristic data sample, and performing difference calculation on each first derivative characteristic data sample and each other first derivative characteristic data sample to obtain a second derivative characteristic data sample;
Summing each basic characteristic data sample with each other basic characteristic data sample and each first derivative characteristic data sample, and summing each first derivative characteristic data sample with each other first derivative characteristic data sample to obtain a third derivative characteristic data sample;
multiplying each basic characteristic data sample with each other basic characteristic data sample and each first derivative characteristic data sample, and multiplying each first derivative characteristic data sample with each other first derivative characteristic data sample to obtain a fourth derivative characteristic data sample;
dividing each basic characteristic data sample by each other basic characteristic data sample and each first derivative characteristic data sample, and dividing each first derivative characteristic data sample by each other first derivative characteristic data sample to obtain a fifth derivative characteristic data sample;
and combining the basic feature data sample, the first derivative feature data sample, the second derivative feature data sample, the third derivative feature data sample, the fourth derivative feature data sample and the fifth derivative feature data sample to obtain derivative feature data samples.
The specific process is referred to the above-mentioned processes of step S201 to step S206, and will not be described here.
The method for extracting the target derivative characteristic data sample from the derivative characteristic data sample obtained by the characteristic derivative operation specifically comprises the following steps:
(31) Performing feature importance calculation on the derivative feature data sample by adopting LASSO regression to obtain the importance of each feature corresponding to the derivative feature data sample;
specifically, the feature importance calculation is carried out on the derivative feature data sample by adopting LASSO regression with three-fold cross verification.
(32) Determining target features in the features corresponding to the derivative feature data samples according to the importance of the features corresponding to the derivative feature data samples, wherein the target features are features with importance larger than a preset threshold value in the features corresponding to the derivative feature data samples;
(33) And extracting target derivative characteristic data samples corresponding to the target characteristics from the derivative characteristic data samples according to the target characteristics.
In the target derived feature data samples, all 3-15 columns are the same and are called the same geological background. In the same geological background, since 1, 2, 16 and 17 columns of data take different values, multiple rows of data are generated. All data are divided into a training set train and a verification set valid according to the proportion of 8:2, and the data of the same geological background belong to the same data set.
(4) Training an original pollutant concentration prediction model by adopting a target derived characteristic data sample and a corresponding pollutant concentration truth value to obtain the pollutant concentration prediction model.
The original pollutant concentration prediction model is an XGBoost model, training is carried out on a training set, and verification is carried out by using a verification set.
The parameters used for XGBoost are as follows:
the absolute average error (MAE) on the valid of the verification set is observed during training, and as long as 300 rounds of MAE are not reduced, model training is stopped, and a model with optimal performance on the valid is output. The model is used as a pre-trained pollutant concentration prediction model, and can replace forward calculation of organic pollutant migration in a hypotonic site in practical application.
The invention aims to provide a prediction method for organic pollutant migration, which is based on big data and a machine learning algorithm, and realizes the preposition of calculation time (namely training a model before high-precision inversion is carried out), so that a forward result (namely the pollutant concentration of a hypotonic pollution site to be predicted) can be quickly obtained by using the trained machine learning model in practical application.
The method for predicting the migration of the organic pollutants has the following advantages that
1. The forward modeling speed of the migration of the organic pollutants in the hypotonic field is greatly improved, and each forward modeling calculation is reduced from tens of seconds or more to less than 1 second;
2. the high-precision hydrogeologic parameter inversion can be applied to a hypotonic pollution site, so that the simulation prediction precision of pollutant migration is improved.
Embodiment two:
the embodiment of the invention also provides a device for predicting the organic pollutant migration, which is mainly used for executing the method for predicting the organic pollutant migration provided in the first embodiment of the invention, and the device for predicting the organic pollutant migration provided in the first embodiment of the invention is specifically introduced below.
FIG. 3 is a schematic view of an apparatus for predicting migration of organic pollutants according to an embodiment of the present invention, as shown in FIG. 3, the apparatus mainly includes: an acquisition unit 10, a feature deriving operation unit 20, and a contaminant concentration prediction unit 30, wherein:
the acquisition unit is used for acquiring basic characteristic data of the low-permeability pollution site to be predicted, wherein the pollutants of the low-permeability pollution site to be predicted are organic pollutants;
a feature deriving operation unit for performing a feature deriving operation on the basic feature data, and extracting target derived feature data from the derived feature data obtained by the feature deriving operation;
The pollutant concentration prediction unit is used for predicting the pollutant concentration of the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining the prediction result of the migration of the organic pollutants.
In an embodiment of the present invention, there is provided a device for predicting migration of organic pollutants, including: basic characteristic data of a low-permeability pollution site to be predicted is obtained, wherein pollutants of the low-permeability pollution site to be predicted are organic pollutants; performing feature derivation operation on the basic feature data, and extracting target derived feature data from the derived feature data obtained by the feature derivation operation; and carrying out pollutant concentration prediction on the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration. According to the device for predicting the organic pollutant migration, disclosed by the invention, the pollutant concentration of the low-permeability pollution site to be predicted is obtained by predicting the pollutant concentration of the target derivative characteristic data by adopting the pollutant concentration prediction model, compared with a forward method for predicting the organic pollutant migration of the low-permeability site, the operation speed of the pollutant concentration prediction model is high, the prediction speed and the prediction efficiency of the organic pollutant migration are improved, and the technical problems of long time consumption and low efficiency when the conventional forward method for predicting the organic pollutant migration of the low-permeability site is used for predicting the organic pollutant migration are solved.
Optionally, the feature-derived operating unit is further configured to: performing square feature derivation operation on each piece of basic feature data to obtain first derived feature data; performing difference calculation on each basic feature data and other basic feature data as well as each first derivative feature data, and performing difference calculation on each first derivative feature data and other first derivative feature data to obtain second derivative feature data; summing each basic characteristic data with other basic characteristic data and each first derivative characteristic data, and summing each first derivative characteristic data with other first derivative characteristic data to obtain third derivative characteristic data; multiplying each basic characteristic data with other basic characteristic data and each first derivative characteristic data, and multiplying each first derivative characteristic data with other first derivative characteristic data to obtain fourth derivative characteristic data; dividing each basic characteristic data with other basic characteristic data and each first derivative characteristic data, and dividing each first derivative characteristic data with other first derivative characteristic data to obtain fifth derivative characteristic data; and combining the basic feature data, the first derivative feature data, the second derivative feature data, the third derivative feature data, the fourth derivative feature data and the fifth derivative feature data to obtain derivative feature data.
Optionally, the feature-derived operating unit is further configured to: and extracting target derivative feature data corresponding to the preset target feature from the derivative feature data according to the preset target feature.
Optionally, the contaminant concentration prediction model comprises: XGBoost model.
Optionally, the device is further configured to: obtaining a basic characteristic data sample of a hypotonic pollution site; calculating the basic characteristic data samples by adopting a Dandy-salt formula to obtain a pollutant concentration true value corresponding to each group of basic characteristic data samples; performing feature derivation operation on the basic feature data sample, and extracting a target derived feature data sample from the derived feature data sample obtained by the feature derivation operation; training an original pollutant concentration prediction model by adopting a target derived characteristic data sample and a corresponding pollutant concentration truth value to obtain the pollutant concentration prediction model.
Optionally, the device is further configured to: performing feature importance calculation on the derivative feature data sample by adopting LASSO regression to obtain the importance of each feature corresponding to the derivative feature data sample; determining target features in the features corresponding to the derivative feature data samples according to the importance of the features corresponding to the derivative feature data samples, wherein the target features are features with importance larger than a preset threshold value in the features corresponding to the derivative feature data samples; and extracting target derivative characteristic data samples corresponding to the target characteristics from the derivative characteristic data samples according to the target characteristics.
Optionally, the basic features corresponding to the basic feature data samples include: the distance from the pollution source, the vertical distance from the pollution source, the pollution feather average load concentration in the permeation layer, the density of the hypotonic layer, the organic carbon distribution coefficient, the organic carbon fraction of the permeation layer, the organic carbon fraction of the hypotonic layer, the effective porosity of the permeation layer, the total porosity of the hypotonic layer, the seepage speed of the permeation layer, the pollutant migration speed of the permeation layer, the length of the pollution source in the x-axis direction, the transverse dispersion of the permeation layer, the effective molecular diffusion coefficient of the permeation layer, the occurrence of the self-pollution source, the pollution loading duration and the pollution source removal time; calculating the basic characteristic data sample by using a Dandy-salt formula comprises: the Dandy-salt formula is used:
calculating basic characteristic data samples to obtain a pollutant concentration true value corresponding to each group of basic characteristic data samples, wherein c trans (x, z, t) represents a true value of the concentration of the contaminant at the current spatial point (x, z) at time t, x represents the distance from the source of the contaminant, z represents the vertical distance of the spatial point from the source of the contaminant, t represents the span of time from the occurrence of the source of the contaminant to the moment of interest, t=t 0 +t 1 ，t 0 Indicating the occurrence of self-pollution source, the loading time of pollution, t 1 Indicating the time after the removal of the contamination source, C 0 Indicating the average load concentration of pollution feathers in the permeable layer above the low permeable layer, b indicating the source characteristics,
v represents the seepage velocity of the osmotic layer, L represents the length of the pollution source in the x-axis direction, and D t Represents the transverse hydrodynamic dispersion coefficient of the permeable layer, D t ＝Vα t +D e ，α t Represents the transverse dispersion of the permeable layer D e Indicating the effective molecular diffusion coefficient of the permeable layer, +.>
Indicating the total porosity of the permeation layer,/o>
V c Indicating the pollutant migration rate of the permeable layer, +.>
R represents the blocking coefficient of the permeation layer,
ρ b density of the permeation layer, K oc Organic carbon partition coefficient, f oc The organic carbon fraction of the permeable layer, the effective porosity of the n permeable layer, and ζ represents the distance between the current integration point and the pollution sourceFrom, γ represents an intermediate variable,>
n 'represents the total porosity of the hypotonic layer, R' represents the coefficient of retardation of the hypotonic layer,/L->
ρ b 'represents the density of the hypotonic layer, f' oc Organic carbon fraction of hypotonic layer, D represents transverse dispersion coefficient of hypotonic layer, D * ＝n′ (p) D o ，D 0 Represents the pollutant migration speed of the permeation layer, p represents the surface bending coefficient index, < >>
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
As shown in fig. 4, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, said memory 602 storing machine readable instructions executable by said processor 601, said processor 601 and said memory 602 communicating over the bus when the electronic device is running, said processor 601 executing said machine readable instructions to perform the steps of the method of predicting migration of organic pollutants as described above.
Specifically, the above-mentioned memory 602 and the processor 601 can be general-purpose memories and processors, and are not particularly limited herein, and the above-mentioned method for predicting migration of organic pollutants can be performed when the processor 601 runs a computer program stored in the memory 602.
The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 601 or instructions in the form of software. The processor 601 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 602, and the processor 601 reads information in the memory 602 and performs the steps of the above method in combination with its hardware.
Corresponding to the above method for predicting the migration of organic pollutants, the embodiments of the present application also provide a computer-readable storage medium storing machine-executable instructions which, when invoked and executed by a processor, cause the processor to execute the steps of the above method for predicting the migration of organic pollutants.
The prediction device for migration of the organic pollutants provided by the embodiment of the application can be specific hardware on equipment or software or firmware installed on the equipment. The device provided in the embodiments of the present application has the same implementation principle and technical effects as those of the foregoing method embodiments, and for a brief description, reference may be made to corresponding matters in the foregoing method embodiments where the device embodiment section is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
As another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the vehicle marking method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. Are intended to be encompassed within the scope of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for predicting migration of organic pollutants, comprising:
basic characteristic data of a low-permeability pollution site to be predicted is obtained, wherein pollutants of the low-permeability pollution site to be predicted are organic pollutants;
performing feature derivation operation on the basic feature data, and extracting target derived feature data from derived feature data obtained by the feature derivation operation;
And carrying out pollutant concentration prediction on the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration.
2. The method of claim 1, wherein performing feature derivation operations on the base feature data comprises:
performing square feature derivation operation on each piece of basic feature data to obtain first derived feature data;
performing difference calculation on each basic feature data and other basic feature data as well as each first derivative feature data, and performing difference calculation on each first derivative feature data and other first derivative feature data to obtain second derivative feature data;
summing each basic characteristic data with each other basic characteristic data and each first derivative characteristic data, and summing each first derivative characteristic data with each other first derivative characteristic data to obtain third derivative characteristic data;
multiplying each basic feature data with each other basic feature data and each first derivative feature data, and multiplying each first derivative feature data with each other first derivative feature data to obtain fourth derivative feature data;
Dividing each basic feature data with each other basic feature data and each first derivative feature data, and dividing each first derivative feature data with each other first derivative feature data to obtain fifth derivative feature data;
and combining the basic feature data, the first derivative feature data, the second derivative feature data, the third derivative feature data, the fourth derivative feature data and the fifth derivative feature data to obtain derivative feature data.
3. The prediction method according to claim 1, wherein extracting target derived feature data from derived feature data obtained by a feature derivation operation, comprises:
extracting target derived feature data corresponding to the preset target features from the derived feature data according to the preset target features.
4. The prediction method according to claim 1, wherein the pollutant concentration prediction model includes: XGBoost model.
5. The prediction method according to claim 1, characterized in that the method further comprises:
obtaining a basic characteristic data sample of a hypotonic pollution site;
Calculating the basic characteristic data samples by adopting a Dandy-salt formula to obtain a pollutant concentration true value corresponding to each group of basic characteristic data samples;
performing feature derivation operation on the basic feature data sample, and extracting a target derived feature data sample from the derived feature data sample obtained by the feature derivation operation;
and training an original pollutant concentration prediction model by adopting the target derived characteristic data sample and the corresponding pollutant concentration truth value to obtain the pollutant concentration prediction model.
6. The method of claim 5, wherein extracting target derived feature data samples from a feature derivation operation comprises:
performing feature importance calculation on the derivative feature data sample by adopting LASSO regression to obtain the importance of each feature corresponding to the derivative feature data sample;
determining target features in the features corresponding to the derivative feature data samples according to the importance of the features corresponding to the derivative feature data samples, wherein the target features are features with importance larger than a preset threshold value in the features corresponding to the derivative feature data samples;
And extracting the target derivative characteristic data sample corresponding to the target characteristic from the derivative characteristic data sample according to the target characteristic.
7. The prediction method according to claim 5, wherein the basic features corresponding to the basic feature data samples include: the distance from the pollution source, the vertical distance from the pollution source, the pollution feather average load concentration in the permeation layer, the density of the hypotonic layer, the organic carbon distribution coefficient, the organic carbon fraction of the permeation layer, the organic carbon fraction of the hypotonic layer, the effective porosity of the permeation layer, the total porosity of the hypotonic layer, the seepage speed of the permeation layer, the pollutant migration speed of the permeation layer, the length of the pollution source in the x-axis direction, the transverse dispersion of the permeation layer, the effective molecular diffusion coefficient of the permeation layer, the occurrence of the self-pollution source, the pollution loading duration and the pollution source removal time;
calculating the basic characteristic data sample by adopting a Dandy-salt formula, wherein the method comprises the following steps:
the Dandy-salt formula is used:
calculating the basic characteristic data samples to obtain pollutant concentration true values corresponding to each group of basic characteristic data samples, wherein c trans (x, z, t) represents a true value of the concentration of the contaminant at the current spatial point (x, z) at time t, x represents the distance from the source, z represents the vertical distance of the spatial point from the source, t represents the span of time from the occurrence of the source to the moment of interest, t=t 0 +t 1 ，t 0 Indicating the occurrence of self-pollution source, the loading time of pollution, t 1 Indicating the time after the removal of the contamination source, C 0 Indicating the pollution plume average load concentration in the permeable layer above the low permeability layer, b indicating the source characteristics,
v represents the seepage velocity of the permeable layer, L represents the length of the pollution source in the x-axis direction, and D t Represents the transverse hydrodynamic dispersion coefficient of the permeable layer, D t ＝Vα t +D e ，α t Represents the lateral dispersion of the permeable layer, D e Representing the effective molecular diffusion coefficient of said permeation layer, < >>
Indicating the total porosity of the permeation layer,/o>
V c Indicating the rate of pollutant transport through the permeable layer,
r represents the blocking coefficient of the permeation layer, and->
ρ b Density of the permeation layer, K oc Organic carbon partition coefficient, f oc The organic carbon fraction of the permeable layer, the effective porosity of the n permeable layer, ζ represents the distance between the current integration point and the pollution source, γ represents an intermediate variable, +.>
n 'represents the total porosity of the hypotonic layer, R' represents the coefficient of retardation of the hypotonic layer,/or->
ρ b 'represents the density of the hypotonic layer, f' oc The organic carbon fraction of the hypotonic layer, D represents the transverse dispersion coefficient of the hypotonic layer, D * ＝n′(p)D o ，D 0 Indicating the rate of contaminant transport through the permeable layer, p indicating the surface bending modulus index,
8. a device for predicting migration of organic pollutants, comprising:
The device comprises an acquisition unit, a prediction unit and a prediction unit, wherein the acquisition unit is used for acquiring basic characteristic data of a low-permeability pollution site to be predicted, and pollutants of the low-permeability pollution site to be predicted are organic pollutants;
the feature deriving operation unit is used for performing feature deriving operation on the basic feature data and extracting target derived feature data from the derived feature data obtained by the feature deriving operation;
the pollutant concentration prediction unit is used for predicting the pollutant concentration of the target derivative characteristic data by adopting a pollutant concentration prediction model to obtain the pollutant concentration of the low-permeability pollution site to be predicted, and further obtaining a prediction result of organic pollutant migration.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of the preceding claims 1 to 7.
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