A method and system for predicting the spread of soil pollution

Abstract

This application relates to the field of pollution diffusion prediction technology, specifically to a method and system for predicting soil pollution diffusion. The method includes: acquiring monitoring data from each of multiple monitoring points within a target area; determining multiple pollution diffusion routes based on the pollutant concentration at each monitoring point; determining the pollution impact weight of each pollution diffusion route based on monitoring data from monitoring points at both ends of each pollution diffusion route; and correcting the diffusion coefficient in the soil pollution diffusion prediction model based on the pollution impact weights of the multiple pollution diffusion routes, for use in predicting soil pollution diffusion in the target area. This method can improve the accuracy and reliability of the soil pollution diffusion prediction model for predicting the target area.

Description

Technical Field

[0001] This application relates to the field of pollution diffusion prediction technology, specifically to a method and system for predicting soil pollution diffusion. Background Technology

[0002] Currently, when predicting the spread of soil pollution, the usual method is to conduct grid-based monitoring around the pollution source and input the monitoring data into a prediction model based on the convection-diffusion equation for simulation and prediction.

[0003] However, in the above methods, the simulation process of the model is usually based on the assumption of terrain homogenization. The model parameters (such as diffusion coefficient and hydraulic conductivity) are often set as a fixed constant, which makes it difficult to effectively characterize the spatial heterogeneity caused by micro-topographic undulation, lithological abrupt changes and dominant flow paths under complex site conditions. This results in insufficient representativeness of the monitoring data itself, and it is impossible to accurately capture the actual diffusion behavior of pollutants, leading to low accuracy of prediction based on the prediction model. Summary of the Invention

[0004] To address the technical problem of low accuracy in predictive models for soil pollution diffusion, this application aims to provide a method and system for predicting soil pollution diffusion. The specific technical solution adopted is as follows:

[0005] This application provides a method for predicting soil pollution diffusion, comprising: acquiring monitoring data of each monitoring point among multiple monitoring points in a target area, wherein the monitoring data of a monitoring point includes the coordinates of the monitoring point, pollutant concentration, and soil environmental parameters; determining multiple pollution expansion routes based on the pollutant concentration of each monitoring point, wherein a pollution expansion route is a route from a polluted monitoring point to an adjacent unpolluted monitoring point; determining the pollution impact weight of each pollution expansion route based on the monitoring data of the monitoring points at both ends of each pollution expansion route, wherein the pollution impact weight is used to characterize the degree of pollution diffusion impact on the unpolluted monitoring point by the polluted monitoring point; and correcting the diffusion coefficient in the soil pollution diffusion prediction model based on the pollution impact weight of the multiple pollution expansion routes for use in predicting soil pollution diffusion in the target area.

[0006] Optionally, the above-mentioned determination of the pollution impact weight of each pollution expansion route based on the monitoring data of the monitoring points at both ends of each pollution expansion route includes: determining the route length and relative elevation difference of the first pollution expansion route based on the coordinates of the first unpolluted monitoring point and the first polluted monitoring point at both ends of the first pollution expansion route, wherein the first pollution expansion route is any one of multiple pollution expansion routes; determining the diffusion impact parameter of the first pollution expansion route based on the pollutant concentration, soil environmental parameters, and route length of the first unpolluted monitoring point and the first polluted monitoring point, wherein the diffusion impact parameter is used to characterize the efficiency of pollutant diffusion from the polluted monitoring point to the unpolluted monitoring point; and determining the pollution impact weight of the first pollution expansion route based on the diffusion impact parameter, relative elevation difference, and route length of the first pollution expansion route.

[0007] Optionally, determining the diffusion impact parameters of the first pollution expansion route based on the pollutant concentrations, soil environmental parameters, and route length of the first pollution expansion route between the first uncontaminated monitoring point and the first contaminated monitoring point includes: determining the comprehensive blocking coefficient of the first uncontaminated monitoring point based on the soil environmental parameters of the first uncontaminated monitoring point, and determining the comprehensive blocking coefficient of the first contaminated monitoring point based on the soil environmental parameters of the first contaminated monitoring point, wherein the comprehensive blocking coefficient is used to characterize the soil's ability to block pollutant migration; determining the concentration comparison parameter of the first uncontaminated monitoring point based on the pollutant concentration and the comprehensive blocking coefficient of the first uncontaminated monitoring point, and determining the concentration comparison parameter of the first contaminated monitoring point based on the pollutant concentration and the comprehensive blocking coefficient of the first contaminated monitoring point, wherein the concentration comparison parameter is used to characterize the expansion capacity of pollutants after the blocking effect of the soil medium has been removed; and determining the diffusion impact parameters of the first pollution expansion route based on the difference between the concentration comparison parameters of the first uncontaminated monitoring point and the first contaminated monitoring point, and the route length of the first pollution expansion route.

[0008] Optionally, determining the pollution impact weight of the first pollution expansion route based on the diffusion impact parameters, relative elevation difference, and route length of the first pollution expansion route includes: determining the elevation impact coefficient of the first pollution expansion route based on the relative elevation difference and route length of the first pollution expansion route; and determining the pollution impact weight of the first pollution expansion route based on the elevation impact coefficient and diffusion impact parameters of the first pollution expansion route.

[0009] Optionally, determining the elevation influence coefficient of the first pollution expansion route based on the relative elevation difference and route length includes: determining the elevation influence coefficient of the first pollution expansion route based on the ratio of the relative elevation difference to the route length and the consistency of the diffusion direction of the first pollution expansion route.

[0010] Optionally, determining the pollution impact weight of the first pollution expansion route based on the elevation influence coefficient and diffusion influence parameter of the first pollution expansion route includes: determining the comprehensive diffusion impact of the first pollution expansion route by multiplying the elevation influence coefficient and diffusion influence parameter of the first pollution expansion route; and determining the pollution impact weight of the first pollution expansion route by the ratio between the comprehensive diffusion impact of the first pollution expansion route and the total comprehensive diffusion impact, wherein the total comprehensive diffusion impact is the sum of the comprehensive diffusion impact of the first unpolluted monitoring point and each adjacent polluted monitoring point.

[0011] Optionally, the above-mentioned modification of the diffusion coefficient in the soil pollution diffusion prediction model based on the pollution impact weights of the multiple pollution expansion routes for use in predicting soil pollution diffusion in the target area includes: modifying the diffusion coefficient of each pollution expansion route in the soil pollution diffusion prediction model based on the pollution impact weights of each pollution expansion route to obtain the modified soil pollution diffusion prediction model; and performing soil pollution diffusion prediction based on the modified soil pollution diffusion prediction model.

[0012] Optionally, the above-mentioned determination of multiple pollution spread routes based on the pollutant concentration at each monitoring point includes: dividing the multiple monitoring points into polluted monitoring points and unpolluted monitoring points based on the pollutant concentration and pollutant concentration threshold at each monitoring point; and determining the first route as the pollution spread route when the first polluted monitoring point is adjacent to the first unpolluted monitoring point and the pollution spread direction of the first route is consistent with the main pollution spread direction. The first route is the route from the first polluted monitoring point to the first unpolluted monitoring point.

[0013] Optionally, the method further includes: obtaining a main pollution diffusion direction vector and a first pollution diffusion direction vector, wherein the first pollution diffusion direction vector is the diffusion direction vector of the first route; determining the cosine value of the angle between the main pollution diffusion direction vector and the first pollution diffusion direction vector as the diffusion direction consistency of the first route; and determining that the pollution diffusion direction of the first route is consistent with the main pollution diffusion direction when the diffusion direction consistency is greater than the consistency threshold.

[0014] This application also provides a soil pollution diffusion prediction system, including a data acquisition unit, a data processing unit, and a model correction unit: the data acquisition unit is used to acquire monitoring data of each monitoring point among multiple monitoring points in a target area, the monitoring data of a monitoring point including the coordinates of the monitoring point, pollutant concentration, and soil environmental parameters; the data processing unit is used to determine multiple pollution expansion routes based on the pollutant concentration of each monitoring point, a pollution expansion route being a route from a polluted monitoring point to an adjacent unpolluted monitoring point; the data processing unit is also used to determine the pollution impact weight of each pollution expansion route based on the monitoring data of the monitoring points at both ends of each pollution expansion route, the pollution impact weight being used to characterize the degree of pollution diffusion impact of the polluted monitoring point on the unpolluted monitoring point; the model correction unit is used to correct the diffusion coefficient in the soil pollution diffusion prediction model based on the pollution impact weights of the multiple pollution expansion routes, for use in predicting soil pollution diffusion in the target area.

[0015] This application has the following beneficial effects:

[0016] By using the coordinates of multiple monitoring points, pollutant concentrations, and soil environmental parameters, pollution diffusion is first visualized as pollution expansion routes. Then, based on the diffusion differences of different pollution expansion routes, the pollution diffusion efficiency is quantified by pollution impact weights. Finally, the diffusion coefficient of the corresponding route in the model is corrected based on the pollution impact weights of each pollution expansion route. This allows the corrected model to differentiate the heterogeneous diffusion characteristics of different routes, dynamically simulate the propagation process of pollutants along each route, and improve the accuracy and reliability of the soil pollution diffusion prediction model for the target area. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for predicting the spread of soil pollution provided in one embodiment of this application;

[0019] Figure 2 This is a flowchart of another method for predicting soil pollution diffusion provided in one embodiment of this application;

[0020] Figure 3 This is a structural diagram of a soil pollution diffusion prediction system provided in one embodiment of this application. Detailed Implementation

[0021] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a soil pollution diffusion prediction method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0023] With the progress of industrialization, soil pollution has become increasingly prominent, and its migration and transformation process is complexly controlled by multiple factors such as micro-topography, soil heterogeneity, and hydrogeological conditions.

[0024] Once soil pollution occurs, pollutants can spread from the source to surrounding areas, posing potential risks to residents' health and the ecological environment. To accurately assess these risks and achieve effective early warning, it is necessary to test the soil at the pollution source and in the surrounding area, analyze the migration rate of pollutants in the soil, and predict their diffusion trends and extent, so as to take early control measures. However, soil media often exhibit significant spatial heterogeneity—affected by factors such as micro-topographical undulations, differences in soil and rock properties, fissure development, stratification structure, and rainfall erosion, the vertical infiltration and horizontal migration of pollutants often show strong anisotropy.

[0025] Existing methods for predicting soil pollution diffusion largely rely on gridded monitoring stations around pollution sources. However, nationally standardized grid monitoring methods typically presuppose homogeneous terrain, making it difficult to characterize the spatial heterogeneity of the environment under complex topography. Especially in areas with numerous gullies or significant impacts from rainwater erosion, topographic relief and differences in soil and rock properties can lead to significant anisotropy in the vertical infiltration depth and horizontal migration path of pollutants. This makes it impossible to capture drastic changes in pollutant concentration driven by micro-topography, resulting in distorted monitoring data and severely weakening the accuracy and reliability of subsequent model predictions.

[0026] In complex terrain areas such as gullies and slopes, pollutant migration is primarily driven by the hydraulic gradient controlled by micro-topography and the dominant pathways formed by soil structure. Its diffusion exhibits strong directionality and localized explosiveness, rather than a homogeneous, gradual "plume" model. Grid-based monitoring, based on the assumption of homogenization, represents an "average value," inevitably smoothing out concentration peaks or rapid pathways caused by preferential flows and surface runoff. This results in data that fundamentally fails to reflect the true transport mechanism, leading to systematic bias in the input of prediction models.

[0027] The following description, in conjunction with the accompanying drawings, details the specific scheme of a soil pollution diffusion prediction method and system provided in this application.

[0028] Please see Figure 1 The diagram illustrates a flowchart of a method for predicting the spread of soil pollution according to an embodiment of this application.

[0029] S101. Obtain monitoring data for each of the multiple monitoring points within the target area.

[0030] The monitoring data for one monitoring point includes the coordinates of the monitoring point, the concentration of pollutants, and soil environmental parameters.

[0031] It should be understood that the target area is an area delineated around a known source of pollution, encompassing the potential spread range determined based on expert experience or preliminary assessment.

[0032] In one alternative implementation, an orthophoto of the target area can be acquired by a drone equipped with a photography device. The target area can then be divided into geomorphological zones based on the topographic features shown in the images. This ensures that the topography and soil type within each zone are relatively consistent. At least one monitoring point can then be deployed in each zone to ensure that the monitoring data covers different terrains, polluted locations, and unpolluted locations. This avoids the situation where all monitoring points are concentrated on a single type of terrain, thus ensuring that the collected data accurately reflects the overall condition of the entire area.

[0033] It should be understood that the coordinates of a monitoring point are three-dimensional coordinates (x, y, z), where x and y are planar coordinates used to characterize the horizontal position of the monitoring point within the target area, and z is elevation data used to characterize the terrain elevation of the monitoring point.

[0034] It should be understood that the pollutant concentration is the concentration of pollutants currently detected in the soil. The target area may have the concentrations of multiple pollutants. Based on the method provided in the embodiments of this application, the concentration of a single pollutant can be analyzed and detected at one time. In the embodiments of this application, the detection of the concentration of a single pollutant and the correction of model parameters are used as an example for illustration.

[0035] Optionally, soil environmental parameters may include volumetric water content, soil bulk density, organic carbon content, saturated hydraulic conductivity, tortuosity, etc.

[0036] Alternatively, fixed soil pollution real-time monitoring devices (such as multi-parameter sensors) can be used to collect pollutant concentrations, volumetric water content, soil bulk density, organic carbon content, etc. at each monitoring point.

[0037] Optionally, ground-penetrating radar can be used to transmit high-frequency electromagnetic waves into the underground of the area where each monitoring point is located. By analyzing the amplitude, phase and travel time of the reflected waves, the electrical differences of the underground medium can be detected, thereby identifying soil structural features such as abrupt soil texture interfaces (such as sand / clay contact zones), fissures and stratification. Based on the detected soil structural features, the saturated hydraulic conductivity (obtained by calibration from the electrical data inverted by radar) and tortuosity (inferred through radar wave attenuation analysis, reflecting the connectivity of soil pores) can be obtained.

[0038] Optionally, the monitoring data at a monitoring point may also include structural integrity parameters, which are used to characterize the development of preferred migration paths such as cracks and root pores in the soil medium and to quantitatively assess the integrity of the soil structure.

[0039] In one optional implementation, on the radar profile image, for the underground area corresponding to each monitoring point, characteristic regions where the phase axis of reflected waves is interrupted, disordered, or scattered due to structural defects such as cracks and fracture zones are identified and counted; the proportion of these characteristic regions to the total area of ​​the analysis area is calculated; through a preset mapping relationship or normalization processing, this proportion is converted into a value between 0 and 1, thus obtaining the structural integrity parameter of the monitoring point.

[0040] S102. Based on the pollutant concentration at each monitoring point, determine multiple pollution spread routes.

[0041] One of the pollution spread routes is a route from a polluted monitoring point to an adjacent unpolluted monitoring point.

[0042] It should be understood that the pollution spread along a directed path.

[0043] It is understandable that when soil is contaminated, pollutants will spread radially from the pollution source along the direction from high concentration to low concentration along with soil water or soil pore structure. During the diffusion process, the contaminated area will form a new pollution source for the uncontaminated area. That is, when pollutants from multiple contaminated nodes spread, they will also cause pollution to spread to adjacent uncontaminated monitoring nodes. The more serious the spread of pollution, the faster the contamination rate.

[0044] In one alternative implementation, the multiple monitoring points can first be divided into polluted monitoring points and unpolluted monitoring points based on the pollutant concentration and pollutant concentration threshold of each monitoring point. If the first polluted monitoring point is adjacent to the first unpolluted monitoring point and the pollution diffusion direction of the first route is consistent with the main pollution diffusion direction, the first route is determined as the pollution expansion route.

[0045] The first route is the route from the first polluted monitoring point to the first unpolluted monitoring point.

[0046] It is understandable that if the pollutant concentration at a monitoring point is greater than the pollutant concentration threshold, the monitoring point can be identified as a polluted monitoring point, and if the pollutant concentration at a monitoring point is less than or equal to the pollutant concentration threshold, the monitoring point can be identified as an unpolluted monitoring point.

[0047] Optionally, the pollutant concentration threshold can be set based on industry standards and pollutant type. For example, the pollutant concentration threshold for cadmium in agricultural land (cultivated land) soil can be 0.25 mg / kg.

[0048] Optionally, the Euclidean distance between two monitoring points can be calculated based on the three-dimensional coordinates of each monitoring point. If the Euclidean distance is less than a preset distance threshold, the two monitoring points are determined to be adjacent.

[0049] Optionally, the preset distance threshold can be set according to the topographic features of the target area and the density of monitoring points to ensure coverage of the range where pollutants may directly spread.

[0050] For example, when the deployment density is 5-10 units / acre, the preset distance threshold can be 5-10 meters; when the deployment density is 10-20 units / acre, the preset distance threshold can be 3-5 meters.

[0051] It should be understood that the main pollution diffusion direction is the diffusion direction of the pollution source. When the pollution diffusion direction of the first route is consistent with the main pollution diffusion direction, it indicates that the pollution diffusion direction is highly consistent with the overall diffusion trend of the pollution source. The pollution diffusion direction of the first route conforms to the overall diffusion law of pollutants, and the pollutant diffusion efficiency of the first route is relatively high. At this time, analyzing the pollution impact weight of the first route and correcting the model based on the pollution impact weight can enable the soil pollution diffusion prediction model to more accurately simulate the rapid migration behavior of pollutants along this route.

[0052] It is understandable that if the first polluted monitoring point is not adjacent to the first unpolluted monitoring point, or if the direction of pollution diffusion is inconsistent with the direction of main pollution diffusion, it indicates that the pollutant diffusion efficiency of the first route is low, and the difference in pollutant concentration may even be caused by random factors or measurement. Excluding the first route can effectively filter out interfering information and reduce the computational load.

[0053] In one optional implementation, the main pollution diffusion direction vector and the first pollution diffusion direction vector can be obtained; the cosine value of the angle between the main pollution diffusion direction vector and the first pollution diffusion direction vector is determined as the diffusion direction consistency of the first route; if the diffusion direction consistency is greater than the consistency threshold, the pollution diffusion direction of the first route is determined to be consistent with the main pollution diffusion direction.

[0054] Wherein, the first pollution diffusion direction vector is the diffusion direction vector from the first polluted monitoring point to the first unpolluted monitoring point.

[0055] Optionally, the two-dimensional coordinates of all polluted monitoring points can be statistically analyzed to obtain a data matrix. The first principal component direction can be calculated based on the principal component analysis (PCA) method. This first principal component direction is the direction with the largest variance in the distribution of polluted monitoring points. This direction best represents the spatial extension trend of polluted points. Therefore, the first principal component direction vector can be defined as the main pollution diffusion direction.

[0056] Optionally, the vector relationship can be calculated based on the three-dimensional coordinates of the first polluted monitoring point and the first unpolluted monitoring point, and a first pollution diffusion direction vector can be constructed with the first polluted monitoring point as the starting point and the first unpolluted monitoring point as the ending point.

[0057] It should be understood that the cosine of the angle between two directional vectors can intuitively reflect the degree of fit between the two directions. The value of the cosine of the angle ranges from 0 to 1. The closer the value is to 1, the smaller the angle between the two vectors, and the more the first pollution diffusion direction fits the main pollution diffusion direction. The closer the value is to 0, the larger the angle between the two vectors, and the lower the degree of fit between the directions.

[0058] It is understandable that when the consistency of the diffusion direction is greater than the consistency threshold, it means that the degree of fit between the first pollution diffusion direction and the main pollution diffusion direction has reached the preset standard, and the probability of pollutants diffusing along this route is significantly higher than that of other directions. At this time, it can be determined that the pollution diffusion direction of the first route is consistent with the main pollution diffusion direction.

[0059] Optionally, a consistency threshold can be set according to the terrain complexity of the target area, the characteristics of pollutant diffusion, and the required prediction accuracy. For example, the consistency threshold can be set to 0.7, that is, only routes with a diffusion direction consistency of more than 70% are retained.

[0060] In another alternative implementation, the consistency of diffusion direction between the first contaminated monitoring point and all adjacent uncontaminated monitoring points (or between the first uncontaminated monitoring point and all adjacent contaminated monitoring points) can be determined. Then, the multiple consistency of diffusion direction is sorted in descending order, and the first preset number of consistency of diffusion direction in the sequence is determined to meet the consistency requirement of diffusion direction.

[0061] For example, the preset quantity can be 2.

[0062] Understandably, selecting a pre-set number of consistent diffusion directions can quickly focus on the high-risk paths where diffusion is most likely to occur, making it particularly suitable for monitoring scenarios with large-scale, high-density deployment, and improving engineering practicality and monitoring efficiency.

[0063] The method provided in S102 above limits the pollution spread route to the line connecting adjacent monitoring points with the same diffusion direction. This can effectively screen out effective paths with real pollution spread potential, eliminate invalid routes, reduce redundant calculations, and ensure that subsequent weight calculations and model corrections focus on the core diffusion path. This improves prediction efficiency and further guarantees the reliability of prediction results.

[0064] S103. Based on the monitoring data from the monitoring points at both ends of each pollution expansion route, determine the pollution impact weight of each pollution expansion route.

[0065] Among them, the pollution impact weight is used to characterize the degree to which unpolluted monitoring points are affected by the pollution spread from polluted monitoring points.

[0066] It should be understood that the smaller the difference in monitoring data between the two ends of a pollution spread path, the greater the impact of the polluted monitoring point on the unpolluted monitoring point, and the greater the weight of the pollution impact.

[0067] S104. Based on the pollution impact weights of multiple pollution expansion routes, the diffusion coefficient in the soil pollution diffusion prediction model is modified for use in predicting soil pollution diffusion in the target area.

[0068] It should be understood that the soil pollution diffusion prediction model is a physical process-based model used to simulate the migration of pollutants in soil.

[0069] For example, the soil pollution diffusion prediction model can be a soil hydrodynamic and solute transport numerical model based on the convection-diffusion equation, which is well known in the art.

[0070] In one alternative implementation, the diffusion coefficient of each pollution expansion path in the soil pollution diffusion prediction model can be modified based on the pollution impact weight of each pollution expansion path to obtain a modified soil pollution diffusion prediction model; then, soil pollution diffusion prediction is performed based on the modified soil pollution diffusion prediction model.

[0071] It should be understood that the diffusion coefficient in soil pollution diffusion prediction models is a preset basic parameter. Typically, the diffusion coefficient is the same for all routes, failing to consider the influence of heterogeneous factors such as topography and soil environment on the diffusion capacity of different routes, making it difficult to accurately reflect the actual diffusion patterns of complex sites. Therefore, for each identified pollution expansion route, the diffusion coefficient can be adjusted based on the pollution impact weight, so that the adjusted diffusion coefficient can differentiate the diffusion capacity of different routes.

[0072] Understandably, this pollution impact weight comprehensively represents the combined effect of multiple dimensions of factors such as diffusion efficiency, topographic conditions, and directional fit of the corresponding pollution expansion route. It can accurately reflect the actual pollution transmission potential of the route. The larger the pollution impact weight, the greater the correction should be, and the larger the corrected diffusion coefficient should be.

[0073] Optionally, the corrected diffusion coefficient satisfies the following formula:

[0074] ;

[0075] in, Indicates the route of pollution expansion The corresponding corrected diffusion coefficient, pollution propagation path For polluted monitoring points Pointing to uncontaminated monitoring points The pollution expansion route, This represents the pre-defined pollution propagation path in the soil pollution diffusion prediction model. diffusion coefficient, Indicates the route of pollution expansion The weight of pollution impact, This represents the diffusion enhancement coefficient, a constant greater than 0, used to control the maximum influence of pollution impact weight on the diffusion coefficient. An example value can be 1.

[0076] In one alternative implementation, monitoring data from all monitoring points within the target area can be input into a modified soil pollution diffusion prediction model. The modified soil pollution diffusion prediction model incorporates the modified diffusion coefficient of each route into the calculation of the pollutant migration process through the built-in convection-diffusion equation, fully considering the heterogeneous diffusion characteristics of different routes, dynamically simulating the propagation process of pollutants along each route, and outputting the prediction results after the model runs.

[0077] Optionally, the prediction results may include core diffusion information of pollutants within the target area, including the overall diffusion range of pollutants, diffusion rates along different pollution expansion routes, and the concentration distribution of pollutants at different spatial locations. These prediction results can accurately reproduce the actual diffusion patterns of pollutants in complex terrain and soil environments, providing a scientific basis for the formulation of subsequent pollution prevention and control measures and the setting of risk warning thresholds.

[0078] The methods provided in S101-S104 above first visualize pollution diffusion as pollution expansion routes by using the coordinates of multiple monitoring points, pollutant concentrations, and soil environmental parameters. Then, based on the diffusion differences of different pollution expansion routes, the pollution diffusion efficiency is quantified by pollution impact weights. Finally, the diffusion coefficient of the corresponding route in the model is corrected based on the pollution impact weights of each pollution expansion route. This allows the corrected model to differentiate the heterogeneous diffusion characteristics of different routes, dynamically simulate the propagation process of pollutants along each route, and improve the accuracy and reliability of the soil pollution diffusion prediction model for the target area.

[0079] Combination Figure 1 ,like Figure 2 As shown, in one implementation of this application embodiment, the above-mentioned S103 can be specifically implemented by the following S201-S203.

[0080] S201. Based on the coordinates of the first unpolluted monitoring point and the first polluted monitoring point at both ends of the first pollution expansion route, determine the route length and relative elevation difference of the first pollution expansion route.

[0081] The first pollution spread route is any one of multiple pollution spread routes.

[0082] Optionally, the route length is the Euclidean distance between the first unpolluted monitoring point and the first polluted monitoring point, which can be calculated based on the three-dimensional coordinates of the first unpolluted monitoring point and the first polluted monitoring point. The relative elevation difference is the height difference between the two monitoring points. The absolute value of the difference between the elevation data in the coordinates of the first polluted monitoring point and the elevation data of the first unpolluted monitoring point can be determined as the relative elevation difference of the first pollution expansion route.

[0083] It should be understood that this relative elevation difference can directly reflect the difference in terrain elevation at both ends of the first pollution expansion route, and can effectively characterize the diffusion-driven or inhibitory effects of soil water and pollutants dissolved in it due to the terrain.

[0084] S202. Based on the pollutant concentrations, soil environmental parameters, and route length of the first pollution expansion route at the first unpolluted monitoring point and the first polluted monitoring point, determine the diffusion impact parameters of the first pollution expansion route.

[0085] Among them, the diffusion impact parameter is used to characterize the efficiency of pollutant diffusion from polluted monitoring points to unpolluted monitoring points.

[0086] In one optional implementation, the comprehensive blocking coefficient of the first uncontaminated monitoring point can be determined based on the soil environmental parameters of the first uncontaminated monitoring point, and the comprehensive blocking coefficient of the first contaminated monitoring point can be determined based on the soil environmental parameters of the first contaminated monitoring point; the concentration comparison parameter of the first uncontaminated monitoring point can be determined based on the pollutant concentration of the first uncontaminated monitoring point and the comprehensive blocking coefficient of the first uncontaminated monitoring point, and the concentration comparison parameter of the first contaminated monitoring point can be determined based on the pollutant concentration of the first contaminated monitoring point and the comprehensive blocking coefficient of the first contaminated monitoring point; the diffusion impact parameter of the first pollution expansion path can be determined based on the difference between the concentration comparison parameters of the first uncontaminated monitoring point and the first contaminated monitoring point, and the path length of the first pollution expansion path.

[0087] The comprehensive retardation coefficient is used to characterize the soil's ability to retard the migration of pollutants, while the concentration comparison parameter is used to characterize the expansion capacity of pollutants after the retardation effect of the soil medium has been removed.

[0088] Based on the description of the above embodiments, it should be understood that soil environmental parameters include volumetric water content, soil bulk density, organic carbon content, saturated hydraulic conductivity, and tortuosity. Tortuosity reflects pore connectivity, i.e., the effect of pore structure on extending and complicating migration paths; the more tortuous and longer the path, the more significantly convection and diffusion processes are hindered, and the stronger the soil's retention capacity. Soil bulk density reflects the mass basis for retention; the greater the soil bulk density, the more solid-phase material provides adsorption sites, and the stronger the soil's retention capacity. High volumetric water content indicates that pollutants have wide channels to move with water, which dilutes and weakens the solid-phase retention effect represented by the numerator term; therefore, the soil's retention capacity is weaker.

[0089] Optionally, the overall hindrance coefficient satisfies the following formula:

[0090] ;

[0091] in, Indicates monitoring point The overall resistance coefficient, monitoring points This can be any monitoring point (e.g., the first uncontaminated monitoring point or the first contaminated monitoring point). Indicates monitoring point Soil bulk density, Indicates monitoring point The solid-liquid partition coefficient, , Indicates monitoring point saturated hydraulic conductivity, Indicates monitoring point The organic carbon content, Indicates monitoring point The degree of curvature, Indicates monitoring point The volumetric water content.

[0092] In this formula, squaring the tortuosity amplifies the nonlinear enhancement of the hindering effect of path curvature. The constant term 1 ensures that the overall hindering coefficient is greater than 1, meaning that even in ideal conditions without soil adsorption, pollutant migration in water still occurs. Based on this, the formula is derived... It is the ability to impede water flow beyond the baseline hydrodynamic migration, resulting from soil properties.

[0093] It should be noted that in practical applications, even dry soil will contain trace amounts of moisture. It will not be zero; if detected If the value approaches zero, the industry-standard minimum value (such as 0.01%) can be used as an alternative for calculation.

[0094] It should be understood that this formula can transform the spatial heterogeneity of soil into a comprehensive retention coefficient that reflects the soil's retention capacity.

[0095] Optionally, the concentration comparison parameter satisfies the following formula:

[0096] in, Indicates monitoring point Concentration comparison parameters, Indicates monitoring point The concentration of pollutants, Indicates monitoring point The overall resistance coefficient, Represents structural integrity parameters (value range 0–1). This indicates the diffusion enhancement effect caused by defects in soil structure. In this formula, This value represents the pollutant concentration that can be held per unit of impermeability, assuming the soil is a homogeneous and porous medium. It reflects the pollutant concentration level after removing the impermeability effect of the soil matrix itself; multiplied by... This amplifies the accelerating or enhancing effect of soil structural defects on pollutant migration.

[0097] The final concentration comparison parameters reflect the effective driving concentration that the soil ultimately exhibits, which can be used to drive the diffusion of pollutants to the surrounding area, under the dual influence of soil impediment capacity and soil structural defects.

[0098] Optionally, the absolute value of the difference between the concentration comparison parameters of the first uncontaminated monitoring point and the first contaminated monitoring point can be determined as the difference between the concentration comparison parameters of the first uncontaminated monitoring point and the first contaminated monitoring point.

[0099] Optionally, the diffusion effect parameters satisfy the following formula:

[0100] ;

[0101] in, Indicates the route of pollution expansion The diffusion effect parameters, Indicates monitoring point Concentration comparison parameters, Indicates monitoring point Concentration comparison parameters, Indicates monitoring point Concentration comparison parameters, monitoring points For monitoring points Another adjacent monitoring point (which can be either a polluted monitoring point or an unpolluted monitoring point). This represents the baseline value for the concentration comparison parameter. This indicates the length of the pollution spread path.

[0102] In this formula, This represents the absolute value of the difference in concentration comparison parameters between adjacent monitoring points, reflecting the concentration difference of pollutants between adjacent monitoring points under conditions other than soil environmental influence. This represents the diffusion rate of soil pollutants per unit route length. The larger the ratio, the faster the pollutants diffuse in the soil.

[0103] This is a second-order difference value, specifically the concentration comparison parameter at the monitoring point. Monitoring points and monitoring points The absolute value of the second-order central difference at the three points measures the difference at the intermediate point (i.e., the monitoring point). The magnitude of the change in pollutant concentration gradient near the monitoring point; the larger the second-order difference value, the more drastic the change. Nearby, a very steep concentration distribution or an inflection point may correspond to a region of accelerated or inhibited diffusion. It is a normalization factor used to normalize the second-order difference value so that... It becomes a dimensionless relative rate of change; This is a global correction term used to analyze whether diffusion anomalies have changed. When this value is greater than 1, it indicates that the monitoring point... The diffusion is accelerated or impeded by geological structures or micro-topography in the vicinity, thus amplifying and correcting the basic rate term.

[0104] It should be noted that the diffusion effect parameter is a parameter with units, specifically m⁻¹.

[0105] Optionally, The value can be monitored at the point. With monitoring points The absolute value of the difference in concentration comparison parameters between the two points, or the average value of the concentration comparison parameters of all monitoring points within the target area.

[0106] It should be noted that when the absolute value or average value of the difference is 0, it can be taken as 0.001 to avoid the denominator being 0.

[0107] The method provided in S202 above calculates the comprehensive hindrance coefficient to remove the hindrance effect of the soil medium, and then determines the diffusion influence parameters based on the differences in concentration comparison parameters and route length. This fully considers the restrictive effect of soil heterogeneity on pollutant diffusion. The introduction of the comprehensive hindrance coefficient quantifies the soil's ability to impede pollutant migration, while the concentration comparison parameters accurately reflect the actual expansion potential of pollutants after removing hindrance interference. Based on this, the diffusion influence parameters calculated by combining concentration differences and route length can more accurately reflect the diffusion efficiency of pollutants under different soil environments, effectively solving the problem of misjudgment of diffusion patterns caused by neglecting soil characteristics in traditional predictions.

[0108] S203. Based on the diffusion impact parameters, relative elevation difference, and route length of the first pollution expansion route, determine the pollution impact weight of the first pollution expansion route.

[0109] In one alternative implementation, the elevation influence coefficient of the first pollution expansion path can be determined based on the relative elevation difference and length of the first pollution expansion path; then, the pollution influence weight of the first pollution expansion path can be determined based on the elevation influence coefficient and diffusion influence parameters of the first pollution expansion path.

[0110] It is understandable that the actual pollution efficiency is affected by the interference of rainwater on the environment. For example, the rate of pollution from high to low locations increases, and soil water flows from higher to lower elevations with the terrain difference. In rainy weather, the soil water content is higher, and the pollutants dissolved in water will increase the diffusion rate and diffusion concentration with the increase of flowing water. Therefore, it is necessary to analyze the impact of elevation on pollution diffusion (i.e., elevation influence coefficient).

[0111] In one alternative implementation, the ratio of the relative elevation difference to the route length can be determined as the elevation influence coefficient of the first pollution expansion route.

[0112] Understandably, this ratio quantifies the potential energy gradient per unit distance caused by topographic elevation differences, characterizing the driving force of pollutant migration with groundwater flow or surface runoff. The larger the ratio, the greater the efficiency of pollutant migration.

[0113] In another alternative implementation, the elevation influence coefficient of the first pollution expansion route can be determined based on the ratio of the relative elevation difference to the route length and the consistency of the diffusion direction of the first pollution expansion route.

[0114] It is understandable that when the direction of the first pollution spread path is highly consistent with the main direction of pollution spread, the promoting effect of topography on the spread is maximized; when the directions are inconsistent, even if the topographic difference is large, its actual contribution to the current spread path will be reduced.

[0115] Optionally, the elevation influence coefficient satisfies the following formula:

[0116] ;

[0117] in, Indicates the route of pollution expansion Elevation influence coefficient, Indicates the route of pollution expansion The relative elevation difference Indicates the route of pollution expansion route length, Indicates the route of pollution expansion The diffusion direction is consistent.

[0118] In one alternative implementation, the product of the elevation influence coefficient and the diffusion influence parameter of the first pollution expansion route can be determined as the comprehensive diffusion influence of the first pollution expansion route; the ratio between the comprehensive diffusion influence of the first pollution expansion route and the total comprehensive diffusion influence can be determined as the pollution influence weight of the first pollution expansion route.

[0119] The total comprehensive diffusion impact is the sum of the comprehensive diffusion impact of the first uncontaminated monitoring point and each adjacent contaminated monitoring point.

[0120] Optionally, the pollution impact weights satisfy the following formula:

[0121] ;

[0122] in, Indicates the route of pollution expansion The weight of pollution impact, Indicates the route of pollution expansion The diffusion effect parameters, Indicates the route of pollution expansion Elevation influence coefficient, Indicates monitoring point The number of all adjacent contaminated nodes, Indicates the route of pollution expansion The combined diffusion impact, This indicates the overall diffusion impact.

[0123] It should be understood that Used to characterize pollution expansion pathways The degree of pollution impact at monitoring points The percentage of all the impacts received.

[0124] Optionally, if the sum of the diffusion effects of all adjacent contaminated monitoring points If the value is zero, then the pollution impact weights of each pollution expansion route are equal.

[0125] The methods provided in S201-S203 above refine the process of determining the pollution impact weight into three key steps: calculation of route length and relative elevation difference, determination of diffusion impact parameters, and integration of multiple factors. This accurately characterizes the combined impact of terrain conditions and diffusion characteristics on pollution propagation, providing more reliable parameter support for the subsequent correction of the diffusion coefficient in the model, thereby improving the scientific nature of the prediction results.

[0126] This application also provides a soil pollution diffusion prediction system, such as... Figure 3 As shown, the soil pollution diffusion prediction system 30 includes a data acquisition unit 301, a data processing unit 302, and a model correction unit 303.

[0127] The data acquisition unit 301 is used to acquire monitoring data from each of the multiple monitoring points within the target area.

[0128] The monitoring data for one monitoring point includes the coordinates of the monitoring point, the concentration of pollutants, and soil environmental parameters.

[0129] The data processing unit 302 is used to determine multiple pollution spread routes based on the pollutant concentration at each monitoring point.

[0130] One of the pollution spread routes is a route from a polluted monitoring point to an adjacent unpolluted monitoring point;

[0131] The data processing unit 302 is also used to determine the pollution impact weight of each pollution expansion route based on the monitoring data of the monitoring points at both ends of each pollution expansion route.

[0132] Among them, the pollution impact weight is used to characterize the degree to which unpolluted monitoring points are affected by the pollution diffusion from polluted monitoring points;

[0133] Model correction unit 303 is used to correct the diffusion coefficient in the soil pollution diffusion prediction model based on the pollution impact weights of the multiple pollution expansion routes, so as to predict the soil pollution diffusion in the target area.

[0134] It should be noted that the soil pollution diffusion prediction system 30 can implement any of the above-mentioned optional soil pollution diffusion prediction methods.

[0135] The soil pollution diffusion prediction method and system provided in this application embodiment, by introducing ground-penetrating radar to finely characterize soil texture, fissures and stratification structure, breaks through the limitations of the traditional grid method's assumption of "topographic homogenization". The constructed comprehensive retardation coefficient and concentration comparison parameters quantify the retardation or enhancement effect of spatial heterogeneity on diffusion, realize the capture of the "preferred flow path" of pollutants driven by micro-topography and weather, and significantly improve the accuracy of diffusion trend prediction and early warning reliability in complex sites.

[0136] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for predicting the spread of soil pollution, characterized in that, include: Acquire monitoring data from each of multiple monitoring points within the target area. The monitoring data for a single monitoring point includes the coordinates of the monitoring point, pollutant concentration, and soil environmental parameters. Based on the pollutant concentration at each monitoring point, multiple pollution spread routes are determined. A pollution spread route is a route from a polluted monitoring point to an adjacent unpolluted monitoring point. Based on the monitoring data from the monitoring points at both ends of each pollution expansion route, the pollution impact weight of each pollution expansion route is determined. The pollution impact weight is used to characterize the degree to which unpolluted monitoring points are affected by the pollution spread from polluted monitoring points. Based on the pollution impact weights of the multiple pollution expansion routes, the diffusion coefficient in the soil pollution diffusion prediction model is modified for use in predicting soil pollution diffusion in the target area. The corrected diffusion coefficient satisfies the following formula: ； in, Indicates the route of pollution expansion The corresponding corrected diffusion coefficient, pollution propagation path For polluted monitoring points Pointing to uncontaminated monitoring points The pollution expansion route, This represents the pre-defined pollution propagation path in the soil pollution diffusion prediction model. diffusion coefficient, Indicates the route of pollution expansion The weight of pollution impact, This represents the diffusion enhancement coefficient, a constant greater than 0, used to control the maximum influence of pollution impact weights on the diffusion coefficient. The pollution impact weight of each pollution spread path is determined based on monitoring data from monitoring points at both ends of each pollution spread path, including: Based on the coordinates of the first unpolluted monitoring point and the first polluted monitoring point at both ends of the first pollution expansion route, the route length and relative elevation difference of the first pollution expansion route are determined. The first pollution expansion route is any one of multiple pollution expansion routes. Based on the soil environmental parameters of the first uncontaminated monitoring point, the comprehensive blocking coefficient of the first uncontaminated monitoring point is determined, and based on the soil environmental parameters of the first contaminated monitoring point, the comprehensive blocking coefficient of the first contaminated monitoring point is determined. The comprehensive blocking coefficient is used to characterize the soil's ability to block the migration of pollutants. Based on the pollutant concentration at the first unpolluted monitoring point and the comprehensive retardation coefficient at the first unpolluted monitoring point, a concentration comparison parameter for the first unpolluted monitoring point is determined. Based on the pollutant concentration at the first polluted monitoring point and the comprehensive retardation coefficient at the first polluted monitoring point, a concentration comparison parameter for the first polluted monitoring point is determined. The concentration comparison parameter is used to characterize the expansion capacity of pollutants after the retardation effect of the soil medium has been removed. Based on the difference between the concentration comparison parameters of the first unpolluted monitoring point and the first polluted monitoring point, and the route length of the first pollution expansion path, the diffusion impact parameters of the first pollution expansion path are determined. The diffusion impact parameters are used to characterize the efficiency of pollutant diffusion from the polluted monitoring point to the unpolluted monitoring point. Based on the diffusion impact parameters, relative elevation difference, and route length of the first pollution expansion route, the pollution impact weight of the first pollution expansion route is determined.

2. The method for predicting the diffusion of soil pollution according to claim 1, characterized in that, The determination of the pollution impact weight of the first pollution expansion path based on the diffusion impact parameters, relative elevation difference, and path length of the first pollution expansion path includes: Based on the relative elevation difference and route length of the first pollution expansion route, the elevation influence coefficient of the first pollution expansion route is determined. Based on the elevation influence coefficient and diffusion influence parameters of the first pollution expansion route, the pollution influence weight of the first pollution expansion route is determined.

3. The method for predicting the diffusion of soil pollution according to claim 2, characterized in that, The determination of the elevation influence coefficient of the first pollution expansion path based on the relative elevation difference and path length includes: Based on the ratio of the relative elevation difference to the route length, and the consistency of the diffusion direction of the first pollution expansion route, the elevation influence coefficient of the first pollution expansion route is determined.

4. The method for predicting the diffusion of soil pollution according to claim 2, characterized in that, The determination of the pollution impact weight of the first pollution expansion path based on the elevation influence coefficient and diffusion influence parameters of the first pollution expansion path includes: The product of the elevation influence coefficient and the diffusion influence parameter of the first pollution expansion route is determined as the comprehensive diffusion influence of the first pollution expansion route. The ratio between the comprehensive diffusion impact of the first pollution expansion route and the total comprehensive diffusion impact is determined as the pollution impact weight of the first pollution expansion route. The total comprehensive diffusion impact is the sum of the comprehensive diffusion impact of the first unpolluted monitoring point and each adjacent polluted monitoring point.

5. The method for predicting the diffusion of soil pollution according to claim 1, characterized in that, The step of adjusting the diffusion coefficient in the soil pollution diffusion prediction model based on the pollution impact weights of the multiple pollution expansion routes for use in predicting soil pollution diffusion in the target area includes: The diffusion coefficient of each pollution expansion route in the soil pollution diffusion prediction model is corrected based on the pollution impact weight of each pollution expansion route, resulting in the corrected soil pollution diffusion prediction model. Soil pollution diffusion prediction is based on the modified soil pollution diffusion prediction model.

6. The method for predicting the diffusion of soil pollution according to claim 1, characterized in that, Based on the pollutant concentration at each monitoring point, multiple pollution propagation routes are determined, including: Based on the pollutant concentration and pollutant concentration threshold at each monitoring point, the multiple monitoring points are divided into polluted monitoring points and unpolluted monitoring points; When the first polluted monitoring point is adjacent to the first unpolluted monitoring point and the pollution diffusion direction of the first route is consistent with the main pollution diffusion direction, the first route is determined as the pollution expansion route. The first route is the route from the first polluted monitoring point to the first unpolluted monitoring point.

7. The method for predicting soil pollution diffusion according to claim 6, characterized in that, The method further includes: Obtain the main pollution diffusion direction vector and the first pollution diffusion direction vector, wherein the first pollution diffusion direction vector is the diffusion direction vector of the first route; The cosine of the angle between the main pollution diffusion direction vector and the first pollution diffusion direction vector is determined as the consistency of the diffusion direction of the first route. If the consistency of diffusion direction is greater than the consistency threshold, the pollution diffusion direction of the first route is determined to be consistent with the main pollution diffusion direction.

8. A soil pollution diffusion prediction system, characterized in that, It includes a data acquisition unit, a data processing unit, and a model correction unit: The data acquisition unit is used to acquire monitoring data of each of the multiple monitoring points in the target area. The monitoring data of a monitoring point includes the coordinates of the monitoring point, pollutant concentration, and soil environmental parameters. The data processing unit is used to determine multiple pollution expansion routes based on the pollutant concentration at each monitoring point. A pollution expansion route is a route from a polluted monitoring point to an adjacent unpolluted monitoring point. The data processing unit is also used to determine the pollution impact weight of each pollution expansion route based on the monitoring data of the monitoring points at both ends of each pollution expansion route. The pollution impact weight is used to characterize the degree to which unpolluted monitoring points are affected by the pollution diffusion of polluted monitoring points. The model correction unit is used to correct the diffusion coefficient in the soil pollution diffusion prediction model based on the pollution impact weights of the multiple pollution expansion routes, so as to predict the soil pollution diffusion in the target area. The corrected diffusion coefficient satisfies the following formula: ； in, Indicates the route of pollution expansion The corresponding corrected diffusion coefficient, pollution propagation path For polluted monitoring points Pointing to uncontaminated monitoring points The pollution expansion route, This represents the pre-defined pollution propagation path in the soil pollution diffusion prediction model. diffusion coefficient, Indicates the route of pollution expansion The weight of pollution impact, This represents the diffusion enhancement coefficient, a constant greater than 0, used to control the maximum influence of pollution impact weights on the diffusion coefficient. The data processing unit is specifically used for: Based on the coordinates of the first unpolluted monitoring point and the first polluted monitoring point at both ends of the first pollution expansion route, the route length and relative elevation difference of the first pollution expansion route are determined. The first pollution expansion route is any one of multiple pollution expansion routes. Based on the soil environmental parameters of the first uncontaminated monitoring point, the comprehensive blocking coefficient of the first uncontaminated monitoring point is determined, and based on the soil environmental parameters of the first contaminated monitoring point, the comprehensive blocking coefficient of the first contaminated monitoring point is determined. The comprehensive blocking coefficient is used to characterize the soil's ability to block the migration of pollutants. Based on the pollutant concentration at the first unpolluted monitoring point and the comprehensive retardation coefficient at the first unpolluted monitoring point, a concentration comparison parameter for the first unpolluted monitoring point is determined. Based on the pollutant concentration at the first polluted monitoring point and the comprehensive retardation coefficient at the first polluted monitoring point, a concentration comparison parameter for the first polluted monitoring point is determined. The concentration comparison parameter is used to characterize the expansion capacity of pollutants after the retardation effect of the soil medium has been removed. Based on the difference between the concentration comparison parameters of the first unpolluted monitoring point and the first polluted monitoring point, and the route length of the first pollution expansion path, the diffusion impact parameters of the first pollution expansion path are determined. The diffusion impact parameters are used to characterize the efficiency of pollutant diffusion from the polluted monitoring point to the unpolluted monitoring point. Based on the diffusion impact parameters, relative elevation difference, and route length of the first pollution expansion route, the pollution impact weight of the first pollution expansion route is determined.

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