A method, device, equipment, medium and product for predicting accumulation of heavy metals in soil

Abstract

The application discloses a kind of prediction methods, devices, equipment, medium and product of soil heavy metal accumulation amount.The method comprises: in response to the pollution prediction request of target enterprise emission plan, according to target emission plan, determine the emission source category, and determine the target production stage in target emission plan that will produce the pollution emission corresponding to emission source category;Determine the target heavy metal emission amount corresponding to target production stage, and determine the corresponding target diffusion intensity based on preset diffusion prediction model;According to target diffusion intensity and preset target characteristic variable of influence soil heavy metal accumulation, a pre-trained target pollution prediction model is used to predict the heavy metal accumulation amount of surrounding soil after target enterprise emission.The technical scheme of the application can accurately predict the heavy metal accumulation amount of surrounding soil after target enterprise emission, thereby helping the risk prevention and control work of soil heavy metal pollution.

Description

Technical Field

[0001] This invention relates to the field of environmental pollution prevention and control technology, and in particular to a method, apparatus, equipment, medium and product for predicting the accumulation of heavy metals in soil. Background Technology

[0002] Non-ferrous metal production enterprises emit large amounts of dust, waste residue, and wastewater containing heavy metals during production. If not properly treated, these substances can enter the surrounding soil through wet and dry deposition, surface runoff, and wastewater irrigation. Heavy metals accumulate in the soil, often exceeding background levels. Furthermore, anthropogenic and natural factors influence the migration and fate of heavy metals, thus affecting their vertical distribution in the soil. Soil heavy metals can be released and migrate into the food chain, groundwater, and surface water, threatening human health. Therefore, the prevention and control of soil heavy metal pollution risks is of paramount importance.

[0003] Therefore, how to analyze the amount and intensity of heavy metal emissions and diffusion based on emission plans, and combine them with pollution prediction models to accurately predict the amount of heavy metal accumulation in the surrounding soil after the target enterprise's emissions, thereby contributing to the risk prevention and control of soil heavy metal pollution, is an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for predicting the accumulation of heavy metals in soil, so as to accurately predict the accumulation of heavy metals in the surrounding soil after the emission of heavy metals by a target enterprise, thereby contributing to the risk prevention and control of heavy metal pollution in soil.

[0005] According to one aspect of the present invention, a method for predicting the accumulation of heavy metals in soil is provided, comprising:

[0006] In response to a pollution forecasting request regarding the target company's emission plan, the emission source categories are determined based on the target emission plan, and the target production phases that will generate pollution emissions corresponding to the emission source categories in the target emission plan are identified.

[0007] Determine the target heavy metal emissions corresponding to the target production stage, and determine the corresponding target diffusion intensity based on the preset diffusion prediction model;

[0008] Based on the target diffusion intensity and the preset target characteristic variables affecting soil heavy metal accumulation, a pre-trained target pollution prediction model is used to predict the amount of heavy metal accumulation in the surrounding soil after the target enterprise's emissions.

[0009] According to another aspect of the present invention, a device for predicting the accumulation of heavy metals in soil is provided, comprising:

[0010] The first determining module is used to respond to a pollution prediction request for the target enterprise's emission plan, determine the emission source category based on the target emission plan, and determine the target production stage in the target emission plan that will generate pollution emissions corresponding to the emission source category.

[0011] The second determination module is used to determine the target heavy metal emissions corresponding to the target production stage, and to determine the corresponding target diffusion intensity based on a preset diffusion prediction model.

[0012] The prediction module is used to predict the amount of heavy metal accumulation in the soil surrounding the target enterprise after its emissions, based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, using a pre-trained target pollution prediction model.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for predicting soil heavy metal accumulation as described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for predicting soil heavy metal accumulation as described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the method for predicting soil heavy metal accumulation according to any embodiment of the present invention.

[0019] The technical solution of this invention, in response to a pollution prediction request regarding a target enterprise's emission plan, determines the emission source category based on the target emission plan, and identifies the target production stage within the target emission plan that will generate pollution emissions corresponding to the emission source category; determines the target heavy metal emission amount corresponding to the target production stage, and determines the corresponding target diffusion intensity based on a preset diffusion prediction model; and, based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, uses a pre-trained target pollution prediction model to predict the heavy metal accumulation in the surrounding soil after the target enterprise's emissions. By analyzing heavy metal emissions and diffusion intensity based on the emission plan, combined with the pollution prediction model, the heavy metal accumulation in the surrounding soil after the target enterprise's emissions can be accurately predicted, thereby contributing to the risk prevention and control of soil heavy metal pollution.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for predicting the accumulation of heavy metals in soil provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for predicting the accumulation of heavy metals in soil provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a structural block diagram of a soil heavy metal accumulation prediction device provided in Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," "target," "candidate," and "alternative," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a method for predicting soil heavy metal accumulation according to Embodiment 1 of the present invention. This embodiment is applicable to situations where heavy metal emissions and diffusion intensity are analyzed based on emission plans, combined with pollution prediction models, to accurately predict the heavy metal accumulation in the surrounding soil after emissions from a target enterprise. It is particularly suitable for predicting the pollution of surrounding soil by gaseous and liquid emissions from non-ferrous metal production enterprises (i.e., predicting soil heavy metal content). This method can be executed by a soil heavy metal accumulation prediction device, which can be implemented in hardware and / or software. This device can be configured in an electronic device and executed by the pollution prediction system of the non-ferrous metal production enterprise, such as... Figure 1 As shown, the method for predicting the accumulation of heavy metals in soil includes:

[0030] S101. In response to a pollution forecasting request for the target enterprise's emission plan, determine the emission source category based on the target emission plan, and determine the target production stage in the target emission plan that will generate pollution emissions corresponding to the emission source category.

[0031] A pollution prediction request refers to a request to predict the accumulation of heavy metals in the surrounding soil after a target company has implemented its target emission plan. The target emission plan refers to the target company's specific plan for discharging waste gas and / or wastewater into the surrounding area. The target emission plan can characterize the target company's expected emissions of waste gas and / or wastewater through different methods at different times. Emission source categories can include gas-based and water-based sources. The target production stage corresponds to the emission source category. Water-based sources correspond to the discharge of various liquid raw materials and products through pipelines, pools, etc.

[0032] Optionally, when the pollution prediction system detects an emission plan issued by the target enterprise, it may consider that a pollution prediction request for the target enterprise's emission plan has been detected. In this case, it may respond to the pollution prediction request by matching the content of the target emission plan to determine the emission source category.

[0033] Optionally, the target production stage for the pollution emissions corresponding to the emission source category in the target emission plan is determined, including: if the emission source category is gas source type, then the target production stage for the pollution emissions corresponding to the emission source category in the target emission plan is determined to be the stockpile loading and unloading transportation stage and the ore crushing process stage; if the emission source category is water source type, then the target production stage for the pollution emissions corresponding to the emission source category in the target emission plan is determined to be the wastewater discharge stage.

[0034] The storage yard loading and unloading transportation stage refers to the emission stages corresponding to the fugitive emissions of heavy metals during the loading and unloading transportation process of non-ferrous metal enterprises and the centralized discharge of heavy metals from wastewater outlets during the production process. The ore crushing process stage refers to the ore crushing process that generates heavy metal emissions during the non-ferrous metal mining and beneficiation process.

[0035] S102. Determine the target heavy metal emissions corresponding to the target production stage, and determine the corresponding target diffusion intensity based on the preset diffusion prediction model.

[0036] The preset diffusion prediction model can be an atmospheric diffusion model and / or a wastewater diffusion model. The atmospheric diffusion model can be, for example, an Atmospheric Environmental Research Model (AERMOD). The target diffusion intensity can include a first diffusion intensity corresponding to gas source diffusion and / or a second diffusion intensity corresponding to water source diffusion.

[0037] Optionally, the target heavy metal emissions corresponding to the target production stage are determined, including: if the emission source is gas-source type and the target production stage is the stockpile loading and unloading transportation stage and the ore crushing process stage, then the first heavy metal emissions corresponding to the stockpile loading and unloading transportation stage are determined based on the total particulate matter emissions and the heavy metal content in the stockpile dust; the second heavy metal emissions corresponding to the ore crushing process stage are determined based on the emission monitoring data of the target enterprise or the data collected by the preset sampler; and the target heavy metal emissions corresponding to the target production stage are determined based on the first heavy metal emissions and the second heavy metal emissions.

[0038] The first heavy metal emission refers to the heavy metal emissions generated during the loading, unloading, and transportation phases at the stockpile, while the second heavy metal emission refers to the heavy metal emissions generated during the ore crushing process. The target heavy metal emission can be the sum of the first and second heavy metal emissions.

[0039] For example, based on the total particulate matter emissions and the heavy metal content in the stockpile dust, the first heavy metal emission level corresponding to the loading, unloading, and transportation phases at the stockpile can be determined using the following formula:

[0040] H i =W y ×C i

[0041] Among them, H i This indicates the amount of heavy metals emitted during loading, unloading, and transportation, specifically the primary heavy metal emission, expressed in tons per year (t / a). y This represents the total particulate matter emissions from the stockpile, in tons per year (t / a), in carbon dioxide (C). i This indicates the heavy metal content in the dust from the stockpile, expressed in mg / kg.

[0042] Optionally, the total particulate matter emission W in the stockpile dust can be determined based on the dust particulate matter emission coefficient during the stockpile loading and unloading process, the material loading and unloading volume during the process, the particulate matter emission coefficient due to wind erosion of the stockpile, and the surface area of ​​the stockpile. y .

[0043] For example, the total particulate matter emission W in stockpile dust can be determined according to the following formula. y :

[0044]

[0045] Among them, E h G represents the particulate matter emission coefficient during the loading and unloading process at the stockyard, expressed in kg / t; M represents the total number of loading and unloading operations; G represents the total number of loading and unloading operations. Yi E represents the amount of material loaded or unloaded in the i-th loading / unloading process. w The particulate matter emission factor representing the impact of wind erosion on the stockpile, expressed in kg / m³. 2 AY Represents the surface area of ​​the stockpile, in meters. 2 .

[0046] Optionally, the particulate matter emission coefficient during the loading and unloading process of the stockyard can be determined based on the particle size multiplier of the material, the average wind speed at ground level, the moisture content of the material, and the dust removal efficiency.

[0047] For example, the particulate matter emission factor during the loading and unloading process in the storage yard can be determined based on the following formula:

[0048]

[0049] Among them, E h This represents the particulate matter emission coefficient during the loading and unloading process at the storage yard, expressed in kg / t, k. i The particle size multiplier represents the material, u represents the average ground wind speed in m / s, M represents the material moisture content in %, and δ represents the dust removal efficiency in %.

[0050] Optionally, the wind erosion dust emission coefficient of the stockpile can be determined based on the particle size multiplier of the stockpile, the wind erosion potential of the maximum wind speed observed during disturbance, and the dust removal efficiency of pollution control technologies.

[0051] For example, the wind erosion dust emission coefficient of the stockpile can be determined based on the following formula:

[0052]

[0053] Among them, E w This indicates the emission coefficient of wind erosion dust from the stockpile, expressed in kg / m³. 2 ;k i The particle size multiplier of the stockpile; n represents the number of disturbances the stockpile experiences per year; P i δ represents the wind erosion potential of the maximum wind speed observed in the i-th disturbance, in g / m2; δ represents the dust removal efficiency of pollution control technology, in %.

[0054] For example, the wind erosion potential P of the maximum wind speed observed in the i-th perturbation. i It can be calculated using the following formula:

[0055]

[0056] Where u* represents the frictional wind speed, m / s; This represents the threshold frictional wind speed, which is the critical frictional wind speed at which dust is generated, in m / s.

[0057] For example, the frictional wind speed u* can be calculated using the following formula:

[0058]

[0059] Where u(z) represents the ground wind speed, m / s; Z represents the ground wind speed detection height, m; and z0 represents the ground roughness, m.

[0060] Optionally, in the non-ferrous metal mining and beneficiation process, heavy metal emissions are mainly generated by the ore crushing process. Therefore, the emission rate and heavy metal content of particulate matter at all particulate matter emission points can be monitored to calculate the heavy metal emissions during non-ferrous metal production. That is, the second heavy metal emission corresponding to the ore crushing process stage can be determined based on the emission monitoring data of the target enterprise. Alternatively, during the mining and beneficiation process, atmospheric particulate matter sampling points can be set up in the material crushing workshop. Particulate matter can be collected using atmospheric particulate matter samplers, and the heavy metal content in the particulate matter can be measured to calculate the heavy metal emissions during the production process. That is, the second heavy metal emission corresponding to the ore crushing process stage can be determined based on the data collected by the preset samplers.

[0061] Optionally, the target heavy metal emissions corresponding to the target production stage are determined, including: if the emission source is water-based and the target production stage is a wastewater discharge stage, then the heavy metal emissions corresponding to the wastewater discharge stage are determined based on the pipeline length, the pond area, the pollutant concentration in the wastewater, the discharge operation time, and the leakage coefficient when the target enterprise discharges through the pipeline and pond under normal operating conditions; the heavy metal emissions corresponding to the wastewater discharge stage are then determined as the heavy metal emissions corresponding to the target production stage.

[0062] The heavy metal emissions at the wastewater discharge stage can include the leakage amounts from pipes and ponds.

[0063] It should be noted that since acidic wastewater from open-air stockpiling of non-ferrous metals is a major source of heavy metals in the soil, when the emission source is determined to be water-source type, the corresponding target production stage can be determined as the wastewater discharge stage.

[0064] Optionally, the amount of heavy metals discharged through pipelines during the wastewater discharge stage can be determined based on the discharge operation time, the leakage coefficient of the target enterprise when discharging through pipelines under normal operating conditions, the pipeline length, and the pollutant concentration in the wastewater; or the amount of heavy metals discharged through ponds during the wastewater discharge stage can be determined based on the discharge operation time, the leakage coefficient of the target enterprise when discharging through ponds under normal operating conditions, the pond area, and the pollutant concentration in the wastewater.

[0065] For example, the amount of heavy metals discharged through pipes and ponds during the wastewater discharge stage can be determined based on the following formulas:

[0066] D i，wastewater(pipe) =T×k i，wastewater(pipe) ×L pipe ×ci，wastewater

[0067] D i，wastewater(pond) =T×k i，wastewater(pond) ×S pond ×c i，wastewater

[0068] Among them, D i，wastewater(pipe) and D i，wastewater(pond) These represent the amount of pollutants discharged into the soil through pipelines and pond leaks, specifically heavy metal emissions, k. i，wastewater(pipe) Let k be the leakage coefficient of the pipeline under normal operating conditions. i，wastewater(pond) Let L be the leakage coefficient of the pond under normal operating conditions. pipe S is the length of the pipe. pond c is the area of ​​the pool. i，wastewater Where is the concentration of pollutants in the wastewater, and T is the discharge operation time.

[0069] Optionally, the total amount of heavy metals discharged through pipes and ponds during the wastewater discharge stage can be determined as the total wastewater leakage, i.e., the heavy metal emissions corresponding to the target production stage.

[0070] Optionally, based on a preset diffusion prediction model, the corresponding target diffusion intensity is determined, including: predicting the first diffusion intensity obtained by the target enterprise through atmospheric diffusion using a preset atmospheric diffusion model based on the target heavy metal emission amount; and predicting the second diffusion intensity obtained by the target enterprise through wastewater diffusion using a preset wastewater diffusion model based on the target heavy metal emission amount.

[0071] The wastewater diffusion model can be a cellular automata. Wastewater diffusion from operating enterprises mainly occurs through leakage, forming surface runoff and thus impacting the surrounding soil environment.

[0072] Optionally, pollution source parameters (including target metal emissions), meteorological data, and topographic data of the study area (i.e., the pre-defined area surrounding the target enterprise) can be input into a pre-defined atmospheric diffusion model, such as the AERMOD model or the CALPUFF model (three-dimensional unsteady Lagrange diffusion model), to obtain the atmospheric heavy metal diffusion intensity, i.e., the first diffusion intensity.

[0073] For example, pollution source parameters may specifically include at least one of the following: chimney location (latitude and longitude coordinates), height (meters), and outlet diameter (m), emission rate (g / s), flue gas temperature (°C), and heavy metal emissions corresponding to the target production stage; meteorological data includes surface meteorological data and upper-air sounding data. Surface meteorological data includes air temperature (°C), wind speed (m / s), wind direction (°), relative humidity (%), and air pressure (hPa); upper-air sounding data includes the vertical distribution of temperature, wind speed, and humidity with altitude. The data sources are mainly meteorological observation stations and numerical weather prediction models. These data are processed by the AERMOD meteorological preprocessing module in the AERMET software to generate the meteorological parameter files (.sfc) required by the model. Topographic data may include slope, aspect, and elevation, covering the latitude and longitude range and projected coordinate system of the simulation area. These data are processed by the AERMAP topographic preprocessing module to generate the topographic elevation files (.dem) required by the model.

[0074] For example, after inputting pollution source parameters (including target metal emissions), meteorological data, and topographic data into the AERMOD model, the AERMOD file is run to output particulate matter concentrations (g / m3) at different time scales (1 hour, 3 hours, 8 hours, and 24 hours). The output results are copied into Excel to obtain the annual average ground-level concentration relative to each grid point of the pollution source. Further data processing can be performed using ArcGIS software's rasterization tool to obtain the pollutant diffusion situation in raster form, and finally, the 1-hour, 3-hour, 8-hour, 24-hour, and annual average diffusion intensity (g / m3) are output.

[0075] Optionally, using a pre-defined wastewater diffusion model, the study area where the target enterprise is located can be divided into a grid. Based on the initial point sources within the grid, combined with the distance from the target enterprise, elevation, slope, and surface roughness, the wastewater heavy metal diffusion intensity, i.e., the second diffusion intensity, can be calculated.

[0076]

[0077] Where Slope represents the slope between grid points (0-90°), Rou represents the roughness, Dis represents the horizontal distance between grid points, and D... i，wastewater The value represents the amount of wastewater leaked (L), and c represents the concentration of heavy metals in the wastewater (mg / L). Finally, the data is standardized. JL i This refers to the diffusion intensity of heavy metals in wastewater, also known as the second diffusion intensity.

[0078] S103. Based on the target diffusion intensity and the preset target characteristic variables affecting soil heavy metal accumulation, a pre-trained target pollution prediction model is used to predict the amount of heavy metal accumulation in the surrounding soil after the target enterprise's emissions.

[0079] Among them, the target characteristic variable refers to the factor variable that has a greater impact on the accumulation of heavy metals in the soil among the candidate characteristic variables. The candidate characteristic variables can represent 26 variable indicators corresponding to the six major influencing factors (i.e., soil properties, climate, geography, agriculture, vegetation and socio-economic factors).

[0080] For example, the candidate feature variables and related information corresponding to different influencing factors can be represented by the following Table 1:

[0081] Table 1: Candidate Feature Variables Corresponding to Different Influencing Factors

[0082]

[0083]

[0084] The target pollution prediction model can refer to a basic learner obtained by integrating learners trained by at least two deep learning algorithms. The deep learning algorithms can include at least two of the following: Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), and Artificial Neural Network (ANN).

[0085] Optionally, a genetic algorithm can be used to optimize the parameter configuration of each basic learner to obtain the optimal solution for each learner's model; and the model can be validated using ten-fold cross-validation, using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). 2 To evaluate the accuracy of the model.

[0086] Optionally, for the selection of base learners: the K-Means algorithm can be used to reduce the number of base learners and remove redundant base learners with similar prediction results; alternatively, a multi-objective programming approach (MOBA) can be used to optimize the selection of base learners and determine the base learners to participate in the final integration.

[0087] Optionally, for the integration of base learners, the "elastic network algorithm" integration strategy can be used, which combines the output weights of the base learners (see the formula below) to integrate the selected base learners, thereby improving the model's prediction accuracy and stability.

[0088] e it =|z it -y i |

[0089]

[0090] Among them, e it z represents the absolute error of the prediction result of the t-th base learner for sample i. it Let y represent the prediction result of the t-th base learner for sample i. i Let a represent the expected outcome for sample i, 0.1 represent the smoothing factor, and a it w represents the weight factor of the t-th base learner for sample i. it This represents the importance weight of the prediction results of the base learner.

[0091] Optionally, based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, a pre-trained target pollution prediction model is used to predict the amount of heavy metal accumulation in the soil surrounding the target enterprise after its emissions. This includes: determining the correlation between candidate characteristic variables and soil heavy metal accumulation based on preset statistical test methods, and determining the target characteristic variable affecting soil heavy metal accumulation from the candidate characteristic variables based on the correlation; inputting the target characteristic variable and the target diffusion intensity into the pre-trained target pollution prediction model to predict the amount of heavy metal accumulation in the soil surrounding the target enterprise after its emissions. The preset statistical test methods can be Spearman's rank correlation coefficient and the Kruskal-Wallis test.

[0092] Optionally, the SHAP (SHapley Additive exPlanations) algorithm can be used to predict the local interpretation (the impact of each candidate feature variable on the accumulation of heavy metals in soil at a specific spatial location) and the global interpretation (the impact of each candidate feature variable on the accumulation of heavy metals in soil throughout the entire region), that is, to determine the degree of association between the candidate feature variables and the accumulation of heavy metals in soil.

[0093] It should be noted that SHAP is a game theory-based model interpretation method used to quantify the contribution of feature variables to model predictions. The SHAP method provides a consistent and interpretable assessment of feature importance by calculating the marginal contribution of each feature across all possible feature combinations. Local interpretation explains the predicted heavy metal accumulation in soil at a specific spatial location, i.e., the contribution of each independent variable to the predicted value at a given sample point. For a specific sample point, SHAP values ​​might show that soil pH and organic matter content contribute significantly to the predicted result, with soil pH contributing negatively and organic matter content contributing positively. Global interpretation explains the predicted heavy metal accumulation in soil over the entire region, i.e., the overall contribution of each independent variable to the predicted value for the entire region. SHAP summary plot results might show that soil pH and organic matter content contribute significantly to the overall predicted result for the entire region, with soil pH contributing negatively and organic matter content contributing positively. SHAP interaction plots might show a significant interaction between soil pH and organic matter content. Through the SHAP algorithm, the independent and interactive effects of feature variables on soil heavy metal accumulation can be quantified.

[0094] Optionally, based on the degree of association and in conjunction with a preset association threshold, candidate feature variables with a degree of association greater than the association threshold can be identified as target feature variables affecting soil heavy metal accumulation.

[0095] Optionally, the target pollution prediction model can be obtained by iteratively training the basic learner based on historical atmospheric diffusion intensity, historical wastewater diffusion intensity, historical characteristic variables, and corresponding historical soil heavy metal accumulation.

[0096] Optionally, suitable characteristic variables can be selected from the six major factors and 26 indicators that affect the accumulation of heavy metals in soil. After determining the target characteristic variables, a pre-trained target pollution prediction model can be used to predict the accumulation of heavy metals in the surrounding soil after the target enterprise's emissions, based on the target characteristic variables and the target diffusion intensity.

[0097] For example, the Bootstrap method can be used to generate 5000 samples to evaluate the uncertainty of the model's prediction results and calculate the 95% confidence interval of the model's estimated value. Specifically, firstly, Bootstrap resampling is used to randomly generate 5000 Bootstrap samples from the established prediction model and the original dataset. The model is then retrained using these 5000 samples to predict the input data and obtain the predicted value. The 2.5% and 97.5% quantiles of the predicted value are directly taken as the upper and lower bounds of the 95% confidence interval.

[0098] The technical solution of this invention, in response to a pollution prediction request regarding a target enterprise's emission plan, determines the emission source category based on the target emission plan, and identifies the target production stage within the target emission plan that will generate pollution emissions corresponding to the emission source category; determines the target heavy metal emission amount corresponding to the target production stage, and determines the corresponding target diffusion intensity based on a preset diffusion prediction model; and, based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, uses a pre-trained target pollution prediction model to predict the heavy metal accumulation in the surrounding soil after the target enterprise's emissions. By analyzing heavy metal emissions and diffusion intensity based on the emission plan, combined with the pollution prediction model, the heavy metal accumulation in the surrounding soil after the target enterprise's emissions can be accurately predicted, thereby contributing to the risk prevention and control of soil heavy metal pollution.

[0099] Example 2

[0100] Figure 2 This is a flowchart of a method for predicting soil heavy metal accumulation according to Embodiment 2 of the present invention; based on the above embodiments, this embodiment provides a preferred example for predicting soil heavy metal accumulation, specifically, as follows: Figure 2 As shown, the method includes the following steps:

[0101] S201. In response to a pollution forecasting request regarding the target company's emission plan, determine the emission source category based on the target emission plan.

[0102] S202. If the emission source category is gas source type, then the target production stage for the pollution emission corresponding to the emission source category in the target emission plan is determined to be the stockpile loading and unloading transportation stage and the ore crushing process stage.

[0103] S203. If the emission source category is water source type, then the target production stage that will generate pollution emissions corresponding to the emission source category in the target emission plan shall be the wastewater discharge stage.

[0104] S204. Determine the target heavy metal emissions corresponding to the target production stage.

[0105] S205. Based on the target heavy metal emissions, use a pre-set atmospheric diffusion model to predict the first diffusion intensity obtained by the target enterprise through atmospheric diffusion.

[0106] S206. Based on the target heavy metal emissions, use a pre-set wastewater diffusion model to predict the second diffusion intensity obtained by the target enterprise through wastewater diffusion.

[0107] S207. Based on the preset statistical test method, determine the degree of correlation between candidate characteristic variables and soil heavy metal accumulation, and determine the target characteristic variable that affects soil heavy metal accumulation from the candidate characteristic variables according to the degree of correlation.

[0108] S208. Input the target characteristic variables and target diffusion intensity into the pre-trained target pollution prediction model to predict the accumulation of heavy metals in the surrounding soil after the target enterprise's emissions.

[0109] Example 3

[0110] Figure 3 This is a structural block diagram of a soil heavy metal accumulation prediction device provided in Embodiment 3 of the present invention. This embodiment is applicable to situations where, based on emission plans, the amount of heavy metal emissions and diffusion intensity are analyzed, and combined with a pollution prediction model, the accumulation of heavy metals in the surrounding soil after emissions from a target enterprise is accurately predicted. The soil heavy metal accumulation prediction device provided in this embodiment can execute the soil heavy metal accumulation prediction method provided in any embodiment of the present invention, possessing the corresponding functional modules and beneficial effects of the execution method. This soil heavy metal accumulation prediction device can be implemented in hardware and / or software and configured in an electronic device with soil heavy metal accumulation prediction function, executed by the pollution prediction system of a non-ferrous metal production enterprise, such as... Figure 3 As shown, the soil heavy metal accumulation prediction device may specifically include:

[0111] The first determining module 301 is used to respond to a pollution prediction request for the target enterprise's emission plan, determine the emission source category according to the target emission plan, and determine the target production stage in the target emission plan that will generate pollution emissions corresponding to the emission source category.

[0112] The second determining module 302 is used to determine the target heavy metal emissions corresponding to the target production stage, and to determine the corresponding target diffusion intensity based on a preset diffusion prediction model.

[0113] The prediction module 303 is used to predict the amount of heavy metal accumulation in the soil surrounding the target enterprise after its emissions, based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, using a pre-trained target pollution prediction model.

[0114] The technical solution of this invention, in response to a pollution prediction request regarding a target enterprise's emission plan, determines the emission source category based on the target emission plan, and identifies the target production stage within the target emission plan that will generate pollution emissions corresponding to the emission source category; determines the target heavy metal emission amount corresponding to the target production stage, and determines the corresponding target diffusion intensity based on a preset diffusion prediction model; and, based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, uses a pre-trained target pollution prediction model to predict the heavy metal accumulation in the surrounding soil after the target enterprise's emissions. By analyzing heavy metal emissions and diffusion intensity based on the emission plan, combined with the pollution prediction model, the heavy metal accumulation in the surrounding soil after the target enterprise's emissions can be accurately predicted, thereby contributing to the risk prevention and control of soil heavy metal pollution.

[0115] Furthermore, the first determining module 301 may include:

[0116] The first determining unit is used to determine, if the emission source type is gas source type, the target production stage that will generate pollution emissions corresponding to the emission source type in the target emission plan is the stockpile loading and unloading transportation stage and the ore crushing process stage.

[0117] The second determining unit is used to determine the target production stage that will generate pollution emissions corresponding to the emission source type in the target emission plan as the wastewater discharge stage if the emission source type is water source type.

[0118] Furthermore, the second determining module 302 is specifically used for:

[0119] If the emission source is gas-source type and the target production stage is the stockpile loading and unloading transportation stage and the ore crushing process stage, then the first heavy metal emission corresponding to the stockpile loading and unloading transportation stage is determined based on the total particulate matter emission and the heavy metal content in the stockpile dust.

[0120] Based on the emission monitoring data of the target enterprise or the data collected by the preset sampler, determine the amount of second heavy metal emissions corresponding to each stage of the ore crushing process.

[0121] The target heavy metal emissions for the target production stage are determined based on the first and second heavy metal emissions.

[0122] Furthermore, the second determining module 302 is also used for:

[0123] If the emission source is water-based and the target production stage is the wastewater discharge stage, then the heavy metal emission amount corresponding to the wastewater discharge stage is determined based on the pipeline length, the pond area, the pollutant concentration in the wastewater, the discharge operation time, and the leakage coefficient of the target enterprise when discharging through the pipeline and pond under normal operating conditions.

[0124] The amount of heavy metal emissions corresponding to the wastewater discharge stage is determined as the amount of heavy metal emissions corresponding to the target production stage.

[0125] Furthermore, the second determining module 302 is also used for:

[0126] Based on the target heavy metal emissions, the first diffusion intensity obtained by the target enterprise through atmospheric diffusion is predicted using a pre-set atmospheric diffusion model;

[0127] Based on the target heavy metal emissions, the second diffusion intensity obtained by the target enterprise through wastewater diffusion is predicted using a pre-set wastewater diffusion model.

[0128] Furthermore, the prediction module 303 is specifically used for:

[0129] Based on the pre-set statistical test method, the degree of correlation between candidate characteristic variables and soil heavy metal accumulation is determined, and based on the degree of correlation, the target characteristic variable affecting soil heavy metal accumulation is determined from the candidate characteristic variables.

[0130] The target characteristic variables and target diffusion intensity are input into a pre-trained target pollution prediction model to predict the accumulation of heavy metals in the surrounding soil after the target enterprise's emissions.

[0131] Example 4

[0132] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0133] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0134] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0135] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for predicting soil heavy metal accumulation.

[0136] In some embodiments, the method for predicting soil heavy metal accumulation can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for predicting soil heavy metal accumulation described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for predicting soil heavy metal accumulation by any other suitable means (e.g., by means of firmware).

[0137] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0139] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0141] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0142] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0143] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the method for predicting soil heavy metal accumulation according to any embodiment of the present invention.

[0144] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0145] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0146] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting the accumulation of heavy metals in soil, characterized in that, include: In response to a pollution forecasting request regarding the target company's emission plan, the emission source categories are determined based on the target emission plan; If the emission source category is gas-based, then the target production stages for the pollution emissions corresponding to the emission source category in the target emission plan are determined to be the stockpile loading and unloading transportation stage and the ore crushing process stage; wherein, the stockpile loading and unloading transportation stage refers to the emission stage corresponding to the fugitive emissions of heavy metals during the loading and unloading transportation process of non-ferrous metal enterprises and the centralized discharge of heavy metals from the production process; the ore crushing process stage refers to the ore crushing process that generates heavy metal emissions during the non-ferrous metal mining and beneficiation process; If the emission source category is water source type, then the target production stage that will generate pollution emissions corresponding to the emission source category in the target emission plan is the wastewater discharge stage; If the emission source is gas-based, and the target production stage is the stockpile loading and unloading transportation stage and the ore crushing process stage, then the first heavy metal emission corresponding to the stockpile loading and unloading transportation stage is determined based on the total particulate matter emission and the heavy metal content in the stockpile dust; the second heavy metal emission corresponding to the ore crushing process stage is determined based on the emission monitoring data of the target enterprise or the data collected by the preset sampler; and the target heavy metal emission corresponding to the target production stage is determined based on the first and second heavy metal emissions. If the emission source is water-based and the target production stage is the wastewater discharge stage, then the heavy metal emission amount corresponding to the wastewater discharge stage is determined based on the pipeline length, the pond area, the pollutant concentration in the wastewater, the discharge operation time, and the leakage coefficient when the target enterprise discharges through the pipeline and pond under normal operating conditions; the heavy metal emission amount corresponding to the wastewater discharge stage is then determined as the heavy metal emission amount corresponding to the target production stage. Based on the target heavy metal emissions, the first diffusion intensity obtained by the target enterprise through atmospheric diffusion is predicted using a pre-set atmospheric diffusion model; based on the target heavy metal emissions, the second diffusion intensity obtained by the target enterprise through wastewater diffusion is predicted using a pre-set wastewater diffusion model. Based on the target diffusion intensity and the preset target characteristic variables affecting soil heavy metal accumulation, a pre-trained target pollution prediction model is used to predict the amount of heavy metal accumulation in the surrounding soil after the target enterprise's emissions.

2. The method according to claim 1, characterized in that, Based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, a pre-trained target pollution prediction model is used to predict the accumulation of heavy metals in the soil surrounding the target enterprise after emissions, including: Based on the pre-set statistical test method, the degree of correlation between candidate characteristic variables and soil heavy metal accumulation is determined, and based on the degree of correlation, the target characteristic variable affecting soil heavy metal accumulation is determined from the candidate characteristic variables. The target characteristic variables and target diffusion intensity are input into a pre-trained target pollution prediction model to predict the accumulation of heavy metals in the surrounding soil after the target enterprise's emissions.

3. A device for predicting the accumulation of heavy metals in soil, characterized in that, include: The first determination module is used to determine the emission source category based on the target emission plan in response to a pollution prediction request for the target enterprise's emission plan. The first determining module includes: a first determining unit and a second determining unit; The first determining unit is used to determine, if the emission source type is gas source type, the target production stage that will generate pollution emissions corresponding to the emission source type in the target emission plan as the stockpile loading and unloading transportation stage and the ore crushing process stage; wherein, the stockpile loading and unloading transportation stage refers to the emission stage corresponding to the fugitive emissions of heavy metals during the stockpile loading and unloading transportation process of non-ferrous metal enterprises and the emissions of heavy metals from centralized discharge outlets during the production process; the ore crushing process stage refers to the ore crushing process that generates heavy metal emissions during the non-ferrous metal mining and beneficiation process; The second determining unit is used to determine the target production stage in the target emission plan that will generate pollution emissions corresponding to the emission source type as the wastewater discharge stage if the emission source type is water source type. The second determining module is used to determine the first heavy metal emission amount corresponding to the stockyard loading and unloading transportation stage based on the total particulate matter emission amount and the heavy metal content in the stockyard dust if the emission source type is gas source and the target production stage is the stockyard loading and unloading transportation stage and the ore crushing process stage; determine the second heavy metal emission amount corresponding to the ore crushing process stage based on the emission monitoring data of the target enterprise or the data collected by the preset sampler; and determine the target heavy metal emission amount corresponding to the target production stage based on the first heavy metal emission amount and the second heavy metal emission amount. The second determining module is further configured to, if the emission source is water-based and the target production stage is the wastewater discharge stage, determine the heavy metal emission amount corresponding to the wastewater discharge stage based on the pipeline length, the pond area, the pollutant concentration in the wastewater, the discharge operation time, and the leakage coefficient when the target enterprise discharges through the pipeline and pond under normal operating conditions; and determine the heavy metal emission amount corresponding to the wastewater discharge stage as the heavy metal emission amount corresponding to the target production stage. The second determining module is further configured to predict the first diffusion intensity obtained by the target enterprise through atmospheric diffusion using a preset atmospheric diffusion model based on the target heavy metal emission amount; and to predict the second diffusion intensity obtained by the target enterprise through wastewater diffusion using a preset wastewater diffusion model based on the target heavy metal emission amount. The prediction module is used to predict the amount of heavy metal accumulation in the soil surrounding the target enterprise after its emissions, based on the target diffusion intensity and preset target characteristic variables affecting soil heavy metal accumulation, using a pre-trained target pollution prediction model.

4. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the method for predicting the accumulation of heavy metals in soil as described in any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for predicting the accumulation of heavy metals in soil as described in any one of claims 1-2.

6. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for predicting the accumulation of heavy metals in soil according to any one of claims 1-2.

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