Analysis and early warning method and system for soil heavy metal pollution

Abstract

This application proposes a method and system for analyzing and warning heavy metal contamination in soil, which relates to the field of environmental monitoring technology. The method includes: determining the particle uniformity of the soil in the target area and triggering a deep pollution collection instruction based on the particle uniformity; in response to receiving the deep pollution collection instruction, collecting migration trend data of pollutants at different depths in the target area soil and environmental data of the target area; based on the migration trend data and environmental data, as well as a pre-trained target pollution dynamic analysis model, determining the migration prediction results of pollutants at different depths in the target area soil; based on the migration trend data, environmental data, and particle uniformity, determining the soil pollution warning index of the target area, and based on the soil pollution warning index and migration prediction results, issuing a warning to the target area. As a result, this solution can improve the response speed of the warning and achieve accurate analysis of the migration path of pollutants.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring technology, and in particular to an analysis and early warning method and system for heavy metal pollution in soil. Background Art

[0002] With the accelerated development of industrialization and urbanization, activities such as mining, metallurgical processing, and chemical production have led to excessive levels of heavy metals such as cadmium, lead, mercury, and arsenic in the soil, posing a threat to the ecological environment and human health. In recent years, the development of the Internet of Things and high-throughput sensors has made soil heavy metal pollution early warning methods more real-time and intelligent, supporting refined environmental management and decision-making. Summary of the Invention

[0003] The purpose of this application is to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of this application is to propose an analytical early warning method for heavy metal pollution in soil, so as to accurately assess the migration path of pollutants and improve the response speed of early warning.

[0005] The second purpose of this application is to propose an analysis and early warning system for heavy metal pollution in soil.

[0006] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for analyzing and warning of heavy metal pollution in soil, comprising: determining the particle uniformity of the soil in the target area, and triggering a deep pollution collection instruction based on the particle uniformity;

[0007] In response to receiving the deep pollution collection instruction, collecting migration trend data of pollutants at different depths in the soil of the target area and environmental data of the target area;

[0008] Determine migration prediction results of pollutants at different soil depths in the target area based on the migration trend data and the environmental data, and a pre-trained target pollution dynamic analysis model;

[0009] Based on the migration trend data, the environmental data and the particle uniformity, a soil pollution early warning index of the target area is determined, and based on the soil pollution early warning index and the migration prediction result, an early warning is issued to the target area.

[0010] To achieve the above objectives, the second embodiment of the present application proposes an analysis and early warning system for heavy metal pollution in soil, comprising: a particle analysis module for determining the particle uniformity of the soil in the target area and triggering a deep pollution collection instruction based on the particle uniformity;

[0011] a migration collection module, configured to collect migration trend data of pollutants at different depths in the soil of the target area and environmental data of the target area in response to receiving the deep pollution collection instruction;

[0012] A dynamic assessment module, configured to determine migration prediction results of pollutants at different soil depths in the target area based on the migration trend data and the environmental data, and a pre-trained target pollution dynamic analysis model;

[0013] A prevention and control early warning module is used to determine a soil pollution early warning index for the target area based on the migration trend data, the environmental data, and the particle uniformity, and to issue an early warning to the target area based on the soil pollution early warning index and the migration prediction result.

[0014] The analysis and early warning method and system for heavy metal pollution in soil provided by the present application determines the particle uniformity of the soil in the target area and judges whether to trigger the deep pollution collection instruction based on the particle uniformity. When the deep pollution collection instruction is triggered, the migration trend data and environmental data of pollutants at different depths in the target area are collected to obtain the migration prediction results of pollutants through the trained target pollution dynamic analysis model, thereby determining the soil pollution early warning index and combining it with the migration prediction results to issue an early warning to the target area. Therefore, by issuing an early warning based on the soil pollution early warning index, the response speed of the early warning can be improved, and by issuing an early warning based on the migration prediction results, accurate analysis of the migration path of pollutants can be achieved, and a rapid response to the entire process from pollution monitoring, trend early warning, risk assessment to emergency disposal can be achieved.

[0015] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0017] Figure 1 A schematic diagram of a process for analyzing and warning of heavy metal pollution in soil provided in an embodiment of the present application;

[0018] Figure 2 A flow chart of another method for analyzing and warning of heavy metal pollution in soil provided in an embodiment of the present application;

[0019] Figure 3 A flow chart of another method for analyzing and warning of heavy metal pollution in soil provided in an embodiment of the present application;

[0020] Figure 4A schematic diagram of the process of training a target pollution dynamic analysis model in a soil heavy metal pollution analysis and early warning method provided in an embodiment of the present application;

[0021] Figure 5 A schematic diagram of the process of the early warning method for heavy metal pollution in soil provided in an embodiment of the present application;

[0022] Figure 6 This is a structural diagram of a soil heavy metal pollution analysis and early warning system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0024] The following describes the soil heavy metal pollution analysis and early warning method and system according to the embodiment of the present application with reference to the accompanying drawings.

[0025] Figure 1 This is a flow chart of a method for analyzing and warning of heavy metal pollution in soil provided by an embodiment of the present application, such as Figure 1 As shown, the analysis and early warning method for heavy metal pollution in soil according to the embodiment of the present application includes but is not limited to the following steps:

[0026] S101, determining the particle uniformity of the soil in the target area, and triggering a deep pollution collection instruction based on the particle uniformity.

[0027] It should be noted that the execution entity of the soil heavy metal pollution analysis and early warning method provided in the embodiments of the present application is an electronic device, which may be a terminal device. Optionally, the terminal device may be a mobile electronic device or a non-mobile electronic device. Exemplary mobile electronic devices may include mobile phones, tablet computers, laptop computers, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), while non-mobile electronic devices may include personal computers (PCs), televisions, and the like. This embodiment of the present application does not impose any specific limitations.

[0028] In some embodiments, an area within a set range on a mining area can be used as a target area, or an area within a set range on an area with industrial pollution can be used as a target area. Alternatively, the set range, and thus the target area, can be determined based on soil distribution information. The soil distribution information can include the distribution of soil types and the distribution of soil properties.

[0029] For example, taking a mining area as an example, the distribution information of the soil in the mining area can be obtained, and the type of soil in the soil distribution information can be determined. The areas in the mining area with the same soil type and in adjacent positions are used as target areas within the set range.

[0030] In some embodiments, the particle uniformity of the soil can be calculated by collecting the particle size standard deviation and the particle size average of the soil in the target area and then calculating the particle size standard deviation and the particle size average.

[0031] In some embodiments, multiple monitoring sensors and sampling devices can be deployed in the target area. Soil at different locations in the target area can be collected by the sampling devices, and the monitoring sensors can be used to monitor the standard deviation and average particle size of the soil to calculate the particle uniformity of the soil.

[0032] In some embodiments, a deep contamination acquisition instruction can be triggered based on a uniformity threshold corresponding to the particle uniformity and by comparing the particle uniformity with the uniformity threshold. Optionally, if the comparison result indicates that the particle uniformity is less than or equal to the uniformity threshold, the deep contamination acquisition instruction is triggered.

[0033] Among them, the deep pollution collection instruction is used to instruct the collection of pollution data of the deep soil in the target area.

[0034] S102 , in response to receiving the deep pollution collection instruction, collecting migration trend data of pollutants at different depths in the soil of the target area and environmental data of the target area.

[0035] In some embodiments, when a deep contamination collection instruction is received, pollutant data at different depths in the soil within the target area may be collected. Optionally, the pollutant data may include pollutant migration trend data, which indicates the vertical diffusion rate and horizontal penetration rate of pollutants in the soil. The pollutants may be heavy metals in the soil.

[0036] In some embodiments, the content data of heavy metals at different depths in the soil of the target area can be collected to determine the concentration distribution of heavy metals in the soil at different depths based on the content data, so that the migration trend data of heavy metals can be calculated based on the concentration distribution of heavy metals.

[0037] In some embodiments, when a deep contamination collection instruction is received, groundwater contamination data within the target area may also be collected as environmental data for the target area. Alternatively, groundwater contamination concentration and pollutant properties may be collected as environmental data.

[0038] S103, based on the migration trend data and environmental data, and the pre-trained target pollution dynamic analysis model, determine the migration prediction results of pollutants at different depths in the target area soil.

[0039] In some embodiments, migration trend data and environmental data are input into a pre-trained target pollution dynamic analysis model, and the model is used to predict the migration path of pollutants in the target area to obtain a predicted migration path as a migration prediction result.

[0040] Optionally, the evolution of pollutants in the soil can be determined based on the migration prediction results, thereby visually demonstrating soil pollution.

[0041] Optionally, the target pollution dynamic analysis model can also predict the concentration distribution of pollutants in the target area in a future time period to predict the concentration distribution of pollutants in the target area, and use the predicted migration path and concentration distribution as the migration prediction result.

[0042] S104: Determine a soil pollution warning index for the target area based on the migration trend data, environmental data, and particle uniformity, and issue a warning to the target area based on the soil pollution warning index and the migration prediction result.

[0043] In some embodiments, the migration dynamic factor can be calculated based on the migration trend data, and combined with the environmental data, the cumulative risk coefficient of the target area can be calculated, so as to correlate the migration dynamic factor, the cumulative risk coefficient and the particle uniformity to calculate the soil pollution warning index of the target area.

[0044] In some embodiments, the risk level of soil pollution risk in the target area can be determined based on the soil pollution warning index, and warning information can be determined based on the risk level and migration prediction results, so as to issue a warning to the target area based on the warning information.

[0045] Optionally, the migration prediction results can indicate the migration path of pollutants in the target area in the future time period, as well as the concentration distribution of pollutants, so as to determine the pollution situation of the target area in the future time period based on the migration prediction results, and to issue early warnings to the target area based on the risk level and pollution situation.

[0046] For example, warning information includes red warning information and orange warning information, among which the urgency of red warning information is greater than that of orange warning information; risk levels include low risk and high risk. When the migration prediction results indicate that the migration speed and migration distance of pollutants in the target area are fast, the concentration distribution of pollutants is high, and the risk level is high, a red warning information can be issued to the target area.

[0047] In some embodiments, the risk level of soil pollution risk in the target area can be determined based on the soil pollution early warning index and a set early warning index threshold. For example, when the soil pollution early warning index is greater than or equal to the early warning index threshold, the risk level is determined to be high risk, and when the soil pollution early warning index is less than the early warning index threshold, the risk level is determined to be low risk.

[0048] In the analysis and early warning method for heavy metal pollution in soil provided in the embodiment of the present application, the particle uniformity of the soil in the target area is determined, and based on the particle uniformity, it is determined whether to trigger the deep pollution collection instruction. When the deep pollution collection instruction is triggered, the migration trend data and environmental data of pollutants at different depths in the soil of the target area are collected to obtain the migration prediction results of pollutants through the trained target pollution dynamic analysis model, thereby determining the soil pollution early warning index and combining it with the migration prediction results to issue an early warning to the target area. Therefore, by issuing an early warning based on the soil pollution early warning index, the response speed of the early warning can be improved, and by issuing an early warning based on the migration prediction results, accurate analysis of the migration path of pollutants can be achieved, and a rapid response to the entire process from pollution monitoring, trend early warning, risk assessment to emergency disposal can be achieved.

[0049] Figure 2 This is a flow chart of a method for analyzing and warning of heavy metal pollution in soil provided by an embodiment of the present application, such as Figure 2 As shown, the analysis and early warning method for heavy metal pollution in soil according to the embodiment of the present application includes but is not limited to the following steps:

[0050] S201 : collecting particle size distribution data of the soil in the target area, and determining the particle size standard deviation and the particle size average of the soil based on the particle size distribution data.

[0051] In some embodiments, by deploying multiple monitoring sensors and sampling equipment in the target area, and having the sampling equipment collect soil at different locations in the target area, and using the monitoring sensors to collect the particle size distribution data of the soil, the particle size standard deviation and the particle size average can be determined based on the particle size distribution data.

[0052] Optionally, particle size distribution data of the soil in the target area may be collected according to a set sampling period.

[0053] In some embodiments, the number of particles corresponding to each particle size value can be determined based on the particle size distribution data, and then the average particle size can be calculated based on the different particle size values ​​and their corresponding particle numbers, and the standard deviation of the particle size can be further calculated based on the average particle size.

[0054] S202 , normalizing the particle size standard deviation and the particle size average, and determining the particle uniformity based on the normalization result.

[0055] In some embodiments, the particle uniformity is calculated by normalizing the particle size standard deviation and the particle size average, and using the normalized particle size standard deviation and the particle size average as the normalization result.

[0056] Alternatively, the formula for calculating particle uniformity is as follows:

[0057]

[0058] Among them, Fp represents the particle uniformity, Fsd represents the standard deviation of particle size, and Fpz represents the average particle size.

[0059] S203: Determine a uniformity threshold.

[0060] S204 : In response to the particle uniformity being less than or equal to the uniformity threshold, triggering a deep contamination collection instruction.

[0061] In some embodiments, a uniformity threshold is determined and compared with the particle uniformity to determine whether to trigger a deep contamination acquisition instruction. Alternatively, the uniformity threshold can be determined based on the instruction received from the client. In other words, the instruction carries the uniformity threshold.

[0062] In some embodiments, by comparing the particle uniformity with a uniformity threshold, and when the particle uniformity is less than or equal to the uniformity threshold, it is determined that a deep contamination acquisition instruction is triggered.

[0063] S205 , in response to receiving the deep pollution collection instruction, collecting migration trend data of pollutants at different depths in the soil of the target area and environmental data of the target area.

[0064] S206, based on the migration trend data and environmental data, and the pre-trained target pollution dynamic analysis model, determine the migration prediction results of pollutants at different depths in the soil of the target area.

[0065] S207: Determine a soil pollution warning index for the target area based on the migration trend data, environmental data, and particle uniformity, and issue a warning to the target area based on the soil pollution warning index and the migration prediction result.

[0066] In the embodiment of the present application, steps S205-S207 can be implemented by any of the methods in the embodiments of the present application, which is not limited here and will not be described in detail.

[0067] In the analytical and early warning method for heavy metal contamination in soil provided in the embodiments of this application, the representativeness and accuracy of soil characteristic data can be improved by obtaining the standard deviation and average particle size of the soil in the target area to determine particle uniformity. Calculating particle uniformity using the standard deviation and average particle size makes the determination of particle uniformity more applicable and enables intelligent identification of soil heterogeneity.

[0068] Figure 3 This is a flow chart of a method for analyzing and warning of heavy metal pollution in soil provided by an embodiment of the present application, such as Figure 3 As shown, the analysis and early warning method for heavy metal pollution in soil according to the embodiment of the present application includes but is not limited to the following steps:

[0069] S301, determining the particle uniformity of the soil in the target area, and triggering a deep pollution collection instruction based on the particle uniformity.

[0070] S302 , in response to receiving the deep pollution collection instruction, collect migration trend data of pollutants at different depths in the soil of the target area and environmental data of the target area.

[0071] In the embodiment of the present application, the implementation method of steps S301-S302 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0072] S303: Determine the vertical diffusion rate and horizontal penetration rate of the pollutants based on the migration trend data.

[0073] In some embodiments, when a deep pollution collection instruction is received, the content data of pollutants at different depths in the soil of the target area are collected to determine the concentration distribution of pollutants in the soil at different depths based on the content data. Therefore, the longitudinal diffusion rate and horizontal infiltration rate of pollutants can be calculated as migration trend data based on the concentration distribution of pollutants. In other words, the longitudinal diffusion rate and horizontal infiltration rate of pollutants can be obtained from the migration trend data.

[0074] S304: Determine the migration dynamic factor of the pollutant based on the longitudinal diffusion rate and the horizontal infiltration rate.

[0075] In some embodiments, the contaminant migration dynamics factor can be determined by determining the depth range of the soil in the target area and integrating the longitudinal diffusion rate and the horizontal infiltration rate within the depth range. Optionally, the depth range can be the depth range for collecting contaminant migration trend data.

[0076] For example, if the migration trend data of pollutants at depth a in the target area are collected, and the migration trend data of pollutants at depth b in the target area are collected, then the depth range of the soil in the target area is from a to b. The formula for calculating the migration dynamic factor of the pollutants is as follows:

[0077]

[0078] Where Mdf represents the migration dynamic factor, Zs(x) represents the longitudinal diffusion rate at depth x, and Hs(x) represents the horizontal permeability rate at depth x.

[0079] S305: Determine the soil pollution warning index of the target area based on the migration dynamic factor, environmental data, and particle uniformity.

[0080] In some embodiments, the migration dynamics factor and environmental data can be combined to determine the cumulative risk coefficient of the pollutants, and the soil pollution warning index of the target area can be determined based on the migration dynamics factor, the cumulative risk coefficient and the particle uniformity.

[0081] In some embodiments, pollutant concentration data can be obtained from environmental data, where the pollutant concentration data includes maximum pollutant concentration, average pollutant concentration, and pollutant half-life, and then the cumulative risk coefficient of the pollutant is determined based on the pollutant concentration data and migration dynamic factors.

[0082] Alternatively, the formula for calculating the cumulative risk factor of a pollutant is as follows:

[0083]

[0084] Among them, Arc represents the cumulative risk coefficient, Cmax represents the maximum pollutant concentration, Cavg represents the average pollutant concentration, Thalf represents the pollutant half-life, α, β and γ represent weight coefficients, and α+β+γ=1.

[0085] Furthermore, the soil pollution early warning index can be obtained by fitting the particle uniformity, migration dynamic factor and cumulative risk coefficient. Optionally, the particle uniformity, migration dynamic factor and cumulative risk coefficient can be linearly normalized to obtain the soil pollution early warning index.

[0086] Optionally, the formula for calculating the soil pollution early warning index is as follows:

[0087]

[0088] Among them, Spwi represents the soil pollution warning index.

[0089] S306: Based on the soil pollution early warning index and migration prediction results, an early warning is issued to the target area.

[0090] In some embodiments, the soil pollution warning index can indicate the risk level of soil pollution risk in the target area. By combining the risk level and migration prediction results, the warning information can be clarified, and the target area can be warned based on the warning information.

[0091] In some embodiments, the soil pollution risk level can be determined based on the soil pollution early warning index and the early warning index threshold. The risk level can be determined by interacting with the client to obtain the set early warning index threshold and comparing the soil pollution early warning index with the early warning index threshold.

[0092] Optionally, the risk level may include a first risk level and a second risk level, wherein the first risk level is greater than the second risk level.

[0093] In some embodiments, if the soil pollution warning index is greater than or equal to the warning index threshold, the current soil pollution risk is determined to be a first risk level; if the soil pollution warning index is less than the warning index threshold, the current soil pollution risk is determined to be a second risk level.

[0094] Furthermore, early warning information for the target area can be determined based on the risk level and migration prediction results.

[0095] That is, in response to the soil pollution warning index being greater than or equal to the warning index threshold, the current soil pollution risk is determined to be the first risk level, and based on the first risk level and the migration prediction result, the first warning information is determined to issue a warning to the target area according to the first warning information.

[0096] In response to the soil pollution early warning index being less than the early warning index threshold, the current soil pollution risk is determined to be a second risk level, and based on the second risk level and the migration prediction result, second early warning information is determined to issue an early warning to the target area according to the second early warning information.

[0097] For example, the soil pollution early warning index is Spwi, and the early warning index threshold is Z. If the number Spwi ≥ Z, it means that the pollution risk of the soil in the target area is high. The distribution of pollution sources can be determined based on the migration prediction results. At the same time, pollution isolation facilities and emergency remediation equipment can be deployed, and the monitoring frequency of the target area can be increased. When it is found that the spread of pollutants is intensifying, physical isolation or chemical remediation measures can be taken quickly to control the scope of pollution.

[0098] If Spwi < Z, it means that the pollution risk of the soil in the target area is at a controllable level. In this case, regular monitoring of soil and groundwater quality will continue, pollution prevention and control drills will be conducted regularly, emergency response capabilities and pollution source tracing and analysis capabilities will be improved, and the pollution risk will be controlled in the long term.

[0099] In the analysis and early warning method for heavy metal pollution in soil provided in the embodiment of the present application, the migration dynamic factor of the pollutant is determined, and the cumulative risk coefficient of the pollutant is calculated based on the migration dynamic factor and the pollutant concentration data. The particle uniformity, migration dynamic factor and cumulative risk coefficient are further correlated and analyzed to determine the soil pollution early warning index of the soil in the target area. By comparing the soil pollution early warning index with the early warning index threshold, the response speed of the early warning can be improved.

[0100] On the basis of the above embodiments, the embodiments of the present application can also explain the training process of the target pollution dynamic analysis model, such as Figure 4 As shown in FIG, the training process of the target pollution dynamic analysis model includes but is not limited to the following steps:

[0101] S401, obtaining sample migration trend data and sample environmental data, and performing numerical simulation based on the sample migration trend data and the sample environmental data, obtaining the sample pollutant migration path and the sample pollutant concentration distribution as simulation results, so as to establish an initial pollution dynamic analysis model.

[0102] In some embodiments, migration trend data and environmental data may be obtained from a database as sample migration trend data and sample environmental data, and / or the sample migration trend data and sample environmental data may be generated by artificial intelligence.

[0103] In some embodiments, based on the sample migration trend data and the sample environmental data, numerical calculation technology can be used to simulate the migration of pollutants to obtain the sample pollutant migration path and the sample pollutant concentration distribution, so that the sample migration trend data and the sample environmental data can be used as the input data of the model, and the sample pollutant migration path and the sample pollutant concentration distribution can be used as the output data of the model. Further, based on the relationship between input and output, an initial pollution dynamic analysis model can be established.

[0104] S402 , obtaining historical migration trend data and historical environmental data of the target area as sample input data, and historical migration paths of pollutants and historical pollutant concentration distribution as sample reference results.

[0105] In some embodiments, historical migration trend data and historical environmental data are obtained from historical data of the target area and used as sample input data of the initial pollution dynamic analysis model, and historical migration paths and historical pollutant concentration distributions of pollutants are obtained from historical data as sample reference results of the output results of the initial pollution dynamic analysis model.

[0106] S403 : Based on the sample input data and the sample reference results, the initial pollution dynamic analysis model is trained to obtain a target pollution dynamic analysis model.

[0107] In some embodiments, the sample input data and the sample reference results may be used as initial pollution dynamic analysis model training data to train the initial pollution dynamic analysis model, thereby obtaining a target pollution dynamic analysis model.

[0108] In some embodiments, by inputting sample input data into the initial pollution dynamic analysis model, the predicted migration path of pollutants and the predicted pollutant concentration distribution are obtained as sample prediction results. Then, the initial pollution dynamic analysis model can be optimized based on the sample prediction results and the sample reference results, and training can be continued until the target pollution dynamic analysis model is obtained after the training is completed.

[0109] That is to say, in the process of continuing to train the initial pollution dynamic analysis model based on the sample reference results and the sample prediction results to obtain the target pollution dynamic analysis model, the initial pollution dynamic analysis model can be optimized based on the sample reference results and the sample prediction results to obtain the candidate pollution dynamic analysis model, and continue to train the candidate pollution dynamic analysis model to obtain the target pollution dynamic analysis model.

[0110] In some embodiments, the candidate pollution dynamics analysis model can be trained based on the prediction results output by the candidate pollution dynamics analysis model in combination with sample input data. The prediction results output by the candidate pollution dynamics analysis model can be obtained, and characteristic information related to pollution migration can be extracted from the prediction results. For example, the sample input data can be input into the candidate pollution dynamics analysis model to output a prediction result, and information such as the pollutant diffusion range, concentration gradient, migration directionality, and migration rate can be extracted from the prediction results as characteristic information.

[0111] Furthermore, a data set is established based on the characteristic information and sample input data, and a candidate pollution dynamic analysis model is trained based on the data set to obtain a target pollution dynamic analysis model. In other words, the data set includes at least historical migration trend data, historical environmental data, and characteristic information.

[0112] Optionally, the candidate pollution dynamic analysis model can be used to determine the prediction results corresponding to the sample input data based on the sample input data, and the loss function of the candidate pollution dynamic analysis model can be determined based on the prediction results and feature information, and the candidate pollution dynamic analysis model can be optimized based on the loss function to obtain the target pollution dynamic analysis model.

[0113] In the analysis and early warning method for soil heavy metal pollution provided in the embodiment of the present application, an initial pollution dynamic analysis model is established by using numerical simulation, and the initial pollution dynamic analysis model is trained in combination with migration trend data and environmental data to obtain a target pollution dynamic analysis model. This can accurately quantify the migration path of pollutants and realize dynamic simulation of the migration path of pollutants.

[0114] Figure 5 The figure shows a flow chart of the early warning method for heavy metal pollution in soil. Figure 5 As shown, by determining the target area and collecting the particle size distribution data of the soil in the target area, the particle uniformity is determined based on the particle size distribution data, and the deep pollution collection instruction is triggered based on the particle uniformity; after receiving the deep pollution collection instruction, the environmental data of the soil in the target area and the migration trend data of pollutants at different depths of the soil are collected; based on the migration trend data and the environmental data, the migration prediction results of the pollutants are obtained through the target pollution dynamic analysis model; based on the migration trend data, the environmental data and the particle uniformity, the soil pollution early warning index is determined, and based on the soil pollution early warning index and the migration prediction results, the target area is warned.

[0115] Corresponding to the analysis and early warning methods for heavy metal pollution in soil proposed in the above-mentioned embodiments, an embodiment of the present application further proposes an analysis and early warning system for heavy metal pollution in soil. Since the analysis and early warning system for heavy metal pollution in soil proposed in the embodiment of the present application corresponds to the analysis and early warning methods for heavy metal pollution in soil proposed in the above-mentioned embodiments, the implementation methods of the above-mentioned analysis and early warning methods for heavy metal pollution in soil are also applicable to the analysis and early warning system for heavy metal pollution in soil proposed in the embodiment of the present application, and will not be described in detail in the following embodiments.

[0116] In order to implement the above embodiment, the present application also proposes an analysis and early warning system for heavy metal pollution in soil.

[0117] Figure 6 This is a schematic diagram of the structure of a soil heavy metal pollution analysis and early warning system provided in an embodiment of the present application.

[0118] like Figure 6 As shown, the soil heavy metal pollution analysis and early warning system 600 includes:

[0119] The particle analysis module 601 is used to determine the particle uniformity of the soil in the target area and trigger the deep pollution collection instruction based on the particle uniformity;

[0120] Migration collection module 602, for collecting migration trend data of pollutants at different depths in the target area soil and environmental data of the target area in response to receiving the deep pollution collection instruction;

[0121] Dynamic assessment module 603, for determining the migration prediction results of pollutants at different soil depths in the target area based on migration trend data and environmental data and a pre-trained target pollution dynamic analysis model;

[0122] The prevention and control warning module 604 is used to determine the soil pollution warning index of the target area based on the migration trend data, environmental data and particle uniformity, and to issue an early warning to the target area based on the soil pollution warning index and migration prediction results.

[0123] In a possible implementation of an embodiment of the present application, the particle analysis module 601 is also used to: collect particle size distribution data of the soil in the target area, and determine the particle size standard deviation and particle size average of the soil based on the particle size distribution data; normalize the particle size standard deviation and particle size average, and determine the particle uniformity based on the normalization result.

[0124] In a possible implementation of the embodiment of the present application, the particle analysis module 601 is further configured to: determine a uniformity threshold; and trigger a deep contamination collection instruction in response to the particle uniformity being less than or equal to the uniformity threshold.

[0125] In a possible implementation of an embodiment of the present application, the dynamic evaluation module 603 is also used to: obtain sample migration trend data and sample environmental data, and perform numerical simulation based on the sample migration trend data and the sample environmental data to obtain the sample pollutant migration path and the sample pollutant concentration distribution as simulation results to establish an initial pollution dynamic analysis model; obtain the historical migration trend data and historical environmental data of the target area as sample input data, and the historical migration path and historical pollutant concentration distribution of the pollutants as sample reference results; and train the initial pollution dynamic analysis model based on the sample input data and the sample reference results to obtain the target pollution dynamic analysis model.

[0126] In a possible implementation of an embodiment of the present application, the dynamic evaluation module 603 is also used to: input sample input data into the initial pollution dynamic analysis model to obtain the predicted migration path of pollutants and the predicted pollutant concentration distribution as sample prediction results; based on the sample reference results and the sample prediction results, continue to train the initial pollution dynamic analysis model to obtain the target pollution dynamic analysis model.

[0127] In a possible implementation of an embodiment of the present application, the dynamic evaluation module 603 is further used to: optimize the initial pollution dynamic analysis model based on the sample reference results and the sample prediction results to obtain a candidate pollution dynamic analysis model; obtain the prediction results output by the candidate pollution dynamic analysis model, and extract feature information related to pollution migration from the prediction results; establish a data set based on the feature information and sample input data, and train the candidate pollution dynamic analysis model based on the data set to obtain a target pollution dynamic analysis model.

[0128] In a possible implementation of an embodiment of the present application, the prevention and control warning module 604 is also used to: determine the longitudinal diffusion rate and horizontal infiltration rate of pollutants based on migration trend data; determine the migration dynamic factor of pollutants based on the longitudinal diffusion rate and horizontal infiltration rate; and determine the soil pollution warning index of the target area based on the migration dynamic factor, environmental data, and particle uniformity.

[0129] In one possible implementation of an embodiment of the present application, the prevention and control warning module 604 is further used to: obtain pollutant concentration data from environmental data; determine the cumulative risk coefficient of the pollutant based on the pollutant concentration data and the migration dynamic factor; and perform linear normalization processing on the particle uniformity, migration dynamic factor, and cumulative risk coefficient to obtain a soil pollution warning index.

[0130] In a possible implementation of an embodiment of the present application, the prevention and control warning module 604 is further used to: obtain a set warning index threshold; in response to the soil pollution warning index being greater than or equal to the warning index threshold, determine that the current soil pollution risk is a first risk level, and based on the first risk level and the migration prediction result, determine the first warning information to issue a warning to the target area according to the first warning information; in response to the soil pollution warning index being less than the warning index threshold, determine that the current soil pollution risk is a second risk level, and based on the second risk level and the migration prediction result, determine the second warning information to issue a warning to the target area according to the second warning information; wherein, the first risk level is greater than the second risk level.

[0131] In the soil heavy metal pollution analysis and early warning system provided by the embodiment of the present application, the particle uniformity of the soil in the target area is determined, and based on the particle uniformity, it is determined whether to trigger the deep pollution collection instruction. When the deep pollution collection instruction is triggered, the migration trend data and environmental data of pollutants at different depths in the target area are collected to obtain the migration prediction results of pollutants through the trained target pollution dynamic analysis model, thereby determining the soil pollution early warning index and combining it with the migration prediction results to issue an early warning to the target area. Therefore, by issuing an early warning based on the soil pollution early warning index, the response speed of the early warning can be improved, and by issuing an early warning based on the migration prediction results, accurate analysis of the migration path of pollutants can be achieved, and a rapid response to the entire process from pollution monitoring, trend early warning, risk assessment to emergency disposal can be achieved.

[0132] It should be noted that the explanation of the embodiment of the analysis and early warning method for heavy metal pollution in soil mentioned above is also applicable to the analysis and early warning system for heavy metal pollution in soil of this embodiment, and will not be repeated here.

[0133] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0134] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0135] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0136] The collection, storage, use, processing, transmission, provision and application of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0137] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0138] This application contemplates providing implementation options for users to selectively block the use or access of personal information data. Specifically, this application contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0139] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0140] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0141] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0142] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0143] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0144] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0145] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0146] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for analyzing and warning of heavy metal pollution in soil, characterized in that: The method comprises: Determining the particle uniformity of the soil in the target area and triggering a deep pollution collection instruction based on the particle uniformity; In response to receiving the deep pollution collection instruction, collecting migration trend data of pollutants at different depths in the soil of the target area and environmental data of the target area; Determine migration prediction results of pollutants at different soil depths in the target area based on the migration trend data and the environmental data, and a pre-trained target pollution dynamic analysis model; Determining a soil pollution early warning index for the target area based on the migration trend data, the environmental data, and the particle uniformity, and issuing an early warning for the target area based on the soil pollution early warning index and the migration prediction result, wherein a migration dynamic factor is calculated based on the migration trend data, and a cumulative risk coefficient of the target area is calculated in combination with the environmental data, and the migration dynamic factor, the cumulative risk coefficient, and the particle uniformity are correlated to calculate the soil pollution early warning index for the target area; The training process of the target pollution dynamic analysis model includes: obtaining sample migration trend data and sample environmental data, and performing numerical simulation based on the sample migration trend data and sample environmental data to obtain sample pollutant migration paths and sample pollutant concentration distributions as simulation results to establish an initial pollution dynamic analysis model, obtaining historical migration trend data and historical environmental data of the target area as sample input data, and historical migration paths and historical pollutant concentration distributions of pollutants as sample reference results, inputting the sample input data into the initial pollution dynamic analysis model to obtain predicted migration paths and predicted pollutant concentration distributions of the pollutants as sample prediction results, and continuing to train the initial pollution dynamic analysis model based on the sample reference results and the sample prediction results to obtain the target pollution dynamic analysis model.

2. The method according to claim 1, characterized in that Determining the particle uniformity of the soil in the target area includes: Collecting particle size distribution data of the soil in the target area, and determining a particle size standard deviation and a particle size average of the soil based on the particle size distribution data; The particle size standard deviation and the particle size average are normalized, and the particle uniformity is determined based on the normalization result.

3. The method according to claim 2, characterized in that The triggering of the deep pollution collection instruction based on the particle uniformity includes: Determine a uniformity threshold; In response to the particle uniformity being less than or equal to the uniformity threshold, the deep contamination collection instruction is triggered.

4. The method according to claim 1, wherein The step of continuing to train the initial pollution dynamic analysis model based on the sample reference result and the sample prediction result to obtain the target pollution dynamic analysis model includes: Based on the sample reference result and the sample prediction result, the initial pollution dynamic analysis model is optimized to obtain a candidate pollution dynamic analysis model; Obtaining prediction results output by the candidate pollution dynamic analysis model, and extracting characteristic information related to pollution migration from the prediction results; A data set is established based on the feature information and the sample input data, and the candidate pollution dynamics analysis model is trained based on the data set to obtain the target pollution dynamics analysis model.

5. The method according to claim 1, wherein Determining the soil pollution early warning index of the target area based on the migration trend data, the environmental data, and the particle uniformity includes: Determining the longitudinal diffusion rate and the horizontal penetration rate of the pollutant based on the migration trend data; Determining a migration dynamic factor of the pollutant based on the longitudinal diffusion rate and the horizontal infiltration rate; A soil pollution early warning index of the target area is determined based on the migration dynamic factor, the environmental data and the particle uniformity.

6. The method according to claim 5, characterized in that Determining the soil pollution early warning index of the target area based on the migration dynamic factor, the environmental data, and the particle uniformity includes: obtaining pollutant concentration data from the environmental data; Determining a cumulative risk coefficient of the pollutant based on the pollutant concentration data and the migration dynamic factor; The particle uniformity, the migration dynamic factor, and the cumulative risk coefficient are linearly normalized to obtain the soil pollution early warning index.

7. The method according to any one of claims 1 to 6, characterized in that The step of issuing an early warning to the target area based on the soil pollution early warning index and the migration prediction result includes: Get the set warning index threshold; In response to the soil pollution early warning index being greater than or equal to the early warning index threshold, determining that the current soil pollution risk is a first risk level, and determining first early warning information based on the first risk level and the migration prediction result, so as to issue an early warning to the target area according to the first early warning information; In response to the soil pollution early warning index being less than the early warning index threshold, determining that the current soil pollution risk is a second risk level, and determining second early warning information based on the second risk level and the migration prediction result, so as to issue an early warning to the target area according to the second early warning information; Wherein, the first risk level is greater than the second risk level.

8. An analysis and early warning system for heavy metal pollution in soil, characterized in that: The system comprises: A particle analysis module is used to determine the particle uniformity of the soil in the target area and trigger a deep pollution collection instruction based on the particle uniformity; a migration collection module, configured to collect migration trend data of pollutants at different depths in the soil of the target area and environmental data of the target area in response to receiving the deep pollution collection instruction; A dynamic assessment module, configured to determine migration prediction results of pollutants at different soil depths in the target area based on the migration trend data and the environmental data, and a pre-trained target pollution dynamic analysis model; a prevention and control warning module, configured to determine a soil pollution warning index for the target area based on the migration trend data, the environmental data, and the particle uniformity, and to issue a warning to the target area based on the soil pollution warning index and the migration prediction result, wherein a migration dynamic factor is calculated based on the migration trend data, and a cumulative risk coefficient of the target area is calculated in combination with the environmental data, and the migration dynamic factor, the cumulative risk coefficient, and the particle uniformity are correlated to calculate the soil pollution warning index for the target area; The training process of the target pollution dynamic analysis model includes: obtaining sample migration trend data and sample environmental data, and performing numerical simulation based on the sample migration trend data and sample environmental data to obtain sample pollutant migration paths and sample pollutant concentration distributions as simulation results to establish an initial pollution dynamic analysis model, obtaining historical migration trend data and historical environmental data of the target area as sample input data, and historical migration paths and historical pollutant concentration distributions of pollutants as sample reference results, inputting the sample input data into the initial pollution dynamic analysis model to obtain predicted migration paths and predicted pollutant concentration distributions of the pollutants as sample prediction results, and continuing to train the initial pollution dynamic analysis model based on the sample reference results and the sample prediction results to obtain the target pollution dynamic analysis model.

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