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

Abstract

The invention provides an analysis and early warning method and system for soil heavy metal pollution, and relates to the technical field of environment monitoring. The method comprises the steps that the particle uniformity of soil in a target area is determined, and a deep pollution collection instruction is triggered based on the particle uniformity; in response to a received deep pollution acquisition instruction, acquiring migration trend data of pollutants in different depths of soil in a target area and environmental data of the target area; on the basis of the migration trend data, the environmental data and a pre-trained target pollution dynamic analysis model, determining migration prediction results of pollutants at different depths of the soil in the target area; and determining a soil pollution early warning index of the target area based on the migration trend data, the environmental data and the particle uniformity, and performing early warning on the target area based on the soil pollution early warning index and a migration prediction result. Therefore, according to the scheme, the response speed of early warning can be improved, and accurate analysis of the pollutant migration path is realized.

Description

Technical Field

[0001] This application relates to the technical field of environmental monitoring, and particularly to an analysis and early warning method and system for soil heavy metal pollution. 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 at least one of the technical problems in the related art to some extent.

[0004] To this end, the first purpose of this application is to propose an analysis and early warning method for soil heavy metal pollution to accurately evaluate 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 soil heavy metal pollution.

[0006] To achieve the above object, an analysis and early warning method for soil heavy metal pollution according to an embodiment of the first aspect of this application 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 the migration trend data of pollutants at different depths in the soil of the target area and the environmental data of the target area; Based on the migration trend data, the environmental data, and a pre-trained target pollution dynamic analysis model, determining the migration prediction results of pollutants at different depths in the soil of the target area; Based on the migration trend data, the environmental data, and the particle uniformity, determining the soil pollution early warning index of the target area, and warning the target area based on the soil pollution early warning index and the migration prediction results.

[0007] To achieve the above object, an analysis and early warning system for soil heavy metal pollution according to an embodiment of the second aspect of this application includes: 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; A migration collection module for collecting the migration trend data of pollutants at different depths in the soil of the target area and the environmental data of the target area in response to receiving the deep pollution collection instruction; A dynamic evaluation module, configured to determine the migration prediction results of pollutants at different depths in the soil of the target area based on the migration trend data, 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 issue a warning for the target area based on the soil pollution warning index and the migration prediction results.

[0008] The analysis and warning method and system for soil heavy metal pollution provided by this application determine the particle uniformity of the soil in the target area, and judge whether to trigger a deep pollution collection instruction according to 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 soil of the target area are collected, so as to obtain the migration prediction results of pollutants through the trained target pollution dynamic analysis model. Thus, by determining the soil pollution warning index and combining the migration prediction results, a warning is issued for the target area. Therefore, warning based on the soil pollution warning index can improve the response speed of the warning, and warning combined with the migration prediction results can accurately analyze the migration path of pollutants and achieve a rapid response in the whole process from pollution monitoring, trend warning, risk assessment to emergency disposal.

[0009] Some of the additional aspects and advantages of this application will be given in the following description, some will become obvious from the following description, or will be understood through the practice of this application. Description of the Drawings

[0010] The above-mentioned and / or additional aspects and advantages of this application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where: Figure 1 It is a schematic flowchart of a method for analyzing and warning soil heavy metal pollution provided by an embodiment of this application; Figure 2 It is a schematic flowchart of another method for analyzing and warning soil heavy metal pollution provided by an embodiment of this application; Figure 3 It is a schematic flowchart of another method for analyzing and warning soil heavy metal pollution provided by an embodiment of this application; Figure 4 It is a schematic flowchart of the training process of a target pollution dynamic analysis model in a method for analyzing and warning soil heavy metal pollution provided by an embodiment of this application; Figure 5 It is a schematic flowchart of a method for warning soil heavy metal pollution provided by an embodiment of this application; Figure 6 It is a schematic structural diagram of an analysis and warning system for soil heavy metal pollution provided by an embodiment of this application. Specific Embodiments

[0011] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as a limitation of the present application.

[0012] The method and system for analyzing and warning of soil heavy metal pollution according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0013] Figure 1 is a flowchart of a method for analyzing and warning of soil heavy metal pollution provided according to the embodiments of the present application. As Figure 1 shown, the method for analyzing and warning of soil heavy metal pollution according to the embodiments of the present application includes but is not limited to the following steps: S101, determining the particle uniformity of the soil in the target area and triggering a deep pollution collection instruction based on the particle uniformity.

[0014] It should be noted that the execution subject of the method for analyzing and warning of soil heavy metal pollution provided in the embodiments of the present application is an electronic device, and the electronic device may be a terminal device. Optionally, the terminal device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a handheld computer, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a personal computer (PC), a television, etc. The embodiments of the present application do not make specific limitations.

[0015] In some embodiments, an area within a set range on a mining area may be used as the target area, and an area within a set range on an area with industrial pollution may also be used as the target area. Optionally, the set range may be determined according to the distribution information of the soil, so as to determine the target area. Among them, the distribution information of the soil may be the distribution of soil types and the distribution of soil properties.

[0016] For example, taking a mining area as an example, the distribution information of the soil in the mining area may be obtained, the type of the soil in the distribution information of the soil may be determined, and an area with the same soil type and adjacent positions within the mining area may be used as the target area within the set range.

[0017] In some embodiments, the standard deviation and average value of the particle size of the soil in the target area can be collected, and based on the standard deviation and average value of the particle size, the particle uniformity of the soil can be calculated.

[0018] In some embodiments, a plurality of monitoring sensors and sampling devices can be deployed within the target area. The sampling devices are used to collect the soil at different positions in the target area, and the monitoring sensors are used to monitor the standard deviation and average value of the particle size of the soil, so as to calculate the particle uniformity of the soil.

[0019] In some embodiments, based on the uniformity threshold corresponding to the particle uniformity, by comparing the particle uniformity with the uniformity threshold, a deep pollution collection instruction can be triggered according to the comparison result. Optionally, if the comparison result indicates that the particle uniformity is less than or equal to the uniformity threshold, a deep pollution collection instruction is triggered.

[0020] Among them, the deep pollution collection instruction is used to indicate the collection of pollution data of the deep soil in the target area.

[0021] S102. In response to receiving the deep pollution collection instruction, collect the migration trend data of pollutants at different depths in the soil of the target area and the environmental data of the target area.

[0022] In some embodiments, when the deep pollution collection instruction is received, the pollutant data at different depths in the soil of the target area can be collected. Optionally, the pollutant data may include the migration trend data of pollutants, and the migration trend data indicates the longitudinal diffusion rate and horizontal penetration rate of pollutants in the soil. Among them, the pollutant can be heavy metals in the soil.

[0023] In some embodiments, the content data of heavy metals at different depths in the soil of the target area can be collected, so as to determine the concentration distribution of heavy metals in the soil at different depths according to the content data, and thus the migration trend data of heavy metals can be calculated according to the concentration distribution of heavy metals.

[0024] In some embodiments, when the deep pollution collection instruction is received, the pollution data of the groundwater in the target area can also be collected as the environmental data of the target area. Optionally, the pollution concentration and pollutant properties of the groundwater can be collected as the environmental data.

[0025] S103. Based on the migration trend data, 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.

[0026] In some embodiments, by inputting migration trend data and environmental data into a pre-trained target pollution dynamic analysis model, the model predicts the migration path of pollutants in the target area to obtain the predicted migration path as the migration prediction result.

[0027] Optionally, based on the migration prediction result, the evolution process of pollutants in the soil can be determined, thereby visually displaying soil pollution.

[0028] Optionally, the target pollution dynamic analysis model can also predict the concentration distribution of pollutants in the target area within 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.

[0029] S104, based on the migration trend data, environmental data, and particle uniformity, determine the soil pollution warning index of the target area, and based on the soil pollution warning index and the migration prediction result, issue a warning for the target area.

[0030] In some embodiments, the migration dynamic factor can be calculated according to 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, and calculate the soil pollution warning index of the target area.

[0031] In some embodiments, the risk level of the soil pollution risk in the target area can be determined according to the soil pollution warning index, and the warning information can be determined according to the risk level and the migration prediction result, so as to issue a warning for the target area according to the warning information.

[0032] Optionally, the migration prediction result can indicate the migration path of pollutants in the target area within a 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 according to the migration prediction result, and issue a warning for the target area according to the risk level and the pollution situation.

[0033] For example, the warning information includes red warning information and orange warning information, where the urgency of the red warning information is greater than that of the orange warning information; the risk levels include low risk and high risk. When the migration prediction result indicates that the migration speed of pollutants in the target area is fast, the migration distance is long, and the concentration distribution of pollutants is high, and the risk level is high, a red warning information can be issued for the target area.

[0034] In some embodiments, the risk level of the soil pollution risk in the target area can be determined according to the soil pollution warning index and the set warning index threshold. For example, when the soil pollution warning index is greater than or equal to the warning index threshold, the risk level is determined to be high risk; when the soil pollution warning index is less than the warning index threshold, the risk level is determined to be low risk.

[0035] In the analysis and early warning method for soil heavy metal pollution provided by the embodiments of the present application, the particle uniformity of the soil in the target area is determined, and whether to trigger a deep pollution collection instruction is judged according to the particle uniformity. When the deep pollution collection instruction is triggered, the migration trend data and environmental data of pollutants at different depths of the soil in the target area are collected, so as to obtain the pollutant migration prediction result through the trained target pollution dynamic analysis model, and then the soil pollution early warning index is determined, and combined with the migration prediction result, the target area is warned. Thus, warning based on the soil pollution early warning index can improve the response speed of the warning, and warning combined with the migration prediction result can accurately analyze the pollutant migration path and achieve a rapid response to the entire process from pollution monitoring, trend warning, risk assessment to emergency disposal.

[0036] Figure 2 is a flowchart of an analysis and early warning method for soil heavy metal pollution provided by the embodiments of the present application, as Figure 2 shown, the analysis and early warning method for soil heavy metal pollution of the embodiments of the present application includes but is not limited to the following steps: S201, collect the particle size distribution data of the soil in the target area, and determine the particle size standard deviation and the average particle size of the soil according to the particle size distribution data.

[0037] In some embodiments, by deploying a plurality of monitoring sensors and sampling devices in the target area, and the sampling device collects the soil at different positions in the target area, and the monitoring sensor is used to collect the particle size distribution data of the soil, so that the particle size standard deviation and the average particle size can be determined according to the particle size distribution data.

[0038] Optionally, the particle size distribution data of the soil in the target area can be collected according to a set sampling period.

[0039] In some embodiments, the number of particles corresponding to each particle size value can be determined according to the particle size distribution data, so that the average particle size can be calculated according to different particle size values and the corresponding number of particles, and further the particle size standard deviation can be calculated according to the average particle size.

[0040] S202, perform normalization processing on the particle size standard deviation and the average particle size, and determine the particle uniformity according to the normalization result.

[0041] In some embodiments, by performing normalization processing on the particle size standard deviation and the average particle size, and taking the normalized particle size standard deviation and the average particle size as the normalization result, the particle uniformity is calculated according to the normalization result.

[0042] Optionally, the formula for calculating the particle uniformity is as follows:

[0043] Wherein, Fp represents the particle uniformity, Fsd represents the standard deviation of the particle size, and Fpz represents the average particle size.

[0044] S203. Determine the uniformity threshold.

[0045] S204. In response to the particle uniformity being less than or equal to the uniformity threshold, trigger a deep contamination collection instruction.

[0046] In some embodiments, by determining the uniformity threshold and comparing the uniformity threshold with the particle uniformity, it is determined whether to trigger a deep contamination collection instruction. Optionally, the uniformity threshold can be determined according to the instruction information received from the client. That is, the uniformity threshold is carried in the instruction information.

[0047] In some embodiments, by comparing the magnitudes of the particle uniformity and the uniformity threshold, and when the particle uniformity is less than or equal to the uniformity threshold, a deep contamination collection instruction is determined to be triggered.

[0048] S205. In response to receiving the deep contamination collection instruction, collect the migration trend data of pollutants at different depths in the soil of the target area and the environmental data of the target area.

[0049] S206. Based on the migration trend data, environmental data, and a pre-trained target pollution dynamic analysis model, determine the migration prediction results of pollutants at different depths in the soil of the target area.

[0050] S207. Based on the migration trend data, environmental data, and particle uniformity, determine the soil pollution warning index of the target area, and issue a warning for the target area based on the soil pollution warning index and the migration prediction results.

[0051] In the embodiments of the present application, the implementation manners of steps S205 - S207 can be respectively implemented by any one of the embodiments of the present application. No limitation is made herein and no further elaboration is provided.

[0052] In the analysis and warning method for soil heavy metal pollution provided by the embodiments of the present application, by obtaining the standard deviation of the particle size and the average particle size of the soil in the target area to determine the particle uniformity, the representativeness and accuracy of the soil characteristic data can be improved. Calculating the particle uniformity through the standard deviation of the particle size and the average particle size can make the determination process of the particle uniformity more applicable and realize the intelligent identification of soil heterogeneity.

[0053] Figure 3It is a flowchart of an analysis and early warning method for soil heavy metal pollution provided by an embodiment of the present application. As Figure 3 shown, the analysis and early warning method for soil heavy metal pollution in the embodiment of the present application includes but is not limited to the following steps: S301, Determine the particle uniformity of the soil in the target area, and trigger a deep pollution collection instruction based on the particle uniformity.

[0054] S302, In response to receiving the deep pollution collection instruction, collect the migration trend data of pollutants at different depths in the soil of the target area and the environmental data of the target area.

[0055] In the embodiment of the present application, the implementation manners of steps S301 - S302 can be implemented in any one of the embodiments of the present application respectively. No limitation is made herein and no further elaboration is provided.

[0056] S303, Based on the migration trend data, determine the longitudinal diffusion rate and the horizontal penetration rate of the pollutants.

[0057] In some embodiments, when receiving the deep pollution collection instruction, by collecting the content data of pollutants at different depths in the soil of the target area, the concentration distribution of pollutants in the soil at different depths can be determined according to the content data. Thus, the longitudinal diffusion rate and the horizontal penetration rate of the pollutants can be calculated as the migration trend data. That is to say, the longitudinal diffusion rate and the horizontal penetration rate of the pollutants can be obtained from the migration trend data.

[0058] S304, Based on the longitudinal diffusion rate and the horizontal penetration rate, determine the migration dynamic factor of the pollutants.

[0059] In some embodiments, by determining the depth range of the soil in the target area and integrating the longitudinal diffusion rate and the horizontal penetration rate within this depth range, the migration dynamic factor of the pollutants can be determined. Optionally, the depth range can be the depth range for collecting the migration trend data of the pollutants.

[0060] For example, collect the migration trend data of pollutants at the depth a of the soil in the target area, and collect the migration trend data of pollutants at the depth b of the soil in the target area. 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:

[0061] Among them, Mdf represents the migration dynamic factor, Zs(x) represents the longitudinal diffusion rate at the depth x, and Hs(x) represents the horizontal penetration rate at the depth x. S305, Based on the migration dynamic factor, environmental data, and particle uniformity, determine the soil pollution early warning index of the target area.

[0062] In some embodiments, the cumulative risk coefficient of pollutants can be determined by combining the migration dynamic factor and environmental data, and the soil pollution warning index of the target area can be determined according to the migration dynamic factor, the cumulative risk coefficient, and the particle uniformity.

[0063] In some embodiments, the pollutant concentration data can be obtained from the environmental data, where the pollutant concentration data includes the maximum pollutant concentration, the average pollutant concentration, and the pollutant half-life, and then the cumulative risk coefficient of the pollutants can be determined according to the pollutant concentration data and the migration dynamic factor.

[0064] Optionally, the formula for calculating the cumulative risk coefficient of pollutants is as follows:

[0065] Where 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.

[0066] Furthermore, the soil pollution warning index can be obtained by fitting the particle uniformity, the migration dynamic factor, and the cumulative risk coefficient. Optionally, the particle uniformity, the migration dynamic factor, and the cumulative risk coefficient can be linearly normalized to obtain the soil pollution warning index.

[0067] Optionally, the formula for calculating the soil pollution warning index is as follows:

[0068] Where Spwi represents the soil pollution warning index.

[0069] S306, issue a warning for the target area based on the soil pollution warning index and the migration prediction result.

[0070] In some embodiments, the soil pollution warning index can indicate the risk level of the soil pollution risk in the target area. By combining the risk level and the migration prediction result, the warning information can be clarified, and thus, a warning can be issued for the target area according to the warning information.

[0071] In some embodiments, the risk level of the soil pollution risk can be determined according to the soil pollution warning index and the warning index threshold. The set warning index threshold can be obtained by interacting with the client, and the soil pollution warning index and the warning index threshold can be compared to determine the risk level.

[0072] Optionally, the risk level can include a first risk level and a second risk level, where the first risk level is greater than the second risk level.

[0073] In some embodiments, if the soil pollution warning index is greater than or equal to the warning index threshold, it is determined that the current pollution risk of the soil is the first risk level; if the soil pollution warning index is less than the warning index threshold, it is determined that the current pollution risk of the soil is the second risk level.

[0074] Furthermore, the warning information for the target area can be determined according to the risk level and the migration prediction result.

[0075] That is to say, in response to the soil pollution warning index being greater than or equal to the warning index threshold, it is determined that the current pollution risk of the soil is the first risk level, and based on the first risk level and the migration prediction result, the first warning information is determined to warn the target area according to the first warning information.

[0076] In response to the soil pollution warning index being less than the warning index threshold, it is determined that the current pollution risk of the soil is the second risk level, and based on the second risk level and the migration prediction result, the second warning information is determined to warn the target area according to the second warning information.

[0077] Exemplarily, the soil pollution warning index is Spwi, and the warning index threshold is Z. If Spwi≥Z, it indicates that the pollution risk of the soil in the target area is relatively high. Then, according to the migration prediction result, the distribution of pollution sources can be determined, and at the same time, pollution barrier facilities and emergency repair equipment can be deployed, and the monitoring frequency of the target area can be increased. When it is found that the spread of pollutants intensifies, physical isolation or chemical repair measures can be quickly taken to control the pollution range.

[0078] If Spwi<Z, it indicates that the pollution risk of the soil in the target area is at a controllable level. Then, the quality of the soil and groundwater will continue to be monitored regularly, and pollution prevention and control drills will be carried out regularly to improve the emergency response ability and the ability of pollution source tracing analysis to ensure that the pollution risk is controllable in the long term.

[0079] In the method for analyzing and warning soil heavy metal pollution provided by the embodiments of the present application, by determining the migration dynamic factor of the pollutant, and based on the migration dynamic factor and the pollutant concentration data, the cumulative risk coefficient of the pollutant is calculated. Further, the particle uniformity, the migration dynamic factor and the cumulative risk coefficient are analyzed in association to determine the soil pollution warning index of the soil in the target area. By comparing the soil pollution warning index with the warning index threshold, the response speed of the warning can be improved.

[0080] 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, the training process of the target pollution dynamic analysis model includes but is not limited to the following steps: S401. 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 the simulation results, so as to establish an initial pollution dynamic analysis model.

[0081] In some embodiments, the migration trend data and the environmental data can be obtained from a database as the sample migration trend data and the sample environmental data, and / or the sample migration trend data and the sample environmental data are generated by artificial intelligence.

[0082] In some embodiments, based on the sample migration trend data and the sample environmental data, numerical calculation techniques can be used to simulate the migration of pollutants to obtain the sample pollutant migration path and the sample pollutant concentration distribution. Thus, 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, according to the relationship between the input and output, an initial pollution dynamic analysis model is established.

[0083] S402. Obtain the historical migration trend data and the historical environmental data of the target area as the sample input data, and the historical migration path of the pollutant and the historical pollutant concentration distribution as the sample reference results.

[0084] In some embodiments, the historical migration trend data and the historical environmental data are obtained from the historical data of the target area and used as the sample input data of the initial pollution dynamic analysis model, and the historical migration path of the pollutant and the historical pollutant concentration distribution are obtained from the historical data as the sample reference results of the output results of the initial pollution dynamic analysis model.

[0085] S403. 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.

[0086] In some embodiments, the sample input data and the sample reference results can be used as the training data of the initial pollution dynamic analysis model to train the initial pollution dynamic analysis model, so that the target pollution dynamic analysis model can be obtained.

[0087] In some embodiments, by inputting the sample input data into the initial pollution dynamic analysis model, the predicted migration path of the pollutant and the predicted pollutant concentration distribution are obtained as the sample prediction results. Furthermore, according to the sample prediction results and the sample reference results, the initial pollution dynamic analysis model can be optimized and continue to be trained until the training is completed to obtain the target pollution dynamic analysis model.

[0088] That is to say, in the process of continuously training the initial pollution dynamic analysis model based on the sample reference results and 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 sample prediction results to obtain a candidate pollution dynamic analysis model, and the candidate pollution dynamic analysis model can be continuously trained to obtain the target pollution dynamic analysis model.

[0089] In some embodiments, the candidate pollution dynamic analysis model can be trained according to the prediction results output by the candidate pollution dynamic analysis model in combination with the sample input data. The prediction results output by the candidate pollution dynamic analysis model can be obtained, and the feature information related to pollution migration can be extracted from the prediction results. For example, the sample input data is input into the candidate pollution dynamic analysis model to output the prediction results, and information such as the diffusion range of pollutants, the concentration change gradient, the migration directionality, and the migration rate is extracted from the prediction results as the feature information.

[0090] Furthermore, a data set is established based on the feature information and the sample input data, and the candidate pollution dynamic analysis model is trained based on the data set to obtain the target pollution dynamic analysis model. That is to say, the data set at least includes historical migration trend data, historical environmental data, and feature information.

[0091] Optionally, through the candidate pollution dynamic analysis model, based on the sample input data, the prediction result corresponding to the sample input data can be determined, and according to the prediction result and the feature information, the loss function of the candidate pollution dynamic analysis model can be determined, and the candidate pollution dynamic analysis model can be optimized based on the loss function to obtain the target pollution dynamic analysis model.

[0092] In the analysis and early warning method for soil heavy metal pollution provided by the embodiments of the present application, by using numerical simulation to establish an initial pollution dynamic analysis model and training the initial pollution dynamic analysis model in combination with migration trend data and environmental data to obtain the target pollution dynamic analysis model, the migration path of pollutants can be accurately quantified, and the dynamic simulation of the migration path of pollutants can be realized.

[0093] Figure 5 Shown is a schematic flow chart of the early warning method for soil heavy metal pollution. As Figure 5As 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 a 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 environmental data, through the target pollution dynamic analysis model, the migration prediction result of the pollutants is obtained; based on the migration trend data, environmental data and particle uniformity, the soil pollution warning index is determined, and based on the soil pollution warning index and the migration prediction result, early warning is carried out on the target area.

[0094] Corresponding to the above-mentioned analysis and early warning methods for soil heavy metal pollution proposed in several embodiments, an embodiment of the present application also proposes an analysis and early warning system for soil heavy metal pollution. Since the analysis and early warning system for soil heavy metal pollution proposed in the embodiment of the present application corresponds to the above-mentioned analysis and early warning methods for soil heavy metal pollution proposed in several embodiments, the implementation manners of the above-mentioned analysis and early warning methods for soil heavy metal pollution are also applicable to the analysis and early warning system for soil heavy metal pollution proposed in the embodiment of the present application, and will not be described in detail in the following embodiments.

[0095] To implement the above embodiments, the present application also proposes an analysis and early warning system for soil heavy metal pollution.

[0096] Figure 6 It is a schematic structural diagram of an analysis and early warning system for soil heavy metal pollution provided by an embodiment of the present application.

[0097] As Figure 6 shown, the analysis and early warning system 600 for soil heavy metal pollution includes: A particle analysis module 601, configured 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 602, configured to collect the migration trend data of pollutants at different depths of the soil in the target area and the environmental data of the target area in response to receiving the deep pollution collection instruction; A dynamic evaluation module 603, configured to determine the migration prediction result of pollutants at different depths of the soil in the target area based on the migration trend data and environmental data, and a pre-trained target pollution dynamic analysis model; A prevention and control early warning module 604, configured to determine the soil pollution warning index of the target area based on the migration trend data, environmental data and particle uniformity, and carry out early warning on the target area based on the soil pollution warning index and the migration prediction result.

[0098] In a possible implementation manner of the embodiment of the present application, the particle analysis module 601 is further configured to: collect the particle size distribution data of the soil in the target area, and determine the standard deviation and average value of the particle size of the soil according to the particle size distribution data; perform normalization processing on the standard deviation and average value of the particle size, and determine the particle uniformity according to the normalization result.

[0099] In a possible implementation manner of the embodiment of the present application, the particle analysis module 601 is further configured to: determine a uniformity threshold; in response to the particle uniformity being less than or equal to the uniformity threshold, trigger a deep pollution collection instruction.

[0100] In a possible implementation manner of the embodiment of the present application, the dynamic evaluation module 603 is further configured to: obtain sample migration trend data and sample environment data, and perform numerical simulation based on the sample migration trend data and sample environment data to obtain the sample pollutant migration path and sample pollutant concentration distribution as simulation results, so as to establish an initial pollution dynamic analysis model; obtain the historical migration trend data and historical environment data of the target area as sample input data, and the historical migration path and historical pollutant concentration distribution of the pollutant as sample reference results; train the initial pollution dynamic analysis model based on the sample input data and sample reference results to obtain a target pollution dynamic analysis model.

[0101] In a possible implementation manner of the embodiment of the present application, the dynamic evaluation module 603 is further configured to: input the sample input data into the initial pollution dynamic analysis model to obtain the predicted migration path and predicted pollutant concentration distribution of the pollutant as sample prediction results; continue to train the initial pollution dynamic analysis model based on the sample reference results and sample prediction results to obtain a target pollution dynamic analysis model.

[0102] In a possible implementation manner of the embodiment of the present application, the dynamic evaluation module 603 is further configured to: optimize the initial pollution dynamic analysis model based on the sample reference results and 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 the 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.

[0103] In a possible implementation manner of the embodiment of the present application, the prevention and control warning module 604 is further configured to: determine the longitudinal diffusion rate and horizontal penetration rate of the pollutant based on the migration trend data; determine the migration dynamic factor based on the longitudinal diffusion rate and horizontal penetration rate; determine the soil pollution warning index of the target area based on the migration dynamic factor, environmental data and particle uniformity.

[0104] In a possible implementation manner of the embodiment of the present application, the prevention and control early warning module 604 is further configured to: obtain pollutant concentration data from environmental data; determine the cumulative risk coefficient of the pollutant according to the pollutant concentration data and the migration dynamic factor; perform linear normalization processing on the particle uniformity, the migration dynamic factor, and the cumulative risk coefficient to obtain a soil pollution early warning index.

[0105] In a possible implementation manner of the embodiment of the present application, the prevention and control early warning module 604 is further configured to: obtain a set early warning index threshold; in response to the soil pollution early warning index being greater than or equal to the early warning index threshold, determine that the current pollution risk of the soil is the first risk level, and based on the first risk level and the migration prediction result, determine the first early warning information 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, determine that the current pollution risk of the soil is the second risk level, and based on the second risk level and the migration prediction result, determine the second early warning information 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.

[0106] In the analysis and early warning system for soil heavy metal pollution provided by the embodiment of the present application, the particle uniformity of the soil in the target area is determined, and whether to trigger a deep pollution collection instruction is judged according to the particle uniformity. When the deep pollution collection instruction is triggered, the migration trend data and environmental data of pollutants at different depths of the soil in the target area are collected, so as to obtain the migration prediction result of the pollutants through the trained target pollution dynamic analysis model. Thus, by determining the soil pollution early warning index and combining the migration prediction result, an early warning is issued to the target area. Therefore, issuing an early warning based on the soil pollution early warning index can improve the response speed of the early warning, and issuing an early warning in combination with the migration prediction result can accurately analyze the migration path of the pollutants and achieve a rapid response to the entire process from pollution monitoring, trend early warning, risk assessment to emergency disposal.

[0107] It should be noted that the foregoing explanation of the embodiment of the analysis and early warning method for soil heavy metal pollution also applies to the analysis and early warning system for soil heavy metal pollution of this embodiment, and will not be elaborated here.

[0108] To implement the above embodiment, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiment. To implement the above embodiment, the present application also proposes a computer-readable storage medium storing computer execution instructions, and the computer execution instructions are used to implement the method provided in the foregoing embodiment when executed by a processor.

[0109] To implement the above embodiments, the present application further provides a computer program product, including a computer program which, when executed by a processor, implements the method provided in the foregoing embodiments.

[0110] The collection, storage, use, processing, transmission, provision, and application, etc., of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0111] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of these legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps need to be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0112] The present application is expected to provide an implementation plan for users to selectively block the use or access of personal information data. That is, the present application is expected to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0113] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection 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 can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0114] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0115] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present application includes additional implementations, where functions may be performed in a substantially simultaneous manner or in an order opposite to that shown or discussed, according to the functions involved, which should be understood by those skilled in the technical field to which the embodiments of the present application pertain.

[0116] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be 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 (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0117] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0118] Those of ordinary skill in the art can understand that all or part of the steps carried out in the methods of the above embodiments can be completed by instructing relevant 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 embodiments.

[0119] In addition, in each of the embodiments of the present application, the functional units can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0120] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An analysis and early warning method for heavy metal pollution in soil, characterized in that The method includes: Determine the particle uniformity of the soil in the target area, and trigger a deep pollution collection instruction based on the particle uniformity; In response to receiving the deep pollution collection instruction, collect the migration trend data of pollutants at different depths in the soil of the target area and the environmental data of the target area; Based on the migration trend data, the environmental data, and a pre-trained target pollution dynamic analysis model, determine the migration prediction results of pollutants at different depths in the soil of the target area; Based on the migration trend data, the environmental data, and the particle uniformity, determine the soil pollution warning index of the target area, and issue a warning for the target area based on the soil pollution warning index and the migration prediction results.

2. The method according to claim 1, characterized in that, The determination of the particle uniformity of the soil in the target area includes: Collect the particle size distribution data of the soil in the target area, and determine the standard deviation of the particle size and the average particle size of the soil according to the particle size distribution data; Perform normalization processing on the standard deviation of the particle size and the average particle size, and determine the particle uniformity according to the normalization result.

3. The method according to claim 2, wherein 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, trigger the deep pollution collection instruction.

4. The method according to claim 1, wherein The training process of the target pollution dynamic analysis model includes: 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 the simulation result, so as 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 of pollutants and the historical pollutant concentration distribution as sample reference results; Based on the sample input data and the sample reference results, train the initial pollution dynamic analysis model to obtain the target pollution dynamic analysis model.

5. The method according to claim 4, wherein The training of 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 includes: Input the sample input data into the initial pollution dynamic analysis model to obtain the predicted migration path of the 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.

6. The method according to claim 5, wherein The training of 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 includes: 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 the feature 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 dynamic analysis model is trained based on the data set to obtain the target pollution dynamic analysis model.

7. The method according to claim 1, characterized in that, Determining the soil pollution warning index of the target area based on the migration trend data, the environmental data, and the particle uniformity includes: Based on the migration trend data, determining the longitudinal diffusion rate and the horizontal penetration rate of the pollutant; Based on the longitudinal diffusion rate and the horizontal penetration rate, determining the migration dynamic factor of the pollutant; Based on the migration dynamic factor, the environmental data, and the particle uniformity, determining the soil pollution warning index of the target area.

8. The method according to claim 7, characterized in that Determining the soil pollution 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; Based on the pollutant concentration data and the migration dynamic factor, determining the cumulative risk coefficient of the pollutant; Performing linear normalization processing on the particle uniformity, the migration dynamic factor, and the cumulative risk coefficient to obtain the soil pollution warning index.

9. The method according to any one of claims 1 - 8, characterized in that Warning the target area based on the soil pollution warning index and the migration prediction result includes: Obtaining a set warning index threshold; In response to the soil pollution warning index being greater than or equal to the warning index threshold, determining that the current pollution risk of the soil is the first risk level, and based on the first risk level and the migration prediction result, determining the first warning information to warn the target area according to the first warning information; In response to the soil pollution warning index being less than the warning index threshold, determining that the current pollution risk of the soil is the second risk level, and based on the second risk level and the migration prediction result, determining the second warning information to warn the target area according to the second warning information; Wherein, the first risk level is greater than the second risk level.

10. An analysis and early warning system for soil heavy metal pollution, characterized in that, The system includes: A particle analysis module, configured 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 the migration trend data of pollutants at different depths in the soil of the target area and the environmental data of the target area in response to receiving the deep pollution collection instruction; A dynamic evaluation module, configured to determine the migration prediction result of pollutants at different depths in the soil of the target area based on the migration trend data, the environmental data, and a pre-trained target pollution dynamic analysis model; A prevention and control warning module, configured to determine the soil pollution warning index of the target area based on the migration trend data, the environmental data, and the particle uniformity, and warn the target area based on the soil pollution warning index and the migration prediction result.

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