An intelligent detection method and system for soil heavy metal pollution

Abstract

The present invention relates to the technical field of soil pollution detection, and particularly to an intelligent detection method and system for soil heavy metal pollution. The method includes: collecting soil multi-source information and regional environmental parameters of the soil to be tested, and identifying soil pollution characteristics according to the soil multi-source information; establishing a pollution identification analysis model based on the soil pollution characteristics and regional environmental parameters to analyze whether the soil to be tested contains heavy metal pollutants; if heavy metal pollutants are contained, predicting the pollution diffusion path of the heavy metal pollutants in time and space according to the pollution identification analysis model; if no heavy metal pollutants are contained, performing a time series analysis on the soil pollution characteristics, monitoring and analyzing the future concentration distribution of the heavy metal pollutants, and establishing a pollution risk warning. Through the present invention, the problems in the traditional soil heavy metal detection method, such as long sampling and analysis time, inability to dynamically monitor the change of pollutant concentration in real time, insufficient accuracy of diffusion prediction, and difficulty in comprehensively evaluating high-concentration and potential risk areas, are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution detection, and particularly to an intelligent detection method and system for soil heavy metal pollution. Background Art

[0002] Soil heavy metal pollution has become a major challenge in environmental governance due to its high toxicity, non-degradability, and persistence. Traditional detection methods mainly rely on laboratory chemical analysis techniques. Although they have high detection accuracy, due to the need for manual sampling and complex experimental operations, the efficiency is low, making it difficult to meet the requirements of large-scale rapid monitoring.

[0003] In addition, most of the existing technologies remain in the static monitoring stage, lacking the ability to dynamically monitor the changes in pollutant concentration over time and space. At the same time, the diffusion prediction is limited by static assumptions and fails to fully consider dynamic environmental factors such as groundwater flow velocity, terrain, and meteorological conditions, resulting in insufficient prediction accuracy and risk assessment capabilities.

[0004] The information disclosed in this background art section is only intended to deepen the understanding of the overall background art of the present disclosure and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides an intelligent detection method and system for soil heavy metal pollution, which can effectively solve the problems in the background art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] An intelligent detection method for soil heavy metal pollution, the method comprising:

[0008] Collecting soil multi-source information and regional environmental parameters of the soil to be tested, and identifying soil pollution characteristics according to the soil multi-source information;

[0009] Establishing a pollution identification analysis model according to the soil pollution characteristics and the regional environmental parameters, and analyzing whether the soil to be tested contains heavy metal pollutants;

[0010] If it contains the heavy metal pollutants, predicting the pollution diffusion path of the heavy metal pollutants in time and space according to the pollution identification analysis model;

[0011] If it does not contain the heavy metal pollutants, performing a time series analysis on the soil pollution characteristics, monitoring and analyzing the future concentration distribution of the heavy metal pollutants, and establishing a pollution risk warning.

[0012] Further, analyzing whether the soil to be tested contains heavy metal pollutants includes:

[0013] Analyze the multi-source information of the soil, and extract the soil pollution characteristics including soil physical parameters, chemical components, and trace heavy metal characteristics;

[0014] Pull the time period sequence, conduct fusion analysis based on the soil pollution characteristics obtained within the time period sequence, and set the heavy metal pollution threshold according to the results of the fusion analysis;

[0015] Combine the regional environmental parameters, correct the heavy metal pollution threshold, and feedback it to the pollution identification analysis model;

[0016] Input the soil pollution characteristics into the pollution identification analysis model, calculate the distribution of the heavy metal pollutants and the concentration values of the heavy metal pollutants, and determine whether the soil to be tested contains the heavy metal pollutants according to the heavy metal pollution threshold.

[0017] Further, calculating the distribution of the heavy metal pollutants and the concentration values of the heavy metal pollutants includes:

[0018] Divide the area of the soil to be tested into several spatial units, and each spatial unit corresponds to the soil multi-source information and the regional environmental parameters;

[0019] Input the soil pollution characteristics of each spatial unit into the pollution identification analysis model, and calculate the concentration values of the heavy metal pollutants in each spatial unit;

[0020] Perform interpolation processing on the concentration values of each calculated spatial unit to generate the pollution concentration distribution of the heavy metal pollutants in the area of the soil to be tested;

[0021] Extract the distribution and pollution degree of the heavy metal pollutants based on the pollution concentration distribution.

[0022] Further, performing interpolation processing on the concentration values of each calculated spatial unit includes:

[0023] Analyze the spatial correlation between spatial units, and construct a semi-variogram according to the spatial correlation to describe the spatial variation law of the concentration values;

[0024] Set the target interpolation points, calculate the weights of each spatial unit around the target interpolation points according to the semi-variogram, and the weights are dynamically adjusted according to the spatial correlation;

[0025] According to the weights, weighted calculate the concentration values of the heavy metal pollutants corresponding to the spatial units to the target interpolation points to generate the interpolation concentration of the heavy metal pollutants in the target area;

[0026] Integrate the interpolation concentrations of the target interpolation points to generate the concentration distribution of heavy metal pollutants, and mark the spatial distribution range of heavy metal pollutants and the high-pollution risk areas.

[0027] Further, predict the pollution diffusion path of the heavy metal pollutants in time and space according to the pollution identification and analysis model, including:

[0028] Based on the soil pollution characteristics and the regional environmental parameters, set the initial regional boundary conditions for pollution diffusion;

[0029] According to the initial regional boundary conditions, delimit the initial pollution diffusion area, and identify the location of the heavy metal pollution diffusion source within the initial pollution diffusion area;

[0030] According to the initial pollution diffusion area, collect the environmental driving factors related to pollution diffusion;

[0031] Combining the initial pollution diffusion area and the environmental driving factors, starting from the location of the heavy metal pollution diffusion source and using the time series as an auxiliary condition, predict the pollution diffusion path of the heavy metal pollutants.

[0032] Further, set the initial regional boundary conditions for pollution diffusion, including:

[0033] Based on the sampling locations of the soil to be measured and their surrounding geographical information, delimit the spatial range of the initial pollution diffusion area;

[0034] According to the soil pollution characteristics within the spatial range of the initial pollution diffusion area, set the boundary values and distribution gradients of the heavy metal pollutant concentrations;

[0035] According to the regional environmental parameters and the distribution gradient, set the key environmental boundary conditions within the initial pollution diffusion area;

[0036] Summarize and generate the initial regional boundary conditions according to the key environmental boundary conditions and the boundary values and distribution gradients of the heavy metal pollutant concentrations.

[0037] Further, monitor and analyze the future concentration distribution of the heavy metal pollutants, including:

[0038] Based on historical monitoring data and real-time sampling data, construct a pollution time-series concentration database, which includes pollutant concentration values, sampling times, sampling locations, and the regional environmental parameters;

[0039] Extract the time-series change characteristics of the concentrations of the heavy metal pollutants from the pollution time-series concentration database;

[0040] According to the described temporal variation characteristics, a future concentration distribution prediction model is established in combination with the pollution diffusion path;

[0041] The historical monitoring data and the real-time sampling data included in the pollution temporal concentration database are input into the future concentration distribution prediction model to generate the future concentration distribution of the heavy metal pollutants at different time points;

[0042] Based on the future concentration distribution, high-concentration regions and potential risk regions are evaluated and stored in the pollution temporal concentration database.

[0043] Further, evaluating high-concentration regions and potential risk regions includes:

[0044] Performing a fusion analysis on the soil pollution characteristics, and setting a heavy metal pollution threshold according to the results of the fusion analysis;

[0045] Comparing the concentration values of the heavy metal pollutants generated by the future concentration distribution prediction model with the heavy metal pollution threshold to calibrate the high-concentration regions exceeding the heavy metal pollution threshold;

[0046] Combining the regional environmental parameters and the pollution diffusion path, analyzing the surrounding geology, hydrology and meteorological conditions of the high-concentration regions to generate a high-concentration region distribution map;

[0047] According to the high-concentration region distribution map and the soil pollution characteristics, delimit the potential risk regions affected by the pollution and generate a pollution diffusion risk region map.

[0048] An intelligent detection system for soil heavy metal pollution, the system includes:

[0049] An information collection module that collects the soil multi-source information and regional environmental parameters of the soil to be measured, and identifies the soil pollution characteristics according to the soil multi-source information;

[0050] A pollution analysis module that establishes a pollution identification and analysis model according to the soil pollution characteristics and regional environmental parameters, and analyzes whether the soil to be measured contains heavy metal pollutants;

[0051] A path identification module. If heavy metal pollutants are contained, the pollution diffusion path of the heavy metal pollutants in time and space is predicted according to the pollution identification and analysis model;

[0052] A temporal monitoring module. If heavy metal pollutants are not contained, temporal analysis of the soil pollution characteristics is performed to monitor and analyze the future concentration distribution of the heavy metal pollutants and establish a pollution risk warning.

[0053] Further, the pollution analysis module includes:

[0054] A feature analysis unit analyzes multi-source soil information and extracts soil pollution features including soil physical parameters, chemical compositions, and trace heavy metal features.

[0055] A time-series fusion unit pulls a time-period sequence, conducts fusion analysis based on the soil pollution features obtained within the time-period sequence, and sets a heavy metal pollution threshold according to the results of the fusion analysis.

[0056] A threshold correction unit combines regional environmental parameters, corrects the heavy metal pollution threshold, and feeds it back to the pollution identification and analysis model.

[0057] A pollution judgment unit inputs the soil pollution features into the pollution identification and analysis model, calculates the distribution of heavy metal pollutants and the concentration values of heavy metal pollutants, and determines whether the soil to be tested contains heavy metal pollutants according to the heavy metal pollution threshold.

[0058] Through the technical solution of the present invention, the following technical effects can be achieved:

[0059] By integrating multi-source information, dynamic monitoring, and diffusion prediction technologies, the problems of low detection efficiency, lack of dynamic monitoring, accurate diffusion prediction, and risk assessment ability in traditional methods are solved, effectively improving the efficiency and intelligent level of soil heavy metal pollution detection.

[0060] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically given. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0062] Figure 1 It is a schematic flowchart of a method for intelligent detection of soil heavy metal pollution;

[0063] Figure 2 It is a schematic flowchart for analyzing whether the soil to be tested contains heavy metal pollutants;

[0064] Figure 3 It is a schematic structural diagram for generating a pollution diffusion path;

[0065] Figure 4 It is a schematic structural diagram for monitoring future concentration distribution. Detailed implementation manners

[0066] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the description of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0068] Embodiment 1;

[0069] As Figure 1 shown, the present application provides an intelligent detection method for soil heavy metal pollution. The method includes:

[0070] S10: Collect the multi-source information of the soil to be tested and the regional environmental parameters, and identify the soil pollution characteristics according to the multi-source information of the soil;

[0071] S20: Establish a pollution identification analysis model according to the soil pollution characteristics and the regional environmental parameters, and analyze whether the soil to be tested contains heavy metal pollutants;

[0072] S30: If heavy metal pollutants are contained, predict the pollution diffusion path of the heavy metal pollutants in time and space according to the pollution identification analysis model;

[0073] S40: If no heavy metal pollutants are contained, perform a time series analysis on the soil pollution characteristics, monitor and analyze the future concentration distribution of the heavy metal pollutants, and establish a pollution risk warning.

[0074] Specifically, the multi-source information of the soil sample to be measured includes the physical properties of the soil (such as permeability, porosity, water content) and chemical compositions (such as organic matter content, pH value, heavy metal concentration). Regional environmental parameters include meteorological data related to pollution diffusion (such as precipitation, wind speed, wind direction), hydrological conditions (such as groundwater flow velocity, flow direction, water level), and topographic and geomorphic characteristics (such as slope, surface undulation), which can be collected in real time through devices such as sensor networks, remote sensing technologies, and monitoring stations. Some historical data can be obtained from environmental monitoring databases; by analyzing the multi-source information of the collected soil, key characteristics related to heavy metal pollution are extracted, including heavy metal concentration values, spatial distribution characteristics, and change trends. Data preprocessing methods (such as denoising, normalization) are used to ensure the effectiveness and consistency of the data. Based on the extracted soil pollution characteristics, it is initially judged whether there are abnormal phenomena in the soil pollution to provide support for subsequent modeling; based on the soil pollution characteristics and regional environmental parameters, a pollution identification and analysis model is established using machine learning algorithms (such as random forest, support vector machine) or statistical analysis methods. The pollution identification and analysis model is used to analyze whether the soil to be measured contains heavy metal pollutants, and to judge the types, concentrations, and distribution characteristics of the pollutants. For the soil identified as containing heavy metal pollutants, it enters the pollution diffusion path prediction step. For the soil without pollutants, it enters the time series analysis and risk warning step; after confirming that the soil to be measured contains heavy metal pollutants, the diffusion path of the pollutants is predicted according to the pollution identification and analysis model. First, the initial regional boundary conditions of pollution diffusion are set, including the source location, initial concentration value, and regional environmental characteristics. Secondly, combined with regional environmental parameters (such as groundwater flow velocity, terrain slope) and dynamic time series, the diffusion path of heavy metal pollutants in time and space is predicted through a pollution diffusion model (such as a numerical simulation model based on Kriging interpolation or finite element method), and a pollution distribution map and diffusion trend map are generated.

[0075] Through the technical solution of the present invention, the problems of low detection efficiency, lack of dynamic monitoring, accurate diffusion prediction, and risk assessment ability in the traditional method are solved, and the detection efficiency and intelligent level of soil heavy metal pollution are effectively improved.

[0076] Furthermore, as Figure 2 shown, analyzing whether the soil to be measured contains heavy metal pollutants includes:

[0077] Analyzing the multi-source information of the soil, and extracting soil pollution characteristics including soil physical parameters, chemical compositions, and trace heavy metal characteristics;

[0078] Pulling the time period sequence, performing fusion analysis according to the soil pollution characteristics obtained within the time period sequence, and setting the heavy metal pollution threshold according to the fusion analysis result;

[0079] Combine regional environmental parameters to correct the heavy metal pollution threshold and feedback it to the pollution identification and analysis model;

[0080] Input the soil pollution characteristics into the pollution identification and analysis model, calculate the distribution of heavy metal pollutants and the concentration values of heavy metal pollutants, and judge whether the soil to be tested contains heavy metal pollutants according to the heavy metal pollution threshold.

[0081] As an optimization of the above embodiment, collect the multi-source soil information of the soil to be tested, extract the soil pollution characteristics including soil physical parameters, chemical components and trace heavy metal characteristics. The soil physical parameters include the permeability, porosity and water content of the soil, the chemical components include the pH value and organic matter content of the soil, and the trace heavy metal characteristics include the concentration values of heavy metal pollutants (such as cadmium, lead, mercury, etc.) in the soil. After the collection is completed, analyze the above soil pollution characteristics and extract the key indicators that can characterize the pollution characteristics, such as the concentration distribution of heavy metal pollutants and the soil chemical structure; pull the time-series data of the soil to be tested to obtain the soil pollution characteristics including different time points. The time-series data includes historical monitoring data and real-time sampling data, where the historical data is extracted from the database and the real-time data is collected by on-site sensors. Conduct a fusion analysis on the changes in the soil pollution characteristics in the time dimension in the time series, extract the change rules of heavy metal concentrations, and based on the results of the fusion analysis, combine the national or regional soil heavy metal pollution limit standards to set the heavy metal pollution threshold for distinguishing polluted soil from non-polluted soil; correct the set heavy metal pollution threshold in combination with the regional environmental parameters of the area where the soil to be tested is located. The regional environmental parameters include the meteorological conditions (such as precipitation, wind speed), hydrological conditions (such as groundwater flow velocity, flow direction) and topographic and geomorphic characteristics (such as slope) of the area. Input the above regional environmental parameters into the correction model to correct the heavy metal pollution threshold based on the regional environmental parameters, generate the corrected heavy metal pollution threshold applicable to the area, and feedback the corrected heavy metal pollution threshold to the pollution identification and analysis model to improve the adaptability of the model to complex environments; input the extracted soil pollution characteristics into the pollution identification and analysis model, and in combination with the corrected heavy metal pollution threshold, analyze whether the soil to be tested contains heavy metal pollutants. The pollution identification and analysis model calculates the distribution and concentration values of pollutants based on the input soil pollution characteristics, and compares the calculation results with the corrected heavy metal pollution threshold. If the concentration value of a certain heavy metal pollutant in the soil sample exceeds the corrected pollution threshold, it is determined that the soil contains heavy metal pollutants; if the concentration values of all heavy metal pollutants are lower than the threshold, it is determined that the soil does not contain heavy metal pollutants.

[0082] Furthermore, calculating the distribution of heavy metal pollutants and the concentration values of heavy metal pollutants includes:

[0083] Divide the area of the soil to be measured into several spatial units, and each spatial unit corresponds to multi-source soil information and regional environmental parameters;

[0084] Input the soil pollution characteristics of each spatial unit into the pollution identification analysis model to calculate the concentration values of heavy metal pollutants in each spatial unit;

[0085] Perform interpolation processing on the calculated concentration values of each spatial unit to generate the pollution concentration distribution of heavy metal pollutants in the area of the soil to be measured;

[0086] Extract the distribution and pollution degree of heavy metal pollutants based on the pollution concentration distribution.

[0087] As an optimization of the above embodiment, divide the area of the soil to be measured into several spatial units to ensure that each spatial unit has sufficient data support. The division of spatial units can be based on the geographical information of the area, such as using the regular grid division method (such as equidistant grid) or adaptive grid division according to the distribution of terrain features. For each spatial unit, extract the corresponding multi-source soil information (such as permeability, porosity, water content, etc.) and regional environmental parameters (such as groundwater flow velocity, meteorological data, terrain slope, etc.) to ensure the data integrity and accuracy of each unit; input the soil pollution characteristics (such as heavy metal concentration values, distribution gradients) extracted from each spatial unit into the pollution identification analysis model, and combine the corrected heavy metal pollution threshold to calculate the concentration values of heavy metal pollutants in each unit. The pollution identification analysis model can be based on multiple regression analysis or machine learning algorithms (such as random forest model or support vector machine model) to process the input feature data and output the concentration values and distribution characteristics of heavy metal pollutants in each spatial unit; perform interpolation processing on the calculated concentration values of heavy metal pollutants in each spatial unit to generate the pollution concentration distribution map of the entire area to be measured. The interpolation processing can use the Kriging interpolation method, a spatial interpolation algorithm, to generate a more accurate pollution concentration distribution result by analyzing the variability between spatial units. The specific steps of interpolation include constructing a variogram based on the known unit concentration values, determining the correlation between spatial units, calculating the concentration value of the interpolation point through weight allocation, and generating a complete pollution concentration distribution to show the concentration distribution of heavy metal pollutants in the entire area; based on the generated pollution concentration distribution, extract the distribution and pollution degree of heavy metal pollutants. The distribution includes the range and diffusion path of pollutants in space, and the pollution degree includes the maximum value, average value of the concentration value and the distribution characteristics of the exceeded standard area. Combine the environmental standards and heavy metal pollution threshold to mark the high pollution risk areas and generate the corresponding pollution distribution map and pollution degree report to provide a scientific basis for pollution control.

[0088] Furthermore, performing interpolation processing on the calculated concentration values of each spatial unit includes:

[0089] Analyze the spatial correlation between spatial units, and construct a semi-variogram according to the spatial correlation to describe the spatial variation law of concentration values;

[0090] Set the target interpolation points, calculate the weights of each spatial unit around the target interpolation points according to the semi-variogram, and the weights are dynamically adjusted according to the spatial correlation;

[0091] According to the weights, weight and calculate the heavy metal pollutant concentration values corresponding to the spatial units to the target interpolation points, and generate the interpolated concentration of heavy metal pollutants in the target area;

[0092] Integrate the interpolated concentrations of the target interpolation points to generate the concentration distribution of heavy metal pollutants, and mark the spatial distribution range and high pollution risk areas of heavy metal pollutants.

[0093] As a preference of the above embodiment, use the heavy metal pollutant concentration values of each spatial unit that have been calculated and their corresponding spatial units as the basic data for interpolation processing. By statistically analyzing the changes in the heavy metal pollution concentration values of each spatial unit, study the correlation with the change of distance, so as to identify the distribution law of heavy metal pollution concentration values in space. According to the results of spatial correlation analysis, construct a semi-variogram describing the change of concentration values between spatial units. The semi-variogram is used to quantify the concentration difference between spatial units and its change law with the increase of distance. Specifically, as the distance between spatial units increases, the correlation between concentration values gradually weakens until it reaches a state of irrelevance; according to the boundary of the area to be measured and the distribution of spatial units, select the unmeasured points as the target interpolation points to ensure that the interpolation points evenly cover the area to be measured. According to the semi-variogram, calculate the weight value of each spatial unit around the target interpolation point. The weight value is directly related to the distance between the spatial unit and the target interpolation point and the correlation. Units with a closer distance or stronger spatial correlation have higher weights, while units with a farther distance or weaker correlation have lower weights. The weight value will be dynamically adjusted to adapt to the correlation changes between different spatial positions and units; weight and sum the heavy metal pollutant concentration values of the spatial units around the target interpolation point according to their weight values, and finally obtain the concentration value of the target interpolation point. The logic of weighted calculation is that the concentration value of the target interpolation point is jointly determined by the concentration values of the surrounding spatial units and their corresponding weights. Units with higher weights have a greater impact on the result, and units with lower weights have a smaller impact; integrate the concentration values of all target interpolation points to generate the concentration distribution of heavy metal pollutants, which is used to display the pollution concentration in the entire area to be measured and provide complete spatial distribution information. According to the generated concentration distribution, combined with the pre-set heavy metal pollution threshold, mark the high pollution risk areas. Any area where the concentration value of the target interpolation point exceeds the threshold will be marked as a high pollution risk area and highlighted in the pollution distribution map.

[0094] Furthermore, as Figure 3As shown, predicting the pollution diffusion path of heavy metal pollutants in time and space according to the pollution identification and analysis model, including:

[0095] Based on the soil pollution characteristics and regional environmental parameters, set the initial regional boundary conditions for pollution diffusion;

[0096] According to the initial regional boundary conditions, delimit the initial pollution diffusion area, and identify the location of heavy metal pollution diffusion sources within the initial pollution diffusion area;

[0097] According to the initial pollution diffusion area, collect environmental driving factors related to pollution diffusion;

[0098] Combining the initial pollution diffusion area and environmental driving factors, starting from the location of heavy metal pollution diffusion sources and using the time series as an auxiliary condition, predict the pollution diffusion path of heavy metal pollutants.

[0099] As a preference of the above embodiments, based on the soil pollution characteristics and regional environmental parameters of the soil to be tested, analyze the initial concentration, distribution range and environmental impact factors of pollutants. The soil pollution characteristics include the concentration values and spatial distribution characteristics of heavy metal pollutants, and the regional environmental parameters include groundwater flow velocity, terrain slope, meteorological conditions, etc. According to the pollution characteristics and regional environmental parameters, determine the initial regional boundary conditions for pollution diffusion. The initial regional boundary conditions specifically include the initial value of pollutant concentration, hydrodynamic conditions within the boundary range (such as groundwater flow velocity and direction), and terrain and meteorological characteristics of the boundary range. The initial regional boundary conditions provide an initial data basis for predicting the pollution diffusion path; based on the set initial regional boundary conditions, combined with the spatial distribution characteristics of pollutants and environmental parameters, delimit the initial regional range of pollution diffusion. The initial regional range usually includes the key pollution areas around the soil sample to be tested. Within the initial pollution diffusion region, locate the diffusion source position of heavy metal pollutants through a pollution identification analysis model. The identification of the diffusion source position is based on the distribution law of pollutant concentration, and select the position with the highest concentration value as the potential diffusion source, providing a starting point for predicting the diffusion path; within the delimited initial regional range, collect the environmental driving factors affecting pollution diffusion, and integrate the environmental driving factors with the boundary conditions of the initial pollution diffusion region to provide multi-dimensional data input for predicting the diffusion path; combine the diffusion source position, initial regional boundary conditions and environmental driving factors to establish a diffusion path model for heavy metal pollutants. The construction of the model can be based on numerical simulation methods (such as finite difference method or finite element method) or pollutant migration simulation algorithms (such as Darcy's law). By introducing time series analysis, combine the diffusion path model with the time dimension to simulate the diffusion of heavy metal pollutants at different times. The time series data includes the change of pollutant concentration over time and the dynamic expansion of the diffusion range. Starting from the diffusion source position, simulate the pollution diffusion path of pollutants in time and space, and generate a pollution diffusion trend map. The trend map shows the diffusion range, concentration distribution and diffusion speed of pollutants in the region.

[0100] Furthermore, setting the initial regional boundary conditions for pollution diffusion includes:

[0101] Based on the sampling position of the soil to be tested and its surrounding geographical information, delimit the spatial range of the initial pollution diffusion region;

[0102] According to the soil pollution characteristics within the spatial range of the initial pollution diffusion region, set the boundary value and distribution gradient of heavy metal pollutant concentration;

[0103] According to the regional environmental parameters and distribution gradient, set the key environmental boundary conditions within the initial pollution diffusion region;

[0104] Summarize and generate the initial regional boundary conditions according to the key environmental boundary conditions and the boundary value and distribution gradient of heavy metal pollutant concentration.

[0105] Preferably, based on the sampling location of the soil sample to be tested, the precise location coordinates of the sampling point and the geographical environment characteristics around it are obtained by combining geographical information. Taking the sampling location as the center, according to the possible diffusion range and geographical characteristics of pollutants in the area to be tested, the spatial range of the initial pollution diffusion area is delimited. The delimitation of the spatial range is carried out by combining the distribution characteristics of heavy metal concentrations, setting a certain diffusion radius, considering the restrictions of regional terrain conditions (such as natural barriers like mountains and rivers) on the diffusion path, adjusting the shape of the spatial range, and optimizing the initial area range by integrating historical monitoring data and environmental parameters to ensure that potential pollution diffusion paths are covered. Within the delimited initial area, combining the sampling point data and the distribution characteristics of heavy metal pollutant concentrations in the area, the boundary values of pollutant concentrations are set. The setting of the boundary values can refer to national or regional soil environmental quality standards or can be dynamically adjusted according to actual monitoring data. Based on the spatial distribution law of heavy metal concentrations within the initial area, the gradient change of concentration values diffusing outward from the pollution source location is analyzed. By quantitatively describing the concentration gradient (such as the change rate of concentration with distance), the distribution gradient of pollutant concentrations is formed to reflect the diffusion intensity of pollutants within the initial area. Regional environmental parameters related to pollution diffusion are collected within the initial area, and combined with the regional environmental parameters, the key environmental boundary conditions within the initial area are set. The key environmental boundary conditions describe the constraint conditions affected by environmental factors during the pollution diffusion process, such as hydrodynamic boundary conditions, topographic and geomorphic boundary conditions, and meteorological boundary conditions. According to the boundary values and distribution gradients of heavy metal pollutants, a comprehensive analysis is carried out with the key environmental boundary conditions to form the complete boundary conditions of the initial area. Using the pollution identification and analysis model, the above-mentioned boundary values, gradients, and environmental parameters are input into the model to uniformly generate the boundary condition data of the initial pollution diffusion area. The generated boundary conditions of the initial area include the initial area range, concentration boundary values, concentration gradients, and numerical descriptions of environmental impact factors, which are used as the basic input for predicting pollution diffusion paths.

[0106] Furthermore, as Figure 4 shown, monitoring and analyzing the future concentration distribution of heavy metal pollutants includes:

[0107] Based on historical monitoring data and real-time sampling data, a pollution time-series concentration database is constructed, which contains pollutant concentration values, sampling times, sampling locations, and regional environmental parameters;

[0108] Extract the time-series change characteristics of the concentrations of heavy metal pollutants from the pollution time-series concentration database;

[0109] According to the time-series change characteristics, a future concentration distribution prediction model is established in combination with the pollution diffusion path;

[0110] Input the historical monitoring data and real-time sampling data contained in the pollution time-series concentration database into the future concentration distribution prediction model to generate the future concentration distribution of heavy metal pollutants at different time points;

[0111] Based on the future concentration distribution, evaluate the high-concentration areas and potential risk areas, and store them in the pollution time-series concentration database.

[0112] As an optimization of the above embodiment, based on the historical monitoring data and real-time sampling data, collect information related to heavy metal pollutants, including pollutant concentration values: the monitored concentrations of heavy metal pollutants (such as cadmium, lead, mercury, etc.); sampling time: the specific time points of each monitoring or sampling; sampling location: the geographical coordinates of the soil sample collection points; regional environmental parameters: environmental factors affecting the change of pollutant concentration, such as meteorological data (precipitation, wind speed, temperature) and hydrological data (groundwater flow velocity, flow direction). Organize, clean, and store the above data in the pollution time-series concentration database; perform denoising and normalization processing on the data in the pollution time-series concentration database to eliminate outliers and data inconsistencies, and extract the time-varying characteristics of pollutant concentration through time-series analysis algorithms, identify the change trends, periodicity, and mutation points of the concentration, such as identifying the peaks and troughs of heavy metal concentration at different time points, analyzing the growth or decline rate of concentration over time, and determining the law of periodic change of concentration (such as seasonal change); according to the extracted time-series characteristics and pollution diffusion path data, establish a future concentration distribution prediction model. The prediction model can adopt statistical methods or machine learning methods, combine the input parameters of the pollution diffusion path (such as diffusion range, diffusion source location, and environmental driving factors), and fuse the time-series characteristics with the spatial diffusion characteristics; input the historical monitoring data and real-time sampling data in the pollution time-series concentration database into the future concentration distribution prediction model, and predict the future concentration distribution of heavy metal pollutants at different time points according to the input data and diffusion path. The generated concentration distribution includes the heavy metal pollutant concentration values at different times and the distribution of pollutants within the spatial range. Visualize the generated future concentration distribution in the form of dynamic graphs or concentration distribution maps to intuitively display the diffusion trend of heavy metal pollutants over time and space; analyze the generated future concentration distribution, identify the high-concentration areas where the pollutant concentration exceeds the set threshold, mark the range of areas that may be severely affected, and combine the regional environmental parameters (such as terrain, groundwater flow direction) to analyze the potential risk areas where the high-concentration areas may further spread. Store the evaluation results of the high-concentration areas and potential risk areas in the pollution time-series concentration database as the basis for subsequent monitoring and dynamic updates.

[0113] Furthermore, evaluating the high-concentration areas and potential risk areas includes:

[0114] Conduct a fusion analysis of soil pollution characteristics and set heavy metal pollution thresholds according to the results of the fusion analysis;

[0115] Compare the heavy metal pollutant concentration values generated by the future concentration distribution prediction model with the heavy metal pollution threshold to calibrate the high-concentration areas exceeding the heavy metal pollution threshold;

[0116] Combine the regional environmental parameters and the pollution diffusion path, analyze the surrounding geological, hydrological and meteorological conditions of the high-concentration areas, and generate a high-concentration area distribution map;

[0117] According to the high-concentration area distribution map and the soil pollution characteristics, delimit the potential risk areas that may be affected by pollution, and generate a pollution diffusion risk area map.

[0118] As an optimization of the above embodiment, combine the historical monitoring data and real-time sampling data in the pollution time-series concentration database, and use data fusion technology to comprehensively analyze the soil pollution characteristics. The analysis content includes the change of heavy metal pollutant concentration in different time periods, the heavy metal concentration distribution characteristics in the spatial range, and the correlation between the concentration value and the regional environmental parameters (such as hydrological and meteorological conditions). According to the fusion analysis results, combined with the national or regional soil environmental quality standards, set the threshold of heavy metal pollutants. The threshold represents the judgment standard of high-concentration pollution and can be dynamically adjusted based on actual data. For example, for heavy metals such as cadmium and lead, when their concentration exceeds a certain value, it can be judged as a high pollution risk; compare the heavy metal pollutant concentration values generated by the future concentration distribution prediction model with the set heavy metal pollution threshold point by point. The comparison results show whether the concentration value at each spatial position exceeds the threshold, calibrate the spatial areas where the concentration value exceeds the threshold, and identify the high-concentration pollution areas. The high-concentration areas may be concentrated downstream of the diffusion path or near the pollution source. Record the calibrated high-concentration area data into the pollution time-series concentration database; combine the calibration results of the high-concentration areas and conduct a comprehensive analysis with the regional environmental parameters (such as geological, hydrological and meteorological conditions). Generate a high-concentration area distribution map according to the analysis results, and mark the range, shape and pollutant concentration value of the exceeded standard area; based on the high-concentration area distribution map and the pollution diffusion path, combine the soil pollution characteristics to delimit the potential risk areas that may be affected by pollution. The specific steps include determining the diffusion trend of the high-concentration areas, comprehensively analyzing the environmental parameters (such as slope and water flow direction) of the potential risk areas and the soil pollution characteristics, and evaluating the migration risk of pollutants to the surrounding areas. According to the above analysis results, generate a pollution diffusion risk area map, and mark the potential impact range and risk level of pollution diffusion.

[0119] Embodiment 2;

[0120] Based on the same inventive concept as a soil heavy metal pollution intelligent detection method in the foregoing embodiment, the present invention also provides a soil heavy metal pollution intelligent detection system, which includes:

[0121] An information collection module that collects multi-source soil information and regional environmental parameters of the soil to be measured, and identifies soil pollution characteristics based on the multi-source soil information;

[0122] A pollution analysis module that establishes a pollution identification and analysis model based on soil pollution characteristics and regional environmental parameters, and analyzes whether the soil to be measured contains heavy metal pollutants;

[0123] A path identification module that, if heavy metal pollutants are present, predicts the pollution diffusion path of heavy metal pollutants in time and space based on the pollution identification and analysis model;

[0124] A time-series monitoring module that, if heavy metal pollutants are not present, performs time-series analysis on soil pollution characteristics, monitors and analyzes the future concentration distribution of heavy metal pollutants, and establishes a pollution risk warning.

[0125] The above adjustment system in the present invention can effectively implement an intelligent detection method for soil heavy metal pollution, and the technical effects that can be achieved are as described in the above embodiments, which will not be elaborated here.

[0126] Furthermore, the pollution analysis module includes:

[0127] A feature analysis unit that analyzes multi-source soil information and extracts soil pollution characteristics including soil physical parameters, chemical components, and trace heavy metal characteristics;

[0128] A time-series fusion unit that pulls a time period sequence, performs fusion analysis based on the soil pollution characteristics obtained within the time period sequence, and sets a heavy metal pollution threshold according to the fusion analysis result;

[0129] A threshold correction unit that combines regional environmental parameters, corrects the heavy metal pollution threshold, and feeds it back to the pollution identification and analysis model;

[0130] A pollution judgment unit that inputs soil pollution characteristics into the pollution identification and analysis model, calculates the distribution of heavy metal pollutants and the concentration value of heavy metal pollutants, and judges whether the soil to be measured contains heavy metal pollutants according to the heavy metal pollution threshold.

[0131] Similarly, for the above optimization solutions of the system, the corresponding optimization effects of the methods in Embodiment 1 can also be realized respectively, which will not be elaborated here either.

[0132] Although the present application has been described in connection with specific features and their embodiments, it will be apparent that various modifications and combinations can be made thereto without departing from the spirit and scope of the present application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined by the appended claims and are considered to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications therein.

Claims

1. An intelligent detection method for heavy metal pollution in soil, characterized in that, The method includes: Collecting the soil multi-source information and regional environmental parameters of the soil to be tested, and identifying the soil pollution characteristics according to the soil multi-source information; Establishing a pollution identification analysis model according to the soil pollution characteristics and the regional environmental parameters, and analyzing whether the soil to be tested contains heavy metal pollutants; If the heavy metal pollutants are contained, predicting the pollution diffusion path of the heavy metal pollutants in time and space according to the pollution identification analysis model; If the heavy metal pollutants are not contained, performing a time series analysis on the soil pollution characteristics, monitoring and analyzing the future concentration distribution of the heavy metal pollutants, and establishing a pollution risk warning; Predicting the pollution diffusion path of the heavy metal pollutants in time and space according to the pollution identification analysis model includes: Based on the soil pollution characteristics and the regional environmental parameters, setting the initial regional boundary conditions for pollution diffusion; Defining the initial pollution diffusion area according to the initial regional boundary conditions, and identifying the positions of heavy metal pollution diffusion sources within the initial pollution diffusion area; Collecting the environmental driving factors related to pollution diffusion according to the initial pollution diffusion area; Combining the initial pollution diffusion area and the environmental driving factors, starting from the position of the heavy metal pollution diffusion source, and using the time series as an auxiliary condition, predicting the pollution diffusion path of the heavy metal pollutants; Setting the initial regional boundary conditions for pollution diffusion includes: Based on the sampling position of the soil to be tested and its surrounding geographical information, defining the spatial range of the initial pollution diffusion area; According to the soil pollution characteristics within the spatial range of the initial pollution diffusion area, setting the boundary value and distribution gradient of the heavy metal pollutant concentration; According to the regional environmental parameters and the distribution gradient, setting the key environmental boundary conditions within the initial pollution diffusion area; Summarizing and generating the initial regional boundary conditions according to the key environmental boundary conditions, the boundary value and distribution gradient of the heavy metal pollutant concentration.

2. The intelligent detection method for soil heavy metal pollution according to claim 1, wherein, Analyzing whether the soil to be tested contains heavy metal pollutants includes: Analyzing the soil multi-source information, and extracting the soil pollution characteristics including soil physical parameters, chemical components and trace heavy metal characteristics; Pulling a time period sequence, performing a fusion analysis according to the soil pollution characteristics obtained within the time period sequence, and setting a heavy metal pollution threshold according to the fusion analysis result; Combining the regional environmental parameters, correcting the heavy metal pollution threshold and feeding it back to the pollution identification analysis model; Inputting the soil pollution characteristics into the pollution identification analysis model, calculating the distribution of the heavy metal pollutants and the heavy metal pollutant concentration value, and judging and identifying whether the soil to be tested contains the heavy metal pollutants according to the heavy metal pollution threshold.

3. The intelligent detection method for soil heavy metal pollution according to claim 2, wherein Calculating the distribution of the heavy metal pollutants and the heavy metal pollutant concentration value includes: Dividing the area of the soil to be tested into several spatial units, and each spatial unit corresponds to the soil multi-source information and the regional environmental parameters; Inputting the soil pollution characteristics of each spatial unit into the pollution identification analysis model, and calculating the heavy metal pollutant concentration value in each spatial unit; Interpolate the concentration values of each of the calculated spatial units to generate the pollution concentration distribution of the heavy metal pollutants in the area of the soil to be measured; Extract the distribution and pollution degree of the heavy metal pollutants based on the pollution concentration distribution.

4. The intelligent detection method for soil heavy metal pollution according to claim 3, wherein, Interpolating the concentration values of each of the calculated spatial units includes: Analyze the spatial correlation between spatial units, and construct a semivariogram according to the spatial correlation to describe the spatial variation law of concentration values; Set target interpolation points, and calculate the weights of each of the spatial units around the target interpolation points according to the semivariogram, and the weights are dynamically adjusted according to the spatial correlation; According to the weights, weighted calculate the concentration values of the heavy metal pollutants corresponding to the spatial units to the target interpolation points to generate the interpolation concentration of the heavy metal pollutants in the target area; Integrate the interpolation concentrations of the target interpolation points to generate the concentration distribution of the heavy metal pollutants, and mark the spatial distribution range and high-pollution risk areas of the heavy metal pollutants.

5. The intelligent detection method for soil heavy metal pollution according to claim 1, wherein Monitor and analyze the future concentration distribution of the heavy metal pollutants, including: Based on historical monitoring data and real-time sampling data, construct a pollution time-series concentration database, which includes pollutant concentration values, sampling times, sampling locations, and the regional environmental parameters; Extract the time-series change characteristics of the concentration of the heavy metal pollutants from the pollution time-series concentration database; According to the time-series change characteristics, combine with the pollution diffusion path to establish a future concentration distribution prediction model; Input the historical monitoring data and the real-time sampling data included in the pollution time-series concentration database into the future concentration distribution prediction model to generate the future concentration distribution of the heavy metal pollutants at different time points; Based on the future concentration distribution, evaluate the high-concentration areas and potential risk areas, and store them in the pollution time-series concentration database.

6. The intelligent detection method for soil heavy metal pollution according to claim 5, wherein, Evaluating the high-concentration areas and potential risk areas includes: Conduct a fusion analysis of the soil pollution characteristics, and set a heavy metal pollution threshold according to the fusion analysis results; Compare the concentration values of the heavy metal pollutants generated by the future concentration distribution prediction model with the heavy metal pollution threshold, and mark the high-concentration areas exceeding the heavy metal pollution threshold; Combined with the regional environmental parameters and the pollution diffusion path, analyze the surrounding geology, hydrology, and meteorological conditions of the high-concentration areas to generate a high-concentration area distribution map; According to the high-concentration area distribution map and the soil pollution characteristics, delimit the potential risk areas affected by the pollution and generate a pollution diffusion risk area map.

7. An intelligent detection system for soil heavy metal pollution, characterized in that, Adopt the intelligent detection method for soil heavy metal pollution as described in claim 1, and the system includes: An information collection module that collects the soil multi-source information and regional environmental parameters of the soil to be measured, and identifies the soil pollution characteristics according to the soil multi-source information; A pollution analysis module that establishes a pollution identification and analysis model according to the soil pollution characteristics and regional environmental parameters, and analyzes whether the soil to be measured contains heavy metal pollutants; A path identification module, if it contains heavy metal pollutants, then predicts the pollution diffusion path of the heavy metal pollutants in time and space according to the pollution identification and analysis model; The time-series monitoring module, if it does not contain heavy metal pollutants, conducts time-series analysis on the soil pollution characteristics, monitors and analyzes the future concentration distribution of heavy metal pollutants, and establishes a pollution risk warning.

8. An intelligent detection system for soil heavy metal pollution according to claim 7, characterized in that, The pollution analysis module includes: A feature analysis unit that analyzes multi-source soil information and extracts soil pollution characteristics including soil physical parameters, chemical components, and trace heavy metal characteristics; A time-series fusion unit that pulls a time period sequence, conducts fusion analysis based on the soil pollution characteristics obtained within the time period sequence, and sets a heavy metal pollution threshold according to the fusion analysis results; A threshold correction unit that combines regional environmental parameters, corrects the heavy metal pollution threshold, and feeds it back to the pollution identification and analysis model; A pollution judgment unit that inputs the soil pollution characteristics into the pollution identification and analysis model, calculates the distribution of heavy metal pollutants and the concentration values of heavy metal pollutants, and determines whether the soil to be tested contains heavy metal pollutants according to the heavy metal pollution threshold.

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