Soil pollutant on-line monitoring and risk assessment method and system

Abstract

The invention relates to the technical field of environmental protection and pollution control, in particular to a soil pollutant on-line monitoring and risk assessment method and system.The method comprises the steps that soil pollutant monitoring areas are divided, and high-precision sensors are deployed, so that the accuracy and reliability of monitoring data are improved; the fluctuation condition of the pollutant concentration is calculated and analyzed according to historical data, a reasonable sampling frequency is determined, and the concentration change can be better captured; by establishing a composite risk assessment function, the risk level of a sampling point is assessed more comprehensively and accurately; the prevention and control measure intensity and range corresponding to the sampling point are dynamically adjusted according to the change of the composite risk assessment value, and the timeliness and effectiveness of prevention and control work are ensured. The method is used for solving the technical problems of low monitoring efficiency, poor risk assessment and lack of pertinence of prevention and control measures caused by no online monitoring of the pollutant concentration in soil and no quantitative assessment of the soil pollution risk in the existing scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection and pollution control, and in particular to a method and system for online monitoring and risk assessment of soil pollutants. Background Art

[0002] Environmental protection and pollution control refers to the use of environmental science theories and methods to protect the environment in a planned manner, prevent the deterioration of environmental quality, and adopt technical, economic, legal and other means to eliminate and reduce pollutant emissions.

[0003] However, in the monitoring and management of soil pollution, there are multiple challenges: lack of real-time online monitoring means, the existing scheme fails to realize online monitoring of pollutant concentration in soil, resulting in low monitoring efficiency and difficulty in timely reflecting the soil pollution status; the risk assessment system is imperfect, the existing scheme fails to conduct a comprehensive and accurate quantitative assessment of soil pollution risks, and it is difficult to determine the key areas of pollution and potential risks; the sampling frequency setting is unreasonable, and the sampling frequency setting without scientific basis is easy to lead to low sampling efficiency or waste of resources, and cannot effectively capture the changes in pollutant concentrations, and there are oversampling or undersampling, which wastes resources; the prevention and control measures lack pertinence and flexibility, and the prevention and control measures are often based on empirical judgments, and fail to be dynamically adjusted according to the results of soil pollution risk assessment, which is insufficient in pertinence and effectiveness, resulting in limited prevention and control effects. To this end, the present invention proposes a method and system for online monitoring and risk assessment of soil pollutants. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose a method and system for online monitoring and risk assessment of soil pollutants.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] Online monitoring and risk assessment methods for soil pollutants, including:

[0007] Step 1: Divide the soil pollutant monitoring area and monitor the pollutant concentration in the soil in real time;

[0008] Step 2: Allocate sampling points within the monitoring area to collect the pollutant concentration in the soil, and calculate and analyze the fluctuation of pollutant concentration based on historical data to determine the sampling frequency;

[0009] Step 3: Establish a composite risk assessment function to assess the risk level of all sampling points and obtain a composite risk assessment value;

[0010] Step 4: Analyze the risk changes of the sampling points based on the composite risk assessment value, and dynamically adjust the intensity and scope of prevention and control measures corresponding to the sampling points.

[0011] It should be noted that the application objects of the online monitoring and risk assessment method and system of soil pollutants in the embodiments of the present invention can be soil pollution monitoring and management in the field of environmental protection. Specifically, it can be for in-depth analysis of soil pollutant concentration data through data analysis technology; by dividing the soil pollutant monitoring area, real-time monitoring of pollutant concentration, dynamically adjusting the sampling frequency according to historical data, establishing a composite risk assessment function to assess the risk level, and dynamically adjusting prevention and control measures according to the composite risk assessment value, to achieve accurate monitoring and effective risk assessment of soil pollutants, improve monitoring efficiency and accuracy, and facilitate the formulation of targeted soil pollution control and protection measures.

[0012] Furthermore, the process of dividing the soil pollutant monitoring area and monitoring the pollutant concentration in the soil in real time includes:

[0013] The soil pollutant monitoring area is mapped into a two-dimensional plane D, whose area is A D ; Where D represents the set of two-dimensional planes, that is, the entire soil pollutant monitoring area; the area of ​​the soil pollutant monitoring area A D Represents the size of a two-dimensional plane;

[0014] Classify soil pollutants into types in monitoring area D;

[0015] It is understandable that the pollution situation of the plot is initially identified through the collection of land pollution data, on-site investigation, interviews with people familiar with the plot, etc. At this stage, some potential pollution sources or types of pollutants may be discovered; sampling and analysis are carried out on the basis of preliminary identification, by collecting soil samples in the monitoring area and using chemical analysis, spectral analysis, mass spectrometry and other means to determine the types of pollutants present in the soil, so as to classify the types of pollutants in the monitoring area;

[0016] According to the types of soil pollutants, N high-precision sensors are deployed in the monitoring area D, and the location coordinates of the high-precision sensors are obtained. Wherein, N represents the number of high-precision sensors, i represents the position coordinate index of the high-precision sensor, and i=1, 2, ..., N;

[0017] Define the monitoring radius of each high-precision sensor as r i , then by calculating the union approximation of the monitoring range of each high-precision sensor, the coverage area A of the online monitoring network is obtained E , which is the sum of the monitoring areas of all high-precision sensors: In the formula, A E ≤A D ;

[0018] It is understandable that the coverage area A of the entire online monitoring network EThe area A of the entire soil pollutant monitoring area should be as close to or as wide as possible. D , that is, A E ≈A D or And A E As large as possible, however, due to the deployment of high-precision sensors and the limitations of the monitoring radius, the coverage area of ​​the online monitoring network does not exceed the area of ​​the soil pollutant monitoring area; the monitoring range of each high-precision sensor is defined as a sensor location with a monitoring radius of r i The resulting circular area or more complex geometric shapes;

[0019] Use high-precision sensors to monitor and record the pollutant concentration C in the soil in real time;

[0020] The soil pollutant concentration monitored by the i-th high-precision sensor at time t is marked as C i (t); where C i represents the pollutant concentration monitored by the i-th high-precision sensor;

[0021] Collect real-time monitoring pollutant concentration data to form a collection {C i (t)i=1,2,...,N}.

[0022] Furthermore, M sampling points are selected in the monitoring area D to collect soil samples of different categories. The location coordinates of each sampling point are Where j represents the position coordinate index of the sampling point, and j = 1, 2, ..., M; M represents the number of sampling points, and the number of sampling points is calculated by the formula Determine, where A D represents the area of ​​soil pollutant monitoring, d represents the distance between sampling points, represents the sampling point adjustment factor;

[0023] It is understandable that the selection of sampling points should be based on a comprehensive consideration of factors such as geographical distribution, distribution of pollution sources, and soil type;

[0024] The position coordinates of each sampling point Match the corresponding high-precision sensors to obtain real-time monitoring data on the concentrations of different types of pollutants.

[0025] Furthermore, the process of calculating and analyzing the fluctuation of pollutant concentration based on historical data to determine the sampling frequency includes:

[0026] Determine the pollutant type and set the basic sampling frequency F0 of the pollutant;

[0027] Obtain historical pollutant concentration data with a time length of pt from the monitoring database; the historical pollutant concentration data is the type of pollutant targeted, including the pollutant concentration values ​​of each sampling point at different time points; pt represents the time span of pollutant concentration data collection, such as the past year;

[0028] For each sampling point, all its historical pollutant concentration values ​​at a time length of pt are regarded as a sample set;

[0029] Use the variance formula to calculate the variance σ of the sample set 2 : In the formula, n represents the number of monitoring times, that is, the number of samples collected in a time length of pt, C q represents the pollutant concentration value of each sample, Indicates the average pollutant concentration value of the sample;

[0030] Combine the calculated variance of the sample set to analyze the fluctuation of pollutant concentration: σ 2 Used to reflect the fluctuation degree of pollutant concentration, if the variance σ 2 The larger the value, the greater the fluctuation of historical pollutant concentration; if the variance σ 2 The smaller the value of , the more stable the fluctuation of pollutant concentration;

[0031] Setting a fluctuation level judgment threshold τ; wherein the threshold τ can be determined according to actual conditions and monitoring requirements, and is used to judge whether the fluctuation of pollutant concentration is significant;

[0032] Based on the variance σ 2 The comparison result with the fluctuation degree judgment threshold τ determines whether to increase or decrease the sampling frequency, and then dynamically adjusts the sampling frequency; when the variance σ of any sampling point 2 When it is greater than the fluctuation degree judgment threshold τ, it is considered that the pollutant concentration at this point fluctuates significantly, and the sampling frequency needs to be increased to better capture the concentration changes. The adjusted sampling frequency is F=F0×(1+α); otherwise, the sampling frequency is reduced, and the adjusted sampling frequency is F=F0×(1+β); among them, α and β are sampling frequency adjustment proportional coefficients, which are used to determine the amplitude of increasing the sampling frequency when the fluctuation is significant or the amplitude of reducing the sampling frequency when the fluctuation is stable, and α and β are both greater than 1.

[0033] Furthermore, a composite risk assessment function is established to assess the risk level of all sampling points. The process of obtaining the composite risk assessment value includes:

[0034] Obtain all monitored soil pollutant types L;

[0035] For the jth sampling point, its pollutant concentration vector is c j =(cj(1) ,c j(2) ,...,c j(L) ), where vector c j is an L-dimensional vector, where each component corresponds to a pollutant concentration;

[0036] Risk level assessment is performed using a composite risk assessment function:

[0037]

[0038] In the formula, R(c j ) represents the composite risk assessment value of the jth sampling point; w k represents the weight coefficient of the kth pollutant, which is used to reflect the importance of the pollutant in risk assessment; c jk represents the concentration of the kth pollutant in the jth sampling point; a k 、b k It is the exponential parameter of the nonlinear term, which determines the influence of the nonlinear term on the concentration of the k-th pollutant in the risk assessment;

[0039] Understandably, the first It is a combination of linear and nonlinear terms, representing the weighted sum of all pollutant concentrations, where a k The concentration of each pollutant is allowed to affect the composite risk assessment value in a nonlinear manner; the second is a product term that takes into account the interaction between all pollutant concentrations through the exponent b k Adjust the contribution of each pollutant concentration in the interaction and control the overall impact of this interaction on the composite risk assessment value by adjusting the parameter γ;

[0040] According to the composite risk assessment value R(c j ) is analyzed, if the composite risk assessment value R(c j ) is larger, the higher the risk level of the sampling point is; if the composite risk assessment value R(c j ) is smaller, the lower the risk level of the sampling point is.

[0041] Further, the process of analyzing the risk change of the sampling point according to the composite risk assessment value includes:

[0042] Obtain the composite risk assessment value of all sampling points;

[0043] When the sampling points are classified according to the composite risk assessment value, the composite risk assessment values ​​of all sampling points are matched with the preset risk threshold T to obtain the low-risk area sampling point set S low , the sampling point set S in the medium risk area mid And the high-risk area sampling point set S hig ;

[0044] If the composite risk assessment value is less than the preset risk threshold, a first risk signal is generated, and the corresponding sampling point j is associated with the low-risk area sampling point set S low If the composite risk assessment value is not less than the preset risk threshold and not greater than X% of the risk threshold, where X is a real number greater than one hundred, a second risk signal is generated and the corresponding sampling point j is associated with the set of sampling points S in the medium-risk area. mid If the composite risk assessment value is greater than the risk threshold value X%, a third risk signal is generated, and the corresponding sampling point j is associated with the high-risk area sampling point set S hig ;

[0045] The difference formula is used to express the change in risk: ΔR j (h) = R j (h+Δh)-R j (h), where ΔR j (t) represents the risk change of the jth sampling point in the hth time period; Δh represents the length of the time period.

[0046] Furthermore, the process of dynamically adjusting the intensity and scope of prevention and control measures corresponding to the sampling points includes:

[0047] Dynamically adjust the intensity of prevention and control measures based on the amount of risk change:

[0048] T p (h+Δh)=T p (h)+δ p ·|ΔR p (h)|,

[0049] Where, T p (h+Δh) represents the intensity of prevention and control measures; p∈{low, mid, high} represents low, medium and high risk areas; δ p Represents the adaptation coefficient, which is used to control the parameters of the adjustment of prevention and control measures, where δ p ={δ low ,δ mid ,δ hig} and δ low <δ mid <δ hig , that is, the adjustment efforts in high-risk areas are the greatest, and those in low-risk areas are the least;

[0050] At the same time, the scope of prevention and control measures is adjusted based on the changes in risk signals at sampling points and the composite risk assessment values ​​of adjacent points.

[0051] Soil pollutant online monitoring and risk assessment system, including:

[0052] The area division and data monitoring module is used to divide the soil pollutant monitoring area and monitor the pollutant concentration in the soil in real time;

[0053] The sampling point division and frequency determination module is used to allocate sampling points in the monitoring area to collect the pollutant concentration in the soil, and calculate and analyze the fluctuation of the pollutant concentration based on historical data to determine the sampling frequency;

[0054] The composite risk assessment function establishment module is used to establish a composite risk assessment function to assess the risk level of all sampling points and obtain a composite risk assessment value;

[0055] The risk prevention and control dynamic adjustment module is used to analyze the risk changes of sampling points according to the composite risk assessment value, and dynamically adjust the intensity and scope of prevention and control measures corresponding to the sampling points.

[0056] Compared with the existing technology, the advantages of the method and system for online monitoring and risk assessment of soil pollutants provided by the present invention are:

[0057] 1. The present invention can accurately cover the entire monitoring area by mapping the soil pollutant monitoring area into a two-dimensional plane and deploying high-precision sensors to ensure that the pollutant concentration in the soil is monitored without omission. The real-time monitoring capability of the high-precision sensors can quickly obtain soil pollutant concentration data, providing timely and accurate information support for subsequent risk assessment and prevention and control measures;

[0058] 2. The present invention can more efficiently capture changes in pollutant concentrations by dynamically adjusting the sampling frequency according to the fluctuations in historical pollutant concentration data, avoiding oversampling or undersampling. At the same time, reasonable sampling frequency settings can reduce unnecessary sampling and analysis work, thereby saving manpower, material resources and time costs;

[0059] 3. The present invention utilizes a composite risk assessment function and comprehensively considers the concentrations of multiple pollutants and their interactions, so as to more comprehensively assess the risk level of soil pollution, identify high-risk areas, medium-risk areas and low-risk areas, and provide a scientific basis for formulating targeted prevention and control measures; by dynamically adjusting the intensity and scope of prevention and control measures according to changes in the risk assessment values ​​of the sampling points, it is possible to more flexibly respond to changes in soil pollution risks and more effectively reduce the risk level of soil pollution.

[0060] In summary, the present invention realizes accurate monitoring, optimized sampling, comprehensive evaluation and dynamic prevention and control of soil pollutant concentrations, provides strong support for environmental protection and sustainable development, and thus ensures the normal operation of subsequent online soil pollutant monitoring and risk assessment methods and systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1This is a flow chart of the online soil pollutant monitoring and risk assessment method proposed in the present invention.

[0062] Figure 2 This is a module diagram of the soil pollutant online monitoring and risk assessment system proposed in the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] See also Figure 1 The present invention provides a method for online monitoring and risk assessment of soil pollutants, comprising:

[0065] Step 1: Divide the soil pollutant monitoring area and monitor the pollutant concentration in the soil in real time;

[0066] Step 2: Allocate sampling points within the monitoring area to collect the pollutant concentration in the soil, and calculate and analyze the fluctuation of pollutant concentration based on historical data to determine the sampling frequency;

[0067] Step 3: Establish a composite risk assessment function to assess the risk level of all sampling points and obtain a composite risk assessment value;

[0068] Step 4: Analyze the risk changes of the sampling points based on the composite risk assessment value, and dynamically adjust the intensity and scope of prevention and control measures corresponding to the sampling points.

[0069] It should be noted that the application objects of the online monitoring and risk assessment method and system of soil pollutants in the embodiments of the present invention can be soil pollution monitoring and management in the field of environmental protection. Specifically, it can be for in-depth analysis of soil pollutant concentration data through data analysis technology; by dividing the soil pollutant monitoring area, real-time monitoring of pollutant concentration, dynamically adjusting the sampling frequency according to historical data, establishing a composite risk assessment function to assess the risk level, and dynamically adjusting prevention and control measures according to the composite risk assessment value, to achieve accurate monitoring and effective risk assessment of soil pollutants, improve monitoring efficiency and accuracy, and facilitate the formulation of targeted soil pollution control and protection measures.

[0070] See also Figure 1 The present invention provides a method for online monitoring and risk assessment of soil pollutants. In step 1, the step of dividing the soil pollutant monitoring area and monitoring the pollutant concentration in the soil in real time includes:

[0071] Step 101: Map the soil pollutant monitoring area into a two-dimensional plane D, whose area is A. D ; Where D represents the set of two-dimensional planes, that is, the entire soil pollutant monitoring area; the area of ​​the soil pollutant monitoring area A D Represents the size of a two-dimensional plane;

[0072] Step 102: Classify soil pollutants in monitoring area D;

[0073] In step 102, the pollution situation of the plot is preliminarily identified through the collection of plot pollution data, on-site investigation, interviews with plot insiders, etc. At this stage, some potential pollution sources or types of pollutants may be found; sampling and analysis are carried out on the basis of the preliminary identification, and the types of pollutants existing in the soil are determined by collecting soil samples in the monitoring area and using chemical analysis, spectral analysis, mass spectrometry, etc., so as to classify the pollutants in the monitoring area;

[0074] Step 103: Deploy N high-precision sensors in the monitoring area D according to the classified soil pollutant types, and obtain the position coordinates of the high-precision sensors. Wherein, N represents the number of high-precision sensors, i represents the position coordinate index of the high-precision sensor, and i=1, 2, ..., N;

[0075] Step 104: Define the monitoring radius of each high-precision sensor as r i , then by calculating the union approximation of the monitoring range of each high-precision sensor, the coverage area A of the online monitoring network is obtained E , which is the sum of the monitoring areas of all high-precision sensors: In the formula, A E ≤A D ;

[0076] In step 104, the coverage area A of the entire online monitoring network E The area A of the entire soil pollutant monitoring area should be as close to or as wide as possible. D , that is, A E ≈A D or And A E As large as possible, however, due to the deployment of high-precision sensors and the limitations of the monitoring radius, the coverage area of ​​the online monitoring network does not exceed the area of ​​the soil pollutant monitoring area; the monitoring range of each high-precision sensor is defined as a sensor location with a monitoring radius of r i The resulting circular area or more complex geometric shapes;

[0077] Step 105: Use a high-precision sensor to monitor and record the pollutant concentration C in the soil in real time;

[0078] Step 106: mark the soil pollutant concentration monitored by the i-th high-precision sensor at time t as C i (t); where C i represents the pollutant concentration monitored by the i-th high-precision sensor;

[0079] Step 107: collect real-time monitored pollutant concentration data to form a set {C i (t)i=1,2,...,N}.

[0080] See also Figure 1 The present invention provides a method for online monitoring and risk assessment of soil pollutants. In step 2, sampling points are allocated in the monitoring area to collect the pollutant concentration in the soil, and the fluctuation of the pollutant concentration is calculated and analyzed based on historical data to determine the sampling frequency. The steps include:

[0081] Step 201: Select M sampling points in the monitoring area D to collect soil samples of different categories. The location coordinates of each sampling point are Where j represents the position coordinate index of the sampling point, and j = 1, 2, ..., M; M represents the number of sampling points, and the number of sampling points is calculated by the formula Determine, where A D represents the area of ​​soil pollutant monitoring, d represents the distance between sampling points, represents the sampling point adjustment factor;

[0082] In step 201, the selection of sampling points should be based on comprehensive consideration of factors such as geographical distribution, pollution source distribution, soil type, etc.;

[0083] Step 202: Position coordinates of each sampling point Match corresponding high-precision sensors to obtain real-time monitoring data on the concentration of different types of pollutants;

[0084] Step 203, determine the pollutant type and set the basic sampling frequency F0 of the pollutant;

[0085] Step 204, obtaining historical pollutant concentration data of a time length of pt from the monitoring database; wherein the historical pollutant concentration data is the type of pollutant targeted, including the pollutant concentration values ​​of each sampling point at different time points; pt represents the time span of pollutant concentration data collection, such as the past year;

[0086] Step 205: for each sampling point, all its historical pollutant concentration values ​​within a time length of pt are regarded as a sample set;

[0087] Step 206: Calculate the variance σ of the sample set using the variance formula2 : In the formula, n represents the number of monitoring times, that is, the number of samples collected in a time length of pt, C q represents the pollutant concentration value of each sample, Indicates the average pollutant concentration value of the sample;

[0088] Step 207: Analyze the fluctuation of pollutant concentration by combining the calculated variance of the sample set: 2 Used to reflect the fluctuation degree of pollutant concentration, if the variance σ 2 The larger the value, the greater the fluctuation of historical pollutant concentration; if the variance σ 2 The smaller the value of , the more stable the fluctuation of pollutant concentration;

[0089] Step 208, setting a fluctuation level judgment threshold τ;

[0090] In step 208, the threshold τ can be determined according to actual conditions and monitoring requirements to determine whether the fluctuation of pollutant concentration is significant;

[0091] Step 209: Based on the variance σ 2 The comparison result with the fluctuation degree judgment threshold τ determines whether to increase or decrease the sampling frequency, and then dynamically adjusts the sampling frequency; when the variance σ of any sampling point 2 When it is greater than the fluctuation degree judgment threshold τ, it is considered that the pollutant concentration at this point fluctuates significantly, and the sampling frequency needs to be increased to better capture the concentration changes. The adjusted sampling frequency is F=F0×(1+α); otherwise, the sampling frequency is reduced, and the adjusted sampling frequency is F=F0×(1+β); among them, α and β are sampling frequency adjustment proportional coefficients, which are used to determine the amplitude of increasing the sampling frequency when the fluctuation is significant or the amplitude of reducing the sampling frequency when the fluctuation is stable, and α and β are both greater than 1.

[0092] See also Figure 1 The present invention provides a method for online monitoring and risk assessment of soil pollutants. In step three, a composite risk assessment function is established to assess the risk level of all sampling points. The steps of obtaining the composite risk assessment value include:

[0093] Step 301, obtaining all monitored soil pollutant types L;

[0094] Step 302: For the jth sampling point, its pollutant concentration vector is c j =(c j(1) ,c j(2) ,...,c j(L) ), where vector c j is an L-dimensional vector, where each component corresponds to a pollutant concentration;

[0095] Step 303: Use the composite risk assessment function to assess the risk level:

[0096]

[0097] In the formula, R(c j ) represents the composite risk assessment value of the jth sampling point; w k represents the weight coefficient of the kth pollutant, which is used to reflect the importance of the pollutant in risk assessment; c jk represents the concentration of the kth pollutant in the jth sampling point; a k 、b k It is the exponential parameter of the nonlinear term, which determines the influence of the nonlinear term on the concentration of the k-th pollutant in the risk assessment;

[0098] In step 303, the first item It is a combination of linear and nonlinear terms, representing the weighted sum of all pollutant concentrations, where a k The concentration of each pollutant is allowed to affect the composite risk assessment value in a nonlinear manner; the second is a product term that takes into account the interaction between all pollutant concentrations through the exponent b k Adjust the contribution of each pollutant concentration in the interaction and control the overall impact of this interaction on the composite risk assessment value by adjusting the parameter γ;

[0099] According to the composite risk assessment value R(c j ) is analyzed, if the composite risk assessment value R(c j ) is larger, the higher the risk level of the sampling point is; if the composite risk assessment value R(c j ) is smaller, the lower the risk level of the sampling point is.

[0100] See also Figure 1 The present invention provides a method for online monitoring and risk assessment of soil pollutants. In step 4, the steps of analyzing the risk changes of the sampling points according to the composite risk assessment value and dynamically adjusting the intensity and scope of the prevention and control measures corresponding to the sampling points include:

[0101] Step 401, obtaining the composite risk assessment value of all sampling points;

[0102] Step 402: When classifying the sampling points according to the composite risk assessment value, the composite risk assessment values ​​of all sampling points are matched with the preset risk threshold T to obtain a set of low-risk area sampling points S low , the sampling point set S in the medium risk area mid And the high-risk area sampling point set S hig ;

[0103] In step 402, if the composite risk assessment value is less than the preset risk threshold, a first risk signal is generated, and the corresponding sampling point j is associated with the low-risk area sampling point set S low If the composite risk assessment value is not less than the preset risk threshold and not greater than X% of the risk threshold, where X is a real number greater than one hundred, a second risk signal is generated and the corresponding sampling point j is associated with the set of sampling points S in the medium-risk area. mid If the composite risk assessment value is greater than the risk threshold value X%, a third risk signal is generated, and the corresponding sampling point j is associated with the high-risk area sampling point set S hig ;

[0104] Step 403: Use the differential formula to express the risk change: ΔR j (h) = R j (h+Δh)-R j (h), where ΔR j (t) represents the risk change of the jth sampling point in the hth time period; Δh represents the length of the time period;

[0105] Step 404: Dynamically adjust the intensity of prevention and control measures based on the risk change:

[0106] T p (h+Δh)=T p (h)+δ p ·|ΔR p (h)|,

[0107] Where, T p (h+Δh) represents the intensity of prevention and control measures; p∈{low, mid, high} represents low, medium and high risk areas; δ p Represents the adaptation coefficient, which is used to control the parameters of the adjustment of prevention and control measures, where δ p ={δ low ,δ mid ,δ hig} and δ low <δ mid <δ hig , that is, the adjustment efforts in high-risk areas are the greatest, and those in low-risk areas are the least;

[0108] Step 405: At the same time, the risk signal changes of the sampling points and the composite risk assessment values ​​of the adjacent points are comprehensively considered to adjust the scope of the prevention and control measures.

[0109] In the embodiment of the present invention, by dividing the soil pollutant monitoring area and deploying high-precision sensors, accurate monitoring of soil pollutants can be achieved, the accuracy and reliability of monitoring data can be improved, the pollutant concentration in the soil can be monitored in real time by high-precision sensors, and timely data support can be provided for risk assessment and the formulation of prevention and control measures. By allocating sampling points and calculating and analyzing the fluctuation of pollutant concentration based on historical data, a reasonable sampling frequency can be determined to avoid the problem of oversampling or undersampling. The sampling frequency can be dynamically adjusted according to the fluctuation of pollutant concentration, which can better capture the change of pollutant concentration and provide more accurate data for risk assessment. By establishing a composite risk assessment function, the concentrations of multiple pollutants and their interactions can be comprehensively considered to conduct a more comprehensive and accurate assessment of the risk level of the sampling point. The composite risk assessment value can provide a scientific basis for the formulation of prevention and control measures and guide the focus and intensity of prevention and control work. According to the change of the composite risk assessment value, the intensity and scope of the prevention and control measures corresponding to the sampling point can be dynamically adjusted to ensure the timeliness and effectiveness of the prevention and control work. By classifying the risk level of the sampling point, different prevention and control measures can be taken for sampling points in different risk areas to improve the pertinence and efficiency of the prevention and control work. Dynamic adjustment of prevention and control measures can timely discover and respond to potential environmental risks and reduce the impact of soil pollution on the environment and human health. In summary, the examples of the present invention involve decision-making on data collection and analysis, risk assessment and result generation, and solve the technical problems that the existing solutions do not monitor the concentration of pollutants in the soil online and quantitatively assess the risk of soil pollution, resulting in low monitoring efficiency, poor risk assessment and lack of targeted prevention and control measures. In actual situations, more data and contextual information may be needed to make specific decisions and optimize solutions.

[0110] See also Figure 2 The present invention provides a soil pollutant online monitoring and risk assessment system, comprising:

[0111] The area division and data monitoring module is used to divide the soil pollutant monitoring area and monitor the pollutant concentration in the soil in real time;

[0112] The sampling point division and frequency determination module is used to allocate sampling points in the monitoring area to collect the pollutant concentration in the soil, and calculate and analyze the fluctuation of the pollutant concentration based on historical data to determine the sampling frequency;

[0113] The composite risk assessment function establishment module is used to establish a composite risk assessment function to assess the risk level of all sampling points and obtain a composite risk assessment value;

[0114] The risk prevention and control dynamic adjustment module is used to analyze the risk changes of sampling points according to the composite risk assessment value, and dynamically adjust the intensity and scope of prevention and control measures corresponding to the sampling points.

[0115] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The proportional coefficient in the formula and the various preset thresholds in the analysis process are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data; the size of the proportional coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the proportional coefficient depends on the amount of sample data and the preliminary setting of the corresponding processing coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0116] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically based on the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0117] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0118] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0123] Finally: The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for online monitoring and risk assessment of soil pollutants, characterized in that: Step 1: Divide the soil pollutant monitoring area and monitor the pollutant concentration in the soil in real time; Step 2: Allocate sampling points within the monitoring area to collect the pollutant concentration in the soil, and calculate and analyze the fluctuation of pollutant concentration based on historical data to determine the sampling frequency; Step 3: Establish a composite risk assessment function to assess the risk level of all sampling points and obtain a composite risk assessment value; Step 4: Analyze the risk changes of the sampling points based on the composite risk assessment value, and dynamically adjust the intensity and scope of prevention and control measures corresponding to the sampling points.

2. The method for online monitoring and risk assessment of soil pollutants according to claim 1, characterized in that: In step 1, the process of dividing the soil pollutant monitoring area and monitoring the pollutant concentration in the soil in real time includes: The soil pollutant monitoring area is mapped into a two-dimensional plane D, whose area is A D ; Where D represents the set of two-dimensional planes, that is, the entire soil pollutant monitoring area; the area of ​​the soil pollutant monitoring area A D Represents the size of a two-dimensional plane; Classify soil pollutants into types in monitoring area D; According to the types of soil pollutants, N high-precision sensors are deployed in the monitoring area D, and the location coordinates of the high-precision sensors are obtained. Wherein, N represents the number of high-precision sensors, i represents the position coordinate index of the high-precision sensor, and i=1, 2, ..., N; Define the monitoring radius of each high-precision sensor as r i , then by calculating the union approximation of the monitoring range of each high-precision sensor, the coverage area A of the online monitoring network is obtained E , which is the sum of the monitoring areas of all high-precision sensors: In the formula, A E ≤A D ; Use high-precision sensors to monitor and record the pollutant concentration C in the soil in real time; The soil pollutant concentration monitored by the i-th high-precision sensor at time t is marked as C i (t); where C i represents the pollutant concentration monitored by the i-th high-precision sensor; Collect real-time monitoring pollutant concentration data to form a collection {C i (t)i=1,2,...,N}.

3. The method for online monitoring and risk assessment of soil pollutants according to claim 1, characterized in that: In step 2, the process of allocating sampling points within the monitoring area to collect the pollutant concentration in the soil includes: Select M sampling points in the monitoring area D to collect soil samples of different categories. The location coordinates of each sampling point are Where j represents the position coordinate index of the sampling point, and j = 1, 2, ..., M; M represents the number of sampling points, and the number of sampling points is calculated by the formula Determine, where A D represents the area of ​​soil pollutant monitoring, d represents the distance between sampling points, represents the sampling point adjustment factor; The position coordinates of each sampling point Match the corresponding high-precision sensors to obtain real-time monitoring data on the concentrations of different types of pollutants.

4. The method for online monitoring and risk assessment of soil pollutants according to claim 3, characterized in that: In step 2, the process of calculating and analyzing the fluctuation of pollutant concentration based on historical data to determine the sampling frequency includes: Determine the pollutant type and set the basic sampling frequency F0 of the pollutant; Obtain historical pollutant concentration data with a time length of pt from the monitoring database; the historical pollutant concentration data is the type of pollutant targeted, including the pollutant concentration values ​​of each sampling point at different time points; pt represents the time span of pollutant concentration data collection; For each sampling point, all its historical pollutant concentration values ​​at a time length of pt are regarded as a sample set; Use the variance formula to calculate the variance σ of the sample set 2 : In the formula, n represents the number of monitoring times, that is, the number of samples collected in a time length of pt, C q represents the pollutant concentration value of each sample, Indicates the average pollutant concentration value of the sample; The fluctuation of pollutant concentration is analyzed by combining the calculated variance of the sample set; Set the fluctuation level judgment threshold τ; Based on the variance σ 2 The comparison result with the fluctuation degree judgment threshold τ determines whether to increase or decrease the sampling frequency, and then dynamically adjusts the sampling frequency; when the variance σ of any sampling point 2 When it is greater than the fluctuation degree judgment threshold τ, it is considered that the pollutant concentration at this point fluctuates significantly, and the sampling frequency needs to be increased to better capture the concentration changes. The adjusted sampling frequency is F=F0×(1+α); otherwise, the sampling frequency is reduced, and the adjusted sampling frequency is F=F0×(1+β); among them, α and β are sampling frequency adjustment proportional coefficients, which are used to determine the amplitude of increasing the sampling frequency when the fluctuation is significant or the amplitude of reducing the sampling frequency when the fluctuation is stable, and α and β are both greater than 1.

5. The method for online monitoring and risk assessment of soil pollutants according to claim 1, characterized in that: In step 3, a composite risk assessment function is established to assess the risk level of all sampling points. The process of obtaining the composite risk assessment value includes: Obtain all monitored soil pollutant types L; For the jth sampling point, its pollutant concentration vector is c j =(c j(1) ,c j(2) ,...,c j(L) ), where vector c j is an L-dimensional vector, where each component corresponds to a pollutant concentration; Risk level assessment is performed using a composite risk assessment function: In the formula, R(c j ) represents the composite risk assessment value of the jth sampling point; w k represents the weight coefficient of the kth pollutant, which is used to reflect the importance of the pollutant in risk assessment; c jk represents the concentration of the kth pollutant in the jth sampling point; a k , b k It is the exponential parameter of the nonlinear term, which determines the degree of influence of the nonlinear term on the concentration of the k-th pollutant in the risk assessment.

6. The method for online monitoring and risk assessment of soil pollutants according to claim 1, characterized in that: In step 4, the process of analyzing the risk changes of the sampling points according to the composite risk assessment value includes: Obtain the composite risk assessment value of all sampling points; When the sampling points are classified according to the composite risk assessment value, the composite risk assessment values ​​of all sampling points are matched with the preset risk threshold T to obtain the low-risk area sampling point set S low , the sampling point set S in the medium risk area mid And the high-risk area sampling point set S hig ; If the composite risk assessment value is less than the preset risk threshold, a first risk signal is generated, and the corresponding sampling point j is associated with the low-risk area sampling point set S low If the composite risk assessment value is not less than the preset risk threshold and not greater than X% of the risk threshold, where X is a real number greater than one hundred, a second risk signal is generated and the corresponding sampling point j is associated with the set of sampling points S in the medium-risk area. mid If the composite risk assessment value is greater than the risk threshold value X%, a third risk signal is generated, and the corresponding sampling point j is associated with the high-risk area sampling point set S hig ; The difference formula is used to express the change in risk: ΔR j (h) = R j (h+Δh)-R j (h), where ΔR j (t) represents the risk change of the jth sampling point in the hth time period; Δh represents the length of the time period.

7. The method for online monitoring and risk assessment of soil pollutants according to claim 6, characterized in that: In step 4, the process of dynamically adjusting the intensity and scope of prevention and control measures corresponding to the sampling points includes: Dynamically adjust the intensity of prevention and control measures based on the amount of risk change: T p (h+Δh)=T p (h)+δ p ·|ΔR p (h)|, Where, T p (h+Δh) represents the intensity of prevention and control measures; p∈{low, mid, high} represents low, medium and high risk areas; δ p Represents the adaptation coefficient, which is used to control the parameters of the adjustment of prevention and control measures, where δ p ={δ low ,δ mid ,δ hig } and δ low <δ mid <δ hig , that is, the high-risk areas have the greatest adjustment efforts, and the low-risk areas have the least adjustment efforts; At the same time, the scope of prevention and control measures is adjusted based on the changes in risk signals at sampling points and the composite risk assessment values ​​of adjacent points.

8. Soil pollutant online monitoring and risk assessment system, characterized by: The method for online monitoring and risk assessment of soil pollutants as claimed in any one of claims 1 to 7 comprises: The area division and data monitoring module is used to divide the soil pollutant monitoring area and monitor the pollutant concentration in the soil in real time; The sampling point division and frequency determination module is used to allocate sampling points in the monitoring area to collect the pollutant concentration in the soil, and calculate and analyze the fluctuation of the pollutant concentration based on historical data to determine the sampling frequency; The composite risk assessment function establishment module is used to establish a composite risk assessment function to assess the risk level of all sampling points and obtain a composite risk assessment value; The risk prevention and control dynamic adjustment module is used to analyze the risk changes of sampling points according to the composite risk assessment value, and dynamically adjust the intensity and scope of prevention and control measures corresponding to the sampling points.

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