Soil environment damage identification and evaluation method and system

Abstract

A soil environment damage identification and evaluation method and system relates to the field of soil identification, and the method comprises the steps: obtaining sampling data, analyzing and calculating the pollution source probability of a sub-region through a traceability model, simulating the diffusion process of a target pollution source based on a hydrodynamic model, determining a pollution risk region, and calculating a regional pollution index. According to the method, the pollution condition in the region can be comprehensively evaluated and analyzed by matching the treatment strategy, the pollution source and the seriously affected region can be judged, the pollution degree index results of different regions can be obtained, and therefore differentiated treatment strategy formulation can be carried out, and compared with traditional fixed point monitoring and average treatment, the method has the advantages that the efficiency is high; according to the scheme, refined analysis and precise treatment of regional pollution can be realized, and the pollution control efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of soil identification, and in particular to a soil environmental damage identification and assessment method and system. Background Art

[0002] With the continuous advancement of industrialization and urbanization, the problem of soil pollution is becoming increasingly serious. Soil pollution not only threatens the health of the ecosystem, but also has a serious impact on human health and socio-economic development.

[0003] In related technologies, soil pollution assessment is usually carried out using a fixed sampling point method. The specific method includes setting up a number of fixed sampling points in the target area, regularly collecting soil samples from each sampling point, and then determining the type and concentration of pollutants in the soil through laboratory analysis, and then conducting treatment and remediation in areas where the concentration exceeds the standard.

[0004] However, the fixed sampling point method can only measure the pollution concentration in a certain area, and the pollution detection in the overall target area is not accurate enough, resulting in inaccurate control of pollutants. Summary of the invention

[0005] The present application provides a soil environmental damage identification and assessment method and system for improving the accuracy of pollution detection and improving the precision of pollutant control.

[0006] In a first aspect, the present application provides a soil environmental damage identification and assessment method, which is applied to a soil environmental damage identification and assessment system. The method comprises: Acquire sampling data of all fixed sampling points in a target area, where the target area contains a plurality of sub-areas, and each sub-area contains one fixed sampling point; The sampling data is input into the source tracing analysis model to obtain the pollution source probability of each sub-area; Simulate the diffusion path and impact range of the target pollution source in the target area based on the hydrodynamic model and the hydrogeological parameters of the target pollution source, where the target pollution source is a pollution source with a probability greater than a preset threshold; Calculate the pollution index of each target area according to the diffusion path and the impact range; Selecting a corresponding governance strategy matching the pollution index in the target strategy database; A prompt message containing the governance policy is sent to the client.

[0007] In the above embodiment, by acquiring sampling data, calculating the probability of pollution sources in the sub-area through source tracing model analysis, and then simulating the diffusion process of the target pollution source based on the hydrodynamic model, the pollution risk area is determined, and then the regional pollution index is calculated and matched with the governance strategy. A comprehensive assessment and analysis of the pollution situation in the area can be conducted, which can not only determine the pollution sources and severely affected areas, but also obtain the pollution degree index results of different areas, so that differentiated governance strategies can be formulated. Compared with traditional fixed-point monitoring and average governance, this solution can realize refined analysis and precise governance of regional pollution, thereby improving pollution control efficiency.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of inputting the sampling data into the source tracing analysis model to obtain the pollution source probability of each sub-area specifically includes: Extracting characteristic parameters from the sampling data, and using the obtained first pollutant concentration parameter and first soil physical and chemical property parameter as inputs of the source tracing analysis model; The first pollutant concentration parameter and the first soil physical and chemical property parameter are input into the source tracing analysis model to calculate the posterior probability that a pollution source exists in each sub-region as the pollution source probability of the sub-region.

[0009] In the above embodiment, by extracting the data features obtained from the pollution monitoring sampling analysis, including pollutant concentration parameters and soil physical and chemical parameters, as the model input, the probability distribution of the pollution sources in each sub-region can be obtained by applying the tracing model calculation. Since the pollution characteristics released by different pollution sources are in a corresponding relationship, the potential specific pollution sources of each sub-region can be determined by analyzing the reverse attribution of the monitoring results, which can achieve effective judgment of the pollution sources in the areas that are difficult to monitor directly.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of simulating the diffusion path and impact range of the target pollution source in the target area based on the hydrodynamic model and the hydrogeological parameters of the target pollution source specifically includes: Obtaining hydrogeological parameters of the target area, the hydrogeological parameters including topographic data, soil structure data and underground hydrogeological data; According to the location coordinates of the target pollution source, the pollution source parameters of the target pollution source and the hydrogeological parameters, a numerical simulation is performed through a hydrodynamic model to obtain a migration and diffusion process of the target pollution source, wherein the migration and diffusion process includes the pollutant concentration of each target sub-area within the target area; Visualize and analyze the migration and diffusion process to obtain the diffusion path of the target pollution source in the target area; The pollutant concentration distribution map of the target pollution source and the boundary map of the target area are superimposed, and the target area where the pollutant concentration exceeds the national standard threshold of the pollutant concentration is extracted as the influence range of the target pollution source. The pollutant concentration distribution map is obtained by counting the pollutant concentrations of all sub-areas within the target area.

[0011] In the above embodiment, by acquiring digital hydrogeological data of the target area, including parameters such as topography, soil and groundwater flow direction, and then based on these data, a hydrodynamic mathematical model is used to simulate the migration and diffusion process of each pollution source. The model output results can directly reflect the impact range and propagation path of different pollution sources, and evaluate the pollution status of each area. The prediction results of this model are more spatially comprehensive than direct monitoring, and can intuitively judge high-risk areas of pollution.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the pollution index of each target area according to the diffusion path and the influence range specifically includes: Determine whether each target sub-area is within the influence range of any target pollution source; If so, calculate the superposition influence coefficients of all target pollution sources on each target sub-area to obtain a collection of superposition influence coefficients; Compare the pollutant concentration of each target sub-area with the preset standard threshold, and calculate the excess index collection; Calculate a collection of exposure risk indices according to the position of each target sub-area in the diffusion path; The pollution index of each target area is obtained by taking a weighted average of the superimposed influence coefficient of each target area in the superimposed influence coefficient collection, the exceeding index of each target area in the exceeding index collection, and the exposure risk index of each target area in the exposure risk index collection.

[0013] In the above embodiment, by judging whether the sub-region is within the pollution influence range, calculating the superposition influence coefficient, determining the exceeding index, calculating the exposure risk index, and weightedly calculating the final pollution index, the comprehensive impact of different pollution sources can be evaluated, and the pollution status of each sub-region in the region can be accurately and quantitatively determined. The method comprehensively considers the multi-source superposition effect, the pollution exceeding the standard, and the exposure risk, and obtains the digital pollution evaluation result of each sub-region through the exponential weighted algorithm, which reflects the pollution distribution in the region with higher precision.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the superposition influence coefficient of all the target pollution sources on each target sub-area specifically includes: Obtaining pollution source parameters of each target pollution source and hydrogeological parameters of each target sub-area; The pollution source parameters and hydrogeological parameters are input into the preset multi-source pollution superposition impact calculation model to obtain the pollutant concentration contribution value of each target pollution source to each target sub-area; The pollutant concentration contribution value of each target pollution source is standardized to obtain the superposition influence coefficient of each target pollution source on each target sub-area.

[0015] In the above embodiment, by obtaining data on pollution sources and sub-regions and applying a multi-source pollution superposition model, the pollution contribution of each pollution source to each sub-region can be calculated and converted into a standardized impact coefficient. The impact contribution of different pollution sources can be quantitatively judged based on the calculation model, eliminating the influence of data dimensions, and achieving unified evaluation index results. The standardized impact coefficient can intuitively reflect the pollution load of each pollution source on different regions.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of sending the prompt information including the governance policy to the client, the method further includes: After receiving the governance confirmation request from the client, obtaining expert label information and geographic information of all experts in the governance expert database; Calculate the first matching degree between the location coordinates of the target area and the target area of ​​the geographic information and all the experts; Calculate the hydrogeological parameters of the target area and the expert label information to calculate the first matching degree between the target area and all the experts; An expert whose weighted matching degree of the first matching degree and the second matching degree exceeds a preset matching degree threshold is taken as a first expert, and instruction information containing the first expert is sent to the client.

[0017] In the above embodiment, by matching the expert's knowledge background with the regional characteristics, calculating the matching degree, and selecting the matching expert as the preferred recommendation, accurate decision-making support for the governance plan can be achieved, and the governance efficiency can be improved. The relevance of the expert's knowledge background is fully utilized, and the matching factors of the spatial location are considered, so that the recommendation results meet the specific conditions and needs of the region, avoiding the phenomenon of blind governance due to insufficient experience. Users can obtain matching professional governance plans and shorten the cost of trial and error.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of taking an expert whose weighted matching degree of the first matching degree and the second matching degree exceeds a preset matching degree threshold as a first expert and sending instruction information containing the first expert to the client, the method further includes: Acquire the second pollutant concentration parameter of the target area, the second soil physical and chemical property parameter of the target area and the control measures sent by the client; A remediation report is generated according to the first pollutant concentration parameter, the first pollutant concentration parameter, the first soil physical and chemical property parameter, the second soil physical and chemical property parameter and the remediation measure, and the remediation report is sent to the client.

[0019] In the above embodiment, by obtaining the sampling data after treatment, generating and feeding back the treatment report, a closed-loop interactive treatment process can be established, the treatment plan can be continuously optimized, and the treatment efficiency can be improved. The method obtains the latest sampling data after the user's treatment, including pollution concentration, soil parameters and the treatment measures adopted, so as to generate a treatment report containing comparative analysis and feed it back to the user.

[0020] In a second aspect, an embodiment of the present application provides a soil environmental damage identification and assessment system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the soil environmental damage identification and assessment system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product is run on a soil environmental damage identification and assessment system, the above-mentioned soil environmental damage identification and assessment system performs the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a soil environmental damage identification and assessment system, the soil environmental damage identification and assessment system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It can be understood that the soil environmental damage identification and assessment system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application obtains sampling data, calculates the probability of pollution sources in the sub-area through source tracing model analysis, and then simulates the diffusion process of the target pollution source based on the hydrodynamic model to determine the pollution risk area, and then calculates the regional pollution index and matches the governance strategy. It can conduct a comprehensive assessment and analysis of the pollution situation in the region, not only determine the pollution sources and severely affected areas, but also obtain the pollution degree index results of different regions, so as to formulate differentiated governance strategies. Compared with traditional fixed-point monitoring and average governance, this solution can realize refined analysis and precise governance of regional pollution, and improve pollution control efficiency.

[0025] 2. This application extracts the data characteristics obtained from pollution monitoring sampling and analysis, including pollutant concentration parameters and soil physical and chemical parameters, as model input, and applies the source tracing model to calculate the probability distribution of pollution sources in each sub-region. Since the pollution characteristics released by different pollution sources are corresponding, the potential specific pollution sources of each sub-region can be determined by analyzing the reverse attribution of the monitoring results, which can achieve effective judgment of pollution sources in areas that are difficult to monitor directly.

[0026] 3. This application obtains digital hydrogeological data of the target area, including parameters such as topography, soil and groundwater flow direction, and then uses a hydrodynamic mathematical model based on these data to simulate the migration and diffusion process of each pollution source. The model output results can directly reflect the impact range and propagation path of different pollution sources and evaluate the pollution status of each area. The prediction results of this model are more spatially comprehensive than direct monitoring, and can intuitively judge high-risk areas of pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the soil environmental damage identification and assessment method in the embodiment of the present application; Figure 2 This is another flow chart of the soil environmental damage identification and assessment method in the embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the soil environmental damage identification and assessment system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] The following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the soil environmental damage identification and assessment method in an embodiment of the present application.

[0031] S101, acquiring sampling data of all fixed sampling points in a target area, where the target area includes a plurality of sub-areas, and each sub-area includes one fixed sampling point.

[0032] Among them, the target area represents the overall area where soil pollution assessment is planned, the sub-area represents multiple small areas divided at a certain distance within the target area, the fixed sampling points represent the sampling locations for collecting soil samples pre-set in each sub-area, and the sampling data refers to the test and analysis data of the soil samples collected from the fixed sampling points.

[0033] This step is to obtain soil sampling data of the target area before conducting soil pollution assessment. Specifically, for each sub-area in the target area, soil samples need to be collected at pre-set fixed sampling points, and then the soil samples are tested and analyzed to obtain sampling data including pollutant concentrations, physical and chemical indicators, etc.

[0034] In some embodiments, sampling data can be obtained in the following two ways: sampling personnel collect soil samples at fixed sampling points in each sub-area according to the sampling plan, and then send them to the laboratory for pollutant concentration and physical and chemical property detection and analysis to obtain sampling data; soil samples are monitored in real time by sensors installed at each fixed sampling point, and the monitoring data is transmitted to the collection device to form sampling data. It is understandable that other data collection methods can also be used, which are not limited here.

[0035] S102: Input the sampling data into a source tracing analysis model to obtain the pollution source probability of each sub-area.

[0036] Among them, the source tracing analysis model refers to a probability model that infers the pollution source by analyzing sampling data, and the pollution source probability refers to the probability value of the existence of a pollution source in each sub-area.

[0037] This step is based on the sampling data and uses the source tracing analysis model to calculate the probability of the existence of a pollution source in each sub-area. Specifically, the sampling data is used as the model input, and the model will analyze the correlation between the sampling data and potential pollution sources, and calculate the probability of the existence of different pollution sources in each sub-area as the pollution source probability of the sub-area.

[0038] In some embodiments, this step can be implemented in two ways: establishing a probability model based on a Bayesian network, extracting characteristic parameters of the sampled data as model input, and calculating the posterior probability of each pollution source as the pollution source probability; building a model based on association rules, analyzing the correlation between the sampled data and the historical pollution source data, and obtaining the correlation of each pollution source as the pollution source probability. It is understandable that other tracing models can also be used, which are not limited here.

[0039] S103, simulating the diffusion path and impact range of the target pollution source in the target area based on the hydrodynamic model and the hydrogeological parameters of the target pollution source, the target pollution source being a pollution source with a probability greater than a preset threshold.

[0040] Among them, the hydrodynamic model refers to a mathematical model that simulates groundwater flow and pollutant migration. The hydrogeological parameters include data such as topography, soil, and groundwater flow direction. The diffusion path refers to the migration route of pollutants, and the impact range refers to the pollution risk area.

[0041] This step uses hydrodynamic models and hydrogeological data to simulate the diffusion path and impact range of target pollution sources with a probability greater than the threshold. Specifically, the model simulates the diffusion process of pollution sources, predicts the path of pollutants migrating through the groundwater system, and the pollution risk areas that exceed the standard.

[0042] In some embodiments, this step can be achieved in the following two ways: using the finite element method to establish a three-dimensional hydrogeological model of the study area, loading pollution source parameters, digitally simulating to solve the diffusion equation, and outputting the pollution migration trajectory and range; using the random walk method, taking the pollution source as the starting point, moving the path line according to probability, and superimposing to generate an impact range distribution map. It can be understood that other hydrodynamic models can also be used, which are not limited here.

[0043] S104: Calculate the pollution index of each target area according to the diffusion path and the impact range.

[0044] Among them, the pollution index refers to a quantitative indicator for evaluating the degree of soil pollution.

[0045] This step calculates the pollution index of each target sub-region based on the pollution diffusion path and impact range obtained above. Specifically, based on the relative position of the sub-region in the diffusion path and whether it is within the impact range, a model is used to calculate an index that comprehensively evaluates the severity of the pollution.

[0046] In some embodiments, the pollution index can be calculated in the following two ways: establishing an evaluation model based on distance attenuation, calculating the exposure risk index and the excess index of each sub-area, and performing weighted summation; constructing an evaluation model based on a BP neural network, using sampling data, diffusion parameters, etc. as model inputs, and outputting the pollution index after network training. It is understandable that other calculation methods can also be used, which are not limited here.

[0047] S105: Select a governance strategy that matches the pollution index in the target strategy database.

[0048] Among them, the target strategy database refers to a database that stores the correspondence between pollution index and governance strategy, and the governance strategy refers to the governance measures taken for different pollution levels.

[0049] This step is to search the strategy database for a governance strategy that matches the pollution index. Specifically, according to the pollution index value calculated above, a governance strategy matching it is searched in the database, that is, a governance measure recommended for the pollution index.

[0050] In some embodiments, this step can be implemented in the following two ways: establishing pollution index classification standards, pre-storing corresponding governance strategies, and directly searching for matching strategies according to the pollution index level; establishing a SVM-based model, inputting the pollution index, and outputting matching governance strategies. It is understandable that matching strategies can also be searched in the database in other ways, which are not limited here.

[0051] S106: Send prompt information including the governance policy to the client.

[0052] Among them, prompt information refers to the governance strategy recommendation information displayed to the user.

[0053] This step is to send the found governance strategy to the user in the form of prompt information. Specifically, the matched governance strategy is organized into prompt information in the form of text, voice or picture, and sent to the client user through the network for reference.

[0054] In some embodiments, this step can be implemented in the following two ways: organizing the governance policy in text form as a prompt and pushing it to the user through the APP; using speech synthesis technology to synthesize the governance policy into a voice prompt and push it to the user through a voice call. It is understandable that the prompt information can also be sent to the user in other ways, which are not limited here.

[0055] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the soil environmental damage identification and assessment method in the embodiment of the present application.

[0056] S201, acquiring sampling data of all fixed sampling points in a target area, where the target area includes a plurality of sub-areas, and each sub-area includes one fixed sampling point.

[0057] It can be understood that this step is similar to step S101 and will not be described in detail here.

[0058] S202: extract characteristic parameters from the sampling data, and use the obtained first pollutant concentration parameter and first soil physical and chemical property parameter as inputs of the source tracing analysis model.

[0059] Among them, characteristic parameters refer to important sampling data indicators that reflect the pollution situation, the first pollutant concentration parameter refers to the quantitative detection concentration of each pollutant, and the first soil physical and chemical property parameter refers to the soil pH value, moisture content and other parameters. This step is that before conducting the source tracing analysis, the soil environmental damage identification and assessment system needs to extract characteristic parameters for model analysis from the acquired sampling data, specifically, to screen out pollutant concentration parameters and soil physical and chemical parameters as the input data source of the source tracing model.

[0060] In some embodiments, the system reads the sampling data file transmitted by the sampling sensor, locates the detection result part, parses the detection result table, finds the column with the pollutant name (such as benzene, formaldehyde, etc.) as the header, saves its detection concentration value as the first pollutant concentration parameter, continues to parse the detection result table, finds the column with the soil physical and chemical property (such as pH value, moisture content, etc.) as the header, saves its detection value as the first soil physical and chemical property parameter, organizes the extracted pollutant concentration parameters and soil physical and chemical parameters in the required format as the input feature parameters of the traceability model.

[0061] S203, inputting the first pollutant concentration parameter and the first soil physical and chemical property parameter into the source tracing analysis model to calculate the posterior probability of the existence of a pollution source in each sub-region as the pollution source probability of the sub-region.

[0062] Among them, the posterior probability is the probability of the target appearing under the given observation sample conditions.

[0063] This step is to input the extracted characteristic parameters into the source tracing model to calculate the posterior probability of the pollution source in each sub-region. Specifically, the model uses the characteristic parameters and historical data to calculate the probability of the pollution source as the probability of the existence of the pollution source in each sub-region.

[0064] In some embodiments, the system constructs a Bayesian network model, in which the input nodes are a first pollutant concentration parameter and a first soil physical and chemical property parameter, and the intermediate nodes are set as the possibilities of different pollution sources. The parameters of the network model are trained by learning historical data, and the specific sampling parameters are input into the model. The posterior probability of each pollution source node is obtained through inference calculation, and the posterior probability of each pollution source is returned as the pollution source probability of the sub-area.

[0065] S204, obtaining hydrogeological parameters of the target area, wherein the hydrogeological parameters include topographic data, soil structure data and underground hydrogeological data.

[0066] Topographic data refers to the topography and terrain data of the target area. Soil structure data refers to the permeability and water content of the soil. Groundwater hydrogeological data refers to the flow direction and water quality of groundwater. This step is to obtain the input parameters for hydrodynamic model simulation, including the topography and geomorphology of the target area, soil physical properties and groundwater flow law data. These data can be obtained from maps, soil survey reports, hydrogeological research reports and other sources, or collected through field surveys. The various types of data obtained will be processed and converted to meet the input format requirements of the hydrodynamic model.

[0067] S205. Numerical simulation is performed through a hydrodynamic model according to the location coordinates of the target pollution source, the pollution source parameters of the target pollution source and the hydrogeological parameters to obtain the migration and diffusion process of the target pollution source, wherein the migration and diffusion process includes the pollutant concentration of each target sub-area within the target area.

[0068] The location coordinates of the target pollution source refer to its longitude and latitude. Pollution source parameters refer to the concentration, liquid properties and other properties of pollutants. This step uses a hydrodynamic model to consider the properties, location and hydrogeological conditions of the pollution source, simulates the migration and diffusion of pollutants, and obtains the pollution concentration distribution results for each period and each sub-area.

[0069] In some embodiments, first, according to the hydrogeological data, the target area is discretized into several grid units using the finite element method to prepare for subsequent numerical calculations. Next, a calculation point is set in each grid unit, and the hydrogeological parameters of the calculation point, such as soil permeability coefficient, groundwater flow rate, etc., are extracted to provide coefficient values ​​for the equation. Then, the location coordinates of the target pollution source are mapped to the model grid structure, and the grid unit corresponding to the pollution source is determined as the source term. Next, various pollution source parameters of the target pollution source, including pollutant concentration, liquidity, diffusion coefficient, etc., are extracted to provide data support for the source term. After that, the diffusion-aggregation equation is applied to describe the material conservation law of pollutants in the region, and a mathematical model for simulation calculation is established. After that, the program code is written in combination with the pollution source parameters and hydrogeological parameters, and the diffusion equation is iterated in each grid unit. Then, the iterative calculation is repeated to output the pollution concentration results of each grid unit at different times. Finally, the calculation results of each grid unit are summarized to obtain the pollution concentration distribution of each sub-area of ​​the target area at different times.

[0070] For example, assuming that the target area is 10 square kilometers, it is discretized into 100 grid cells, and the area of ​​each grid cell is 0.1 square kilometers. The target pollution source is located in the (25, 35)th grid cell. The parameters of the pollution source include: the pollutant is methanol, the original concentration is 10 mg / L; the liquid viscosity coefficient is 1.2 cP; the diffusion coefficient is 0.6 m2 / s. By consulting the hydrogeological data, the soil permeability coefficient kx, ky and the groundwater flow rate vx, vy of each grid cell are extracted. Calculation points are set in each grid cell, and the diffusion equation is applied. The above parameters are brought in for iterative calculation to simulate the pollution concentration distribution after 5 hours. After calculation, the methanol concentration in the (30, 40)th sub-area in the target area is 2.3 mg / L. Repeat the execution to obtain the pollutant concentrations of all sub-areas in each period.

[0071] S206: Visualize and analyze the migration and diffusion process to obtain the diffusion path of the target pollution source in the target area.

[0072] Specifically, the system reads the result data of the pollution diffusion simulation, which contains the pollution concentration values ​​of different periods and different regional units. Then, the visualization module is called to map the concentration value to the color spectrum, and high concentration values ​​correspond to high-saturation colors. Next, the color value of each regional unit is rendered on the regional map to form a heat map of the pollution concentration distribution at different periods. Finally, the continuous change trajectory of the high-value pollution area is extracted from the heat map sequence as the main diffusion path of the pollution source. In this way, the simulation result data is analyzed by visualization means, and the spatial-temporal migration law and diffusion path of pollutants are intuitively presented.

[0073] For example, suppose that a 10×10 regional grid is obtained through numerical simulation, and the pollution concentration data of each grid is as follows: [[0.1, 0.2, 0.3, ...], [0.5, 1.2, 0.8, ...], ...], first, set the color mapping rule: 0-0.2 is mapped to green, 0.2-0.4 is mapped to yellow, 0.4-0.6 is mapped to orange, and so on. Then, traverse the concentration value of each grid, determine its RGB color value according to the mapping rule, and then draw each small grid on the map and color it. For example, the concentration of the (3, 5) grid is 1.2, and the corresponding color is red. Fill the polygonal area of ​​the (3, 5) grid with red.

[0074] Finally, on the grid maps at different times, the movement trajectories of the red high-concentration grid cells are tracked as the main diffusion paths.

[0075] S207, superimposing the pollutant concentration distribution map of the target pollution source and the boundary map of the target area, extracting the target area where the pollutant concentration exceeds the national standard threshold of the pollutant concentration as the influence range of the target pollution source, the pollutant concentration distribution map is obtained by counting the pollutant concentrations of all sub-areas within the target area.

[0076] Specifically, the system first summarizes the average pollution concentration of all units in each sub-region and generates a pollutant concentration distribution heat map. Then, the regional boundary map data is read. Next, the two layers are superimposed, the concentration heat map is cropped, and the sub-region range that exceeds the national pollutant concentration standard limit is extracted as the impact range. In this way, the impact range of the excessive pollution is determined based on the numerical simulation results, providing a basis for the subsequent formulation of governance strategies.

[0077] For example, suppose there are 5 sub-areas in the area, and their average concentrations are [0.2, 0.6, 1.5, 3.2, 0.8]. The national standard limit is 1.0. First, draw the color block map of the sub-area according to the average concentration, then load the area boundary data, overlay the same color block layer, then traverse all sub-areas to find areas with concentrations exceeding 1.0, such as the 3rd and 4th sub-areas, and finally, cut out the 3rd and 4th sub-area polygons in the overlay layer as the impact range. In this way, the sub-areas exceeding the standard are determined by overlay, and the impact range result is obtained.

[0078] S208: Determine whether each target sub-area is within the influence range of any target pollution source.

[0079] Specifically, the system will first obtain the geographical range data of all pollution sources generated in the early stage. Then load the regional boundary data of all sub-regions. For each sub-region, use the spatial analysis algorithm to check whether its range polygon has an intersection with any pollution source's range polygon. If there is an intersection, it is determined that the sub-region is within the influence range of the pollution source. By traversing and judging, a Boolean result can be obtained to determine whether each sub-region is affected by each pollution source.

[0080] S209: If yes, calculate the superposition influence coefficients of all the target pollution sources on each target sub-area to obtain a collection of superposition influence coefficients.

[0081] Specifically, for the sub-area determined to be affected in step S208, the system needs to consider the superposition effect of all pollution sources, and will conduct a comprehensive analysis of the spatial distance between the sub-area and each pollution source, the pollution level, etc., calculate the contribution coefficient of each pollution source to the sub-area, and then use the weighted superposition algorithm to integrate the influence coefficients of all pollution sources to obtain the final comprehensive superposition influence coefficient of the sub-area. Repeat the calculation to obtain the collection of superposition influence coefficients of all affected sub-areas.

[0082] If not, it indicates that the target sub-area is not within the influence range of any target pollution source, and no operation is performed on the target sub-area.

[0083] This step specifically includes the following steps: In this step, the pollution source parameters of each of the target pollution sources and the hydrogeological parameters of each of the target sub-areas are obtained. The system needs to obtain relevant data of the pollution sources and sub-areas as model inputs. The pollution source parameters include attribute data such as pollutant type, pollutant concentration, and pollutant liquid properties. The hydrogeological parameters include regional environmental data such as groundwater flow direction, groundwater flow rate, and soil permeability. The system can obtain these data from monitoring records of pollution source enterprises, hydrogeological research reports, etc.

[0084] Input the pollution source parameters and hydrogeological parameters into a preset multi-source pollution superposition impact calculation model to obtain the pollutant concentration contribution value of each target pollution source to each target sub-area; In this step, according to the diffusion equation, the concentration distribution of pollutants from each pollution source in different sub-areas is calculated, and the pollution concentration value of each pollution source in each sub-area is counted as the pollutant concentration contribution value. First, the source strength S of each pollution source is extracted to represent the amount of pollutants released per unit time. According to the hydrogeological parameters, the diffusion coefficient D of the study area is obtained, and the diffusion equation is established: C=S / (πDX*Y), which expresses the relationship between the pollutant concentration C and the distance X, Y. The area is divided into grids, and the concentration C of each grid unit is iteratively calculated. The C value of each pollution source in each sub-area grid is counted, and the average is taken as its concentration contribution value to the sub-area.

[0085] The pollutant concentration contribution value of each target pollution source is standardized to obtain the superposition influence coefficient of each target pollution source on each target sub-area.

[0086] First, find the minimum and maximum values ​​of the concentration contribution values ​​of all pollution sources. For the concentration contribution value of each pollution source in each sub-area, apply the formula: X'=(X-Min) / (Max-Min). The calculated X' is the superposition influence coefficient in the range of 0-1 after standardization. Finally, the superposition influence coefficient of each target sub-area is obtained.

[0087] S210: Compare the pollutant concentration of each target sub-area with a preset standard threshold, and calculate a set of exceeding standard indexes.

[0088] The system first needs to obtain the average pollution concentration value in each sub-area, which requires collecting the simulated concentrations of all grid cells output by the pollution diffusion model, averaging them by sub-area, and obtaining representative pollution concentration results for the sub-area. Next, the system needs to load the preset pollution concentration limit standards, which are proposed by the environmental monitoring department based on the pollutant category and risk to form a limit table or database. Then, the system needs to traverse the access sub-area list, read the pollution concentration values ​​of the sub-areas one by one, and find matching limit standards. If no matching standards are found, similar pollutants need to be converted accordingly. Then compare the size relationship between the pollution concentration of the sub-area and the corresponding standard limit. If the limit is exceeded, calculate the multiple of the excess and convert it into an excess index in the range of 0-1. If the limit is not exceeded, the excess index is 0. In this way, the excess index of all sub-areas is traversed and calculated, and finally a combination set of excess index numbers is formed.

[0089] S211. Compare the pollutant concentration of each target sub-area with a preset standard threshold, and calculate a set of exceeding standard indexes.

[0090] The system first needs to determine the coordinates of the center point of each sub-region. The system can automatically calculate the geometric center of the sub-region polygon, or use the population center or asset center within the sub-region to determine the center point of focus. Next, the system needs to access the list of target pollution sources and read the coordinate location information of the pollution sources one by one. Then access the list of sub-region center points, use the formula to calculate the spatial distance, and find the linear distance from the center point of each sub-region to the location of each pollution source. Subsequently, apply the inverse function to the distance value to convert it into an index value in the range of 0-1. The farther the distance, the smaller the index, and the closer the distance, the larger the index. If there are multiple pollution sources affecting the same sub-region, the maximum index can be taken to represent the final exposure risk index of the sub-region. After calculating all sub-regions in this way, a collection of sub-region exposure risk index arrays is formed.

[0091] S213, taking a weighted average of the superimposed influence coefficient of each target area in the superimposed influence coefficient collection, the exceeding index of each target area in the exceeding index collection, and the exposure risk index of each target area in the exposure risk index collection to obtain the pollution index of each target area.

[0092] The system needs to take out the result data of the three index collections, and then preset the weight percentages, such as 40% for the impact coefficient, 30% for the excess index, and 30% for the exposure index. Next, traverse each sub-area, take out the corresponding three index values, and perform weighted calculations according to the weight percentages to obtain the comprehensive pollution index of the sub-area. For example, if the three indexes of a sub-area are 0.6, 0.4, and 0.8, respectively, then its pollution index is calculated as 0.4*0.6+0.30.4+0.3*0.8=0.5. After calculating all partitions in this way, the pollution index of all sub-areas is obtained.

[0093] S214: Select a governance strategy that matches the pollution index in the target strategy database.

[0094] It can be understood that this step is similar to step S105 and will not be described in detail here.

[0095] S215: Send prompt information containing the governance policy to the client.

[0096] It can be understood that this step is similar to step S106 and will not be described in detail here.

[0097] After step S215, the following steps are also included: After receiving the governance confirmation request from the client, obtaining expert label information and geographic information of all experts in the governance expert database; In this step, the system needs to enable the expert matching process after receiving the governance service confirmation request sent by the user. The process first needs to extract detailed information of all experts from the preset governance expert database, which mainly includes two parts of data. The first is the expert label information. This type of information can be expressed in a list of words such as the expert's areas of expertise and familiar scenarios, such as "soil remediation, heavy metal governance", etc. The second is the expert's geographic information, mainly the latitude and longitude coordinates of the city or institution where the expert is located. The system can obtain the above two types of information of all experts from the expert database through SQL query or interface call, and store them as variables for subsequent calculations.

[0098] Calculate the first matching degree between the location coordinates of the target area and the target area of ​​the geographic information and all the experts; In this step, after obtaining the expert information, the system needs to calculate the geographic location matching degree. First, the system needs to obtain the coordinate location information of the target area to be served as requested by the user, such as the center point coordinates or the bounding box coordinates. Then, the system needs to obtain the geographic coordinate information of each expert one by one, calculate the spatial distance from the target area coordinates, and use the inverse distance method to map and convert it into the first matching degree in the range of 0-1. The conversion function can be obtained through model training. In simple terms, the closer the expert is to the target area, the higher the first matching degree is. The first matching degree of each expert is calculated in this way in a loop. Calculate the hydrogeological parameters of the target area and the expert label information to calculate the first matching degree between the target area and all the experts; In this step, the system also needs to calculate the degree of professional knowledge matching. First, the system needs to extract the hydrogeological data of the target service area, including groundwater quality parameters, soil permeability indicators, etc. Then, the system accesses the professional label vocabulary of each expert and uses the string matching algorithm to calculate the degree of label coverage. The more professional knowledge points are covered, the higher the second degree of matching. For example, if the groundwater in the target area contains sodium, and the expert label contains "groundwater without sodium", the second degree of matching is higher. This cycle is repeated to obtain the knowledge matching degree of each expert.

[0099] An expert whose weighted matching degree of the first matching degree and the second matching degree exceeds a preset matching degree threshold is taken as a first expert, and instruction information containing the first expert is sent to the client.

[0100] In this step, after obtaining the first and second matching degrees of each expert, the system needs to perform a weighted calculation to obtain a comprehensive matching degree. First, the system needs to set the weight ratio of the matching degree, for example, the geographical location matching degree weight is 0.3, and the professional knowledge matching degree weight is 0.7. The weight setting can be adjusted according to actual business needs. Then, the system needs to take out the two matching degrees of each expert one by one, perform a weighted sum calculation, and obtain the overall matching score of the expert. For example, if the first matching degree of an expert is 0.8 and the second matching degree is 0.6, then the weighted matching degree calculation formula is: 0.3*0.8+0.7*0.6=0.66. Next, the system needs to preset a matching degree threshold, for example, set it to 0.8. The threshold can determine the optimal expert by simulating the indicator effects of each value of the test. After that, the system compares the matching score of each expert with the threshold, and screens out experts whose matching degree exceeds the threshold as the first expert candidates who meet the requirements. Finally, the system needs to organize the information of the first expert into an instruction format, including the expert's name, organization, contact information, etc., and send it to the user client through the API interface so that the client can obtain detailed information about the matching expert, so that the user can subsequently initialize contact with the expert to carry out the governance project.

[0101] After the step of taking the expert whose weighted matching degree of the first matching degree and the second matching degree exceeds the preset matching degree threshold as the first expert and sending the instruction information containing the first expert to the client, the method further includes: Acquire the second pollutant concentration parameter of the target area, the second soil physical and chemical property parameter of the target area and the control measures sent by the client; In this step, after completing the preliminary pollution assessment and expert matching, the system needs to obtain relevant parameter data from the user after the governance is implemented. This includes the pollutant concentration parameters obtained from the second sampling test of the target area, such as the concentration of various heavy metals and organic matter; the physical and chemical parameters of the soil from the second sampling, such as pH value, moisture content, etc.; and the information on the governance measures actually taken by the user. The system can receive these three types of data sent by the client through the API interface and store them in variables for future use.

[0102] A remediation report is generated according to the first pollutant concentration parameter, the first pollutant concentration parameter, the first soil physical and chemical property parameter, the second soil physical and chemical property parameter and the remediation measure, and the remediation report is sent to the client.

[0103] In this step, after the system receives the data uploaded by the user, it needs to call the report generation module to integrate all the data to generate the final governance report. The report needs to include a comparison of the pollution concentrations of the two samples to explain the governance effect; changes in soil physical and chemical parameters, analysis of the governance mechanism; governance measures taken and effect evaluation. The system can set up a report template, extract key data points, insert placeholders to generate text or charts. The generated report is then sent to the client through the network for the user to view the effect of the governance process. In this way, through comparative analysis, the governance plan can be continuously optimized and the efficiency of pollution remediation can be improved.

[0104] The soil environmental damage identification and evaluation system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the structure of a physical device of the soil environmental damage identification and assessment system in an embodiment of the present application.

[0105] It should be noted that Figure 3 The structure of the soil environmental damage identification and assessment system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0106] like Figure 3 As shown, the soil environmental damage identification and assessment system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0107] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0108] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0109] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0111] Specifically, the soil environmental damage identification and assessment system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the soil environmental damage identification and assessment method provided in the above embodiment is implemented.

[0112] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the soil environmental damage identification and assessment system described in the above embodiment; or may exist independently without being assembled into the soil environmental damage identification and assessment system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the soil environmental damage identification and assessment system, the soil environmental damage identification and assessment system implements the soil environmental damage identification and assessment method provided in the above embodiment.

[0113] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0114] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0115] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A soil environmental damage identification and assessment method, characterized in that: Applied to a soil environmental damage identification and assessment system, the method comprises: Acquire sampling data of all fixed sampling points in a target area, wherein the target area includes a plurality of sub-areas, and each sub-area includes one fixed sampling point; Inputting the sampling data into a source tracing analysis model to obtain the pollution source probability of each sub-area; Simulating the diffusion path and impact range of the target pollution source in the target area based on the hydrodynamic model and the hydrogeological parameters of the target pollution source, wherein the target pollution source is a pollution source with a probability greater than a preset threshold; Calculate the pollution index of each target area according to the diffusion path and the impact range; Selecting a governance strategy corresponding to the pollution index in a target strategy database; A prompt message including the governance policy is sent to the client.

2. The method according to claim 1, characterized in that The step of inputting the sampling data into the source tracing analysis model to obtain the pollution source probability of each sub-area specifically includes: Extracting characteristic parameters from the sampling data, and using the obtained first pollutant concentration parameter and first soil physical and chemical property parameter as inputs of the source tracing analysis model; The first pollutant concentration parameter and the first soil physical and chemical property parameter are input into the source tracing analysis model to calculate the posterior probability that a pollution source exists in each sub-area as the pollution source probability of the sub-area.

3. The method according to claim 1, characterized in that The step of simulating the diffusion path and impact range of the target pollution source in the target area based on the hydrodynamic model and the hydrogeological parameters of the target pollution source specifically includes: Acquiring hydrogeological parameters of the target area, wherein the hydrogeological parameters include topographic data, soil structure data, and underground hydrogeological data; Numerical simulation is performed through a hydrodynamic model according to the location coordinates of the target pollution source, the pollution source parameters of the target pollution source and the hydrogeological parameters to obtain the migration and diffusion process of the target pollution source, wherein the migration and diffusion process includes the pollutant concentration of each target sub-area within the target area; Performing a visual analysis on the migration and diffusion process to obtain the diffusion path of the target pollution source in the target area; The pollutant concentration distribution map of the target pollution source and the boundary map of the target area are superimposed, and the target area where the pollutant concentration exceeds the national standard threshold of the pollutant concentration is extracted as the influence range of the target pollution source. The pollutant concentration distribution map is obtained by counting the pollutant concentrations of all sub-areas within the target area.

4. The method according to claim 1, characterized in that: The step of calculating the pollution index of each target area according to the diffusion path and the influence range specifically includes: Determine whether each target sub-area is within the influence range of any of the target pollution sources; If yes, then calculate the superposition influence coefficients of all the target pollution sources on each target sub-area to obtain a collection of superposition influence coefficients; Compare the pollutant concentration of each target sub-area with a preset standard threshold, and calculate a set of exceeding standard indexes; Calculating a collection of exposure risk indices according to the position of each of the target sub-areas in the diffusion path; The pollution index of each target area is obtained by taking a weighted average of the superimposed influence coefficient of each target area in the superimposed influence coefficient collection, the exceeding index of each target area in the exceeding index collection, and the exposure risk index of each target area in the exposure risk index collection.

5. The method according to claim 4, characterized in that The step of calculating the superposition influence coefficient of all the target pollution sources on each target sub-area specifically includes: Obtaining pollution source parameters of each of the target pollution sources and hydrogeological parameters of each of the target sub-areas; Input the pollution source parameters and hydrogeological parameters into a preset multi-source pollution superposition impact calculation model to obtain the pollutant concentration contribution value of each target pollution source to each target sub-area; The pollutant concentration contribution value of each target pollution source is standardized to obtain the superposition influence coefficient of each target pollution source on each target sub-area.

6. The method according to claim 2, characterized in that After the step of sending the prompt information including the governance policy to the client, the method further includes: After receiving the governance confirmation request from the client, obtaining expert tag information and geographic information of all experts in the governance expert database; Calculate the first matching degree between the location coordinates of the target area and the target area of ​​the geographic information and all the experts Calculating the hydrogeological parameters of the target area and the expert label information to calculate a first matching degree between the target area and all the experts; An expert whose weighted matching degree of the first matching degree and the second matching degree exceeds a preset matching degree threshold is taken as a first expert, and instruction information containing the first expert is sent to the client.

7. The method according to claim 6, characterized in that After the step of selecting an expert whose weighted matching degree of the first matching degree and the second matching degree exceeds a preset matching degree threshold as a first expert and sending instruction information containing the first expert to the client, the method further includes: Acquire the second pollutant concentration parameter of the target area, the second soil physical and chemical property parameter of the target area and the control measures sent by the client; A remediation report is generated according to the first pollutant concentration parameter, the first pollutant concentration parameter, the first soil physical and chemical property parameter, the second soil physical and chemical property parameter and the remediation measures, and the remediation report is sent to the client.

8. A soil environmental damage identification and assessment system, characterized in that: The soil environmental damage identification and assessment system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the soil environmental damage identification and assessment system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the soil environment damage identification and assessment system, the soil environment damage identification and assessment system is caused to execute the method as described in any one of claims 1-7.

10. A computer program product, characterized in that When the computer program product is run on a soil environment damage identification and assessment system, the soil environment damage identification and assessment system is enabled to execute the method as described in any one of claims 1 to 7.

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