Soil pollution risk analysis method and system based on internet of things sensor

Abstract

The application discloses a soil pollution risk analysis method and system based on an Internet of Things sensor, and relates to the technical field of data analysis.The technical scheme is as follows: collecting pollutant concentration distribution information of different soil layers; taking the migration of a first detection point with high pollutant concentration in the pollutant concentration distribution information to a second detection point with low pollutant concentration as a benchmark, generating an initial migration path; filtering false paths in the initial migration path in combination with geological permeability, a height difference between the first detection point and the second detection point and / or historical migration data; determining a pollutant migration rate between the first detection point and the second detection point based on a concentration change amount of the first detection point and the second detection point, migration path distribution and geological parameters; and predicting a pollutant diffusion range according to the pollutant migration rate.The application not only improves the accuracy and rationality of overall distribution analysis of the migration path, but also effectively reduces the prediction error of the local migration rate.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and more specifically, to a method and system for soil pollution risk analysis based on Internet of Things (IoT) sensors. Background Technology

[0002] Soil pollution risk analysis is a core decision-making basis for environmental governance because persistent and bioaccumulative substances such as heavy metals and organic pollutants can easily enter the human body through the food chain, causing serious impacts on human health. In addition, the spread of pollutants can also destroy soil biological communities, leading to reduced crop yields. Therefore, assessing soil pollution risk can identify high-risk areas and provide a scientific basis for environmental monitoring and governance.

[0003] Existing methods for analyzing soil pollution migration are mostly based on Darcy's law and Fick's law, constructing diffusion models using pollutant concentration gradients. However, these methods only use concentration gradients as the migration direction reference and do not consider the blocking effect of low-permeability strata on the path, easily leading to the generation of many false migration paths that do not conform to actual geological conditions. Furthermore, existing techniques struggle to exclude reverse migration of pollutants; additionally, model parameters are overly dependent on experimental data, resulting in significant discrepancies between predicted results and real-world scenarios.

[0004] Therefore, how to research and design a soil pollution risk analysis method and system based on IoT sensors that can overcome the above-mentioned shortcomings is an urgent problem that we need to solve. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for soil pollution risk analysis based on Internet of Things (IoT) sensors. This method first generates an initial path driven by concentration gradients, then integrates a multi-dimensional correction mechanism to filter out false paths. Furthermore, it dynamically weights and calculates migration rates while considering concentration changes, migration path distribution, and geological parameters. This not only improves the accuracy and rationality of the overall migration path distribution analysis but also effectively reduces the prediction error of local migration rates.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0007] Firstly, a method for soil pollution risk analysis based on IoT sensors is provided, including the following steps:

[0008] Collect information on the distribution of pollutant concentrations in different soil layers within the target area;

[0009] Based on the migration from the first detection point with high pollutant concentration to the second detection point with low pollutant concentration in the pollutant concentration distribution information, an initial migration path is generated.

[0010] By combining geological permeability, the height difference between the first and second detection points, and / or historical migration data, false paths in the initial migration path are filtered out to obtain a corrected migration path;

[0011] Based on the concentration changes, migration path distribution, and geological parameters of the first and second detection points, the pollutant migration rate between the first and second detection points is determined.

[0012] The extent of pollutant diffusion is predicted based on the pollutant migration rate.

[0013] Furthermore, the process of generating the initial migration path includes:

[0014] Based on the direction of the concentration gradient decrease, a straight path is generated from the first detection point to the adjacent second detection point, and one or more of the straight paths constitute the initial migration path.

[0015] Furthermore, the process of generating the initial migration path includes:

[0016] Spatial interpolation algorithms are used to fill in the concentration distribution in areas not covered by the sensor.

[0017] Based on the direction of the concentration gradient decrease, a curved path is generated from the first detection point to the adjacent second detection point, and one or more of the curved paths constitute the initial migration path.

[0018] Furthermore, filtering out false paths in the initial migration path includes:

[0019] If the initial migration path traverses a low-permeability formation, the permeability coefficient K of the low-permeability formation is less than 1 × 10⁻⁶. -6 If the speed is cm / s, then the corresponding initial migration path is deleted;

[0020] If the altitude of the second detection point is higher than that of the first detection point and there is no external driving force, then the corresponding initial migration path is deleted.

[0021] By comparing the actual migration paths of similar geological conditions in historical pollution events, paths that do not conform to historical patterns are eliminated.

[0022] Furthermore, the process for determining the pollutant migration rate includes:

[0023] The sum of the concentration changes at the first detection point and the second detection point is determined, and the pollutant accumulation / release rate is determined by the ratio of the sum of the concentration changes to the corresponding time interval.

[0024] The migration distance between the first detection point and the second detection point is determined based on the migration path distribution.

[0025] A geological weighting function is constructed based on geological parameters, including geological permeability coefficient and porosity.

[0026] The pollutant migration rate between the first detection point and the second detection point is determined by combining the pollutant accumulation / release rate, migration distance, geological weighting function, and calibration coefficient.

[0027] Furthermore, the method also includes:

[0028] For the migration path from the same first detection point to multiple second detection points, pollutant migration weights are dynamically assigned based on concentration difference, geological permeability coefficient, and migration distance.

[0029] The pollutant migration rate is adjusted according to the pollutant migration weight to obtain the final pollutant migration rate.

[0030] Furthermore, the process of determining the calibration coefficient includes:

[0031] The initial calibration coefficients were obtained through inversion using laboratory soil column experiments.

[0032] Furthermore, the process of determining the calibration coefficient includes:

[0033] Based on extended Kalman filtering, the calibration coefficients are dynamically updated with the goal of minimizing the error between the predicted migration rate and the measured migration rate.

[0034] Furthermore, predicting the pollutant diffusion range based on the pollutant migration rate includes:

[0035] The diffusion distance of pollutants in the soil is predicted by integrating the pollutant migration rate with time.

[0036] The pollutant diffusion boundary is determined based on multiple diffusion distances, thus obtaining the pollutant diffusion range.

[0037] Secondly, a soil pollution risk analysis system based on Internet of Things (IoT) sensors is provided. This system is used to implement the soil pollution risk analysis method based on IoT sensors as described in any one of the first aspects, including:

[0038] The information acquisition module is used to collect information on the distribution of pollutant concentrations in different soil layers within the target area;

[0039] The path generation module is used to generate an initial migration path based on the migration from the first detection point with high pollutant concentration to the second detection point with low pollutant concentration in the pollutant concentration distribution information.

[0040] The path correction module is used to combine geological permeability, the height difference between the first detection point and the second detection point, and / or historical migration data to filter out false paths in the initial migration path and generate a corrected migration path.

[0041] The rate analysis module is used to determine the pollutant migration rate between the first detection point and the second detection point based on the concentration change, migration path distribution and geological parameters of the first detection point and the second detection point.

[0042] A diffusion prediction module is used to predict the diffusion range of pollutants based on the pollutant migration rate.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The soil pollution risk analysis method based on IoT sensors provided by this invention first generates an initial path driven by concentration gradient, and then integrates a multi-dimensional correction mechanism to filter out false paths. Furthermore, it dynamically weights and calculates the migration rate while considering the concentration change, migration path distribution, and geological parameters. This not only improves the accuracy and rationality of the overall migration path distribution analysis, but also effectively reduces the prediction error of local migration rates.

[0045] 2. This invention eliminates physically unreasonable paths caused by relying solely on concentration gradients in traditional methods, such as crossing impermeable layers or migrating against topography, by using geological permeability thresholds, topographic elevation difference constraints, and historical data comparison. It can accurately determine the direction of pollution diffusion and can be applied to scenarios of abrupt changes in permeability or reverse migration of topography.

[0046] 3. This invention dynamically allocates pollutant migration weights by jointly calculating concentration difference, permeability, and migration distance, ensuring that pollutants preferentially migrate to paths with strong permeability, short distance, and large concentration difference, thus solving the prediction deviation of pollutant diffusion range caused by neglecting multi-path allocation in traditional single-path models.

[0047] 4. This invention uses a nonlinear combination of permeability coefficient and porosity to demonstrate the physical law that permeability dominates migration rate and porosity affects pollutant transport channels in loose sediments; in addition, laboratory soil column experiments provide initial parameters and extend Kalman filtering to correct model errors in real time, which can adapt to a variety of different geological conditions and pollutant types. Attached Figure Description

[0048] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0049] Figure 1 This is a flowchart from Embodiment 1 of the present invention;

[0050] Figure 2 This is a schematic diagram of the initial migration path in Embodiment 1 of the present invention;

[0051] Figure 3 This is a schematic diagram of the nearest neighbor point in Embodiment 1 of the present invention;

[0052] Figure 4 This is a system block diagram in Embodiment 2 of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0054] Example 1: A soil pollution risk analysis method based on IoT sensors, such as Figure 1 As shown, it includes the following steps:

[0055] S1: Collect information on the distribution of pollutant concentrations in different soil layers within the target area;

[0056] S2: Based on the migration from the first detection point with high pollutant concentration to the second detection point with low pollutant concentration in the pollutant concentration distribution information, an initial migration path is generated;

[0057] S3: Combining geological permeability, the height difference between the first and second detection points, and / or historical migration data, filter out false paths in the initial migration path to obtain a corrected migration path;

[0058] S4: Determine the pollutant migration rate between the first and second detection points based on the concentration changes, migration path distribution, and geological parameters between the first and second detection points;

[0059] S5: Predict the extent of pollutant diffusion based on pollutant migration rate.

[0060] In step S1, pollutant concentration sensors are deployed in the target area in a three-dimensional grid layout. Generally, the horizontal spacing between sensors is ≤5m and the vertical spacing is ≤0.5m. The pollutant concentration sensors include, but are not limited to, electrochemical sensors and heavy metal sensors. In addition, each pollutant concentration sensor has a built-in GPS positioning module to record its three-dimensional coordinates (x,y,z) in real time. The collected pollutant concentration and three-dimensional coordinates are combined to form a data set, which is then wirelessly transmitted to the central processing unit via LoRa. The central processing unit then executes steps S2-S5.

[0061] In step S2, the initial migration path is generated from the detection point with high pollutant concentration to the detection point with low pollutant concentration. The actual display of the initial migration path can be a straight line or a curve.

[0062] In some examples, the initial migration path generation process includes: generating a straight path from a first detection point to an adjacent second detection point, based on the direction of the concentration gradient decrease; one or more straight paths constitute the initial migration path. It should be noted that the initial migration path composed of multiple straight paths is a polygonal line, which can also be converted into a curve. Furthermore, the pollutant concentration in an initial migration path exhibits a monotonically changing pattern.

[0063] like Figure 2 As shown, the lead metal concentration collected at detection point a is 65 mg / kg, which is the highest concentration among all detection points. Detection points adjacent to detection point a are detection points b, c, and d. Therefore, the straight-line paths starting from detection point a are a→b, a→c, and a→d, and the resulting initial migration paths are a→b→e→f, a→b→d→f, a→b→d→e→f, a→d→f, and a→c→f.

[0064] In some examples, the process of generating the initial migration path may also include: filling in the concentration distribution in areas not covered by the sensor using a spatial interpolation algorithm; generating a curved path from the first detection point to the adjacent second detection point based on the direction of concentration gradient descent, with one or more curved paths constituting the initial migration path.

[0065] In step S3, this invention considers that traditional pollution path analysis only takes into account concentration gradients and ignores geological and topographical limitations, resulting in false paths. This can lead to significant analytical errors and a large amount of data processing. Therefore, in some examples, false paths in the initial migration path are filtered out, including one or more of the following methods: if the initial migration path crosses a low-permeability stratum, the permeability coefficient K of the low-permeability stratum is less than 1 × 10⁻⁶. -6 If the speed is cm / s, the corresponding initial migration path is deleted; if the elevation of the second detection point is higher than that of the first detection point and there is no external driving force, such as groundwater extraction, the corresponding initial migration path is deleted; compare the actual migration paths of similar geological conditions in historical pollution events and eliminate paths that do not conform to historical patterns.

[0066] In step S4, the process of determining the pollutant migration rate includes: determining the sum of the concentration changes at the first and second detection points, and determining the pollutant accumulation / release rate by the ratio of the sum of concentration changes to the corresponding time interval; determining the migration distance between the first and second detection points based on the migration path distribution; constructing a geological weighting function based on geological parameters, including geological permeability and porosity; and determining the pollutant migration rate between the first and second detection points by combining the pollutant accumulation / release rate, migration distance, geological weighting function, and calibration coefficient.

[0067] In this invention, geological conditions exert a weighted influence on pollutant transport through a nonlinear combination of permeability and porosity. The pollutant accumulation / release rate characterizes the flux change of pollutants per unit time, and the migration distance reflects the diffusion path length. All three factors jointly determine the migration rate to conform to the law of conservation of mass.

[0068] In some examples, for each detection point pair A→B on the initial migration path, the expression for calculating the pollutant migration rate is as follows:

[0069]

[0070] Among them, v A→B The value represents the pollutant migration rate between the first detection point A and the second detection point B, in m / s; k represents the calibration coefficient, determined based on historical data inversion, in m. 2 kg / mg; ΔC A→B Δd represents the sum of the decrease in concentration at point A and the increase in concentration at point B within the time interval Δt, in mg / kg; Δt represents the time interval, in seconds; Δd A→B The distance between the first detection point A and the second detection point B can be expressed as Euclidean distance in meters; f(K,n) represents the geological weighting function of the geological parameters, which is dimensionless; K is the geological permeability coefficient, which is dimensionless; and n is the porosity, which is dimensionless.

[0071] In the above formula, the pollutant migration rate characterizes convective migration, diffusion migration, and adsorption / desorption, ΔC A→B Divide by Δt to represent the change in pollutant flux per unit time, and then divide by Δd. A→B The change in pollutant flux is converted into linear velocity, which represents the change in pollutant concentration per unit time and unit distance in the direction of migration, ultimately yielding the pollutant migration rate consistent with the seepage velocity in Darcy's law.

[0072] Specifically, in the nonlinear influence of pollutant migration rate, the geological permeability coefficient plays a dominant role while porosity plays a secondary role. Therefore, this invention uses an exponential multiplication of K and n to construct a geological weighting function. Laboratory soil column experiments were conducted to simulate different K(10) values.-6 -10 -3 The pollutant migration rate under the combination of K and n (0.2-0.5) was determined using multivariate nonlinear regression to quantify the nonlinear influence of geological permeability and porosity on the pollutant migration rate, ultimately identifying the optimal index. Data obtained from laboratory soil column experiments included measured rates, concentration increases, time, and distance. Before multivariate nonlinear regression, an initial calibration coefficient k (e.g., 1) was set. The measured concentration increases, time, distance, and simulation conditions K and n were substituted into the formula to calculate the predicted rate. Then, the sum of squared residuals between the predicted and measured rates was calculated. Finally, the K and n with the smallest sum of squared residuals among multiple sets of K and n was selected as the final result.

[0073] Experimental data show that K 0.5 ×n 0.3 The combination of these factors results in a goodness-of-fit between the predicted and measured rates (R = 1 minus the ratio of the sum of squared residuals to the total sum of squares). 2 =0.92, R 2 In statistics, f(K,n) is a goodness-of-fit index; the closer it is to 1, the stronger the model's interpretability. Therefore, f(K,n) = K 0.5 ×n 0.3 It is applicable to most loose sediments and common pollutants, including but not limited to sand and clay, and common pollutants including but not limited to heavy metals and non-polar organic matter.

[0074] This invention introduces ΔC A→B The sign of the virus can be used to distinguish between true migration and local release / adsorption phenomena, such as defining ΔC as the value of a decrease at point A and an increase at point B. A→B The value is positive, and by quantifying the influence of geological conditions on the migration rate, biases caused by a single concentration driving force can be effectively avoided.

[0075] Considering that previous pollutant migration analyses only showed migration along a single path, making it difficult to accurately handle multi-path competition and allocation issues, this invention introduces a migration weight coefficient to represent the allocation ratio of pollutants from A to different B for the migration from the same detection point (e.g., A) to multiple target detection points (e.g., multiple different Bs).

[0076] Specifically, this invention dynamically allocates pollutant migration weights based on concentration difference, geological permeability coefficient, and migration distance for the migration path from the same first detection point to multiple second detection points; and corrects the pollutant migration rate according to the pollutant migration weights to obtain the final pollutant migration rate.

[0077] In some examples, the formula for calculating the pollutant migration weight is as follows:

[0078]

[0079] Among them, w i This indicates the first detection point A and the second detection point B. i Pollutant migration weights along migration paths between them; This represents the decrease in concentration at point A within the time interval Δt and the change in concentration at point B. i The sum of the increases in point concentration; K i This indicates the first detection point A and the second detection point B. i Geological permeability coefficient of the migration path between them; This indicates the first detection point A and the second detection point B. i The migration distance between them; This represents the decrease in concentration at point A within the time interval Δt and the change in concentration at point B. j The sum of the increases in point concentration; K j This indicates the first detection point A and the second detection point B. j Geological permeability coefficient of the migration path between them; This indicates the first detection point A and the second detection point B. j The migration distance between them; m represents the number of second detection points.

[0080] It should be noted that the eight-neighborhood method can be used to determine the adjacent B points, and then the concentration of all adjacent B points is compared with the concentration of point A. B points with a concentration less than that of point A are all considered as candidate directions, and then multi-path branches are generated by Darcy streamline tracing.

[0081] Taking a horizontal plane as an example, the neighboring B points of each detection point A are determined by the eight-neighbor method, that is, the nearest neighbor points in the eight directions of east, south, west, north, northeast, southeast, northwest and southwest on the horizontal plane; for each detection point A, the B points in its eight-neighbor area are traversed. If the concentration of B point is lower than that of A point, an initial migration path A→B is generated.

[0082] like Figure 3 As shown, the neighboring points of detection point d can be a, b, c, e, and f. Although detection point g is also in the southeast direction of detection point d, the distance between detection point f and detection point d is closer in the same direction. Therefore, detection point g is not considered as the nearest neighbor of detection point d.

[0083] In addition, in three-dimensional space, the six directions of up, down, left, right, front, and back can be used directly to determine the adjacent point B.

[0084] It should be noted that a search distance is set when determining adjacent points B. If there are no adjacent points within the search distance in one direction, it is directly regarded as having no adjacent points B.

[0085] This invention assumes that the amount of pollutant reduction at point A is equal to the amount of pollutants reduced at point B. iWith the total increase in points as a constraint, pollutants preferentially migrate to paths with large concentration differences, high permeability, and short distances, which can effectively reduce multipath allocation errors.

[0086] In some examples, the calculation expression for the multipath pollutant migration rate after introducing pollutant migration weights is as follows:

[0087]

[0088] in, This indicates the first detection point A and the second detection point B. i The migration rate of pollutants between them; n i For the first detection point A and the second detection point B i Porosity of the migration path between them.

[0089] In some examples, the initial calibration coefficients can be obtained through inversion from laboratory soil column experiments. For instance, representative soil columns are constructed to simulate the soil texture, moisture content, and contaminant type of the target area. Contaminants are injected under controlled conditions (such as constant water flow rate and temperature), and the concentration is monitored over time and space. The input data for the entire process includes: concentration gradient ΔC, migration distance Δd, time interval Δt, permeability coefficient K, and porosity n; the output data is the measured migration rate v. s .

[0090] The expression for the inversion calculation is as follows:

[0091]

[0092] Here, k0 represents the initial calibration coefficient, which is the average of multiple experiments. It should be noted that when determining the exponent of the geological weighting function through laboratory soil column experiments, a fixed calibration coefficient can be preset first. The exponent of the geological weighting function can be obtained by fitting the data with the fixed calibration coefficient. Then, the exponent of the obtained geological weighting function can be substituted into the inversion experiment to determine the calibration coefficient. The optimal data can be determined through staged optimization experiments.

[0093] In some examples, the calibration coefficients can also be dynamically updated based on extended Kalman filtering, with the goal of minimizing the error between the predicted and measured migration rates. The predicted migration rate is the pollutant migration rate calculated above, coupled with Darcy's law. The values ​​of the calibration coefficients are dynamically updated through Kalman filtering, and the calculated pollutant migration rate is then updated using the updated calibration coefficients.

[0094] Specifically, the expression for dynamically updating the calibration coefficients is as follows:

[0095] k t+1 =k t +G(v s -vc );

[0096] Where, k t+1 Indicates the updated calibration coefficient; k t The calibration coefficients before the update are represented; G represents the Kalman gain, calculated based on the error covariance matrix, observation matrix, and sensor noise covariance corresponding to the sensor data; v c This indicates the predicted migration rate.

[0097] In step S5, the pollutant diffusion range is predicted based on the pollutant migration rate, including: predicting the diffusion distance of pollutants in the soil based on the integral calculation of the pollutant migration rate and time; determining the pollutant diffusion boundary based on multiple diffusion distances to obtain the pollutant diffusion range.

[0098] Using integral operations can ensure that the cumulative effect of the pollutant front position changing over time meets the numerical solution requirements of Fick's second law under unsteady diffusion conditions.

[0099] This invention enables timely intervention by accurately predicting pollutant diffusion boundaries, thus preventing cascading ecosystem collapse. In some examples, the distance to sensitive targets can be determined by the pollutant diffusion boundary, thereby assessing the risk level of those targets. Furthermore, in some examples, source tracing analysis can be performed based on the distribution of multiple migration paths and the magnitude of pollutant migration rates. Additionally, in some examples, accurate prediction of pollutant diffusion boundaries can be used to delineate prohibited planting areas or provide a reference for recommending crop varieties.

[0100] Experimental verification:

[0101] Experimental analysis was conducted on sites with homogeneous soil, low permeability layer, and reverse topography. The specific site parameters are shown in Table 1.

[0102] Table 1 Site Parameters

[0103]

[0104] The results of the false path filtering comparison are shown in Table 2. The filtering rate in Table 2 refers to the proportion of paths that do not meet the geological conditions that are deleted, and the false judgment rate refers to the proportion of reverse migration paths that are retained.

[0105] Table 2 Comparison Results of Pseudo-Path Filtering

[0106] Method type Path count in scenario 1 Scenario 2 Filtration Rate Scenario 3 False Positive Rate Traditional concentration gradient method 152 32％ 68％ This invention provides multipath correction 89(-41％) 89％ 12％

[0107] The comparison results of migration rate prediction error (unit: cm / day) are shown in Table 3.

[0108] Table 3 Comparison of Migration Rate Prediction Errors

[0109] Detection point pair Measured value Traditional methods Method of the present invention A(0,0,0)→B(5,5,0) 3.2 5.8±1.2 3.5±0.3 C(10,10,0)→D(15,15,0) 1.7 4.1±0.9 1.9±0.2 Overall MAE - 2.34 0.47

[0110] Example 2: Soil pollution risk analysis system based on IoT sensors, such as Figure 4 As shown, the system is used to implement the soil pollution risk analysis method based on IoT sensors as described in Example 1, including an information acquisition module, a path generation module, a path correction module, a rate analysis module, and a diffusion prediction module.

[0111] The system comprises the following modules: an information acquisition module for collecting pollutant concentration distribution information from different soil layers within the target area; a path generation module for generating an initial migration path based on the migration from a first detection point with high pollutant concentration to a second detection point with low pollutant concentration; a path correction module for filtering out false paths in the initial migration path and generating a corrected migration path by combining geological permeability, the height difference between the first and second detection points, and / or historical migration data; a rate analysis module for determining the pollutant migration rate between the first and second detection points based on the concentration change between the first and second detection points, the migration path distribution, and geological parameters; and a diffusion prediction module for predicting the pollutant diffusion range based on the pollutant migration rate.

[0112] Working principle: Initial paths are generated first by driving the concentration gradient, and then a multi-dimensional correction mechanism is used to filter out false paths. Furthermore, the migration rate is dynamically weighted and calculated by considering the concentration change, migration path distribution, and geological parameters. This not only improves the accuracy and rationality of the overall migration path distribution analysis, but also effectively reduces the prediction error of local migration rates.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0114] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0115] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0117] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for soil pollution risk analysis based on Internet of Things sensors, characterized in that, The method comprises the following steps: Collecting the concentration distribution information of pollutants in different soil layers in the target area; Generating an initial migration path based on the migration of a first detection point with high pollutant concentration to a second detection point with low pollutant concentration in the concentration distribution information of pollutants; Filtering false paths in the initial migration path in combination with the geological permeability, the height difference between the first detection point and the second detection point, and / or historical migration data to obtain a corrected migration path; Determining the pollutant migration rate between the first detection point and the second detection point based on the concentration change amount, migration path distribution, and geological parameters of the first detection point and the second detection point; Predicting the pollutant diffusion range according to the pollutant migration rate; The filtering of the false paths in the initial migration path comprises: if the initial migration path traverses a low permeability formation, the permeability coefficient of the low permeability formation then the corresponding initial migration path is deleted; If the altitude of the second detection point is higher than that of the first detection point and there is no external driving force, the corresponding initial migration path is deleted; Comparing the actual migration path under similar geological conditions in historical pollution events to eliminate paths that do not conform to historical laws; The determination process of the pollutant migration rate comprises: Determining the sum of the concentration change amounts of the first detection point and the second detection point, and determining the pollutant accumulation / release rate based on the ratio of the sum of the concentration change amounts to the corresponding time interval; Determining the migration distance between the first detection point and the second detection point according to the migration path distribution; Constructing a geological weighting function based on geological parameters, wherein the geological parameters include the geological permeability coefficient and the porosity; Determining the pollutant migration rate between the first detection point and the second detection point in combination with the pollutant accumulation / release rate, the migration distance, the geological weighting function, and the calibration coefficient; The determination process of the calibration coefficient comprises: Obtaining an initial calibration coefficient through laboratory soil column experiments; The determination process of the calibration coefficient comprises: Dynamically updating the calibration coefficient based on the extended Kalman filter to minimize the error between the predicted migration rate and the measured migration rate; The prediction of the pollutant diffusion range according to the pollutant migration rate comprises: Predicting the diffusion distance of the pollutant in the soil according to the integral operation of the pollutant migration rate and time; Determining the pollutant diffusion boundary based on multiple diffusion distances to obtain the pollutant diffusion range.

2. The Internet of Things sensor-based soil pollution risk analysis method according to claim 1, characterized in that, The generation process of the initial migration path comprises: Generating a straight line path from the first detection point to the adjacent second detection point based on the descending direction of the concentration gradient, and one or more straight line paths constitute the initial migration path. 3.The soil pollution risk analysis method based on the Internet of Things sensor according to claim 1, wherein, The generation process of the initial migration path comprises: Filling the concentration distribution in the area not covered by the sensor through a spatial interpolation algorithm; Generating a curved path from the first detection point to the adjacent second detection point based on the descending direction of the concentration gradient, and one or more curved paths constitute the initial migration path. 4.The method of claim 1, wherein, The method further comprises: Dynamically assigning a pollutant migration weight to the migration path of the same first detection point to multiple second detection points based on the concentration difference, the geological permeability coefficient, and the migration distance; The contaminant migration rate is corrected according to the contaminant migration weight, and a final contaminant migration rate is obtained.

5. A soil pollution risk analysis system based on Internet of Things sensors, characterized in that, The system is used to implement the soil pollution risk analysis method based on the Internet of Things sensor as claimed in any one of claims 1-4, and comprises: An information acquisition module is configured to acquire contaminant concentration distribution information of different soil layers in a target area; A path generation module is configured to generate an initial migration path based on migration of a first detection point with high contaminant concentration to a second detection point with low contaminant concentration in the contaminant concentration distribution information; A path correction module is configured to filter false paths in the initial migration path in combination with geological permeability, a height difference between the first detection point and the second detection point, and / or historical migration data, and generate a corrected migration path; A rate analysis module is configured to determine a contaminant migration rate between the first detection point and the second detection point based on a concentration change amount of the first detection point and the second detection point, migration path distribution, and geological parameters; A diffusion prediction module is configured to predict a contaminant diffusion range according to the contaminant migration rate.

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