A river non-point source pollution source identification method, device and equipment

Abstract

The application relates to the technical field of ecological environment data processing, and in particular to a river non-point source pollution source identification method, device and equipment. The method comprises the following steps: based on obtained hydrological information, a first pollutant influence factor of a target basin grid is calculated; according to the first pollutant influence factor and the hydrological information, a second pollutant influence factor of the target basin grid is calculated by using a constructed conditional distribution function; a residual pollutant influence factor is determined according to the target basin grid and a target river; according to the first pollutant influence factor, the second pollutant influence factor and the residual pollutant influence factor, a pollutant accessibility index of the target basin grid relative to the target river is calculated; and based on the pollutant accessibility index, the potential non-point source pollution source degree of the target basin grid to the target river is evaluated, so that the problem of low non-point source pollution source identification accuracy in the prior art can be effectively solved.

Description

Technical Field

[0001] This application relates to the field of ecological and environmental data processing technology, and in particular to a method, apparatus and equipment for identifying river non-point source pollution sources. Background Technology

[0002] In existing technologies, the identification of watershed non-point source pollution is based on topography and rainfall, using watershed hydrological models to simulate the watershed distribution of pollutants. However, this method has low accuracy in identifying pollution sources and the distribution of non-point source pollution, and may even fail to identify the distribution of non-point source pollution altogether. Summary of the Invention

[0003] In view of this, the embodiments of this application provide a method, apparatus and equipment for identifying river non-point source pollution sources, which can effectively solve the problem of low accuracy in identifying non-point source pollutants in existing non-point source pollutant identification methods.

[0004] In a first aspect, embodiments of this application provide a method for identifying river non-point source pollution, including:

[0005] Based on the acquired hydrological information, the first pollutant impact factor of the target watershed grid is calculated.

[0006] Based on the first pollutant impact factor and the hydrological information, the second pollutant impact factor of the target watershed grid is calculated using the constructed conditional distribution function.

[0007] The remaining pollutant impact factors are determined based on the target watershed grid and the target river.

[0008] Based on the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor, the pollutant accessibility index of the target watershed grid relative to the target river is calculated;

[0009] The potential non-point source pollution level of the target watershed grid to the target river is assessed based on the pollutant accessibility index.

[0010] In some embodiments, the hydrological information includes target hydrological parameters;

[0011] The step of calculating the second pollutant impact factor of the target watershed grid based on the first pollutant impact factor and the hydrological information using the constructed conditional distribution function includes:

[0012] Based on the first pollutant impact factor and the target hydrological parameters, the conditional distribution function is constructed by constructing the conditional distribution function based on the first joint distribution function and the second joint distribution function, and the second pollutant impact factor is calculated.

[0013] In some embodiments, the method further includes:

[0014] The first joint distribution function is constructed based on the optimal marginal distribution functions corresponding to the target hydrological parameters, the first pollutant impact factor, and the second pollutant impact factor, as well as the selected optimal connection function.

[0015] The second joint distribution function is constructed based on the optimal marginal distribution functions corresponding to the first pollutant impact factor and the second pollutant impact factor, respectively, and the optimal connection function.

[0016] The conditional distribution function is constructed based on the first joint distribution function and the second joint distribution function.

[0017] In some embodiments, calculating the pollutant accessibility index of the target watershed grid relative to the target river based on the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor includes:

[0018] Based on the maximum value of each pollutant impact factor within the target watershed, the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factors are standardized respectively.

[0019] The first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor after standardization are substituted into the pollutant accessibility calculation formula to calculate the pollutant accessibility index.

[0020] In some embodiments, the residual pollutant impact factor includes the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid; the first pollutant impact factor is the runoff of the target watershed grid; and the second pollutant impact factor is the pollutant concentration of the target watershed grid.

[0021] The step of substituting the standardized first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor into the pollutant accessibility calculation formula to calculate the pollutant accessibility index includes calculating the pollutant accessibility index using the following pollutant accessibility calculation formula:

[0022]

[0023] Let x be the pollutant accessibility index for the x-th watershed grid. Let be the pollutant accessibility standardized component of the i-th pollutant impact factor; This represents the number of pollutant influencing factors.

[0024] In some embodiments, the residual pollutant impact factors include the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid;

[0025] The step of determining the remaining pollutant impact factors based on the target watershed grid and the target river includes:

[0026] The relative elevation difference is calculated based on the elevation of the target watershed grid and the average and minimum elevations of all river grids of the target river within the target watershed.

[0027] Based on the river raster data and watershed raster data of the target watershed, calculate the Euclidean distance from the center point of the target watershed raster to the center point of the target river raster within the target watershed; wherein, the target river raster is the river raster that is closest to the watershed raster in the target river.

[0028] In some embodiments, the unit watershed is obtained by dividing it using the following method:

[0029] The digital elevation model data of the target watershed is subjected to depression filling processing;

[0030] Based on the digital elevation model data after filling depressions, the flow direction of the target watershed grid is calculated using a preset tool;

[0031] Connect the points corresponding to the highest elevations between two adjacent rivers to form watersheds for each unit watershed; based on each watershed, use a preset method to divide the target watershed into individual unit watersheds.

[0032] In some embodiments, the first pollutant impact factor is runoff; the hydrological information includes target hydrological parameters and surface data; the target hydrological parameter is rainfall intensity;

[0033] The calculation of the first pollutant impact factor of the target watershed grid based on the acquired hydrological information includes:

[0034] Based on the acquired rainfall intensity and ground data, the rainfall runoff of the target watershed is calculated using a preset model, and the runoff volume of the target watershed grid is obtained based on the rainfall runoff and the gridded information of the target watershed.

[0035] Secondly, embodiments of this application provide a river non-point source pollution source identification device, comprising:

[0036] The first factor calculation module is used to calculate the first pollutant impact factor of the target watershed grid based on the acquired hydrological information.

[0037] The second factor calculation module is used to calculate the second pollutant impact factor of the target watershed grid based on the first pollutant impact factor and the hydrological information, using the constructed conditional distribution function.

[0038] The residual factor calculation module is used to determine the residual pollutant impact factors based on the target watershed grid and the target river.

[0039] The index calculation module is used to calculate the pollutant accessibility index of the target watershed grid relative to the target river based on the first pollutant impact factor, the second pollutant impact factor and the remaining pollutant impact factor;

[0040] An assessment module is used to assess the potential non-point source pollution level of the target watershed grid on the target river based on the pollutant accessibility index.

[0041] Thirdly, embodiments of this application provide a terminal device, which includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement a river non-point source pollution source identification method provided in the first aspect of this application.

[0042] The embodiments of this application have the following beneficial effects:

[0043] This application calculates the first pollutant impact factor of a target watershed grid based on acquired hydrological information; calculates the second pollutant impact factor of the target watershed grid using a constructed conditional distribution function based on the first pollutant impact factor and hydrological information; determines the remaining pollutant impact factor based on the target watershed grid and the target river; calculates the pollutant accessibility index of the target watershed grid relative to the target river based on the first, second, and remaining pollutant impact factors; and assesses the potential non-point source pollution level of the target watershed grid to the target river based on the pollutant accessibility index. This application constructs a comprehensive pollutant accessibility index, rather than a single impact factor assessment, achieving a more comprehensive evaluation through this pollutant accessibility index. It can effectively solve the problem of low accuracy in identifying non-point source pollutants in existing methods. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1A flowchart of a river non-point source pollution source identification method according to an embodiment of this application is shown;

[0046] Figure 2 A flowchart illustrating the unit watershed division method of the river non-point source pollution source identification method according to an embodiment of this application is shown;

[0047] Figure 3 This illustration shows a schematic diagram of the first type of watershed division involved in the river non-point source pollution source identification method according to an embodiment of this application;

[0048] Figure 4 This illustration shows a second type of watershed division involved in the river non-point source pollution source identification method according to an embodiment of this application;

[0049] Figure 5 Another flowchart of the river non-point source pollution source identification method according to an embodiment of this application is shown;

[0050] Figure 6 A schematic diagram of a river non-point source pollution source identification device according to an embodiment of this application is shown.

[0051] Explanation of key component symbols:

[0052] 610 - First factor calculation module; 620 - Second factor calculation module; 630 - Residual factor calculation module; 640 - Index calculation module; 650 - Evaluation module. Detailed Implementation

[0053] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0054] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0055] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0056] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0057] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0058] Existing methods for identifying non-point source pollutants have low accuracy and cannot identify the distribution of non-point source pollution. Therefore, this application provides a method, apparatus, and equipment for identifying river non-point source pollution, which can effectively solve the problems of low accuracy and inability to identify the distribution of non-point source pollution in existing methods.

[0059] The river non-point source pollution identification method in this application includes, but is not limited to, methods applicable to river non-point source pollution identification in areas of high human activity. Areas of high human activity refer to regions with high intensity of human activity and significant impacts on the natural environment. These areas typically have high population density, frequent economic activities, and substantial construction activity.

[0060] The following examples illustrate the method for identifying non-point source pollution in this river.

[0061] Figure 1 A flowchart illustrating a method for identifying river non-point source pollution according to an embodiment of this application is shown. Exemplarily, this method includes the following steps:

[0062] S100, based on the acquired hydrological information, calculates the first pollutant impact factor of the target watershed grid.

[0063] For example, hydrological information includes target hydrological parameters and surface data. The target hydrological parameters can be any one of parameters such as rainfall intensity, runoff, and pollutant concentration. Surface data includes: digital elevation model data (DEM data), soil type, land use type, etc.

[0064] The primary pollutant influencing factor is the runoff or pollutant concentration of the target watershed grid.

[0065] S200, based on the first pollutant impact factor and hydrological information, uses the constructed conditional distribution function to calculate the second pollutant impact factor of the target watershed grid.

[0066] The second pollutant influencing factor is the runoff or pollutant concentration of the target watershed grid, and it is different from the first pollutant influencing factor. Hydrological information includes target hydrological parameters.

[0067] In one implementation, if the target hydrological parameter is rainfall intensity, then the pollutant concentration of the target watershed grid is calculated using a conditional distribution function based on the rainfall intensity and runoff of the target watershed grid. In another implementation, if the target hydrological parameter is rainfall intensity, then the runoff of the target watershed grid is calculated using a conditional distribution function based on the rainfall intensity and pollutant concentration of the target watershed grid.

[0068] S300 determines the remaining pollutant impact factors based on the target watershed grid and the target river.

[0069] For example, residual pollutant impact factors include at least two of the following: Euclidean distance between the target watershed grid and the target river grid, land use type, and relative elevation difference. The distance and relative elevation difference between the target watershed grid and the target river are both key factors in evaluating non-point source pollution sources.

[0070] The target watershed raster is one of the raster cells to be evaluated after the target watershed has been rasterized at a preset resolution. The target river raster is one of the raster cells to be evaluated after the target river region has been rasterized at a preset resolution.

[0071] S400 calculates the pollutant accessibility index of the target watershed grid relative to the target river based on the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor.

[0072] By comprehensively considering multiple pollutant influencing factors, the pollutant accessibility index of the target river can be calculated more accurately.

[0073] S500 assesses the potential non-point source pollution levels of a target watershed grid on a target river based on the pollutant accessibility index.

[0074] The Pollutant Accessibility Index (PAI) is used to quantify the likelihood of a pollutant reaching a target river from a given location (such as a watershed grid).

[0075] For example, a higher Pollutant Accessibility Index (PAI) indicates a greater likelihood that a target watershed grid is a potential non-point source pollution source for the target river. For instance, a threshold can be set, and a critical PAI threshold can be determined based on experience or statistical analysis. If the PAI value of a target watershed grid exceeds the PAI threshold, the grid is considered a potential non-point source pollution source for the target river. The location distribution of target watershed grids corresponding to each potential non-point source pollution source is then statistically analyzed to obtain the distribution of non-point source pollution.

[0076] This application also includes verification of the evaluation results, for example, comparing the evaluation results with historical monitoring data to verify the accuracy of the model. Sensitivity analysis is performed on different scenarios (such as changes in rainfall intensity) to evaluate the robustness of the model.

[0077] This application constructs a Pollutant Accessibility Index (PAI) that comprehensively considers factors such as topography (relative elevation difference), runoff, land use type, and distance. It adopts a multi-factor comprehensive assessment approach to accurately evaluate the potential non-point source pollution level of the target watershed grid on the target river.

[0078] In one implementation, the hydrological information includes target hydrological parameters;

[0079] In step S200, based on the first pollutant impact factor and hydrological information, the second pollutant impact factor of the target watershed grid is calculated using the constructed conditional distribution function, including:

[0080] S210, based on the first pollutant impact factor and the target hydrological parameters, the conditional distribution function is constructed by constructing a conditional distribution function based on the first joint distribution function and the second joint distribution function, and the second pollutant impact factor is calculated.

[0081] Furthermore, the method also includes:

[0082] S211, the first joint distribution function is constructed based on the optimal marginal distribution functions corresponding to the target hydrological parameters, the first pollutant influence factor, and the second pollutant influence factor, as well as the selected optimal linkage function (Copula function). In other words, the first joint distribution function... It describes the relationship between three variables: target hydrological parameters, the first pollutant impact factor, and the second pollutant impact factor.

[0083] S212, a second joint distribution function is constructed based on the optimal marginal distribution functions and optimal connection functions corresponding to the first pollutant impact factor and the second pollutant impact factor, respectively. In other words, the second joint distribution function... It describes the relationship between any two variables among the target hydrological parameter, the first pollutant impact factor, and the second pollutant impact factor.

[0084] S213, the conditional distribution function is constructed based on the first joint distribution function and the second joint distribution function.

[0085] Furthermore, embodiments of this application also include determining the optimal edge distribution using the following method:

[0086] Optimal marginal distributions are selected for variables such as target hydrological parameters, the first pollutant impact factor, and the second pollutant impact factor (e.g., rainfall intensity X, runoff Y, and pollutant concentration Z). Candidate marginal distributions include Gamma, Log-normal, and Weibull distributions. The parameters of the marginal distributions are fitted using maximum likelihood estimation (MLE) or moment estimation, and the rationality of the distributions is verified by KS.

[0087] As an example, the steps for determining the optimal marginal distribution include:

[0088] (1) Select appropriate candidate distributions based on the actual characteristics of each variable and its common distribution assumptions. For example, rainfall intensity X: usually has a positively skewed distribution, and candidate distributions include Gamma and Weibull distributions. Runoff Y: generally exhibits a log-normal or Gamma distribution. Pollutant concentration Z: usually has a positively skewed distribution, and candidate distributions include Log-normal and Gamma distributions.

[0089] (2) Estimate the parameters of the marginal distribution function. For example, fit the parameters of the marginal distribution function using maximum likelihood estimation (MLE) or the method of moments. In the maximum likelihood estimation (MLE) method, for each variable and candidate marginal function distribution, construct a likelihood function and maximize its value to estimate the distribution parameters.

[0090] (3) Goodness-of-fit test. The Kolmogorov-Smirnov (KS) test was used to verify the reasonableness of the distribution.

[0091] For example, for rainfall intensity X, candidate distributions include the Gamma distribution and the Weibull distribution. During parameter estimation, the Method of Moments (MLE) is used to fit the parameters of the Gamma distribution (e.g., k, theta). The method of moments (MMS) is used to fit the parameters of the Weibull distribution (lambda, k). Finally, the KS test is used to test both distributions separately, and the distribution with the better fit is selected. For example, the optimal marginal distribution for rainfall intensity X is the Gamma distribution with parameters (k=2.5, theta=1.2).

[0092] This application does not impose restrictions on the optimal marginal distribution of variables such as target hydrological parameters, first pollutant influence factor, and second pollutant influence factor (e.g., rainfall intensity X, runoff Y, pollutant concentration Z), but determines it specifically based on the data used and KS validation results.

[0093] Furthermore, embodiments of this application also include selecting the optimal Copula function using the following method:

[0094] Choose an appropriate multivariate Copula function (such as Gaussian, Student-t, Clayton, Gumbel, etc.) based on the correlation characteristics between variables, perform joint estimation using the semi-parametric method (marginal distribution parameters + Copula parameters) or the full-parametric method, and select the optimal model using the AIC / BIC criterion.

[0095] Semi-parametric methods include: determining the parameters of the marginal distribution through independent fitting (such as MLE); and obtaining the parameters of the Copula function by maximizing the log-likelihood estimate of the Copula function. Advantages: The marginal distribution and the Copula part can be optimized separately, offering high flexibility.

[0096] The full-parameter method treats the marginal distribution and the Copula function as a single model, estimating all parameters simultaneously. Its advantage lies in its ability to capture the potential interactions between the marginal distribution and the Copula function.

[0097] As an example, the optimal marginal distributions are: Gamma distribution (rainfall intensity X), Log-normal distribution (runoff Y), and Gamma distribution (pollutant concentration Z). Copula candidates include: Gaussian Copula, Student-t Copula, Clayton Copula, and Gumbel Copula.

[0098] Based on parametric estimation, the marginal distribution parameters of each variable are fitted separately; the data are converted into a standard uniform distribution; for each Copula candidate model, the Copula parameters are estimated using MLE; the log-likelihood value is calculated and substituted into the AIC / BIC formula.

[0099] Based on the full-parameter estimation method, a joint model is constructed, treating the marginal distribution and Copula as a whole; all parameters are estimated simultaneously, maximizing the log-likelihood function; and the AIC / BIC values ​​are calculated.

[0100] For model selection, compare the AIC / BIC values ​​of different Copula models; select the model with the smallest AIC / BIC value.

[0101] After determining the optimal Copula function, the joint probability can be calculated based on the optimal marginal distributions and the Copula function. The specific calculation method depends on the chosen Copula function, yielding the joint probability of the three variables. The joint distribution function of the three variables can be expressed as:

[0102]

[0103] in, Let X be the marginal distribution function of rainfall intensity X, runoff Y, and pollutant concentration Z, respectively, and C be the selected optimal Copula function. Based on the first and second joint distribution functions, the conditional distribution function is constructed as follows:

[0104]

[0105] This indicates the probability that one variable will occur under the given conditions, given a range of values ​​for two other variables. This application can obtain target hydrological parameters for different application types, predict the impact of different land use changes on hydrological-pollution relationships, and assess the risk of high runoff and high pollution caused by extreme rainfall.

[0106] In one implementation, the pollutant accessibility index of the target watershed grid relative to the target river is calculated based on the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor, including:

[0107] Based on the maximum values ​​of the impact factors of each pollutant within the target watershed, the impact factors of the first pollutant, the second pollutant, and the remaining pollutants are standardized respectively.

[0108] The standardized impact factors of the first pollutant, the second pollutant, and the remaining pollutant are substituted into the pollutant accessibility calculation formula to obtain the pollutant accessibility index.

[0109] Furthermore, the remaining pollutant impact factors include the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid; the first pollutant impact factor is the runoff of the target watershed grid; and the second pollutant impact factor is the pollutant concentration of the target watershed grid.

[0110] The standardized impact factors of the first, second, and remaining pollutants are substituted into the pollutant accessibility calculation formula to obtain the pollutant accessibility index. This includes calculating the pollutant accessibility index using the following formula:

[0111]

[0112] Let x be the pollutant accessibility index for the x-th watershed grid. Let be the pollutant accessibility standardized component of the i-th pollutant impact factor; This represents the number of pollutant influencing factors.

[0113] Based on the maximum values ​​of the impact factors of each pollutant within the target watershed, the impact factors of the first pollutant, the second pollutant, and the remaining pollutants are standardized, including:

[0114] The Euclidean distance is standardized using the following formula, where the greater the distance, the lower the accessibility of pollutants:

[0115]

[0116] in, This represents the pollutant accessibility normalized component of the Euclidean distance in the i-th watershed grid. This represents the Euclidean distance from the i-th watershed grid cell to the center point of the target river grid cell; This represents the maximum Euclidean distance among all watershed grids within the target watershed.

[0117] The relative elevation difference is standardized using the following formula, where a larger relative elevation difference indicates higher pollutant accessibility:

[0118]

[0119] in, This represents the standardized component of pollutant accessibility in the k-th unit watershed representing the relative elevation difference; This represents the relative elevation difference between the watershed grid in the i-th row and j-th column of the target watershed and the rivers within the target watershed. This represents the maximum relative elevation difference among all unit watersheds within the target watershed.

[0120] The pollutant concentration is standardized using the following formula, where a higher average pollutant concentration indicates greater accessibility:

[0121]

[0122] This represents the pollutant accessibility normalized component of runoff in the i-th watershed grid;

[0123] This represents the pollutant concentration in the i-th watershed grid.

[0124] This represents the maximum pollutant concentration across all watershed grids within the target watershed.

[0125] Similarly, the greater the runoff, the higher the accessibility of pollutants.

[0126] In one implementation, the residual pollutant impact factors include the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid;

[0127] In step S300, the remaining pollutant impact factors are determined based on the target watershed grid and the target river, including:

[0128] S310: Calculate the relative elevation difference based on the elevation of the target watershed grid in the target watershed and the average and minimum elevations of all river grids of the target river in the target watershed.

[0129] As an example, based on DEM data, each watershed unit is coded. Using a zonal statistics tool, the average and minimum elevations of all river grids within the target watershed unit are calculated according to the coded fields. Finally, a raster calculator is used to calculate the relative elevation difference between each watershed grid and the river within the target watershed unit. ):

[0130]

[0131] Where i∈1,2,...,n,j∈1,2,...,m, they represent the rows and columns of the watershed raster, respectively; k∈1,2,...,z, represents the coding sequence number of the unit watershed; This represents the relative elevation difference between the watershed grid in the i-th row and j-th column and the rivers within the target watershed unit; This represents the elevation of the watershed raster in the i-th row and j-th column; This represents the average elevation of all river grid cells within the target river in the k-th unit watershed; This represents the minimum elevation among all river grids of the target river within the k-th unit watershed.

[0132] S320, Based on the river raster data and watershed raster data of the target watershed, calculate the Euclidean distance from the center point of the target watershed raster to the center point of the target river raster; wherein, the target river raster is the watershed raster that is closest to the river raster in the target river.

[0133] Distance: The distance from the river affects the degree of pollution that pollutants cause to the river water.

[0134] ArcGIS is a professional geographic information mapping software that provides users with a scalable and comprehensive GIS platform. It helps users quickly create maps, supports single-user and multi-user editing, and enables complex automated workflows.

[0135] Using the Euclidean distance toolset in ArcGIS, calculate the Euclidean distance between the target watershed raster and the target river raster within the target watershed:

[0136]

[0137] In the formula, , This represents the coordinates of the center point of the i-th river grid. , This represents the coordinates of the center point of the j-th watershed grid.

[0138] In one implementation, such as Figure 2 As shown, in this embodiment of the application, the target watershed is first re-divided into multiple unit watersheds. The unit watersheds are obtained using the following method:

[0139] S610 performs depression filling processing on the digital elevation model data of the target watershed.

[0140] As an example, the "Fill" tool is used to fill depressions in the DEM (Digital Elevation Model) data of the target watershed. Local depressions or depressions in the DEM data can cause flow interruptions or unreasonable flow directions in flow calculations. Open ArcToolbox → Spatial Analyst Tools → Hydrology → Fill. Input the original DEM data, and the process will generate a DEM with filled depressions. This process fills in all unreasonable low-lying points in the DEM data, ensuring the consistency of subsequent flow direction analysis.

[0141] S620, based on the digital elevation model data after filling depressions, uses preset tools to calculate the flow direction of the target watershed raster.

[0142] The "Flow Direction" tool is used to calculate the flow direction, using the filled DEM data as input to generate flow direction information for each small grid cell. Open ArcToolbox → Spatial Analyst Tools → Hydrology → Flow Direction. Input the filled DEM data. The tool, based on the D8 algorithm, calculates the steepest descent direction of the water flow for each watershed grid cell and uses an encoding (typically 1, 2, 4, 8, 16, 32, 64, 128) to represent one of eight directions. This gives each watershed grid cell a corresponding flow direction value.

[0143] It is important to note that these river networks formed by runoff are not the actual river networks that flow continuously within the city. Therefore, this application overlays the actual river network vector data with the river network generated from DEM data, forcing the water flow to follow the true river direction.

[0144] S630 connects the points corresponding to the highest elevation between two adjacent rivers to serve as the watershed of a unit watershed; based on each watershed, the target watershed is divided using a preset method to obtain each unit watershed.

[0145] Specifically, the line connecting the highest points of two adjacent rivers is used as the watershed of a unit watershed. The target watershed is manually vectorized to divide it into unit watersheds based on the rivers. Each grid cell within the divided unit watershed (small watershed) is considered a potential non-point source pollution source for that river. Whether a source is a non-point source pollution source is determined based on the pollutant accessibility index. For example... Figure 3 The diagram shown is a schematic representation of watershed division in the prior art. Figure 4 The diagram shown is a schematic representation of the unit watershed division in this application. Figure 3 , Figure 4 The serial numbers in the code represent the codes for each watershed. Each watershed grid within a unit watershed can be matched with a corresponding watershed grid in each existing watershed, based on its location and size.

[0146] In one implementation, the first pollutant influencing factor is runoff; the hydrological information includes target hydrological parameters and surface data; the target hydrological parameter is rainfall intensity.

[0147] Based on the acquired hydrological information, the first pollutant impact factor of the target watershed grid is calculated, including:

[0148] Based on the acquired rainfall intensity and ground data, a preset model is used to calculate the rainfall runoff of the target watershed, and the raster runoff of the target watershed is obtained based on the rainfall runoff and the rasterized information of the target watershed.

[0149] The preset model adopts the SWAT model. The SWAT (Soil and Water Assessment Tool) model is a distributed model widely used in watershed hydrological simulation. It can comprehensively consider multiple factors such as rainfall, soil properties, topography, and vegetation cover to simulate and predict runoff within the watershed. Its core principle is to perform detailed quantitative analysis of hydrological processes by dividing the watershed into sub-watersheds and hydrological response units (HRUs) and combining measured data and geographic information data.

[0150] In practical applications, rainfall runoff calculation is one of the key functions of the SWAT model. By inputting rainfall intensity and surface data (such as soil permeability, slope, and vegetation cover type), the model can simulate the flow process of rainfall on the land surface and output the rainfall runoff of the target watershed. Furthermore, based on rasterization, the watershed runoff results can be mapped onto specific raster cells, thereby obtaining the runoff of the target watershed raster.

[0151] The following is a detailed description of the river non-point source pollution source identification method of this application embodiment, using a specific example. In this example, the residual pollutant impact factors include the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid; the first pollutant impact factor is the runoff of the target watershed grid; the second pollutant impact factor is the pollutant concentration of the target watershed grid; the hydrological information includes target hydrological parameters and surface data; the target hydrological parameter is rainfall intensity. For example... Figure 5 As shown, it includes the following steps:

[0152] S710 uses the SWAT model to calculate the rainfall runoff of the target watershed based on the acquired rainfall intensity and ground data, and obtains the runoff volume of the target watershed grid based on the rainfall runoff and the gridded information of the target watershed.

[0153] For example, this application is used to predict the pollutant accessibility index of a target watershed grid for a future time period, and to determine whether there are potential pollution sources in the target watershed based on this pollutant accessibility index. Rainfall intensity and surface data are acquired through preset data platforms, including CMIP6, WorldClim, NASA Earth Exchange, and CHELSA, to obtain geographic information data packages for the future time period. For example, data on rainfall intensity and land use types for 2021-2100 are acquired. After downscaling using tools such as SDSM, WRF, and DeepSD, the river non-point source pollution source identification method of this application can predict the river non-point source pollution situation up to 2100. CMIP6 (Coupled Model Intercomparison Project Phase 6) is an international climate model comparison project led by the World Climate Research Programme (WCRP). It aims to assess and improve the accuracy of global climate models (GCMs) through standardized experiments (such as DECK and ScenarioMIP), supporting IPCC assessment reports and climate change prediction research. Its data covers key climate variables such as temperature, precipitation, and sea ice, and is a core tool for studying future greenhouse gas emission scenarios (such as SSPs). WorldClim (a global climate data platform) provides high-resolution (e.g., 1km) global climate raster data, including current climate variables (such as temperature and precipitation) and future downscaled projections based on the CMIP5 / CMIP6 model. Its data is widely used in ecological modeling, species distribution prediction, and regional climate change research, and is often combined with PRISM interpolation technology to improve spatial accuracy. NASA Earth Exchange (NEX), through the NEX-GDDP-CMIP6 project, provides global downscaled climate data, downscaling the raw GCM output of CMIP6 to daily resolution (approximately 25km), supporting the analysis of regional-scale extreme climate events (such as floods and heat waves). Its data is based on SSP scenarios and is suitable for climate risk assessment in scientific research and policymaking. CHELSA is a high-resolution environmental layer for climate data.

[0154] The geographic information data package in this application can also be a geographic information data package for the current time period. Therefore, this application is used to predict the potential locations of current non-point source pollution sources and understand the distribution of river non-point source pollution.

[0155] S720 takes the known runoff and rainfall intensity of the target watershed grid as input into the conditional distribution function and calculates the pollutant concentration of the target watershed grid.

[0156] S730, using a raster calculator to calculate the relative elevation difference between each watershed raster and the river within the target watershed unit ( ); Calculate the Euclidean distance between the target watershed grid and the target river grid within the target watershed.

[0157]

[0158]

[0159] in, For relative elevation difference, The distance is Euclidean.

[0160] S740 calculates the pollutant accessibility index by substituting the standardized pollutant concentration, runoff, Euclidean distance, and relative elevation difference into the pollutant accessibility calculation formula.

[0161]

[0162] in, The accessibility index of pollutants, The pollutant accessibility standardized component is the pollutant concentration. For the pollutant accessibility standardized component of runoff, For the pollutant accessibility standardized component of Euclidean distance, This represents the standardized component of pollutant accessibility based on relative elevation differences.

[0163] S750 assesses the potential pollution sources of the target river for the target watershed grid based on the pollutant accessibility index.

[0164] This application constructs a comprehensive pollutant accessibility index, rather than a single impact factor assessment. This application can more accurately identify potential non-point source pollution sources, thereby improving the efficiency of pollution control. This application achieves a more comprehensive assessment through the pollutant accessibility index.

[0165] Figure 6 A schematic diagram of a river non-point source pollution source identification device according to an embodiment of this application is shown. Exemplarily, the river non-point source pollution source identification device includes: a first factor calculation module 610, a second factor calculation module 620, a residual factor calculation module 630, an index calculation module 640, and an evaluation module 650.

[0166] The first factor calculation module 610 is used to calculate the first pollutant impact factor of the target watershed grid based on the acquired hydrological information.

[0167] The second factor calculation module 620 is used to calculate the second pollutant impact factor of the target watershed grid based on the first pollutant impact factor and hydrological information, using the constructed conditional distribution function.

[0168] The residual factor calculation module 630 is used to determine the residual pollutant impact factors based on the target watershed grid and the target river.

[0169] The index calculation module 640 is used to calculate the pollutant accessibility index of the target watershed grid relative to the target river based on the first pollutant impact factor, the second pollutant impact factor and the remaining pollutant impact factor.

[0170] Assessment module 650 is used to assess the potential non-point source pollution levels of a target watershed grid on a target river based on the pollutant accessibility index.

[0171] It is understood that the device in this embodiment corresponds to the river non-point source pollution source identification method in the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0172] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the various modules in the above-described river non-point source pollution source identification method or the above-described river non-point source pollution source identification device.

[0173] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0174] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0175] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0177] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0178] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0179] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for identifying non-point source pollution in rivers, characterized in that, include: Based on the acquired hydrological information, the first pollutant impact factor of the target watershed grid is calculated. Based on the first pollutant impact factor and the hydrological information, the second pollutant impact factor of the target watershed grid is calculated using the constructed conditional distribution function; the remaining pollutant impact factors are determined based on the target watershed grid and the target river. Based on the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor, the pollutant accessibility index of the target watershed grid relative to the target river is calculated. Specifically, this includes: standardizing the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor based on the maximum value of each pollutant impact factor within the target watershed; and substituting the standardized first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor into the pollutant accessibility calculation formula to calculate the pollutant accessibility index. The pollutant accessibility index is used to assess the potential non-point source pollution level of the target watershed grid on the target river. The remaining pollutant impact factors include the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid; the first pollutant impact factor is the runoff of the target watershed grid; and the second pollutant impact factor is the pollutant concentration of the target watershed grid.

2. The method for identifying river non-point source pollution according to claim 1, characterized in that, The hydrological information includes target hydrological parameters; The step of calculating the second pollutant impact factor of the target watershed grid based on the first pollutant impact factor and the hydrological information using the constructed conditional distribution function includes: Based on the first pollutant impact factor and the target hydrological parameters, the conditional distribution function is constructed by constructing the conditional distribution function based on the first joint distribution function and the second joint distribution function, and the second pollutant impact factor is calculated.

3. The method for identifying river non-point source pollution according to claim 2, characterized in that, The method further includes: The first joint distribution function is constructed based on the optimal marginal distribution functions corresponding to the target hydrological parameters, the first pollutant impact factor, and the second pollutant impact factor, as well as the selected optimal connection function. The second joint distribution function is constructed based on the optimal marginal distribution functions corresponding to the first pollutant impact factor and the second pollutant impact factor, respectively, and the optimal connection function. The conditional distribution function is constructed based on the first joint distribution function and the second joint distribution function.

4. The method for identifying river non-point source pollution according to claim 1, characterized in that, The step of substituting the standardized first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor into the pollutant accessibility calculation formula to calculate the pollutant accessibility index includes calculating the pollutant accessibility index using the following pollutant accessibility calculation formula: Let x be the pollutant accessibility index for the x-th watershed grid. Let be the pollutant accessibility standardized component of the i-th pollutant impact factor; This represents the number of pollutant influencing factors.

5. The method for identifying river non-point source pollution according to claim 1, characterized in that, The remaining pollutant impact factors include the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid; The step of determining the remaining pollutant impact factors based on the target watershed grid and the target river includes: The relative elevation difference is calculated based on the elevation of the target watershed grid and the average and minimum elevations of all river grids of the target river within the target watershed. Based on the river grid data and watershed grid data of the target watershed, calculate the Euclidean distance from the center point of the target watershed grid to the center point of the target river network grid within the target watershed; wherein, the target river network grid is the river grid that is closest to the target river in the watershed grid.

6. The method for identifying river non-point source pollution according to claim 5, characterized in that, The unit watershed is obtained by the following method: The digital elevation model data of the target watershed is subjected to depression filling processing; Based on the digital elevation model data after filling depressions, the flow direction of the target watershed grid is calculated using a preset tool; Connect the points corresponding to the highest elevations between two adjacent rivers to form watersheds for each unit watershed; based on each watershed, use a preset method to divide the target watershed into individual unit watersheds.

7. The method for identifying river non-point source pollution according to any one of claims 1-6, characterized in that, The first pollutant influencing factor is runoff; the hydrological information includes target hydrological parameters and surface data; the target hydrological parameter is rainfall intensity. The calculation of the first pollutant impact factor of the target watershed grid based on the acquired hydrological information includes: Based on the acquired rainfall intensity and ground data, the rainfall runoff of the target watershed is calculated using a preset model, and the runoff volume of the target watershed grid is obtained based on the rainfall runoff and the gridded information of the target watershed.

8. A river non-point source pollution source identification device, characterized in that, include: The first factor calculation module is used to calculate the first pollutant impact factor of the target watershed grid based on the acquired hydrological information. The first pollutant impact factor is the runoff volume of the target watershed grid; The second factor calculation module is used to calculate the second pollutant impact factor of the target watershed grid based on the first pollutant impact factor and the hydrological information, using a constructed conditional distribution function; the second pollutant impact factor is the pollutant concentration of the target watershed grid. The residual factor calculation module is used to determine the residual pollutant impact factor based on the target watershed grid and the target river; the residual pollutant impact factor includes the Euclidean distance and relative elevation difference between the target watershed grid and the target river grid. The index calculation module is used to calculate the pollutant accessibility index of the target watershed grid relative to the target river based on the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor. Specifically, the index calculation module is used to: standardize the first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor based on the maximum value of each pollutant impact factor within the target watershed; and substitute the standardized first pollutant impact factor, the second pollutant impact factor, and the remaining pollutant impact factor into the pollutant accessibility calculation formula to calculate the pollutant accessibility index. An assessment module is used to assess the potential non-point source pollution level of the target watershed grid on the target river based on the pollutant accessibility index.

9. A terminal device, characterized in that, The terminal device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the river non-point source pollution source identification method according to any one of claims 1-7.

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