River non-point source pollution source identification method, device and equipment

Abstract

The application relates to the technical field of ecological environment data processing, in particular to a river non-point source pollution source identification method, device and equipment, which comprises the following steps: based on geographical information data packets corresponding to a target river basin, calculating a plurality of pollution influence factors of a target river basin grid in the target river basin on a target river; based on the plurality of pollution influence factors, calculating a pollutant accessibility index of the target river basin grid on the target river, and evaluating whether a potential non-point source pollution source exists in the target river basin grid according to the pollutant accessibility index. Therefore, the application can effectively solve the problems of low identification accuracy of the existing non-point source pollution identification method.

Description

TECHNICAL FIELD

[0001] The present 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. BACKGROUND

[0002] In the prior art, a method for identifying non-point source pollution of a river basin is as follows: based on terrain and rainfall, a river basin hydrological model is used to simulate the distribution of pollutants in the river basin, and then the non-point source pollution is identified. However, this method has a narrow application range and low accuracy in identifying the distribution of pollution sources and non-point source pollution. SUMMARY

[0003] Therefore, the embodiments of the present application provide a river non-point source pollution source identification method, device and equipment, which can effectively solve the problem of low accuracy of the prior art in identifying non-point source pollution.

[0004] In a first aspect, the embodiments of the present application provide a river non-point source pollution source identification method, comprising:

[0005] Based on the geographical information data packet corresponding to the target river basin, a plurality of pollution influence factors of the target river basin grid in the target river basin on the target river are calculated.

[0006] Based on the plurality of pollution influence factors, a pollutant accessibility index of the target river basin grid on the target river is calculated, and the potential non-point source pollution source of the target river basin on the target river is evaluated according to the pollutant accessibility index.

[0007] In some embodiments, the calculation of the plurality of pollution influence factors of the target river basin grid in the target river basin on the target river based on the geographical information data packet corresponding to the target river basin comprises:

[0008] Based on the geographical information data packet corresponding to the target river basin, the Euclidean distance, the relative height difference, the runoff and the average value of the pollutant concentration of the target river basin grid in the target river basin on the target river are calculated.

[0009] The calculation of the pollutant accessibility index of the target river basin grid on the target river based on the plurality of pollution influence factors comprises:

[0010] Based on the Euclidean distance, the relative height difference, the runoff and the average value of the pollutant concentration of the target river basin grid in the target river basin on the target river, the pollutant accessibility index of the target river basin grid on the target river is calculated.

[0011] In some embodiments, the calculation of the Euclidean distance, the relative height difference, the runoff and the average value of the pollutant concentration of the target river basin grid in the target river basin on the target river based on the geographical information data packet corresponding to the target river basin comprises:

[0012] According to the river network grid data and the basin grid data of the target basin in the geographic information data packet, the Euclidean distance from the center point of the target basin grid in the target basin to the center point of the target river network grid is calculated; wherein the target river network grid is the river network grid closest to the target river among the basin grids.

[0013] In some embodiments, the Euclidean distance, the relative height difference, the runoff, and the average pollutant concentration of the target river basin grid to the target river in the target basin are calculated based on the geographic information data packet corresponding to the target basin, including:

[0014] The relative height difference of the target basin grid is calculated based on the elevation of the target basin grid in the target unit basin and the average elevation and the minimum elevation corresponding to all river network grids of the target river in the target unit basin.

[0015] In some embodiments, the unit basin is obtained by dividing the following method:

[0016] The digital elevation model data included in the geographic information data packet is subjected to depression filling processing;

[0017] Based on the depression-filled digital elevation model data, a preset tool is used to calculate the flow direction of the target basin grid;

[0018] The points corresponding to the highest elevation between adjacent two rivers are connected to serve as the divide of the unit basin; based on each divide, the target basin is segmented by a preset method to obtain each unit basin.

[0019] In some embodiments, the Euclidean distance, the relative height difference, the runoff, and the average pollutant concentration of the target river basin grid to the target river in the target basin are calculated based on the geographic information data packet corresponding to the target basin, including:

[0020] Based on the basin runoff data and the land use type data of the target basin in the geographic information data packet, the runoff of each basin grid is estimated by using a preset model;

[0021] and / or,

[0022] Based on the total pollutant emission amount in the target basin grid, the runoff, the area, and the land use type or the percentage of the land use type in the geographic information data packet, the average pollutant concentration of the target basin grid is calculated.

[0023] In some embodiments, the method further includes:

[0024] Based on the drainage basin grid data in the geographic information data packet, a preset tool is used to create a regular fishing net according to a set resolution, to generate a grid vector file same as each drainage basin grid; based on the grid vector file, a preset area tabulation tool is used to count the percentage of different land use types in the grid cell, and the area of different land use types is determined according to the percentage of different land use types.

[0025] In some embodiments, the calculation of the pollutant accessibility index of the target drainage basin grid to the target river based on the average values of the Euclidean distance, the relative height difference, the runoff and the pollutant concentration of the target drainage basin grid to the target river comprises:

[0026] Based on the corresponding maximum values in the target drainage basin, the average values of the Euclidean distance, the relative height difference, the runoff and the pollutant concentration are standardized;

[0027] The average values of the Euclidean distance, the relative height difference, the runoff and the pollutant concentration after the standardization are multiplied to obtain the pollutant accessibility index.

[0028] In a second aspect, the embodiments of the present application provide a river non-point source pollution source identification device, comprising:

[0029] An impact factor calculation module is configured to calculate a plurality of pollution impact factors of a target drainage basin grid to a target river in a target drainage basin based on a geographic information data packet corresponding to the target drainage basin;

[0030] An accessibility calculation module is configured to calculate a pollutant accessibility index of the target drainage basin grid to the target river based on the plurality of pollution impact factors, and to evaluate a potential non-point source pollution source of the target drainage basin to the target river according to the pollutant accessibility index.

[0031] In a third aspect, the embodiments of the present application provide a terminal device, which comprises a processor and a memory, the memory stores a computer program, and the processor is configured to execute the computer program to implement the river non-point source pollution source identification method provided in the first aspect of the present application.

[0032] The embodiments of the present application have the following beneficial effects:

[0033] The application calculates a plurality of pollution influence factors of a target river basin grid on a target river based on geographic information data packets corresponding to the target river basin; calculates a pollution reachability index of the target river basin grid on the target river based on the plurality of pollution influence factors, and evaluates potential non-point source pollution sources of the target river basin on the target river according to the pollution reachability index. The application calculates a pollution reachability index of a river basin grid on a river based on a plurality of pollution influence factors, can effectively identify whether the target river basin grid is a potential non-point source pollution source based on the size of the pollution reachability index, and has high identification accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be considered as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0035] Figure 1 A flowchart of a river non-point source pollution source identification method of an embodiment of the application is shown;

[0036] Figure 2 Another flowchart of a river non-point source pollution source identification method of an embodiment of the application is shown;

[0037] Figure 3 A flowchart of a calculation method of an influence factor of a river non-point source pollution source identification method of an embodiment of the application is shown;

[0038] Figure 4 A flowchart of a division method of a unit river basin of a river non-point source pollution source identification method of an embodiment of the application is shown;

[0039] Figure 5 A first river basin division schematic diagram involved in a river non-point source pollution source identification method of an embodiment of the application is shown;

[0040] Figure 6 A second river basin division schematic diagram involved in a river non-point source pollution source identification method of an embodiment of the application is shown;

[0041] Figure 7 A structure schematic diagram of a river non-point source pollution source identification device of an embodiment of the application is shown.

[0042] Main element symbol explanation:

[0043] 710 - influence factor calculation module; 720 - reachability calculation module. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application.

[0045] The components of the embodiments of the present application generally described and illustrated in the accompanying drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0046] Hereinafter, the terms "include", "have", and their conjugates used in various embodiments of the present application are only intended to denote that specific features, numbers, steps, operations, elements, components, or combinations thereof are present, and should not be construed as excluding the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof. In addition, the terms "first", "second", "third", and the like are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0047] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present application belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is the same as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized or overly formal meaning, unless clearly defined in various embodiments of the present application.

[0048] Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments and features in the embodiments can be combined with each other without conflict.

[0049] In order to solve the problems of low recognition accuracy and narrow application range of the existing surface source pollution recognition method, the present application finds that whether the pollutants can reach the river depends on the distance from the pollution source to the river, the height difference, the runoff generated by rainfall, and the concentration of pollutants in the rainfall runoff. Therefore, based on the concept of water resource accessibility, based on the above four influencing factors, and considering the specificity of pollutants generated by land use types, the present application proposes a river surface source pollution source recognition method, device and equipment.

[0050] The river non-point source pollution source identification method will be described below in combination with some specific embodiments.

[0051] Figure 1 A flow chart of the river non-point source pollution source identification method of the embodiments of the present application is shown. Exemplarily, the river non-point source pollution source identification method comprises the following steps:

[0052] S100, based on the geographic information data packet corresponding to the target river basin, calculating a plurality of pollution influence factors of the target river basin grid in the target river basin on the target river.

[0053] Exemplarily, the geographic information data packet comprises the following data of the target river basin: 1) digital elevation model (DEM) data; 2) river basin grid data; 3) river network grid data; 4) land use type data; 5) runoff data.

[0054] 1) Digital elevation model (DEM) data includes: (a) regular grid point elevation, regular grid point planar coordinates (X, Y) and corresponding elevation value (Z) stored in the form of a grid, which constitutes the basic data set of the terrain undulation; (b) terrain factors such as slope, slope direction, curvature; (c) hydrological analysis parameters such as flow direction, flow accumulation, and basin boundary.

[0055] 2) River basin grid data: River basin grid data is a river basin geographic information data set stored in a regular grid form, which divides the study area into a plurality of regular small grids (river basin grids), each grid contains specific geographic and attribute information. The following is the geographic and attribute information usually included in the river basin grid data: (a) basic elevation grid, storing the elevation value (Z) of the regular grid unit in the form of a two-dimensional matrix, each grid unit corresponds to the planar coordinates (X, Y), which is a direct expression of the spatial distribution of the terrain. (b) Hydrological characteristic grid, flow direction, flow accumulation. (c) Basin boundary.

[0056] 3) River network grid data, river network grid data represents the river in the form of a regular grid, which is convenient for superimposed analysis with other grid data (such as river basin grid data, DEM data, etc.). Main contents include: (a) river location information, (b) river level information, (c) higher level, (d) river width and depth information, (e) flow velocity information, (f) river connectivity information, (g) river elevation information, (h) river buffer zone information, (i) hydrological characteristic information.

[0057] 4) Land use type data, the purpose and cover type of the land in the study area. The main contents usually included in the land use type data are:

[0058] (a) Land use classification, according to certain standards (such as China Land Use Status Classification, Corine Land Cover, USGS Land Use Classification, etc.), the study area is divided into different land use types. Common classifications include:

[0059] Urban land: such as residential areas, commercial areas, industrial areas, etc.

[0060] Farmland: such as paddy fields, dry land, etc.

[0061] Forest: such as coniferous forest, broadleaf forest, mixed forest, etc.

[0062] Grassland: such as natural grassland, artificial grassland, etc.

[0063] Water area: such as rivers, lakes, reservoirs, etc.

[0064] Bare land: such as desert, gobi, sandy land, etc.

[0065] Wetland: such as marsh, mudflat, etc.

[0066] Other special land use: such as airport, port, mine, etc.

[0067] (b) Land use proportion; in the rasterized study area, each river network grid may contain single or mixed land use types. If it is a mixed type, record the proportion (percentage) of each land use type. For example: a grid may have 60% farmland, 30% grassland, and 10% water area. (c) Land use change information; (d) soil property information; (e) human activity intensity, different land use types correspond to different human activity intensity, for example: urban land: building density, traffic congestion, pollutants mainly from domestic sewage, industrial emissions, etc. Farmland: agricultural fertilization, pesticide use may cause nitrogen and phosphorus pollution. Industrial area: may discharge heavy metals, organic matter and other pollutants. (f) Pollutant emission characteristics. (g) Rainfall runoff characteristics.

[0068] 5) Runoff data, which describes the flow of surface water and groundwater in the study area, directly affecting the transport and diffusion of pollutants. The following are the main contents of the runoff data usually included: (a) Total runoff; (b) Surface runoff and groundwater runoff; (c) Runoff depth; (d) Runoff coefficient; (e) Runoff time distribution; (f) Runoff spatial distribution; (g) Runoff path; (h) Runoff pollutant concentration; (i) Runoff model simulation data, runoff data simulated by hydrological models (such as SWAT, HSPF, HEC-HMS, etc.).

[0069] S200, based on multiple pollution influencing factors, calculate the pollutant accessibility index of the target river basin grid to the target river, and evaluate the potential non-point source pollution of the target river basin to the target river according to the pollutant accessibility index.

[0070] The application calculates a pollution influence factor of each basin grid in the target basin, and determines whether there is a potential pollution source in the basin grid. The area corresponding to the basin grid is planned and designed according to a pollution accessibility index.

[0071] Exemplarily, the pollution influence factor includes but is not limited to distance (Euclidean distance), relative height difference, runoff and average pollution concentration, etc.

[0072] The pollution accessibility refers to the potential ability of the pollution to enter a sensitive environment or a living body through physical migration or chemical diffusion, and the sensitive environment in the application refers to the target river.

[0073] The geographic information data packet in the application can also be a geographic information data packet of a current time stage. Thus, the application is used to predict a potential position of a current non-point source pollution source and understand the situation of river non-point source pollution.

[0074] The geographic information data packet in the present application can obtain the geographic information data packet of the future time period through a preset data platform, thereby the present application is used for predicting the pollutant accessibility index of the target flow basin grid in the future time period, and judging whether the target flow basin exists a potential pollution source based on the pollutant accessibility index. Exemplarily, the preset data platform includes CMIP6, WorldClim, NASA Earth Exchange, CHELSA and the like to obtain the geographic information data packet of the future time period. For example, the data of rainfall, land use type and the like in 2021-2100 is obtained. After the downscaling through SDSM, WRF, DeepSD and the like, the river surface source pollution identification method based on the embodiments of the present application can predict the river surface source pollution condition until 2100. Wherein, CMIP6 (the sixth phase of the coupled model comparison plan) is an international climate model comparison project led by the world climate research plan (WCRP), aiming to evaluate and improve the accuracy of global climate models (GCMs) through standardized experiments (such as DECK and ScenarioMIP), support IPCC assessment report and climate change prediction research. Its data covers key climate variables such as temperature, precipitation, sea ice, etc., and is the core tool for studying future greenhouse gas emission scenarios (such as SSPs). WorldClim (Global Climate Data Platform) provides high-resolution (such as 1km) global climate grid data, including current climate variables (such as temperature, precipitation) and future downscaling prediction data based on CMIP5 / CMIP6 models. Its data is widely used in ecological modeling, species distribution prediction and regional climate change research, often combined with PRISM interpolation technology to improve spatial accuracy. NASA EarthExchange (NEX), Earth Exchange Platform provides global downscaling climate data through NEX-GDDP-CMIP6 project, which downscaling the original GCM output of CMIP6 to daily resolution (about 25km), supporting regional scale extreme climate event analysis (such as flood, heat wave). Its data is based on SSPs scenario, suitable for climate risk assessment of scientific research and policy making3. CHELSA is a high-resolution environmental layer of climate data.

[0075] The pollutant accessibility index (Pollutant Accessibility Index, PAI) is used to quantify the possibility of pollutants reaching the target river from a certain location (such as a flow basin grid).

[0076] Exemplarily, the higher the pollutant accessibility index is, the greater the possibility that the target watershed grid is a potential non-point pollution source of the target river is. For example, a threshold is set, and a critical PAI threshold is determined according to experience or statistical analysis. If the PAI value of the target watershed grid exceeds the PAI threshold, the target watershed grid is considered as a potential non-point pollution source of the target river. The position distribution of each potential non-point pollution source corresponding to the target watershed grid is counted to obtain the distribution of non-point pollution sources.

[0077] The present application constructs a comprehensive pollutant accessibility index instead of a single factor evaluation, which can more accurately identify potential non-point pollution sources and improve the efficiency of pollution prevention and control.

[0078] In one embodiment, as shown in Figure 2 Based on the geographic information data packet corresponding to the target watershed, a plurality of pollution influence factors of the target watershed grid in the target watershed on the target river are calculated, including:

[0079] S110, based on the geographic information data packet corresponding to the target watershed, the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target watershed grid in the target watershed on the target river are calculated;

[0080] Based on the plurality of pollution influence factors, the pollutant accessibility index of the target watershed grid on the target river is calculated, including:

[0081] S210, based on the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target watershed grid in the target watershed on the target river, the pollutant accessibility index of the target watershed grid on the target river is calculated.

[0082] In the embodiment of the present application, the distance, the height difference, the runoff and the average pollutant concentration of the potential pollution source in the watershed to the river are calculated first, and then the pollutant accessibility of the potential pollution source to the river is evaluated through the pollutant accessibility index, which can effectively solve the problem of low recognition accuracy of the existing non-point pollution recognition method.

[0083] Further, as shown in Figure 3 Based on the geographic information data packet corresponding to the target watershed, the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target watershed grid in the target watershed on the target river are calculated, including:

[0084] S111, according to the river network grid data and the watershed grid data of the target watershed in the geographic information data packet, the Euclidean distance from the center point of the target watershed grid to the center point of the target river network grid is calculated; wherein the target river network grid is the river network grid closest to the target river.

[0085] Exemplarily, in the embodiments of the present application, the basin grid is one of the grids obtained by dividing the target basin according to a set resolution; and the river network grid is one of the grids obtained by dividing the target river according to a set resolution. In other words, the basin grid and the river network grid are both a small area obtained by dividing the target basin according to a certain resolution.

[0086] Distance: The distance from the river affects the degree of pollution of the river water by the pollutants.

[0087] ArcGIS is a professional geographic information mapping software, which provides a scalable and comprehensive GIS platform for users. It can help users quickly make maps, support single-user and multi-user editing, and also can perform complex automated workflows.

[0088] In the ArcGIS platform, the Euclidean distance tool set is used to calculate the Euclidean distance between the target basin grid and the river network grid in the target basin:

[0089]

[0090] In the formula, , Xi, Yi represent the coordinates of the center point of the i-th river network grid; , Xj, Yj represent the coordinates of the center point of the j-th basin grid.

[0091] Further, the present application also needs to standardize the Euclidean distance (also known as the proximity factor), which is specifically standardized by the following formula, wherein the farther the distance, the lower the pollutant accessibility:

[0092]

[0093] In the formula, represents the standardized component of the distance factor in the i-th basin grid; represents the Euclidean distance from the i-th basin grid to the center point of the target river network grid; represents the maximum value of the Euclidean distance among all basin grids in the target basin.

[0094] In one embodiment, as shown in Figure 4 , the embodiments of the present application first redivide the target basin to obtain a plurality of unit basins, which are obtained by the following method:

[0095] S310, the digital elevation model data included in the geographic information data packet is filled and processed.

[0096] Exemplarily, the DEM data (Digital Elevation Model data) of the target watershed is subjected to fill depression processing using the "Fill" tool, in which local depressions or depressions in the DEM data can cause flow calculation to be interrupted or to have unreasonable flow direction. Open ArcToolbox→Spatial Analyst Tools→Hydrology→Fill. Input the original DEM data, and a filled DEM data is generated after running. After such processing, all unreasonable low-lying points in the DEM data are filled, thereby ensuring the continuity of subsequent flow direction analysis.

[0097] S320, based on the filled digital elevation model data, a preset tool is used to calculate the flow direction of the target watershed grid;

[0098] The "Flow Direction" tool is called to calculate the flow direction, and the filled DEM data is used as input to generate flow direction information for each small square. Open ArcToolbox→Spatial Analyst Tools→Hydrology→FlowDirection. Input the filled DEM data. The tool calculates the steepest descent direction of water flow for each watershed grid based on the D8 algorithm, and uses a code (usually 1, 2, 4, 8, 16, 32, 64, 128) to represent one of the eight directions. In this way, each watershed grid has a corresponding flow direction value.

[0099] It should be noted that these river networks formed by runoff are not the actual river networks that we have been flowing in the city. Therefore, in this application, the actual river network vector data is superimposed on the river network generated by the DEM data, and the water flow is forced to follow the true river direction.

[0100] S330, connecting each point corresponding to the highest elevation between two adjacent rivers as a divide of a unit watershed; based on each divide, the target watershed is segmented using a preset method to obtain each unit watershed.

[0101] Specifically, the highest line between two adjacent rivers is taken as the divide of a unit watershed, and the target watershed is segmented into unit watersheds by rivers using manual vectorization. Each watershed grid cell in the segmented unit watershed (small watershed) is a potential non-point source pollution source of the river, and whether it is a non-point source pollution source is determined according to the pollutant accessibility index. For example Figure 5 Fig. 1 shows a schematic diagram of watershed division in the prior art, and Figure 6 Fig. 2 shows a schematic diagram of unit watershed division in this application, Figure 5 , Figure 6 The serial numbers in Figs. 1 and 2 are the codes of each watershed. In each unit watershed, each watershed grid can find a corresponding watershed grid in the existing watersheds in terms of position and size.

[0102] Further, based on the geographic information data packet corresponding to the target river basin, the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target river basin grid to the target river in the target river basin are calculated, including:

[0103] In S112, based on the elevation of the target river basin grid in the target unit river basin and the average elevation and the minimum elevation of all river network grids of the target river in the target unit river basin, the relative height difference of the target river basin grid is calculated.

[0104] Illustratively, based on the DEM data, each unit river basin is coded, and the average elevation and the minimum elevation of all river network grids of the target river in the target unit river basin are calculated according to the coding field by using the zoning statistical tool. Finally, the relative height difference of each river basin grid to the river in the target unit river basin is calculated by using the grid calculator.

[0105]

[0106] Wherein, i∈1, 2, …, n, j∈1, 2, …, m, respectively represent the row and column of the river basin grid; k∈1, 2, …, z, represents the coding serial number of the unit river basin; represents the relative height difference of the river basin grid in the i-th row and the j-th column to the river in the target unit river basin; represents the elevation of the river basin grid in the i-th row and the j-th column; represents the average elevation of all river network grids of the target river in the k-th unit river basin; represents the minimum elevation of all river network grids of the target river in the k-th unit river basin.

[0107] The relative height difference is standardized by the following formula, and the larger the relative height difference, the higher the pollutant accessibility:

[0108]

[0109] Wherein, represents the pollutant accessibility standardized component of the relative height difference factor in the k-th unit river basin; represents the relative height difference of the river basin grid in the i-th row and the j-th column in the target unit river basin to the river in the target unit river basin; represents the maximum value of the relative height difference in all unit river basins in the target river basin.

[0110] In one embodiment, based on the geographic information data packet corresponding to the target river basin, the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target river basin grid to the target river in the target river basin are calculated, including:

[0111] ​S113, based on the watershed runoff data and the land use type data of the target watershed in the geographic information data packet, a preset model is used to estimate the runoff of each watershed grid.

[0112] The amount of runoff is an important factor in determining the accessibility of pollutants. In order to quickly and accurately estimate the direct surface runoff under a specific rainfall event, especially in the absence of detailed hydrological observation data, in the embodiments of the present application, based on the watershed runoff data and the land use type data, the direct surface runoff under a specific rainfall event is estimated by using ArcSWAT on the ArcGIS platform based on the SCS-CN (Soil Conservation Service Curve Number method) model. SCS-CN is an empirical model developed for estimating the direct surface runoff under a specific rainfall event. The runoff curve parameter CN (Curve Number) is the only key parameter of the model, which is determined by soil type, soil antecedent moisture condition and land use type. In the case of relatively stable regional soil type and moisture, the change of CN value is mainly caused by the change of land use type. The CN value usually ranges from 0 to 100, and the larger the value, the more runoff is generated. The specific steps of calculating the runoff are, for example: input rainfall data and watershed characteristic parameters; obtain CN value according to soil type and land use type. Calculate the direct surface runoff using the formula and output the result.

[0113] In order to improve the accuracy, in the embodiments of the present application, the CN value is optimized by introducing a machine learning algorithm, or the CN value is dynamically adjusted in combination with remote sensing data. The machine learning algorithm optimizes the CN value in the SCS-CN model, which specifically includes: input features: including but not limited to the following variables: rainfall, soil type, land use type, topographic features (slope, elevation), vegetation coverage, initial wetness condition.

[0114] Target variable: actual observed runoff or historical CN value data.

[0115] Exemplarily, a regression model is used to optimize the CN value, such as linear regression, random forest regression, gradient boosting regression (GBDT), which is suitable for predicting continuous CN value.

[0116] Objective: to establish the mapping relationship between input features and CN value.

[0117] The steps include: collecting historical data (rainfall, runoff, soil type, etc.); using a regression model to fit the relationship between input features and actual CN value; verifying the model performance and adjusting the hyperparameters to improve the prediction accuracy.

[0118] Further, the runoff factor is standardized by the following formula, and the larger the runoff, the higher the accessibility of pollutants:

[0119]

[0120] wherein, represents the pollutant accessibility normalized component of the runoff factor at the i-th basin grid; represents the runoff of the i-th basin grid; represents the maximum value of the runoff among all the basin grids within the target basin.

[0121] Further, based on the geographic information data packet corresponding to the target basin, the Euclidean distance, the relative height difference, the runoff and the average pollutant concentration of the target basin grid to the target river within the target basin are calculated, including:

[0122] S114, based on the total pollutant emission amount, the runoff, the area and the land use type or the percentage of the land use type in the target basin grid in the geographic information data packet, the average pollutant concentration of the target basin grid is calculated.

[0123] In an embodiment, the basin grid is suitable for a single land use type, and the land use type in each basin grid is only one. For example, the average pollutant concentration is calculated by using the following formula:

[0124]

[0125] wherein, represents the average value of the pollutant in the rainfall runoff; M represents the total emission amount of a certain pollutant in the rainfall runoff, in grams g; V represents the runoff of the i-th basin grid, which is obtained according to the above calculation method of the runoff; represents the area of the i-th basin grid; generally consistent with the precision in the DEM data, for example, if the precision of the DEM data is 30 m, then the basin grid is 30 x 30 m 2 . represents the average value of the pollutant index corresponding to different land use types, for example, any one of the average COD, the average TSS and the average TP. The land use type of the i-th basin grid is determined from the land use type data.

[0126] The average COD (Chemical Oxygen Demand) represents the total amount of organic and inorganic substances that can be oxidized by a strong oxidizing agent in the water body, reflecting the degree of organic pollution of the water body.

[0127] The average TSS (Total Suspended Solids) refers to the total mass of suspended particulate matter in a unit volume of water (usually in mg / L), which is used to measure the turbidity and particulate pollution of the water body. The average TP (Total Phosphorus) refers to the total amount of all forms of phosphorus in the water body (including dissolved and particulate states), which is an important indicator for assessing water eutrophication, as shown in Table 1.

[0128] Table 1 Average values of pollutant indicators corresponding to different land use types and pollution levels

[0129]

[0130] In practical applications, many watershed grids may contain multiple land use types (for example, half of the watershed grid is urban impervious surface and the other half is green land). If the entire watershed grid is simply classified as one type, errors may occur. Therefore, in the embodiments of the present application, the contribution of each land use type is proportionally distributed.

[0131] In another embodiment, in order to improve the identification accuracy and increase the range of use, such as to meet the watershed grid of mixed land use type, the following calculation formula is used to calculate the average value of pollutant concentration:

[0132]

[0133] wherein N represents the total number of land use types; represents the average value of the pollutant in the first rainfall runoff; M represents the total discharge of a certain pollutant in the first rainfall runoff, in grams g; V represents the runoff of the i-th watershed grid, which is obtained according to the above calculation method; represents the area of the n-th land use type in the i-th watershed grid; represents the average value of the pollutant indicator corresponding to the n-th land use type.

[0134] Suppose that the impervious surface in the i-th watershed grid accounts for 50% and the green land accounts for 50%, then in the formula S1 (impervious surface) x (impervious surface) + S2 (green land) x (green land).

[0135] Further, the method further comprises:

[0136] S410, based on the drainage grid data in the geographic information data packet, a preset tool is used to create a regular fishing net (Grid Vector File) according to a set resolution to generate a grid vector file identical to each drainage grid (both location and size are identical). The set resolution is equal to the resolution used when dividing the grid in the target drainage basin. The grid vector file includes data division and block management, and the grid vector file divides the target drainage basin into regular small units (one grid unit) for easy block processing of large-scale data. Each grid unit is an independent analysis object.

[0137] S420, based on the grid vector file, a preset area tabulation tool is used to count the percentage of different land use types in the grid unit, and the area of different land use types is determined according to the percentage of different land use types.

[0138] Illustratively, a fishing net is created in ArcGIS to generate a grid vector file identical in size to the above-mentioned drainage grid. Specifically, 1) open the ArcGIS software. 2) use the "Create Fishnet" tool. 3) set the unit size of the fishing net to be the same as the size of the existing grid (for example, 10m x 10m). 4) Ensure that the fishing net covers the entire study area (target drainage basin). 5) output the generated grid vector file.

[0139] The area tabulation tool is used to count the percentage of different land use types in the grid. The area of different land use types is determined according to the percentage of different land use types. Specifically, 1) load the land use type data (usually in raster format) in ArcGIS. 2) use the "Tabulate Area" tool. 3) input the following parameters:

[0140] Zone Layer: Select the fishing net vector file just generated (each grid unit represents a drainage grid).

[0141] Class Layer: Select the land use type raster data.

[0142] Output Table: Specify the table path for outputting the statistical results.

[0143] 4) After running the tool, a table file will be generated, listing the area and percentage of different land use types in each drainage grid.

[0144] Result: A detailed statistical table is obtained, recording the proportion of various land use types in each drainage grid.

[0145] The average concentration of pollutants is standardized by the following formula, and the higher the average concentration of pollutants, the higher the accessibility of pollutants:

[0146]

[0147] represents the pollutant accessibility factor of the i-th watershed grid;

[0148] represents the average value of the pollutant of the i-th watershed grid;

[0149] represents the maximum value of the average value of the pollutant concentration of all watershed grids in the target watershed.

[0150] The present application draws on the concept of water resource accessibility, and based on four influencing factors of distance, height difference, runoff and average value of pollutant concentration, comprehensively considers the specificity of pollutants generated by land use types, so that the present application can effectively solve the problems of low identification accuracy and narrow application range of the existing non-point source pollution identification method.

[0151] In one embodiment, based on the Euclidean distance, relative height difference, runoff and average value of pollutant concentration of the target watershed grid to the target river in the target watershed, the pollutant accessibility index of the target watershed grid to the target river is calculated, including:

[0152] Based on the corresponding maximum values in the target watershed, the Euclidean distance, relative height difference, runoff and average value of pollutant concentration are standardized.

[0153] The standardized Euclidean distance, relative height difference, runoff and average value of pollutant concentration are multiplied to obtain the pollutant accessibility.

[0154] The pollutant accessibility of each potential pollution source in the watershed is evaluated by using the pollutant accessibility index A, and the calculation formula is:

[0155]

[0156] wherein, is the pollutant accessibility index of the x-th watershed grid; is the standardized component of the pollutant accessibility caused by each influencing factor; is the number of influencing factors of the accessibility evaluation model, and the number of influencing factors n is n, for example, n=4 in the embodiment of the present application.

[0157] Figure 7 Fig. 1 shows a structural schematic diagram of a river non-point source pollution source identification device according to an embodiment of the present application. Exemplarily, the river non-point source pollution source identification device includes an influencing factor calculation module 710 and an accessibility calculation module 720.

[0158] The influence factor calculation module 710 is configured to calculate a plurality of pollution influence factors of the target river basin grid on the target river based on the geographic information data packet corresponding to the target river basin.

[0159] The reachability calculation module 720 is configured to calculate a pollution reachability index of the target river basin grid on the target river based on the plurality of pollution influence factors, and identify whether the target river basin grid is a potential non-point source pollution source according to the pollution reachability index.

[0160] It can be understood that the device of the embodiment corresponds to the river non-point source pollution source identification method of the above-mentioned embodiment, and the optional items in the above-mentioned embodiment are also applicable to the embodiment, and thus will not be described here again.

[0161] The application further provides a terminal device. Illustratively, the terminal device includes a processor and a memory. The memory stores a computer program. The processor runs the computer program, so that the terminal device performs the functions of each module in the above-mentioned river non-point source pollution source identification method or the above-mentioned river non-point source pollution source identification device.

[0162] The processor can be an integrated circuit chip with a signal processing capability. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or at least one of the above. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps, and logic block diagrams in the embodiments of the application.

[0163] The memory can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), and the like. Among them, the memory is used to store a computer program, and the processor can execute the computer program correspondingly after receiving an execution instruction.

[0164] The application further provides a computer readable storage medium for storing the computer program used in the terminal device. For example, the computer readable storage medium can include, but is not limited to, a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0165] In several embodiments provided in the application, it should be understood that the disclosed apparatus and method can also be implemented by other ways. The apparatus embodiments described above are only schematic, for example, the flow charts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the apparatus, method and computer program product according to the embodiments of the application. In this regard, each block in the flow chart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that, in alternative implementation ways, the functions noted in the block can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the structural diagram and / or flow chart, and the combination of blocks in the structural diagram and / or flow chart, can be implemented by a dedicated hardware-based system for executing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0166] In addition, each functional module or unit in the embodiments of the 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.

[0167] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0168] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A river non-point source pollution source identification method, characterized in that, The method comprises the following steps: Based on the geographical information data packet corresponding to the target river basin, the multiple pollution influence factors of the target river basin grid in the target river basin to the target river are calculated, specifically including: based on the geographical information data packet corresponding to the target river basin, the Euclidean distance, the relative height difference, the runoff and the average pollution concentration of the target river basin grid in the target river basin to the target river are calculated. Based on the multiple pollution influence factors, the pollution accessibility index of the target river basin grid to the target river is calculated, specifically including: based on the Euclidean distance, the relative height difference, the runoff and the average pollution concentration of the target river basin grid in the target river basin to the target river, the pollution accessibility index of the target river basin grid to the target river is calculated; wherein, the pollution accessibility index is used to quantify the possibility of pollution from the target river basin grid to the target river. According to the pollution accessibility index, the potential non-point source pollution source of the target river basin to the target river is evaluated.

2. The river area source pollution source identification method according to claim 1, characterized in that, The method comprises the following steps: According to the river network grid data and the river basin grid data of the target river basin in the geographical information data packet, the Euclidean distance from the center point of the target river basin grid to the center point of the target river network grid is calculated; wherein, the target river network grid is the river network grid closest to the target river basin grid in the target river.

3. The river area source pollution source identification method according to claim 1, characterized in that, The method comprises the following steps: Based on the elevation of the target river basin grid in the target unit river basin and the average elevation and the minimum elevation corresponding to all river network grids of the target river in the target unit river basin, the relative height difference of the target river basin grid is calculated.

4. The river area source pollution source identification method according to claim 3, characterized in that, The unit river basin is obtained by the following method: The digital elevation model data included in the geographical information data packet is filled and processed; Based on the filled and processed digital elevation model data, a preset tool is used to calculate the flow direction of the target river basin grid; The points corresponding to the highest elevation between adjacent two rivers are connected as the divide of the unit river basin; based on each divide, the target river basin is segmented by a preset method to obtain each unit river basin.

5. The river area non-point source pollution identification method of claim 1, wherein, The method comprises the following steps: Based on the river basin runoff data and the land use type data of the target river basin in the geographical information data packet, the runoff of each river basin grid is estimated by using a preset model; And / or, Based on the total emission amount of pollutants in the target river basin grid, the runoff, the area and the land use type or the percentage of the land use type in the geographical information data packet, the average pollution concentration of the target river basin grid is calculated.

6. The river area source pollution source identification method according to claim 5, characterized in that, The method further comprises the following steps: Based on the drainage basin grid data in the geographic information data packet, a preset tool is used to create a regular fishing net according to a set resolution to generate a grid vector file same as each drainage basin grid; based on the grid vector file, a preset area tabulation tool is used to count the percentage of different land use types in the grid cell, and the area of different land use types is determined according to the percentage of different land use types.

7. The river area non-point source pollution identification method according to any one of claims 2-6, characterized in that, The target river pollution reachability index of the target basin grid is calculated based on the average values of the Euclidean distance, relative height difference, runoff and pollutant concentration of the target river in the target basin grid, including: The average values of the Euclidean distance, relative height difference, runoff and pollutant concentration are standardized based on the corresponding maximum values in the target basin; The average values of the Euclidean distance, relative height difference, runoff and pollutant concentration after the standardization are multiplied to obtain the pollutant reachability index.

8. A river non-point source pollution source identification device, characterized in that, Including: The influence factor calculation module is configured to calculate a plurality of pollution influence factors of the target river in the target basin grid based on the geographic information data packet corresponding to the target basin; The influence factor calculation module is specifically configured to calculate the average values of the Euclidean distance, relative height difference, runoff and pollutant concentration of the target river in the target basin grid based on the geographic information data packet corresponding to the target basin; The reachability calculation module is configured to calculate the pollutant reachability index of the target river in the target basin grid based on the plurality of pollution influence factors, and to evaluate the potential non-point source pollution of the target river in the target basin according to the pollutant reachability index; the reachability calculation module is specifically configured to calculate the pollutant reachability index of the target river in the target basin grid based on the average values of the Euclidean distance, relative height difference, runoff and pollutant concentration of the target river in the target basin grid; wherein the pollutant reachability index is used to quantify the possibility of the pollutant reaching the target river from the target basin grid. The terminal device includes a processor and a memory, the memory stores a computer program, and the processor is configured to execute the computer program to implement the river non-point source pollution source identification method of any one of claims 1-7.

9. A terminal device, comprising: ​

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