Planting industry non-point source pollution risk spatial pattern identification method, device, equipment, medium and product

Abstract

The invention discloses a planting industry non-point source pollution risk spatial pattern identification method, device and equipment, a medium and a product, and relates to the field of non-point source pollution, and the method comprises the steps: carrying out the grade division of the land of a preset region according to the capacity of outputting non-point source pollutants of different land utilization types, and obtaining the output grade of the non-point source pollutants; according to the output level of the non-point source pollutant and the source factor of the non-point source pollutant, obtaining the source ground of the non-point source pollutant, calculating the weight of each migration resistance factor, and determining the migration factor of the preset area according to the weight; determining a resistance base plane based on the migration factor, and obtaining a resistance surface of the non-point source pollutant by using the resistance base plane and the source ground of the non-point source pollutant; calculating a minimum accumulated resistance surface according to the resistance surface of the non-point source pollutant; and determining a planting industry non-point source pollution risk spatial pattern of the preset region based on the minimum accumulated resistance surface. According to the invention, the accuracy of identifying the planting industry non-point source pollution risk area can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of non-point source pollution, and in particular to a method, device, equipment, medium and product for identifying the spatial pattern of non-point source pollution risks in the planting industry. Background Art

[0002] China's farmland is widely distributed, and farmland pollution is scattered and hidden, with strong randomness and great uncertainty. It is necessary to combine monitoring, tracing, governance and management to systematically solve related problems. Effective control of large-scale agricultural non-point source pollution requires a large amount of manpower, material and financial resources.

[0003] Identifying risk areas of non-point source pollution in the agricultural industry will help to rationally allocate resources and take targeted measures to effectively prevent, control and manage non-point source pollution in the agricultural industry. However, in related technologies, the identification methods of risk areas of non-point source pollution in the agricultural industry mostly focus on the identification of "source-sink" attribution, and often ignore the comprehensive impact of the characteristics of terrestrial space (soil, vegetation, etc.) on pollutant loss. Specifically, only land use type is used as the "source" or only the resistance factor of non-point source pollutants in the migration process is considered, while ignoring its coupling with the source factor. Therefore, there is a problem of low accuracy in identifying risk areas of non-point source pollution in the agricultural industry in related technologies. Summary of the invention

[0004] The purpose of this application is to provide a method, device, equipment, medium and product for identifying the spatial pattern of non-point source pollution risks in the agricultural industry, which can improve the accuracy of identifying non-point source pollution risk areas in the agricultural industry.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In the first aspect, the present application provides a method for identifying the spatial pattern of non-point source pollution risk in the planting industry, including:

[0007] According to the capacity of different land use types to output non-point source pollutants, the land in the preset area is graded to obtain the non-point source pollutant output level; wherein the non-point source pollutants include nitrogen and phosphorus; the land use types include dry land, rural settlements, breeding ponds, urban land, construction land, saline-alkali land, sandy land, first-cover grassland, second-cover grassland, third-cover grassland, swamp, beach and woodland;

[0008] According to the non-point source pollutant output level and the source factor of the non-point source pollutant, the source of the non-point source pollutant is obtained, wherein the source factor represents the factor that produces the non-point source pollutant, and the source factor includes the crop planting area, the amount of fertilizer applied, and the effective content of the corresponding non-point source pollutant in the soil;

[0009] Calculating the weight of each migration resistance factor, and determining the migration factor of the preset area according to the weight; the migration resistance factor represents the factor that hinders the migration of the non-point source pollutant;

[0010] Determine a resistance base based on the migration factor, wherein the resistance base represents the resistance of the preset area to the migration of surface source pollutants;

[0011] Obtaining the resistance surface of the non-point source pollutants by using the resistance base surface and the source of the non-point source pollutants;

[0012] Calculate the minimum cumulative resistance surface based on the resistance surface of the non-point source pollutant;

[0013] The spatial pattern of non-point source pollution risk in the agricultural industry in the preset area is determined based on the minimum cumulative resistance surface.

[0014] In the second aspect, the present application provides a device for identifying spatial pattern of non-point source pollution risk in planting industry, comprising:

[0015] An output classification identification module is used to classify the land in a preset area according to the capacity of different land use types to output non-point source pollutants, and obtain the output level of non-point source pollutants; wherein the non-point source pollutants include nitrogen and phosphorus; the land use types include dry land, urban land, construction land, saline-alkali land, sandy land, first-covered grassland, second-covered grassland, third-covered grassland, swamp, beach and woodland;

[0016] A source identification module, used to obtain the source of non-point source pollutants according to the non-point source pollutant output level and the source factor of the non-point source pollutants, wherein the source factor represents the factor that produces the non-point source pollutants, and the source factor includes the crop planting area, the amount of fertilizer applied, and the effective content of the corresponding non-point source pollutants in the soil;

[0017] A migration factor determination module, used to calculate the weights of each migration resistance factor, and determine the migration factor of a preset area according to the weights, wherein each migration resistance factor exhibits a different resistance effect on the migration of the non-point source pollutants;

[0018] A resistance base surface determination module, used to determine a resistance base surface according to the migration factor, wherein the resistance base surface represents the resistance of the preset area to the migration of surface source pollutants;

[0019] A resistance surface determination module, used for obtaining the resistance surface of non-point source pollutants according to the resistance base surface and the source of non-point source pollutants;

[0020] A minimum cumulative resistance surface calculation module, used to calculate the minimum cumulative resistance surface according to the resistance surface of the non-point source pollutants;

[0021] The risk determination module is used to determine the spatial pattern of non-point source pollution risk in the crop industry in a preset area based on the minimum cumulative resistance surface.

[0022] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for identifying the spatial pattern of non-point source pollution risks in the agricultural industry.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for identifying the spatial pattern of non-point source pollution risks in the agricultural industry.

[0024] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for identifying the spatial pattern of non-point source pollution risks in the agricultural industry.

[0025] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0026] The present application provides a method, device, equipment, medium and product for identifying the spatial pattern of non-point source pollution risk in the planting industry. The source of non-point source pollutants is obtained by analyzing the output capacity of non-point source pollutants of different land use types in a preset area and the factors that produce non-point source pollutants; the resistance surface of non-point source pollutants is obtained based on the source of non-point source pollutants and the resistance base surface, and the minimum cumulative resistance surface is calculated on this basis to finally determine the spatial pattern of non-point source pollution risk in the planting industry. This process takes into account the hindering effect of land space on the migration of non-point source pollutants. The minimum cumulative resistance surface can be used to more accurately identify the distribution of non-point source pollutants in different land spaces. The smaller the minimum cumulative resistance, the greater the risk of non-point source pollution, thereby improving the accuracy of identifying non-point source pollution risk areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 This is an application environment diagram of a method for identifying spatial pattern of non-point source pollution risk in the planting industry in one embodiment of the present application;

[0029] Figure 2 A schematic diagram of a flow chart of a method for identifying spatial pattern of non-point source pollution risk in the planting industry provided in one embodiment of the present application;

[0030] Figure 3 It is a schematic diagram of the spatial distribution of the land use "source" level;

[0031] Figure 4 The spatial distribution maps of nitrogen and phosphorus in the preset area; (a) is the spatial distribution of nitrogen "source" level, (b) is the spatial distribution of phosphorus "source" level, (c) is the spatial distribution of nitrogen source, and (d) is the spatial distribution of phosphorus source;

[0032] Figure 5 Schematic diagram of the spatial distribution of each migration factor in the preset area; (a) is the spatial distribution of vegetation cover, (b) is the spatial distribution of soil type, (c) is the spatial distribution of soil structure, (d) is the spatial distribution of rainfall, (e) is the spatial distribution of distance to water bodies, and (f) is the spatial distribution of distance to roads;

[0033] Figure 6 A schematic diagram of the distribution of soil sampling points provided in an embodiment of the present application;

[0034] Figure 7 Schematic diagram of the spatial distribution of nitrogen resistance surface and phosphorus resistance surface; (a) is the spatial distribution of nitrogen resistance surface, and (b) is the spatial distribution of phosphorus resistance surface;

[0035] Figure 8 A schematic diagram of the spatial distribution of the risk level of non-point source pollution in the planting industry provided in one embodiment of the present application;

[0036] Fig. 9 A schematic diagram of the functional modules of a device for identifying the spatial pattern of non-point source pollution risks in the agricultural industry provided in one embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0039] The spatial pattern recognition method of planting non-point source pollution risk provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the output level of the non-point source pollutants in the preset area to the server 104. After the server 104 receives the output level of the non-point source pollutants in the preset area, for the output level of the non-point source pollutants in the preset area, the server 104 obtains the source of the non-point source pollutants according to the output level of the non-point source pollutants and the source factor of the non-point source pollutants, calculates the weight of each migration resistance factor, and determines the migration factor of the preset area according to the weight; determines the resistance base surface based on the migration factor, and obtains the resistance surface of the non-point source pollutants using the resistance base surface and the source of the non-point source pollutants; calculates the minimum cumulative resistance surface according to the resistance surface of the non-point source pollutants; and determines the spatial pattern of the non-point source pollution risk of the planting industry in the preset area based on the minimum cumulative resistance surface. The server 104 can feedback the obtained spatial pattern of the non-point source pollution risk of the planting industry in the preset area to the terminal 102. In addition, in some embodiments, the method for identifying the spatial pattern of non-point source pollution risks in the agricultural industry can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the output level of non-point source pollutants in a preset area, or the server 104 can obtain the output level of non-point source pollutants in a preset area from the data storage system and process the output level of non-point source pollutants in the preset area.

[0040] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0041] In an exemplary embodiment, Figure 2 As shown, a method for identifying the spatial pattern of non-point source pollution risk in the planting industry is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 207.

[0042] in:

[0043] Step 201, according to the capacity of different land use types to output non-point source pollutants, the land in the preset area is graded to obtain the non-point source pollutant output level, that is, the land use "source" level. The non-point source pollutants include nitrogen and phosphorus; the land use types include dry land, rural settlements, breeding ponds, urban land, construction land, saline-alkali land, sandy land, first cover grassland, second cover grassland, third cover grassland, swamp, beach and woodland.

[0044] Step 202, obtaining the source of the non-point source pollutants according to the non-point source pollutant output level and the source factor of the non-point source pollutants, wherein the source factor represents the factor that produces the non-point source pollutants, and the source factor includes the crop planting area, the amount of fertilizer applied, and the effective content of the corresponding non-point source pollutants in the soil.

[0045] Step 203, calculating the weights of the various migration resistance factors, and determining the migration factor of the preset area according to the weights, wherein the migration resistance factor represents the factor that hinders the migration of the non-point source pollutants.

[0046] Step 204: determining a resistance base based on the migration factor, wherein the resistance base represents the resistance of the preset area to the migration of area source pollutants.

[0047] Step 205, obtaining the resistance surface of the non-point source pollutants by using the resistance base surface and the source of the non-point source pollutants.

[0048] Step 206, calculate the minimum cumulative resistance surface based on the resistance surface of the non-point source pollutants. Specifically, the resistance surface includes a phosphorus resistance surface and a nitrogen resistance surface; use the conditional analysis function in ArcGIS to extract the minimum value of the phosphorus resistance surface and the nitrogen resistance surface, and obtain the minimum cumulative resistance surface based on the minimum value.

[0049] Step 207, determining the spatial pattern of non-point source pollution risk of the crop industry in the preset area based on the minimum cumulative resistance surface.

[0050] Through steps 201 to 207, the present application can quickly obtain the spatial pattern of non-point source pollution risks in the crop industry in a preset area, thereby improving the accuracy of identifying risk areas for non-point source pollution in the crop industry.

[0051] In this application, since the dimensions of each indicator are different and not comparable, they need to be converted into dimensionless parameters for calculation. Specifically, the linear normalization method is used to convert the numerical value of each indicator into a normalized value of 0 to 1, where each indicator includes the land use "source" level, source factor and migration resistance factor.

[0052] The calculation formula is as follows:

[0053]

[0054] Where X1 * is the normalized value of the positive indicator, X2 * is the normalized value of the reverse index, max represents the maximum value of the data, and min represents the minimum value of the data. Among them, each index includes the land use "source" level, source factor and migration resistance factor. The larger the value of the index, the greater the resistance, and the index is a positive index; the smaller the value of the index, the greater the resistance, and the index is a reverse index.

[0055] The higher the land use "source" level (the smaller the value, that is, the greater the output capacity of the land use type for nitrogen and phosphorus), the smaller the resistance, which is a positive indicator; the greater the amount of fertilizer applied and soil nutrients (that is, the effective nitrogen content and the effective phosphorus content), the smaller the resistance, which is a negative indicator; the higher the vegetation coverage (the greater the anti-interference ability), the greater the resistance, which is a positive indicator; the higher the soil type level (the smaller the value, that is, the greater the risk of the land type outputting nitrogen and phosphorus), the smaller the resistance, which is a positive indicator; the larger the soil structure value, the greater the resistance, which is a positive indicator; the greater the rainfall, the smaller the resistance, which is a negative indicator; the closer the distance, the smaller the resistance, which is a positive indicator.

[0056] In an exemplary embodiment, step 201 specifically includes:

[0057] Different land use types contribute differently to non-point source pollution. According to the output capacity of nitrogen and phosphorus of land use type, the land in the preset area is graded to obtain the output level of non-point source pollutants, that is, the spatial distribution of the land use "source" level. The higher the land use type level, the smaller the output capacity of nitrogen and phosphorus. Figure 3 As shown in the figure, the stronger the output capacity of nitrogen and phosphorus of different land use types, the higher the level, where "source" refers to nitrogen and phosphorus.

[0058] In a specific embodiment, referring to the previous research results and the team's years of work experience, the preset area is located in Wuyuan County, Bayannur City, Inner Mongolia Autonomous Region. This area is the sixth and seventh drainage areas of the Hetao Irrigation District, with a total area of ​​2503km 2 , accounting for 1 / 4 of the total area of ​​the Hetao Irrigation District, with an altitude of 1042 to 1052 meters.

[0059] Dry land (the preset area, i.e. the cultivated land in the study area, has only dry land and no paddy fields) is the most important source of non-point source pollution in the crop industry, with a level of 1; aquaculture is one of the important sources of non-point source pollution, with a level of 2 in aquaculture ponds; the nitrogen and phosphorus pollution generated by rural settlements is higher than other pollution sources, with a level of 2; the non-point source pollutant output levels of other land use types are: urban land level 3; construction land level 3; saline-alkali land level 3; sandy land level 4; low-cover grassland, i.e. the first-cover grassland level 5, medium-cover grassland, i.e. the second-cover grassland level 6, swamp level 6; beach level 6; high-cover grassland, i.e. the third-cover grassland level 7; forest land level 7. In this application, drainage ditches, rivers, lakes and other water bodies that receive non-point source pollutants are defined as "sinks".

[0060] In an exemplary embodiment, according to the land use "source" level and source factor obtained in step 201, the source of non-point source pollutants is obtained, and step 202 specifically includes steps 301-303:

[0061] Step 301: Perform spatial equal-weighted superposition analysis on the non-point source pollutant output level and the source factor of the non-point source pollutant to obtain the source area of ​​the non-point source pollutant.

[0062] Specifically, source factors refer to the factors that produce non-point source pollutants, and source factors include crop planting area, fertilizer application amount and the effective content of corresponding non-point source pollutants in the soil.

[0063] Based on the crop planting area and the general pollution discharge coefficient of the second pollution, the total nitrogen and total phosphorus emissions from the planting industry in each township in Wuyuan County were calculated, taking the average of 2019 and 2020.

[0064] The amount of fertilizer applied is an important factor in the input of nitrogen and phosphorus into the soil, and is calculated by converting the total amount of fertilizer applied in 2019 and 2020 into pure amount. The effective content of soil nitrogen and the effective content of phosphorus are important indicators of the level of soil nitrogen and phosphorus nutrient supply, and are also the material basis for the loss of nitrogen and phosphorus. The higher the effective content of soil nitrogen and phosphorus, the greater the possibility of entering the water body through surface runoff, leaching and other pathways.

[0065] The effective content of nitrogen and phosphorus in the soil samples were determined by alkaline diffusion method and NaHCO3 extraction-molybdenum antimony colorimetric method. Among them, the effective content of nitrogen and phosphorus are measured values.

[0066] Spatial equal-weighted overlay means that if there are 4 source factors, the weight of each source factor is the same, which is 0.25; if there are 5 source factors, the weight of each source factor is the same, which is 0.2; and so on. Use the raster calculator to multiply each source factor by the weight and then add them together, which is equal-weighted overlay.

[0067] Step 302: Classify the source area by using the natural breakpoint method to obtain source areas of different levels.

[0068] The data generated after the processing in step 301 is divided into different levels using the natural breakpoint method to obtain source areas of different levels, which specifically include five levels, represented by numbers 1, 2, 3, 4 and 5, respectively. Number 1 represents the highest level, and number 5 represents the lowest level. Figure 4 As shown, according to step 302, the nitrogen source area and the phosphorus source area, that is, the nitrogen "source" level and the phosphorus "source" level are obtained respectively; Figure 4 (a) is the spatial distribution of nitrogen "source" level. Figure 4 (b) in the figure shows the spatial distribution of phosphorus “source” levels.

[0069] Step 303: The area with the highest level among the source areas is used as the source of nonpoint source pollutants.

[0070] The highest level area among the different levels of source areas obtained in step 302 is used as the initial source area, and in order to reduce the fragmentation of the source area, the initial source area with an area less than 1km 2 The spots are removed, and finally the source of non-point source pollutants is obtained. The nitrogen source area and the phosphorus source area are processed in step 303 to obtain the nitrogen source area and the phosphorus source area, such as Figure 4 As shown, Figure 4 (c) shows the spatial distribution of nitrogen sources, where the red part is the nitrogen source; Figure 4 (d) in the figure shows the spatial distribution of phosphorus sources, where the red part is the phosphorus source.

[0071] In an exemplary embodiment, step 203 specifically includes steps 401 and 402:

[0072] Step 401 , spatially analyzing each migration resistance factor using a spatial principal component analysis method to obtain a comprehensive score coefficient, and normalizing the comprehensive score coefficient to obtain a weight of each migration resistance factor.

[0073] The values ​​calculated after linear normalization of each migration resistance factor are resistance values, where 0 represents the smallest resistance value and 1 represents the largest resistance value. These values ​​reflect relative resistance, not absolute values.

[0074] Specifically, spatial principal component analysis (SPCA) was used to analyze each migration resistance factor to obtain a comprehensive score coefficient. The principal component analysis tool is included in the multivariate analysis of the Spatial Analyst toolbox in ArcGIS software. The principal component analysis tool was used to perform spatial analysis on each migration resistance factor to obtain a comprehensive score coefficient. The comprehensive score coefficient was linearly normalized to obtain the weight of each migration resistance factor.

[0075] The non-point source pollution risk assessment index system includes various migration resistance factors, which represent factors that hinder the migration of non-point source pollutants, including vegetation cover, soil type, soil structure, rainfall, distance from water bodies, distance from roads, elevation and slope.

[0076] The migration process of non-point source pollutants is determined by a variety of geographical factors. Elevation and slope are used to represent topographic factors. Generally speaking, spaces with higher altitudes and steeper slopes are more conducive to the transmission of non-point source pollutants, and the risk of non-point source pollution is higher.

[0077] Vegetation coverage: The higher the vegetation coverage, the more stable the ecosystem is, the stronger its ability to resist external interference, and the greater the obstruction to non-point source pollutants, which is expressed by the Normalized Vegetation Index (NDVI). In this embodiment, the NDVI data is based on the Landsat8 satellite images from August to September 2021 and is calculated using the BandMath tool of the ENVI5.2 software.

[0078] Soil type: Soil types are graded by scoring according to the output risk of non-point source pollutants, and different soil types are divided into different levels according to their output risk of non-point source pollutants. The soil type level is divided into 7 levels, and is represented by numbers 1 to 7. Level 1 is the worst and has the lowest resistance value; Level 7 is the best and has the highest resistance value. Among them, the soil types in the preset area are mainly dark brown soil level 7, wet tidal soil level 6, salinized tidal soil level 5, tidal soil / yellow tidal soil level 5, irrigation silt tidal soil level 4, chestnut calcium soil level 4, tidal irrigation silt soil level 4, loess soil level 3, new accumulation soil level 2, alluvial soil level 2, and grassland wind sand soil level 1.

[0079] Soil structure: Soil structure is the basis for soil to perform ecological functions. Good soil structure is conducive to reducing surface runoff. It is expressed by the generalized soil structure index (GSSI). The larger the value, the more ideal the soil structure. The soil structure index is a measured value. The calculation formula of the soil structure index is:

[0080] GSSI=[(g-25)×y×q]×0.4769;

[0081] Where GSSI is the soil structure index, g is the solid phase volume percentage (g is greater than 25%), y is the liquid phase volume percentage (y is greater than 0), and q is the gas phase volume percentage (q is greater than 0).

[0082] Precipitation: Precipitation is an important driving force affecting nitrogen and phosphorus loss, and is calculated based on the three-year average rainfall from 2020 to 2022 at relevant meteorological stations.

[0083] Distance from water bodies: The farther from the receiving water body, the greater the possibility that nonpoint source pollutants will be intercepted and reduced during migration, and the lower the potential risk of nitrogen and phosphorus loss.

[0084] Distance from roads: The closer to the road, the more intense the human socio-economic activities, the greater the impact on non-point source pollution, and the higher the risk.

[0085] Figure 5 This is a schematic diagram of the spatial distribution of each migration factor in the preset area. Figure 5 (a) is the spatial distribution of vegetation cover, (b) is the spatial distribution of soil types, (c) is the spatial distribution of soil structure, (d) is the spatial distribution of rainfall, (e) is the spatial distribution of distance to water bodies, and (f) is the spatial distribution of distance to roads.

[0086] The data sources for the preset regions in this application include:

[0087] ①Land use data (2020) are from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (http: / / www.resdc.cn), with a spatial resolution of 30 m.

[0088] ②Landsat8 remote sensing images and data obtained using the Digital Elevation Model (DEM) were downloaded from the Geospatial Data Cloud (http: / / www.gscloud.cn) with a spatial resolution of 30m.

[0089] ③Soil type data are from the National Tibetan Plateau Data Center (https: / / data.tpdc.ac.cn), based on the Chinese soil dataset (v1.1) (2009) of the Harmonized World Soil Database (HWSD).

[0090] ④ Meteorological data (precipitation data of relevant meteorological stations in Wuyuan County from 2020 to 2022) were downloaded from the National Meteorological Science Data Center (http: / / data.cma.cn).

[0091] ⑤The data on crop planting area and fertilizer application in Wuyuan County from 2019 to 2020 were obtained from the Agriculture, Animal Husbandry and Science and Technology Bureau of Wuyuan County.

[0092] ⑥ Soil measurement data. Soil sampling points were set up in different spaces of cultivated land in the preset area. The collection time was April 2023 (before spring irrigation). A total of 41 soil samples were collected, and each soil sample was mixed from 5 surface soil samples (0-20 cm). The sampling was selected by non-grid method, and the sample point layout took into account the sampling principles of representativeness, typicality, randomness and uniformity. The distribution map of soil sampling points is shown in the figure below. Figure 6 shown.

[0093] Step 402: remove the migration resistance factors whose weights are less than a preset value, and retain the migration resistance factors whose weights are greater than or equal to the preset value as migration factors.

[0094] Through SPCA analysis, it was found that the weights of elevation and slope were almost 0, so the two migration resistance factors of elevation and slope were abandoned and the migration factor was obtained. The migration factor included vegetation cover, soil type, soil structure, rainfall, distance to water bodies and distance to roads.

[0095] In an exemplary embodiment, step 204 specifically includes steps 501 and 502:

[0096] Step 501: perform spatial analysis on each of the migration factors using a spatial principal component analysis method to obtain a weight of each of the migration factors.

[0097] Specifically, SPCA was used to calculate the weights of each migration factor, as shown in Table 1.

[0098] Table 1

[0099] Migration Factor Vegetation Cover Soil Type Soil structure Rainfall Distance from water Distance from road Weight 0.104 0.298 0.030 0.389 0.049 0.131

[0100] Step 502: Use the raster calculator in ArcGIS to perform overlay analysis on the weights of each migration factor to determine the resistance base.

[0101] Specifically, the grid calculator tool is used to perform weighted summation of each migration factor to obtain the resistance base surface.

[0102] The weighted summation is performed using the raster calculator in the map algebra of the SpatialAnalyst toolbox in ArcGIS software. The resistance base surface is obtained according to each migration factor. The calculation formula is as follows:

[0103]

[0104] Among them, R i is the resistance base, W j represents the weight of the jth migration factor, A ij represents the resistance value of the jth migration factor in the ith resistance base, and n represents the total number of migration factors.

[0105] According to the resistance base surface obtained in step 204 and the source of the non-point source pollutants obtained in step 202, the cost distance tool in the distance of the SpatialAnalyst toolbox in ArcGIS software is used to obtain the resistance surface of the non-point source pollutants, including the nitrogen resistance surface and the phosphorus resistance surface; Figure 7 As shown, Figure 7 (a) shows the spatial distribution of nitrogen resistance surface, and (b) shows the spatial distribution of phosphorus resistance surface.

[0106] In an exemplary embodiment, step 207 specifically includes:

[0107] The natural breakpoint method is used to identify the risk level of agricultural non-point source pollution on the minimum cumulative resistance surface, and the spatial pattern of agricultural non-point source pollution risk in the preset area is obtained.

[0108] The Minimum Cumulative Resistance Model (MCR) is a derivative of the distance-loss model and is widely used in ecology. In the study of non-point source pollution, MCR reflects the minimum cost required for the migration of non-point source pollutants. The minimum cumulative resistance model comprehensively considers the source, resistance base and spatial distance. The calculation formula is as follows:

[0109]

[0110] Where MCR is the minimum cumulative resistance surface value from the source to any point in space, the dimensionless quantity f is an unknown monotonically increasing function, indicating the negative correlation between MCR and the pollution process; min means taking the minimum cumulative resistance value for the source; D qp R is the spatial distance from the non-point source pollutant position q to the source p, reflecting the horizontal resistance; q is the vertical resistance of the location q that integrates the external factors, that is, the resistance base, m is the number of grids; v is the number of sources.

[0111] In the actual operation, the cost distance tool in ArcGIS software is used to obtain the nitrogen resistance surface and the phosphorus resistance surface according to the source in step 202 and the resistance base surface in step 204, respectively. Then, the Con function in the raster calculator is used to extract the minimum values ​​of the nitrogen resistance surface and the phosphorus resistance surface to generate the minimum cumulative resistance surface of the non-point source pollution in the crop industry. The minimum cumulative resistance surface of non-point source pollution in the crop industry, that is, the resistance that needs to be overcome in the process of non-point source pollution. The smaller the minimum cumulative resistance, the greater the risk of non-point source pollution; conversely, the greater the resistance, the smaller the risk of non-point source pollution. The natural breakpoint method is used to divide the non-point source pollution risk into five levels, namely high-risk area, relatively high-risk area, medium-risk area, relatively low-risk area and low-risk area. Figure 8 shown.

[0112] This application evaluates the risk level of non-point source pollution generated and lost from cultivated land from the perspective of the resistance surface of non-point source pollution in the crop industry, and obtains a more accurate spatial pattern of non-point source pollution risk in the crop industry, thereby providing a decision-making basis for the scientific prevention and control of agricultural non-point source pollution.

[0113] Based on the same inventive concept, the embodiment of the present application also provides a device for identifying the spatial pattern of non-point source pollution risk in the planting industry for implementing the above-mentioned method for identifying the spatial pattern of non-point source pollution risk in the planting industry. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for identifying the spatial pattern of non-point source pollution risk in the planting industry provided below can be referred to the limitations of the method for identifying the spatial pattern of non-point source pollution risk in the planting industry above, and will not be repeated here.

[0114] In an exemplary embodiment, Fig. 9 As shown, a device for identifying spatial pattern of non-point source pollution risk in planting industry is provided, comprising:

[0115] The output classification identification module 21 is used to classify the land in a preset area according to the ability of different land use types to output non-point source pollutants, so as to obtain the output level of non-point source pollutants; wherein the non-point source pollutants include nitrogen and phosphorus; the land use types include dry land, urban land, construction land, saline-alkali land, sandy land, first-covered grassland, second-covered grassland, third-covered grassland, swamp, beach and woodland.

[0116] The source identification module 22 is used to obtain the source of non-point source pollutants based on the output level of non-point source pollutants and the source factors of non-point source pollutants, wherein the source factors represent the factors that produce non-point source pollutants, and the source factors include the crop planting area, the amount of fertilizer applied and the effective content of the corresponding non-point source pollutants in the soil.

[0117] The migration factor determination module 23 is used to calculate the weights of various migration resistance factors and determine the migration factor of a preset area according to the weights, wherein each migration resistance factor exhibits a different resistance effect on the migration of the non-point source pollutants.

[0118] The resistance base determination module 24 is used to determine the resistance base according to the migration factor, and the resistance base represents the resistance of the preset area to the migration of area source pollutants.

[0119] The resistance surface determination module 25 is used to obtain the resistance surface of the non-point source pollutants according to the resistance base surface and the source of the non-point source pollutants.

[0120] The minimum cumulative resistance surface calculation module 26 is used to calculate the minimum cumulative resistance surface according to the resistance surface of the non-point source pollutants.

[0121] The risk determination module 27 is used to determine the spatial pattern of non-point source pollution risk of the crop industry in a preset area based on the minimum cumulative resistance surface.

[0122] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for identifying the spatial pattern of non-point source pollution risk in the planting industry is implemented.

[0123] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above-mentioned method embodiments are implemented when the processor executes the computer program.

[0124] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0125] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0127] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0128] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0129] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for identifying the spatial pattern of non-point source pollution risk in crop production, characterized in that: The method for identifying the spatial pattern of non-point source pollution risk in the planting industry includes: According to the capacity of different land use types to output non-point source pollutants, the land in the preset area is graded to obtain the non-point source pollutant output level; wherein the non-point source pollutants include nitrogen and phosphorus; the land use types include dry land, rural settlements, breeding ponds, urban land, construction land, saline-alkali land, sandy land, first-cover grassland, second-cover grassland, third-cover grassland, swamp, beach and woodland; According to the non-point source pollutant output level and the source factor of the non-point source pollutant, the source of the non-point source pollutant is obtained, wherein the source factor represents the factor that produces the non-point source pollutant, and the source factor includes the crop planting area, the amount of fertilizer applied, and the effective content of the corresponding non-point source pollutant in the soil; Calculating the weight of each migration resistance factor, and determining the migration factor of the preset area according to the weight; the migration resistance factor represents the factor that hinders the migration of the non-point source pollutant; Determine a resistance base based on the migration factor, wherein the resistance base represents the resistance of the preset area to the migration of surface source pollutants; Obtaining the resistance surface of the non-point source pollutants by using the resistance base surface and the source of the non-point source pollutants; Calculate the minimum cumulative resistance surface based on the resistance surface of the non-point source pollutant; The spatial pattern of non-point source pollution risk in the agricultural industry in the preset area is determined based on the minimum cumulative resistance surface.

2. The method for identifying spatial pattern of non-point source pollution risk in crop production according to claim 1, characterized in that: According to the output level of non-point source pollutants and the source factors of non-point source pollutants, the source of non-point source pollutants is obtained, including: Performing spatial equal-weighted superposition analysis on the output level of the non-point source pollutants and the source factors of the non-point source pollutants to obtain the source area of ​​the non-point source pollutants; The source area is graded using a natural breakpoint method to obtain source areas of different grades; The area with the highest level among the source areas is taken as the source of non-point source pollutants.

3. The method for identifying spatial pattern of non-point source pollution risk in crop production according to claim 1, characterized in that: Calculating the weights of the various migration resistance factors and determining the migration factor of the preset area according to the weights includes: The spatial principal component analysis method is used to perform spatial analysis on each migration resistance factor to obtain a comprehensive score coefficient, and the comprehensive score coefficient is normalized to obtain the weight of each migration resistance factor; Migration resistance factors with weights less than the preset value are eliminated, and migration resistance factors with weights greater than or equal to the preset value are retained as migration factors.

4. The method for identifying spatial pattern of non-point source pollution risk in crop production according to claim 1, characterized in that: Determining a resistance base based on the migration factor includes: Performing spatial analysis on each of the migration factors using a spatial principal component analysis method to obtain a weight for each of the migration factors; The raster calculator in ArcGIS is used to perform overlay analysis on the weights of the various migration factors to determine the resistance base.

5. The method for identifying spatial pattern of non-point source pollution risk in crop production according to claim 1, characterized in that: The resistance surface includes a phosphorus resistance surface and a nitrogen resistance surface; The minimum cumulative resistance surface is calculated according to the resistance surface of the non-point source pollutant, specifically including: The conditional analysis function in ArcGIS is used to extract the minimum values ​​of the phosphorus resistance surface and the nitrogen resistance surface, and the minimum cumulative resistance surface is obtained based on the minimum values.

6. The method for identifying spatial pattern of non-point source pollution risk in crop production according to claim 1, characterized in that: Based on the minimum cumulative resistance surface, the spatial pattern of the non-point source pollution risk of the crop industry in the preset area is determined, specifically including: The natural breakpoint method is used to identify the risk level of agricultural non-point source pollution on the minimum cumulative resistance surface, and the spatial pattern of agricultural non-point source pollution risk in the preset area is obtained.

7. A device for identifying spatial pattern of non-point source pollution risk in crop production, characterized in that: The device for identifying spatial pattern of planting industry non-point source pollution risk comprises: An output classification identification module is used to classify the land in a preset area according to the capacity of different land use types to output non-point source pollutants, and obtain the output level of non-point source pollutants; wherein the non-point source pollutants include nitrogen and phosphorus; the land use types include dry land, urban land, construction land, saline-alkali land, sandy land, first-covered grassland, second-covered grassland, third-covered grassland, swamp, beach and woodland; A source identification module, used to obtain the source of non-point source pollutants according to the non-point source pollutant output level and the source factor of the non-point source pollutants, wherein the source factor represents the factor that produces the non-point source pollutants, and the source factor includes the crop planting area, the amount of fertilizer applied, and the effective content of the corresponding non-point source pollutants in the soil; A migration factor determination module, used to calculate the weights of each migration resistance factor, and determine the migration factor of a preset area according to the weights, wherein each migration resistance factor exhibits a different resistance effect on the migration of the non-point source pollutants; A resistance base surface determination module, used to determine a resistance base surface according to the migration factor, wherein the resistance base surface represents the resistance of the preset area to the migration of surface source pollutants; A resistance surface determination module, used for obtaining the resistance surface of non-point source pollutants according to the resistance base surface and the source of non-point source pollutants; A minimum cumulative resistance surface calculation module, used to calculate the minimum cumulative resistance surface according to the resistance surface of the non-point source pollutants; The risk determination module is used to determine the spatial pattern of non-point source pollution risk in the crop industry in a preset area based on the minimum cumulative resistance surface.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for identifying the spatial pattern of non-point source pollution risks in the crop industry as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying the spatial pattern of non-point source pollution risks in the crop industry described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for identifying the spatial pattern of non-point source pollution risks in the crop industry described in any one of claims 1 to 6 is implemented.

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