A method for dividing the origin of agricultural products based on the risk of heavy metal pollution

Abstract

The present invention provides a method for dividing the origin of agricultural products based on the risk of heavy metal pollution, including: constructing a quality judgment function of the origin of agricultural products according to the soil pH value, soil heavy metal content, and heavy metal content of agricultural products at the monitoring point; constructing a matrix M according to the quality judgment function of the origin of agricultural products and the rule of exceeding the standard; adding the longitude and latitude of the monitoring point to the matrix M to form a matrix N; constructing Thiessen polygons according to the matrix N; making the Thiessen polygon face have type attributes; finding polygons adjacent to the Thiessen polygon and with the same type attributes to merge them to form a new face domain; superimposing the new face domain with the distribution map of the origin of agricultural products, and finally determining the type of the origin of agricultural products. The method provided by the present invention improves the accuracy of origin division by combining soil and agricultural product quality to judge the origin risk.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product origin classification, and in particular to an agricultural product origin classification method based on the heavy metal pollution risk. Background Art

[0002] Due to industrial and urban "three wastes", agricultural product production areas are polluted by heavy metals to varying degrees, and the quality and safety of agricultural products are also threatened. Heavy metals can enter the human body through the food chain, threatening human health. In order to carry out targeted restoration and management of agricultural product production areas according to the pollution characteristics, agricultural product production areas will be classified and zoned, and appropriate measures will be adopted for classified management of different types of pollution risk areas.

[0003] There are two main methods for dividing the risk areas of heavy metal pollution in soil in agricultural product producing areas: first, spatially interpolate the heavy metal content data of soil monitoring points to form a raster layer from points and surfaces, and then reclassify the raster values ​​according to the soil heavy metal exceeding risk threshold specified in relevant technical standards, into risky and non-risky areas, and finally convert the raster to surface to form a surface layer. The surface areas in the surface layer are divided into two categories, risky and non-risky. The second is to divide the origin of agricultural products in advance and divide them into monitoring units. In each monitoring unit, soil and agricultural product samples are collected for testing and analysis of their heavy metal content. According to the heavy metal content data, it is judged whether the soil and agricultural products exceed the standard in accordance with the soil and agricultural product heavy metal exceeding thresholds specified in relevant technical standards; then, the risk category of the entire monitoring unit is judged according to the proportion of soil heavy metal exceeding the standard at the monitoring points in the monitoring unit. For example, if the proportion of soil heavy metal exceeding points in the monitoring unit exceeds a certain proportion, the monitoring unit is considered to be a risk area, otherwise it is a risk-free area. Finally, after a preliminary division based on the soil heavy metal content data, the agricultural product exceeding the standard at the monitoring points in each monitoring unit is examined. For example, when the proportion of agricultural product not exceeding the standard exceeds a certain proportion, the risk level of the initially divided risk area is reduced to a risk-free area or a low-risk area.

[0004] However, the above methods for classifying agricultural product origins have the following problems: (1) The classification of agricultural product origin quality is mostly based on soil heavy metal content data, ignoring the mismatch between soil heavy metal excess and agricultural product heavy metal excess, that is, soil excess does not necessarily mean agricultural product excess, and soil non-exceeding does not necessarily mean agricultural product non-exceeding. There is a lack of consideration for agricultural product quality and safety, and the classification accuracy is not high. It does not meet the requirement that the classification and classification management of agricultural product origin quality is to ensure the quality and safety of agricultural products as the core purpose; (2) When classifying the environmental quality of agricultural product origins, either spatial interpolation is directly performed, and the grid is reclassified after interpolation, or the monitoring unit boundary defined before the monitoring survey begins is directly used as the quality category boundary. This is highly subjective and the boundary is inaccurate, which can easily lead to errors or invalidity in risk control measures based on the classification results. Summary of the invention

[0005] The present invention provides a method for dividing the origin of agricultural products based on the heavy metal pollution risk, so as to solve the problem that the current origin division is not accurate enough.

[0006] A method for dividing the origin of agricultural products based on the risk of heavy metal pollution, comprising the following steps:

[0007] Step 1: Construct the attribute data set corresponding to the monitoring points of agricultural product production areas: The attribute data set includes: the longitude, latitude, soil pH value, soil heavy metal content, and agricultural product heavy metal content of the monitoring points;

[0008] Step 2: Determine the rules for exceeding the standard for heavy metal content in soil and agricultural products;

[0009] Step 3: Construct the agricultural product origin quality evaluation function f(x i ,y i , z i ), where x i is the soil pH value at the i-th monitoring point in the study area, y i is the soil heavy metal content at the i-th monitoring point in the study area, z i is the heavy metal content of agricultural products at the i-th monitoring point in the study area, i = {1, 2, 3, … n};

[0010] Step 4: construct a matrix M according to the agricultural product origin quality evaluation function and the exceeding standard rule; the first column of the matrix M represents the exceeding standard of soil; the second column represents the exceeding standard of agricultural products;

[0011] Step 5: Add the longitude and latitude of the monitoring point to the matrix M to form a matrix N;

[0012] Step 6: construct Thiessen polygons according to the matrix N; and make the Thiessen polygons have type attributes; the type attributes include: soil and agricultural products are both within the standard, soil is within the standard and agricultural products are within the standard, soil is within the standard and agricultural products are within the standard, soil is within the standard and agricultural products are within the standard, soil is within the standard and agricultural products are within the standard, and soil and agricultural products are within the standard;

[0013] Step 7: Find polygons that are adjacent to the Thiessen polygon and have the same type attributes, and merge them to form a new face region;

[0014] Step 8: Overlay the new face area with the agricultural product origin distribution map, find out the agricultural product origin distribution area from the generated new face area, and assign the risk type of the corresponding face area to the agricultural product origin plot.

[0015] Furthermore, in the above-mentioned method for dividing the origin of agricultural products based on the risk of heavy metal pollution, the step six comprises:

[0016] The latter two columns of the matrix N are used to construct Thiessen polygons, and the first two columns of the matrix N are assigned to Thiessen polygon faces, so that the Thiessen polygon faces have type attributes.

[0017] Furthermore, in the above-mentioned method for dividing the origin of agricultural products based on the risk of heavy metal pollution, step seven includes:

[0018] Taking a Thiessen polygon face as the starting point, search for the adjacent Thiessen polygons around it, and determine whether the attributes of its adjacent Thiessen polygon faces are the same. If they are adjacent and have the same type attributes, the two types are recorded as the same and merged to form a new face domain; otherwise, they are not merged, and the process is iterated in sequence to finally obtain a new merged face domain set.

[0019] Furthermore, in the above-mentioned method for dividing the origin of agricultural products based on the risk of heavy metal pollution, the construction of Thiessen polygons includes:

[0020] (1) Construct a Delaunay triangulation network based on the monitoring points, number the monitoring points and the Delaunay triangulation network they form, and record which three monitoring points each triangle is composed of;

[0021] (2) In the constructed triangulated network, find the numbers of all triangles adjacent to each monitoring point and record them;

[0022] (3) Sort all triangles adjacent to each monitoring point in a clockwise or counterclockwise direction, starting from a known monitoring point and adjacent triangles, and gradually expanding along the edges and vertices of the triangles until returning to the starting monitoring point and edge;

[0023] (4) For each triangle, calculate the center of its circumscribed circle and record the positions of these center points;

[0024] (5) According to the adjacent triangles of each monitoring point, the centers of the circumscribed circles of these adjacent triangles are connected to obtain Thiessen polygons; for the Thiessen polygons at the edge of the triangulated network, a perpendicular bisector is drawn to intersect with the outline of the graph, and together with the outline of the graph, a Thiessen polygon is formed.

[0025] The method for dividing the origin of agricultural products based on the risk of heavy metal pollution provided by the present invention improves the accuracy of origin division by combining the quality of soil and agricultural products to judge the origin risk. In line with the situation that the soil and agricultural products exceeding the standard found in field monitoring do not completely correspond, the origin risk area is divided by taking into account the soil risk and the agricultural product risk; with the monitoring point as the center, the minimum area that it can represent is drawn, and then the adjacent minimum area areas of the same risk type are merged, and the resulting boundary is more objective, scientific and representative. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a schematic flow chart of the method for dividing the origin of agricultural products based on the heavy metal pollution risk provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The present invention integrates the soil and agricultural product quality data of the monitoring points, constructs a comprehensive index as the basis for the classification of agricultural product origin quality; takes the monitoring point location as the center point, constructs its peripheral polygon, so that the distance from any point in the polygon to the center point is smaller than the distance from other points to the center point, ensuring that the drawn polygon can reflect the representativeness of the center point. Then, the adjacent polygonal surfaces of the same risk level type are merged to reduce the degree of regional fragmentation, and finally the different types of surface layers are superimposed on the agricultural product origin distribution map to obtain different types of agricultural product origin quality classification partitions.

[0030] Figure 1is a flow chart of the method for dividing the origin of agricultural products based on the heavy metal pollution risk provided by the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0031] Step 1: Construct the attribute data set corresponding to the monitoring points of agricultural product production areas: The attribute data set includes: the longitude, latitude, soil pH value, soil heavy metal content, and heavy metal content of agricultural products of the monitoring points.

[0032] Specifically, we construct a data set of agricultural product production monitoring point attributes: the data set indicators include the longitude, latitude, soil pH value, soil heavy metal content, and agricultural product heavy metal content of the point. The longitude and latitude are used to identify the location of the point so that the risk area can be divided spatially based on the attribute data of the point; the pH value, soil heavy metal content, and agricultural product heavy metal content are used to determine whether the soil and agricultural products at the point exceed the standard.

[0033] Step 2: Determine the rules for exceeding the standard for heavy metal content in soil and agricultural products.

[0034] Specifically, the present invention can adopt the following method: Determine the exceeding standard rule:

[0035] Agreed rules for recording excessive content of soil and agricultural products: Based on the threshold values ​​for judging whether the heavy metal content exceeds the standard stipulated in the Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard (Trial) (GB15618) and the National Food Safety Standard Limits of Contaminants in Food (GB 2762) or other technical standards, the soil and agricultural product data are evaluated for exceeding the standard. If the soil does not exceed the standard, it is marked as 0, and if it exceeds the standard, it is marked as 1; if the agricultural product does not exceed the standard, it is marked as 0, and if it exceeds the standard, it is marked as 1.

[0036] Step 3: Construct the agricultural product origin quality evaluation function f(x i ,y i , z i ), where x i is the soil pH value at the i-th monitoring point in the study area, y i is the soil heavy metal content at the i-th monitoring point in the study area, z i is the heavy metal content of agricultural products at the i-th monitoring point in the study area, i = {1, 2, 3, … n}.

[0037] Step 4: construct a matrix M according to the agricultural product origin quality evaluation function and the exceeding standard rule; the first column of the matrix M represents the exceeding standard situation of the soil; the second column represents the exceeding standard situation of the agricultural product.

[0038] Specifically, according to the exceeding standard rule determined in step 2, f(x i ,y i, z i ) results in an n*2 matrix M. The first column of the matrix M indicates the soil exceeding the standard, with 0 for no exceeding and 1 for exceeding. The second column of the matrix M indicates the agricultural product exceeding the standard, with 0 for no exceeding and 1 for exceeding. In this way, the soil and agricultural product data are combined to characterize the heavy metal pollution risk of the monitoring point, that is, the soil and agricultural product heavy metal pollution risk of the point is recorded using the binary method. For example, if the soil at the point does not exceed the standard and the agricultural product exceeds the standard, the attribute of the point is (0, 1); if the soil at the point does not exceed the standard and the agricultural product does not exceed the standard, the attribute of the point is recorded as (0, 0); the soil exceeding the standard and the agricultural product not exceeding the standard is marked as (1, 0); the soil exceeding the standard and the agricultural product also exceeding the standard is marked as (1, 1).

[0039] Step 5: Add the longitude and latitude of the monitoring point to the matrix M to form a matrix N.

[0040] Step six: construct Thiessen polygons according to the matrix N; and make the Thiessen polygons have type attributes; the type attributes include: both soil and agricultural products are within the standard, the soil does not exceed the standard and the agricultural products exceed the standard, the soil exceeds the standard and the agricultural products do not exceed the standard, and both soil and agricultural products exceed the standard.

[0041] Specifically, the longitude and latitude of the monitoring points stored in the last two columns of the matrix N are used to construct the minimum face represented by the monitoring points, namely, the Thiessen polygon (the Thiessen polygon is a subdivision of the spatial plane, and its characteristic is that any position within the polygon is closest to the sample point of the polygon (such as the production environment monitoring point), and is far away from the sample point in the adjacent polygon, and each polygon contains and only contains one sample point), and the first two columns of the matrix N are assigned to the Thiessen polygon face so that the face has a type attribute. The type attribute is divided into four types, namely (0,0), (0,1), (1,0), and (1,1), which respectively represent that neither the soil nor the agricultural products exceed the standard, the soil does not exceed the standard and the agricultural products exceed the standard, the soil exceeds the standard and the agricultural products do not exceed the standard, and both the soil and the agricultural products exceed the standard.

[0042] Step 7: Find polygons that are adjacent to the Thiessen polygons and have the same type attributes, and merge them to form a new face region.

[0043] Specifically, taking a Thiessen polygon face as the starting point, search for the adjacent Thiessen polygons around it, and determine whether the attributes of its adjacent polygon faces are the same. If they are adjacent and have the same attributes, the two types are recorded as the same and merged to form one face; otherwise, they are not merged, and the process is iterated in sequence to obtain a new merged face domain set.

[0044] Step 8: Overlay the new face area with the agricultural product origin distribution map, find out the agricultural product origin distribution area from the generated new face area, and assign the risk type of the corresponding face area to the agricultural product origin plot.

[0045] Specifically, since not every piece of land is an agricultural product production area, the present invention superimposes the new area with the agricultural product production distribution map to determine the agricultural product production distribution area.

[0046] Among them, the steps to establish Thiessen polygons are:

[0047] (1) Construct a Delaunary triangulation network for the monitoring points, number the monitoring points and the triangles they form, and record which three monitoring points each triangle is composed of.

[0048] (2) Find the triangles adjacent to each monitoring point: In the constructed triangulated network, find the numbers of all triangles adjacent to each monitoring point and record them. This can usually be achieved by finding all triangles with the same vertex.

[0049] (3) Sort adjacent triangles: Sort the triangles adjacent to each monitoring point in a clockwise or counterclockwise direction. The sorting starts from a known monitoring point and adjacent triangles, and gradually expands along the edges and vertices of the triangles until it returns to the starting monitoring point and edge.

[0050] (4) Calculate the center of the circumscribed circle of each triangle: For each triangle, calculate the center of its circumscribed circle and record the positions of these centers.

[0051] (5) Connect the centers of the circumscribed circles to form Thiessen polygons: According to the adjacent triangles of each monitoring point, connect the centers of the circumscribed circles of these adjacent triangles to obtain Thiessen polygons. For the Thiessen polygons at the edge of the triangulated network, a perpendicular bisector can be drawn to intersect with the outline of the graph, and together with the outline of the graph, a Thiessen polygon is formed.

[0052] (6) The type of the monitoring point within each Thiessen polygon, that is, the combination of the first two columns of the matrix M, is assigned to the Thiessen polygon to mark the type of the Thiessen polygon surface.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dividing the origin of agricultural products based on the risk of heavy metal pollution, characterized in that: The following steps are involved: Step 1: Construct the attribute data set corresponding to the monitoring points of agricultural product production areas: The attribute data set includes: the longitude, latitude, soil pH value, soil heavy metal content, and heavy metal content of agricultural products of the monitoring points; Step 2: Determine the rules for exceeding the standard for heavy metal content in soil and agricultural products; Step 3: Construct the agricultural product origin quality evaluation function f(x i ,y i , z i ); where x i is the soil pH value at the i-th monitoring point in the study area, y i is the soil heavy metal content at the i-th monitoring point in the study area, z i is the heavy metal content of agricultural products at the i-th monitoring point in the study area, i = {1, 2, 3, … n}; Step 4: construct a matrix M according to the agricultural product origin quality evaluation function and the exceeding standard rule; the first column of the matrix M represents the exceeding standard of soil; the second column represents the exceeding standard of agricultural products; Step 5: Add the longitude and latitude of the monitoring point to the matrix M to form a matrix N; Step 6: construct Thiessen polygons according to the matrix N; and make the Thiessen polygons have type attributes; the type attributes include: soil and agricultural products are both within the standard, soil is within the standard and agricultural products are within the standard, soil is within the standard and agricultural products are within the standard, soil is within the standard and agricultural products are within the standard, soil is within the standard and agricultural products are within the standard, and soil and agricultural products are within the standard; Step 7: Find polygons that are adjacent to the Thiessen polygon and have the same type attributes, and merge them to form a new face region; Step 8: Overlay the new face area with the agricultural product origin distribution map, find out the agricultural product origin distribution area from the generated new face area, and assign the risk type of the corresponding face area to the agricultural product origin plot.

2. The method for dividing the origin of agricultural products based on the risk of heavy metal pollution according to claim 1, characterized in that: The step six comprises: The latter two columns of the matrix N are used to construct Thiessen polygons, and the first two columns of the matrix N are assigned to Thiessen polygon faces, so that the Thiessen polygon faces have type attributes.

3. The method for dividing the origin of agricultural products based on the risk of heavy metal pollution according to claim 1, characterized in that: Step seven includes: Taking a Thiessen polygon face as the starting point, search for the adjacent Thiessen polygons around it, and determine whether the attributes of its adjacent Thiessen polygon faces are the same. If they are adjacent and have the same type attributes, the two types are recorded as the same and merged to form a new face domain; otherwise, they are not merged, and the process is iterated in sequence to finally obtain a new merged face domain set.

4. The method for dividing the origin of agricultural products based on the risk of heavy metal pollution according to claim 1, characterized in that: The constructing of Thiessen polygons includes: (1) Construct a Delaunay triangulation network based on the monitoring points, number the monitoring points and the Delaunay triangulation network they form, and record which three monitoring points each triangle is composed of; (2) In the constructed triangulated network, find the numbers of all triangles adjacent to each monitoring point and record them; (3) Sort all triangles adjacent to each monitoring point in a clockwise or counterclockwise direction, starting from a known monitoring point and adjacent triangles, and gradually expanding along the edges and vertices of the triangles until returning to the starting monitoring point and edge; (4) For each triangle, calculate the center of its circumscribed circle and record the positions of these center points; (5) According to the adjacent triangles of each monitoring point, the centers of the circumscribed circles of these adjacent triangles are connected to obtain Thiessen polygons; for the Thiessen polygons at the edge of the triangulated network, a perpendicular bisector is drawn to intersect with the outline of the graph, and together with the outline of the graph, a Thiessen polygon is formed.

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