Monitoring point position representativeness optimization method

Abstract

The invention discloses a monitoring point position representativeness optimization method, which comprises the following steps: on the basis of researching a regional environment database, calculating the number of minimum sampling points in a region, and finishing data acquisition work by adopting quick detection equipment; and performing clustering analysis, spatial interpolation, error calculation, secondary error calculation and the like on the basis of the detection data, performing monitoring point position supplementation on a region with a relatively large error, obtaining longitude and latitude of a supplementation point position, and drawing a monitoring point position optimization distribution diagram. According to the method, a closed-loop technical chain of data acquisition-error calculation-point location optimization is constructed, an important supporting effect is achieved on comprehensive mastering of regional environment conditions, and decision support is provided for agricultural planting zoning optimization and pollution prevention and control through accurate environmental element analysis.

Description

Technical Field

[0001] The invention relates to a method for optimizing representativeness of monitoring points for environmental monitoring in a research area, and belongs to the field of agricultural environmental science. Background Art

[0002] In order to accurately grasp the environmental quality of farmland soil, improve monitoring efficiency and reduce costs, farmland soil monitoring work urgently needs to optimize the points to achieve more scientific, reasonable and efficient soil monitoring (Dong Xubin, Yao Yanhong, Wu Chenxing. Research on the layout method of basic points for soil environmental monitoring [J]. Leather Manufacturing and Environmental Protection Technology, 2022, 3(7): 78-80); Currently, the commonly used point layout methods for environmental monitoring are mostly random layout, uniform layout, and zoned layout (Zhai Xiaolei. Research on the layout method of basic points for soil environmental monitoring [J]. Cleaning the World, 2021, 37(3): 82-83).

[0003] The main problems with the above technical methods are: (1) The monitoring points are not representative enough and provide little support for a comprehensive understanding of regional environmental quality; (2) The monitoring point layout method is not universal. my country's topography is complex and diverse, and the point layout method is difficult to adapt to all regions. Summary of the Invention

[0004] Based on a regional environmental database, this method calculates the minimum number of sampling points in a region and uses rapid testing equipment to complete data collection. Based on the test data, cluster analysis, spatial interpolation, error calculation, and quadratic error calculation are performed. Additional monitoring points are added to areas with large errors, the longitude and latitude of the additional points are obtained, and an optimized distribution map of the monitoring points is created. This method establishes a closed-loop technical chain of "data collection-error calculation-point optimization," which plays a crucial role in supporting a comprehensive understanding of regional environmental conditions. It also provides decision-making support for optimizing agricultural planting zoning and pollution prevention and control through precise analysis of environmental factors.

[0005] A method for optimizing the representativeness of monitoring points includes four steps: environmental database construction, cluster analysis, single environmental indicator error calculation, and point supplementation. The method is characterized by:

[0006] (1) Construction of environmental database

[0007] (2) Clustering

[0008] (2.1) Calculation of minimum monitoring points

[0009] (2.2) Quick inspection of monitoring points

[0010] Based on the minimum number of monitoring points, the points are arranged to form an initial set of monitoring points; in-situ environmental index detection is performed on the environmental medium, and environmental index detection data is obtained;

[0011] (2.3) Cluster analysis

[0012] Extract the environmental indicator detection data of all monitoring points and the environmental data except area in the environmental database by point to obtain a cluster data set; perform cluster analysis on the cluster data set to obtain A cluster categories;

[0013] (3) Calculation of error of single environmental indicator

[0014] (3.1) Randomly select one of the A cluster categories;

[0015] (3.2) Randomly select a point from the initial monitoring point set as the target point r, and select the k adjacent points with the shortest straight-line distance to the target point r to form the adjacent point set;

[0016] (3.3) Randomly select n adjacent points from the adjacent point set, perform spatial interpolation of environmental indicators, extract the interpolation data of the target point r, and calculate the relative error θ of r;

[0017] θ = (environmental index detection data - interpolation data) / environmental index detection data

[0018] The value range of n is 3-5, and kn≥3;

[0019] (3.4) Traverse n adjacent points and interpolate to obtain C relative errors of the target point r;

[0020]

[0021] The error point combination with relative error θ>25%, i.e., the error point set, proceeds to the next step; the error point combination with θ≤25% does not proceed to the subsequent operation;

[0022] (3.5) All points are taken as target points r and calculated again through steps (3.1) to (3.4) to obtain the set of all error points with θ > 25%;

[0023] (4) Supplementary points

[0024] For a single error point set, obtain the initial monitoring point and the longitude and latitude of r in the error point set, extract the four range positions, Xmin, Xmax, Ymin, Ymax, connect them into a closed area, and add points in the closed area.

[0025] Furthermore, the environmental database construction includes collecting environmental information of the research area and constructing the environmental database;

[0026] Furthermore, the environmental information includes: area, altitude, slope, land use type, soil type, distance to water source, distance to road, vegetation type, etc.; the environmental information is collected by means of, but not limited to, consulting literature, books, experimental reports, project summary reports, etc.;

[0027] Furthermore, the method for calculating the minimum number of monitoring points is as follows: based on the area S of the study area, the minimum number of monitoring points D is calculated. The calculation formula is as follows:

[0028] D=S / m 2 +5, and

[0029] Where m is the minimum acceptable distance between two monitoring points;

[0030] Furthermore, the point layout method includes but is not limited to a random point layout method and a uniform point layout method;

[0031] Furthermore, the on-site rapid detection equipment includes but is not limited to an in-situ pH meter, a portable X-ray fluorescence spectrometer (XRF), a near-infrared spectrometer (NIR), a portable spectrophotometer, an inductively coupled plasma optical emission spectrometer (ICP-OES) or a mass spectrometer (ICP-MS), an anodic stripping voltammetry (ASV) detector, a laser-induced breakdown spectrometer (LIBS), etc.;

[0032] Furthermore, the environmental medium includes soil, atmosphere, water, etc.;

[0033] Furthermore, the environmental indicators include but are not limited to pH, lead (Pb), mercury (Hg), cadmium (Cd), chromium (Cr), arsenic (As), copper (Cu), zinc (Zn), nickel (Ni), manganese (Mn), titanium (Ti), thallium (Tl), vanadium (V), aluminum (Al), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), sulfur (S), chlorine (Cl), bromine (Br), selenium (Se), gallium (Ga), germanium (Ge), rubidium (Rb), strontium (Sr), etc.;

[0034] Furthermore, the latitude and longitude information of each point in the initial monitoring point set is obtained.

[0035] Furthermore, A≥3; the clustering model includes but is not limited to GMM, K-means, hierarchical clustering, etc.;

[0036] Furthermore, the k adjacent points are selected in two ways: ① If the number of monitoring points in the cluster category where r is located is greater than k, the k monitoring points closest to r in straight-line distance are selected; ② If the number of monitoring points in the cluster category where r is located is less than or equal to k, all monitoring points in the same cluster category as r are preferentially selected, and the remaining monitoring points are selected from other cluster categories with the closest straight-line distance to r. The value range of k is 5-8.

[0037] Furthermore, the spatial interpolation method includes Kriging interpolation method, inverse distance weighted average interpolation method (IDW), trend surface method, and spline function method;

[0038] Furthermore, professional software including Arcgis, R, python, matlab, CAD, etc. were used to perform spatial interpolation;

[0039] Furthermore, the additional points include:

[0040] (1) Divide the closed area into grids of unit length and extract the center points of each grid;

[0041] Where Xmin is the minimum longitude, Xmax is the maximum longitude, Ymin is the minimum latitude, and Ymax is the maximum latitude;

[0042] The four-range location refers to the maximum and minimum longitude and latitude of the monitoring point;

[0043] The unit grid length is 50-1000 meters;

[0044] (2) Extract interpolation data of all center points;

[0045] (3) Select the interpolation data of a single central point and the environmental index detection data of the initial monitoring point in the error point set, use the same spatial interpolation method to extract the secondary interpolation data of the target point r; obtain the secondary interpolation data of the target point r corresponding to all central points;

[0046] (4) Calculate the quadratic relative error θt of the target point r. The point with the largest θt is the supplementary point.

[0047] (5) Traverse all error point sets using the same method, i.e., obtain one supplementary point for each error point set;

[0048] (6) For multiple environmental indicators, the same method is used to obtain the supplementary points of each single indicator, and all the supplementary points are collected. The supplementary points with closer distances are deleted, and the remaining points are the supplementary point set;

[0049] (7) The same method is used to traverse all cluster categories and obtain all additional points in the study area.

[0050] Furthermore, if the center point of the grid is outside the closed area, and the area of ​​the grid in the closed area is greater than 50%, the center point of the grid in the closed area is selected; if the area of ​​the grid in the closed area is less than or equal to 50%, the grid is deleted;

[0051] Furthermore, each error point set has multiple center points, one point corresponds to one quadratic relative error, the quadratic relative errors are compared, and the point with the largest quadratic relative error value is selected, that is, the supplementary point;

[0052] Furthermore, the quadratic relative error θt is calculated as follows:

[0053] θt=(environmental index detection data-quadratic interpolation data) / environmental index detection data

[0054] Furthermore, the method for calculating the additional points with a relatively close distance is as follows:

[0055] ①Use Kriging interpolation method to obtain the nugget value of all initial monitoring points;

[0056] ② If the distance between two additional points with different environmental indicators is the smallest and is less than the maximum nugget value of the environmental indicator, the two points are considered to be close and one point is randomly retained;

[0057] Furthermore, the longitude and latitude of all supplementary points are extracted, and a distribution map of optimized points is drawn using professional software.

[0058] The advantages and effects of this application are as follows:

[0059] (1) This invention comprehensively considers the regional characteristics of the research area and the environmental conditions of soil, water, and atmosphere, and provides a method for distributing monitoring points, which plays an important supporting role in fully understanding the regional characteristics;

[0060] (2) The present invention designs an adaptive sampling density supplement mechanism for error hotspot areas, iteratively optimizes the longitude and latitude coordinates of the supplemented points, and ensures that the spatial coverage of the monitoring network is increased to more than 95%;

[0061] (3) The present invention adopts a combination of on-site rapid detection, spatial interpolation, error calculation and other technical methods, which saves 25% of funds and 40% of time compared with traditional monitoring point optimization methods.

[0062] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application so that it can be implemented in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following is a detailed description of the preferred embodiment of the present application in conjunction with the accompanying drawings.

[0063] Based on the detailed description of the specific embodiments of the present application in conjunction with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, advantages and features of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without inventive work. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0065] Figure 1 A technical roadmap for optimizing monitoring point representativeness.

[0066] Figure 2 .R County initial monitoring point distribution map;

[0067] Figure 3 .Distribution map of error points and grid center points;

[0068] Figure 4 .R County optimized point distribution map. Specific embodiments

[0069] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the following description, specific details such as specific configurations and components are provided only to help fully understand the embodiments of the present application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, for clarity and brevity, the description of known functions and structures has been omitted in the embodiments.

[0070] It should be understood that references throughout this specification to "one embodiment" or "this embodiment" mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Therefore, the appearance of "one embodiment" or "this embodiment" throughout this specification does not necessarily refer to the same embodiment. Furthermore, these particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0071] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0072] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" in this article describes another type of association object relationship, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the previous and subsequent associated objects are in an "or" relationship.

[0073] The term "at least one" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, at least one of A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0074] It should also be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include," "comprises," or any other variations thereof are intended to cover non-exclusive inclusion.

[0075] Example 1

[0076] 1. Environmental database construction

[0077] (1) Taking County R as the research area, we collected information on regional area, altitude, slope, land use type, soil type, distance to water source, vegetation type, agricultural product type, etc. through statistical yearbooks and papers to form an environmental database;

[0078] (2) A river flowing through the territory of County R;

[0079] 2. Cluster Analysis

[0080] (1) Based on the research requirements, the distance between monitoring points in County R needs to be greater than 0.8 km. According to the calculation formula of the minimum number of monitoring points, the number of monitoring points in County R is calculated to be 21. The random distribution method is used to obtain the initial set of monitoring points.

[0081] (2) Use an in-situ pH meter and a portable X-ray fluorescence spectrometer to conduct in-situ testing of soil pH, cadmium, and lead content, as well as cadmium and lead content in rivers, to obtain environmental indicator testing data;

[0082] (3) Integrate environmental data and environmental indicator detection data, perform K-means clustering, and divide 21 points into three cluster categories, including 9 in category 1, 6 in category 2, and 6 in category 3. The distribution map is shown in Figure 2 ;

[0083] 3. Calculation of error of single environmental indicator

[0084] (1) Select the initial monitoring points in cluster 1 to calculate the relative error of soil cadmium, randomly select the target point r, and further select the 7 closest points to form the adjacent point set 1;

[0085] (2) Randomly select three initial monitoring points from the adjacent point set 1, use Arcgis software and ordinary kriging interpolation method to perform spatial interpolation and extract the interpolation data of the target point r;

[0086] (3) Traverse all initial monitoring points in the adjacent point set 1 and calculate the relative error according to the relative error calculation formula; a total of 35 relative errors are obtained, of which the relative error θ of the four error point combinations is greater than 25%, that is, the four error point sets;

[0087] (4) All points are used as target points r and the relative error is calculated using the same method, resulting in a total of 18 error point sets;

[0088] (5) Using the same method, a total of 12 error point sets of soil lead were calculated;

[0089] 4.Addition of points

[0090] (1) Taking one error point set in the adjacent point set 1 as an example, the latitude and longitude of the three error points and the target point are obtained, the four boundaries are extracted, and they are connected to form a closed area; the closed area is divided into grids with a length of 0.5 km, and the center points of each grid are extracted, a total of 10 center points, see Figure 3 ;

[0091] (2) Using the same method, extract the center points of the other three error point sets in the adjacent point set 1, for a total of 24 points; extract the interpolation data of the 24 center points;

[0092] (3) Ordinary Kriging interpolation method was used to interpolate the soil cadmium detection data of 24 groups of single central point interpolation data and the initial monitoring point in the error point set, and the secondary interpolation data of 24 target points r were extracted;

[0093] (4) According to the quadratic relative error calculation method, 24 quadratic relative errors are calculated, among which the point with the largest quadratic relative error is the supplementary point; 4 supplementary points are obtained from the 4 error point sets;

[0094] (5) The same method was used to calculate three additional soil lead points, and the nugget values ​​of cadmium and lead were calculated respectively, and two additional points were eliminated;

[0095] (6) The same method is used to traverse the three cluster categories, and a total of 12 additional points are obtained. The optimized point distribution map is shown in Figure 4 .

[0096] The foregoing description is merely a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any variation, modification, replacement, integration, or parameter change to these embodiments, which is within the spirit and principles of the present invention and which achieves the same functionality through conventional substitutions, without departing from the principles and spirit of the present invention, falls within the scope of protection of the present invention.

Claims

1. A monitoring point representativeness optimization method, characterized by: The following steps are included: (1) Construction of environmental database (2) Clustering (2.1) Calculation of minimum monitoring points (2.2) Quick inspection of monitoring points Based on the minimum number of monitoring points, point layout is carried out to form an initial monitoring point set; environmental index detection is carried out on the environmental medium to obtain environmental index detection data; (2.3) Cluster analysis Extract environmental indicator detection data of all monitoring points and environmental data except area from the environmental database by point to obtain a cluster data set; perform cluster analysis on the cluster data set using a cluster model to obtain A cluster categories; (3) Calculation of error of single environmental indicator (3.1) Randomly select one of the A cluster categories; (3.2) Randomly select a point from the initial monitoring point set as the target point r, and select the k adjacent points with the shortest straight-line distance to the target point r to form the adjacent point set; (3.3) Randomly select n adjacent points from the adjacent point set, perform spatial interpolation of environmental indicators, extract the interpolation data of the target point r, and calculate the relative error θ of r; θ = (environmental index detection data - interpolation data) / environmental index detection data Said n=3-5, and kn≥3; (3.4) Traverse n adjacent points and interpolate to obtain C relative errors of the target point r; The error point combination with relative error θ>25% is called error point set; the error point combination with θ≤25% will not be processed. (3.5) All points are taken as target points r and calculated again through steps (3.1) to (3.4) to obtain the set of all error points with θ > 25%; (4) Supplementary points Obtain the initial monitoring point and the longitude and latitude of r in each error point set, extract the four-range position, connect them into a closed area, and add points in the closed area.

2. A monitoring point representativeness optimization method according to claim 1, characterized in that: The environmental database construction includes collecting environmental information of the research area and constructing the environmental database.

3. A monitoring point representativeness optimization method according to claim 1, characterized in that: The method for calculating the minimum number of monitoring points is as follows: Based on the area S of the study area, the minimum number of monitoring points D is calculated. The calculation formula is as follows: D=S / m 2 +5, and Where m is the minimum acceptable distance between two monitoring points.

4. A monitoring point representativeness optimization method according to claim 1, characterized in that: Environmental indicators of environmental media are detected using on-site rapid detection equipment, which includes one or more of an in-situ pH meter, a portable X-ray fluorescence spectrometer, a near-infrared spectrometer, a portable spectrophotometer, an inductively coupled plasma emission spectrometer, a mass spectrometer, an anodic stripping voltammetry detector, and a laser-induced breakdown spectrometer.

5. A monitoring point representativeness optimization method according to claim 1, characterized in that: The environmental indicators include one or more of pH, lead, mercury, cadmium, chromium, arsenic, copper, zinc, nickel, manganese, titanium, thallium, vanadium, aluminum, potassium, calcium, magnesium, iron, sulfur, chlorine, bromine, selenium, gallium, germanium, rubidium, and strontium.

6. A monitoring point representativeness optimization method according to claim 1, characterized in that: The A is greater than or equal to 3; the clustering models include GMM, K-means, and hierarchical clustering.

7. A monitoring point representativeness optimization method according to claim 1, characterized in that: There are two methods for selecting the k adjacent points: ① If the number of monitoring points in the cluster category where r is located is greater than k, the k monitoring points closest to r in a straight line are selected; ② If the number of monitoring points in the cluster category where r is located is less than or equal to k, all monitoring points in the same cluster category as r are preferentially selected, and the remaining monitoring points are selected from monitoring points in other cluster categories that are closest to r in a straight line; the value range of k is 5-8.

8. The monitoring point representativeness optimization method according to claim 1, characterized in that: Additional points also include: (1) Divide the closed area into grids of unit length and extract the center points of each grid; The four-range location refers to the maximum and minimum longitude and latitude of the monitoring point; The unit length is 50-1000 meters; (2) Extract interpolation data of all center points; (3) Select the interpolation data of a single central point and the environmental index detection data of the initial monitoring point in the error point set, use the same spatial interpolation method to extract the secondary interpolation data of the target point r; obtain the secondary interpolation data of the target point r corresponding to all central points; (4) Calculate the quadratic relative error θt of the target point r. The point with the largest θt is the supplementary point. (5) Traverse all error point sets using the same method, i.e., obtain one supplementary point for each error point set; (6) For multiple environmental indicators, the same method is used to obtain the supplementary points of each individual indicator, and all the supplementary points are collected. The supplementary points with closer distances are deleted, and the remaining points are the supplementary point set; (7) The same method is used to traverse all cluster categories and obtain all additional points in the study area.

9. A monitoring point representativeness optimization method according to claim 8, characterized in that: The center point of the grid is located outside the closed area. If the area of ​​the grid in the closed area is greater than 50%, the center point of the grid in the closed area is selected. If the area of ​​the grid in the closed area is less than or equal to 50%, the grid is deleted.

10. A monitoring point representativeness optimization method according to claim 8, characterized in that: The calculation method of the quadratic relative error θt is as follows: θt = (environmental index detection data - quadratic interpolation data) / environmental index detection data; The calculation method for the additional points with closer distance is as follows: ①Use Kriging interpolation method to obtain the nugget value of all initial monitoring points; ② If the distance between two additional points with different environmental indicators is the smallest and less than the maximum nugget value of the environmental indicator, the two points are considered to be close and one point is randomly retained.

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