A method and system for optimizing the layout of monitoring points based on regional scale nitrogen leaching

Abstract

The application discloses a monitoring point layout optimization method and system based on regional scale nitrogen leaching, and relates to the field of agricultural environmental protection.The method comprises the following steps: acquiring a regional area and regional data; based on the regional data, drawing a slope grade map, a groundwater level grade map and a nitrogen accumulation map of the region respectively; performing pixel-to-pixel alignment, superposition and combination calculation on the slope grade map, the groundwater level grade map and the nitrogen accumulation map of the region to generate a plurality of basic units; under a constraint condition, performing initial layout of monitoring points in each basic unit according to the regional area, an obstacle layer and a preset monitoring point spacing; based on an average distance minimization criterion, constructing a target function of the monitoring points; and based on the target function, optimizing the monitoring points in the initial layout by using a space simulated annealing algorithm to obtain an optimized layout of the monitoring points.The application can improve the monitoring accuracy of regional scale nitrogen leaching and the layout efficiency of monitoring well points.

Description

Technical Field

[0001] This application relates to the field of agricultural environmental protection, and in particular to a method and system for optimizing the layout of monitoring points for nitrogen leaching at the regional scale. Background Technology

[0002] Research on nitrogen leaching monitoring in farmland primarily focuses on the field and regional scales. At the field scale, scholars both domestically and internationally have conducted extensive research, employing various techniques such as the undisturbed soil column method, clay head permeameter method, leaching pan (bucket) method, and real-time nitrogen sensor monitoring to meticulously observe and analyze the nitrogen leaching process. The field scale is small-scale, making analysis relatively simple.

[0003] However, research on monitoring nitrogen leaching at the regional scale is relatively scarce. This is mainly because regional-scale monitoring faces numerous challenges: the monitoring area is vast, the terrain is highly varied, and soil types and land use patterns exhibit significant spatial heterogeneity; simultaneously, groundwater flow is complexly influenced by various factors such as geological structure, groundwater level fluctuations, and surface runoff, making it difficult to accurately track and effectively monitor nitrogen migration pathways. Furthermore, the spatial layout of monitoring points often suffers from incomplete coverage and insufficient data representativeness, further exacerbating the difficulty of regional-scale nitrogen leaching monitoring. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for optimizing the layout of monitoring points for regional-scale nitrogen leaching, which can improve the monitoring accuracy of regional-scale nitrogen leaching and the efficiency of monitoring well site layout.

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

[0006] Firstly, this application provides a method for optimizing the layout of monitoring points for nitrogen leaching at a regional scale, comprising: acquiring regional area and regional data; drawing a slope level map, a groundwater level map, and a nitrogen accumulation map of the region based on the regional data; performing pixel alignment overlay and combination calculations on the slope level map, groundwater level map, and nitrogen accumulation map of the region to generate multiple basic units; under constraints, initially laying out monitoring points in each basic unit according to the regional area, obstacle layer, and preset monitoring point spacing; the constraints are greater than a nitrogen accumulation threshold and less than a groundwater depth threshold; the obstacle layer is a layer where monitoring points cannot be set; constructing an objective function for monitoring points based on the average distance minimization criterion; and optimizing the monitoring points in the initial layout within each basic unit using a spatial simulated annealing algorithm based on the objective function to obtain an optimized layout of monitoring points.

[0007] Secondly, this application provides a monitoring well location optimization system based on regional-scale nitrogen leaching, comprising: an acquisition module for acquiring regional area and regional data; a drawing module for drawing a slope level map, a groundwater level map, and a nitrogen accumulation map of the region based on the regional data; a basic unit generation module for performing pixel alignment overlay and combination calculations on the slope level map, groundwater level map, and nitrogen accumulation map of the region to generate multiple basic units; a monitoring point number generation module for initially laying out monitoring points in each basic unit under constraints, based on the regional area, obstacle layer, and preset monitoring point spacing; the constraints are greater than a nitrogen accumulation threshold and less than a groundwater depth threshold; the obstacle layer is a layer where monitoring points cannot be set; a construction module for constructing an objective function for monitoring points based on an average distance minimization criterion; and an optimization module for optimizing the monitoring points in the initial layout of each basic unit using a spatial simulated annealing algorithm based on the objective function to obtain an optimized layout of monitoring points.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects.

[0009] (1) High efficiency in the layout of monitoring wells: This application accurately identifies and divides the basic units of the region by combining pixel alignment superposition and combination calculation, the objective function of monitoring points and spatial simulated annealing algorithm. The simulated annealing algorithm can efficiently search for the global optimal solution, avoiding the blindness and redundancy of monitoring point distribution in traditional methods, and significantly improving the efficiency of monitoring well layout.

[0010] (2) High monitoring accuracy: This application proposes constraints that are greater than the nitrogen accumulation threshold and less than the groundwater depth threshold, ensuring that monitoring points occupy a sufficient proportion in key risk areas with high nitrogen accumulation and shallow groundwater depth. This targeted layout avoids the data deviation problem caused by the unreasonable proportion of monitoring points in the past, and effectively improves the accuracy and representativeness of monitoring data.

[0011] (3) Strong spatial representativeness: By performing pixel-by-pixel superposition and combination calculation on the slope level map, nitrogen accumulation map and groundwater level map of the region, the topographic relief, soil nitrogen distribution and groundwater depth changes in the region are fully considered, so that the generated basic unit effectively improves the spatial representativeness of the monitoring point and ensures that the obtained data can more accurately reflect the monitoring accuracy of nitrogen leaching at the regional scale. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a method for optimizing the layout of monitoring points based on regional-scale nitrogen leaching in one embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] In one exemplary embodiment, such as Figure 1 As shown, a method for optimizing the layout of monitoring points for nitrogen leaching at the regional scale is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S6.

[0017] Step S1: Obtain the area and data of the region.

[0018] Further, obtaining the area of ​​the region specifically includes: acquiring region image data; performing spatial registration on the region image data to generate a polygonal shape with closed boundaries; and calculating the area of ​​the region using planar geometry methods based on the polygonal shape with closed boundaries.

[0019] Furthermore, the formula for calculating the area of ​​a region is as follows.

[0020] .

[0021] in, The area is the region. The number of vertices of the polygon; For the first The coordinates of the vertices of a polygonal shape along the X-axis; For the first The coordinates of the vertices of a polygonal shape along the Y-axis; For the first The coordinates of the vertices of a polygonal shape along the X-axis; For the first The coordinates of the vertices of a polygonal shape along the Y-axis.

[0022] Specifically, obtaining regional area and data is the first step in optimizing the deployment of monitoring points for nitrogen leaching groundwater in farmland. The aim is to provide data support and decision-making basis for subsequent monitoring point deployment through a comprehensive understanding of the target area. Obtaining the regional area involves: understanding the terrain undulations, slopes, and geomorphic units (such as plains, hills, and basins) by acquiring regional image data. The acquired regional image data consists of remote sensing images or UAV aerial images of the target area. Spatial registration of the images is performed using known ground control points to ensure consistency between the geographical location and the actual space. Subsequently, the boundary of the study area is digitally vectorized to generate a closed polygonal shape. Then, based on the constructed closed polygonal boundary, the regional area is calculated using planar geometry methods. Specifically, the planar coordinates of all vertices of the polygonal shape are extracted, and the area is calculated using the formula for calculating the regional area.

[0023] Step S2: Based on the regional data, draw the regional slope level map, groundwater level map and nitrogen accumulation map respectively.

[0024] Furthermore, the regional data includes: digital elevation model data, groundwater depth data at known observation points within the region, and nitrogen content of soil layers at preset depths at known observation points within the region. Step S2 specifically includes steps S21-S23.

[0025] Step S21: Based on the digital elevation model data, calculate the slope value of each grid cell within the area.

[0026] Specifically, the slope map is constructed based on Digital Elevation Model (DEM) data. It calculates the surface tilt (slope value) of each raster cell by analyzing the elevation differences between each raster cell and its neighboring cells. The specific method involves selecting eight neighboring cells around each raster cell as the center, estimating the horizontal and vertical elevation gradients using third-order finite difference or triangulation methods, and then calculating the slope value of that cell. Further, the formula for calculating the slope value of each raster cell within the region is as follows.

[0027] .

[0028] in, This represents the slope value. For digital elevation model data in Rate of change of elevation in a direction; For digital elevation model data in Rate of change of elevation in the direction.

[0029] Step S22: Classify the slope values ​​using the natural breakpoint classification method, divide the slope into grades, and draw a slope grade map based on the slope grades; the slope grade map includes: flat slope map, sloping slope map and steep slope map.

[0030] Specifically, after calculating the slope values, the natural breakpoint classification method was used to group all slope values. This method maximizes inter-group differences and reduces intra-group dispersion, achieving optimal grading results. Ultimately, the slopes were divided into three levels: flat slope (small slope, gentle terrain), sloping slope (medium slope, increased potential leaching and runoff risk), and steep slope (large slope, potentially causing rapid runoff and soil leaching). The angle range for flat slopes was 0–5°, for sloping slopes it was 5–15°, and for steep slopes it was >15°.

[0031] Step S23: Based on the groundwater depth data of known observation points in the region and the nitrogen content of different soil layers at preset depths of known observation points in the region, the Kriging interpolation method is used to obtain the groundwater level map and the nitrogen accumulation map; the groundwater level map includes: shallow buried area map, medium buried area map and deep buried area map; the nitrogen accumulation map includes: low content area map, medium content area map and high content area map.

[0032] Specifically, the groundwater depth data of known observation points in the region are obtained from long-term observation data of groundwater monitoring wells in the study area obtained from water conservancy departments, environmental protection departments, geological departments, etc., as well as from hydrogeological reports, environmental impact assessment reports, etc., to determine the groundwater type (unconfined water, confined water), aquifer thickness, depth and dynamic changes in water level.

[0033] Specifically, for the nitrogen content of soil layers at predetermined depths at known observation points within the region, data on soil type and nitrogen content should be obtained primarily from national or local soil databases (such as the China Soil Database, the Second National Soil Survey data, etc.). If publicly available data is insufficient, sampling points can be evenly distributed within the region for stratified sampling, and the nitrogen content of each soil layer within the 0–2m range can be recorded for subsequent nitrogen accumulation analysis.

[0034] Furthermore, step S23 specifically includes steps S231-S239.

[0035] Step S231: Based on the groundwater depth data of known observation points in the region, establish a groundwater variability function model.

[0036] Specifically, based on the groundwater depth data of known observation points in the region, an experimental variogram is obtained; the experimental variogram is then fitted to obtain a groundwater variogram model.

[0037] The experimental variability function is as follows.

[0038] .

[0039] .

[0040] in, Let be the experimental variability function, representing the distance between two observation points. The expected value of the difference between the variable values ​​reflects how spatial correlation changes with distance; The distance is The number of all observation point pairs, where two observation points constitute one observation point pair; This represents the total number of known observation points within the region. For the first Groundwater depth data at known observation points; For the first Groundwater depth data at known observation points; The distance between observation point pairs; For the first The x-coordinates of the known observation points; No. The ordinates of the known observation points; For the first The x-coordinates of the known observation points; No. The ordinates of the known observation points.

[0041] A set of data points obtained from the experimental variogram. To determine a unique groundwater variability model (mainly the spherical model, Gaussian model or exponential model).

[0042] Step S232: Based on the groundwater variogram model, interpolate the groundwater depth data of unknown observation points in the region to obtain the groundwater depth data of unknown observation points in the region.

[0043] Specifically, based on the groundwater variogram model, a set of Kriging equations is constructed to determine the weighting coefficients for known points. Then, combined with the known burial depth values, a weighted calculation is performed to estimate the groundwater burial depth data for unknown observation points.

[0044] Among them, the interpolation of the first Unknown observation points The groundwater depth value needs to be calculated using a variogram model to determine the relationships between observation points and between known observation points and the first observation point. Unknown observation points Given a spatial structure, construct the following Kriging equations to calculate the weights. .

[0045] .

[0046] .

[0047] The first step is calculated using interpolation with linear combination formulas. Unknown observation points Data on the depth of groundwater.

[0048] .

[0049] in, The higher the interpolation density, the greater the number of unknown observation points. The larger; For each unknown observation point A set of weights is required To perform Kriging interpolation, representing the... The first unknown observation point corresponding to the The Kriging weights for each known observation point represent the influence coefficient of each known observation point on the result. For the first The observation point and the first Experimental variation function values ​​between unknown observation points; For the first Unknown points The corresponding Lagrange multipliers are used to ensure that the weight sum is 1; For the first Unknown observation points Groundwater depth data; For the first The first unknown observation point corresponding to the Kriging weights for known observation points.

[0050] Step S233: Generate a groundwater depth raster map based on the groundwater depth data of known observation points and unknown observation points within the region.

[0051] Specifically, the grid size is set according to the required research accuracy (e.g., 10m × 10m), and the area is divided into a two-dimensional regular grid according to this grid size. Based on the groundwater depth data of known observation points and unknown observation points in the area, the groundwater depth data of the center point of each grid in the two-dimensional regular grid is determined by interpolation, thereby generating a groundwater depth raster map.

[0052] Step S234: Divide the groundwater depth grid map according to the preset range of groundwater depth data and draw a groundwater level map.

[0053] Specifically, the groundwater depth data from known observation points within the region are first compiled into a data table, and its completeness and logical consistency are checked. Outliers that significantly deviate from the regional trend are removed to ensure data reliability. Kriging interpolation is then used to spatially fit the groundwater level data. This includes: analyzing the differences in water levels between known observation points based on their depth data, establishing a suitable groundwater variability model (such as a spherical model, Gaussian model, or exponential model), and fitting the spatial variation structure of the data; based on the groundwater variability model, interpolation calculations are performed on the groundwater depth data from unknown observation points throughout the region to generate a continuously distributed groundwater depth raster map. Based on the preset range of groundwater depth data, the groundwater depth raster map is divided into three levels: shallow buried area (groundwater level is close to the surface, with a high risk of pollution), medium buried area (groundwater is relatively deep, with a medium pollution potential), and deep buried area (groundwater is at a greater depth, with a weaker nitrogen migration capacity). The preset range for the shallow buried area is 0–2m, the preset range for the medium buried area is 2–5m, and the preset range for the deep buried area is >5m.

[0054] Step S235: Based on the nitrogen content of different soil layers at preset depths of known observation points within the region, calculate the cumulative nitrogen content at preset depths of known observation points within the region.

[0055] Specifically, based on the nitrogen content (unit: g / kg) and soil bulk density (unit: g / cm³) of different soil layers at known observation points in each region... 3 ) and soil layer thickness (unit: cm), calculate the nitrogen accumulation (kg / ha) within the preset depth range of 0–2m, the specific calculation formula is as follows.

[0056] ;

[0057] in, This refers to the cumulative amount of nitrogen. For the first Soil nitrogen content in each soil layer; For the first The soil bulk density of each soil layer; For the first The thickness of each soil layer; 10 is the unit conversion factor.

[0058] Step S236: Based on the preset depth nitrogen accumulation of known observation points in the region, establish a nitrogen accumulation variability function model.

[0059] Step S237: Based on the nitrogen accumulation variability function model, interpolate the nitrogen accumulation of different soil layers at preset depths of unknown observation points in the region to obtain the nitrogen accumulation at preset depths of unknown observation points in the region.

[0060] Step S238: Generate a nitrogen accumulation raster map based on the preset depth nitrogen accumulation in the region and the preset depth nitrogen accumulation of unknown observation points in the region.

[0061] Specifically, the experimental variability function established in steps S236-S238 is based on steps S231-S233.

[0062] Step S239: Divide the nitrogen accumulation raster map according to the preset range of nitrogen accumulation and draw the nitrogen accumulation map.

[0063] Specifically, the calculated nitrogen accumulation at a predetermined depth at known observation points within the region is used as input data, and spatial fitting is performed using Kriging interpolation. The specific steps are as follows: Based on the predetermined nitrogen accumulation at known observation points within the region, the spatial variability of nitrogen accumulation among these points is analyzed. A suitable nitrogen content variability function model (such as a spherical model, exponential model, or Gaussian model) is selected and fitted. Based on this model, interpolation calculations are performed on the nitrogen accumulation at non-sampling locations within the study area to achieve prediction and estimation, thereby generating a continuously distributed nitrogen accumulation raster layer. The nitrogen accumulation raster map is divided according to the predetermined range of nitrogen accumulation, resulting in three levels: low-content area, medium-content area, and high-content area. The predetermined range for the low-content area is <400 kg / ha, for the medium-content area it is 400–1000 kg / ha, and for the high-content area it is >1000 kg / ha.

[0064] Step S3: Perform pixel alignment and combination calculations on the slope level map, groundwater level map, and nitrogen accumulation map of the region to generate multiple basic units.

[0065] Specifically, the slope grade map, groundwater depth grade map, and nitrogen accumulation grade map of the region are overlaid on a raster base for pixel-by-pixel analysis. A unique identifier (such as slope grade) is formed by combining the classification codes of each pixel in the three layers. Figure 1 – Groundwater depth level map 2 – Nitrogen accumulation level map 3 (corresponding to numbers 123), thus dividing the area into 3×3×3=27 basic units. Each unit represents a specific combination of natural geography and nitrogen environment, which serves as the basis for the subsequent deployment of monitoring points.

[0066] Step S4: Under constraints, based on the area, obstacle layer, and preset monitoring point spacing, initially lay out the monitoring points in each basic unit; the constraints are greater than the nitrogen accumulation threshold and less than the groundwater depth threshold; the obstacle layer is a layer where monitoring points cannot be set.

[0067] Specifically, based on the area and in accordance with the "Technical Standards for Geological Survey of China Geological Survey" (DD06-2014), while also referencing similar studies at home and abroad or empirical data on the spatial influence radius of pollution sources, it is recommended that the horizontal length and vertical length of the area be 1 / 10 of the preset monitoring point spacing. Within the boundary of the study area, a regular rectangular grid is constructed at the set spacing, with the grid center point serving as the initial candidate set of monitoring points. Simultaneously, obstacle layers such as water bodies, roads, and buildings (e.g., vector boundaries) are introduced, and points intersecting with these layers are removed, retaining only points located within the effective area. The number of samples falling within this area is the number of monitoring points. Based on the area weight of the basic unit, a proportional allocation method is used to distribute the divided monitoring points. Furthermore, constraints are added to consider areas with high nitrogen leaching risk in farmland and areas with shallow groundwater depth. Specifically, the number of monitoring points within areas with nitrogen accumulation (>1000 kg / ha, i.e., high nitrogen accumulation areas) and groundwater depth (<2 m, i.e., shallow groundwater areas) must not be less than 30%~50% of the total number of monitoring points.

[0068] Furthermore, the formula for calculating the number of monitoring points within each basic unit is as follows.

[0069] .

[0070] st .

[0071] in, For the first The number of monitoring points within a basic unit; For the first The area of ​​each basic unit; The area is the region. is the total number of monitoring points; st is the constraint condition; For the first The basic unit belongs to the high-content area map; For the first Number of monitoring points in each basic unit; This is a constraint scaling factor.

[0072] Step S5: Construct the objective function for the monitoring points based on the average distance minimization criterion.

[0073] Furthermore, the expression for the objective function is as follows.

[0074] .

[0075] in, The objective function is... For the first The initial number of monitoring points for each basic unit; The serial number of the monitoring point; The distance is Euclidean. For the first basic unit One monitoring point; Distance monitoring points within the basic unit The nearest monitoring point.

[0076] Specifically, when setting up monitoring points, if there is no prior knowledge, the geographical layout is optimized based on the Minimizations of the Mean of Shortest Distances (MMSD) criterion. The MMSD criterion takes into account the uniform distribution of sample points, so as not to lose some important information. This criterion minimizes the Euclidean distance between any point in the basic unit and the nearest monitoring point, so as to avoid the monitoring points being too concentrated or sparse, thereby improving the representativeness of the monitoring data.

[0077] Step S6: Based on the objective function, the spatial simulated annealing algorithm is used to optimize the monitoring points in the initial layout of each basic unit to obtain the optimized layout of the monitoring points.

[0078] Furthermore, step S6 specifically includes steps S61-S63.

[0079] Step S61: Initialize annealing parameters; annealing parameters include: initial temperature, cooling rate and termination iteration condition.

[0080] Step S62: For the set of monitoring points in the initial layout of each basic unit, under the constraints, generate a set of disturbance monitoring points in the basic unit using a disturbance method, and calculate the objective function of the set of monitoring points and the objective function of the set of disturbance monitoring points respectively.

[0081] Step S63: Based on the objective function of the monitoring point set, the objective function of the disturbance monitoring point set, and the current iteration temperature, the monitoring points in the initial layout within each basic unit are optimized using the Metropolis criterion until the termination iteration condition is met, thus completing the optimized layout of the monitoring points. The termination iteration condition is that the current iteration temperature is less than the temperature threshold. The current iteration temperature is determined based on the initial temperature, cooling rate, and number of iterations.

[0082] Specifically, simulated annealing is a general optimization algorithm. It first sets a high initial temperature and a given cooling rate, then continuously searches for the optimal solution to the objective function. Its advantage lies in its ability to escape local optima and find the global optimum. Spatial simulation algorithms are suitable for situations where a small amount of sample data from the study area is known, and where a semivariance function model characterizes the spatial properties of the monitoring points is available. It is a stochastic computation technique that effectively avoids local optima, becoming a combinatorial optimization algorithm that converges to the global optimum. Furthermore, it does not have strict requirements on the initial state of the research object and can be used to solve complex deterministic combinatorial optimization problems. Its specific steps are as follows.

[0083] Step 1: Initialize annealing parameters, including initial temperature. Cooling rate β (0< β <1) and termination iteration conditions, etc.

[0084] Step 2: For the first All monitoring points in the initial layout within each basic unit are set as a set. Execute the target function .

[0085] Step 3: Based on sets A set of disturbance monitoring points is generated using a disturbance method, and this set of disturbance monitoring points is set as set. and execute the target function. The disturbance methods include, but are not limited to, the following three operations: adding, deleting, and replacing. Any operation or a combination thereof can be used to generate a set of disturbance monitoring points.

[0086] In this process, monitoring points are added by randomly generating a set of several monitoring points within the basic unit according to the constraints. And add it to the current layout set to form a disturbance monitoring point set. .

[0087] Remove monitoring points: Within the basic unit, based on constraints, remove monitoring points from the current layout set. A set of several monitoring points randomly selected in the middle And remove them to form a set of disturbance monitoring points. .

[0088] Replace monitoring points: Within the basic unit, based on constraints, from the current layout A set of several monitoring points randomly selected in the middle Replace it with a set of new monitoring points randomly generated within the basic unit. This forms a set of disturbance monitoring points. .

[0089] After the disturbance operation is completed, the set of disturbance monitoring points is obtained. Then, the objective function value of the perturbation solution is calculated. .

[0090] Step 4: Compare the objective functions of the monitoring point sets Objective function of the set of disturbance monitoring points The Metropolis criterion is used to determine whether to accept the set. .

[0091] Specifically, if a new solution is better than the current solution, then the new solution is accepted unconditionally. This means... < (i.e., set) A smaller objective function value indicates a more uniform distribution of monitoring points. If... The probability of accepting the new solution is calculated using the Metropolis criterion, and this probability depends on the difference in the objective function and the current "temperature" state. That is, it is based on probability. Accept collection ,in, For the first The iteration temperature of the next iteration.

[0092] Step 5: Determine if the termination iteration condition is met (current iteration temperature) Less than the temperature threshold If not satisfied, then calculate Iteration temperature of the next iteration Continue repeating step 3; otherwise, complete the optimization and output the optimized layout of the monitoring points.

[0093] The beneficial effects of the proposed method for optimizing the layout of monitoring points based on regional-scale nitrogen leaching are mainly reflected in the following aspects:

[0094] (1) By performing pixel alignment and combination calculations on the slope level map, groundwater level map and nitrogen accumulation map of the region, the basic units of the region are accurately laid out. Then, by combining the objective function of the monitoring points and the spatial simulation annealing algorithm, the global optimal solution is searched efficiently, avoiding the blindness and redundancy of the distribution of monitoring points in the traditional method, and significantly improving the efficiency of monitoring well layout.

[0095] (2) In the initial layout, the constraints of being greater than the nitrogen accumulation threshold and less than the groundwater depth threshold ensured that the monitoring points in key risk areas with high nitrogen accumulation and shallow groundwater depth occupied a sufficient proportion, effectively avoiding the data deviation problem caused by the unreasonable proportion of monitoring points in the past, and improving the accuracy and representativeness of the data monitored by the monitoring points.

[0096] (3) By performing pixel-by-pixel superposition and combination calculation on the slope level map, nitrogen accumulation map and groundwater level map of the region, the topographic relief, soil nitrogen distribution and groundwater depth changes in the region are fully considered. Multiple basic units are generated according to the level of each map, so that each generated basic unit represents a specific combination type of natural geography and nitrogen environment, which effectively improves the spatial representativeness of the monitoring points and ensures that the obtained data can more accurately reflect the monitoring accuracy of nitrogen leaching at the regional scale.

[0097] (4) The final optimized layout of monitoring points can provide scientific support for the accurate assessment of groundwater nitrogen pollution and the prediction of its evolution trend, and at the same time provide a more systematic and efficient decision-making basis for regional agricultural nitrogen management and groundwater pollution prevention and control.

[0098] Based on the same inventive concept, this application also provides a monitoring well location optimization system based on regional-scale nitrogen leaching. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the monitoring well location optimization system based on regional-scale nitrogen leaching provided below can be found in the limitations of the monitoring well location optimization method based on regional-scale nitrogen leaching described above, and will not be repeated here.

[0099] In an exemplary embodiment, a monitoring well location optimization system based on regional-scale nitrogen leaching is provided, comprising: an acquisition module for acquiring regional area and regional data; a drawing module for drawing a slope level map, a groundwater level map, and a nitrogen accumulation map of the region based on the regional data; a basic unit generation module for performing pixel alignment overlay and combination calculations on the slope level map, groundwater level map, and nitrogen accumulation map of the region to generate multiple basic units; a monitoring point number generation module for initially laying out monitoring points in each basic unit under constraints, based on the regional area, obstacle layer, and preset monitoring point spacing; the constraints are greater than the nitrogen accumulation threshold and less than the groundwater depth threshold; the obstacle layer is a layer where monitoring points cannot be set; a construction module for constructing an objective function for monitoring points based on the average distance minimization criterion; and an optimization module for optimizing the monitoring points in the initial layout within each basic unit using a spatial simulated annealing algorithm based on the objective function to obtain an optimized layout of monitoring points.

[0100] 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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0101] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0102] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0103] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0104] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the layout of monitoring points based on regional scale nitrogen leaching, characterized in that, The monitoring point layout optimization method based on regional scale nitrogen leaching includes: Obtaining a region area and region data; the region data includes digital elevation model data, groundwater depth data of known observation points in the region, and nitrogen content of different soil layers at a preset depth of the known observation points in the region; Based on the region data, a slope grade map, a groundwater level grade map, and a nitrogen accumulation map of the region are respectively drawn; Pixel alignment superposition and combination calculation are performed on the slope grade map, the groundwater level grade map, and the nitrogen accumulation map of the region to generate a plurality of basic units; Under the constraint condition, the monitoring points are initially laid out in each basic unit according to the region area, an obstacle layer, and a preset monitoring point spacing; the constraint condition is that the nitrogen accumulation is greater than a nitrogen accumulation threshold and the groundwater depth is less than a groundwater depth threshold; the obstacle layer is a layer on which the monitoring points cannot be set; Based on the minimum average distance criterion, a target function of the monitoring points is constructed; Based on the target function, a spatial simulated annealing algorithm is used to optimize the monitoring points in the initial layout in each basic unit to obtain an optimized layout of the monitoring points; Based on the region data, a slope grade map, a groundwater level grade map, and a nitrogen accumulation map of the region are respectively drawn, specifically including: Based on the digital elevation model data, the slope value of each grid unit in the region is calculated; The calculation formula of the slope value of each grid unit in the region is: ； wherein is a slope value; is a rate of change of elevation of the digital elevation model data in direction; is a rate of change of elevation of the digital elevation model data in direction; The natural breakpoint classification method is used to classify the slope value, divide the slope grade, and draw the slope grade map according to the slope grade; the slope grade map includes a flat slope map, an inclined slope map, and a steep slope map; Based on the groundwater depth data of the known observation points in the region and the nitrogen content of different soil layers at a preset depth of the known observation points in the region, the Kriging interpolation method is used to obtain the groundwater level grade map and the nitrogen accumulation map; the groundwater level grade map includes a shallow buried area map, a medium buried area map, and a deep buried area map; the nitrogen accumulation map includes a low content area map, a medium content area map, and a high content area map.

2. The method of claim 1, wherein, The region area is obtained, specifically including: Obtaining region image data; Spatial registration is performed on the region image data to generate a closed boundary polygon graph; Based on the closed boundary polygon graph, a planar geometric method is used to calculate the region area.

3. The method of claim 2, wherein, The calculation formula of the region area is: ； in, The area is the region. The number of vertices of the polygon; For the first The coordinates of the vertices of a polygonal shape along the X-axis; For the first The coordinates of the vertices of a polygonal shape along the Y-axis; For the first The coordinates of the vertices of a polygonal shape along the X-axis; For the first The coordinates of the vertices of a polygonal shape along the Y-axis.

4. The method of claim 1, wherein, Based on the groundwater depth data of the known observation points in the region and the nitrogen content of different soil layers at a preset depth of the known observation points in the region, the Kriging interpolation method is used to obtain the groundwater level grade map and the nitrogen accumulation map, specifically including: Based on the groundwater depth data of the known observation points in the region, a groundwater variation function model is established; Based on the groundwater variation function model, the groundwater depth data of unknown observation points in the region is calculated by interpolation to obtain the groundwater depth data of the unknown observation points in the region; Based on the groundwater depth data of the known observation points in the region and the groundwater depth data of the unknown observation points in the region, a groundwater depth grid map is generated; According to a preset range of the groundwater depth data, the groundwater depth grid map is divided to draw the groundwater level grade map; Calculate nitrogen accumulation amounts of the known observation points in the region at the preset depth based on nitrogen contents of different soil layers at the preset depth of the known observation points in the region; Establish a nitrogen accumulation variogram model based on the nitrogen accumulation amounts of the known observation points in the region at the preset depth; Interpolate and calculate nitrogen accumulation amounts of different soil layers at the preset depth of unknown observation points in the region based on the nitrogen accumulation variogram model, to obtain the nitrogen accumulation amounts of the unknown observation points in the region at the preset depth; Generate a nitrogen accumulation grid map based on the nitrogen accumulation amounts at the preset depth in the region and the nitrogen accumulation amounts of the unknown observation points in the region at the preset depth; Divide the nitrogen accumulation grid map according to a preset range of nitrogen accumulation amounts, and draw a nitrogen accumulation map.

5. The method of claim 1, wherein, The number of monitoring points in each basic unit is calculated according to the following formula: ； s.t. ; wherein, is the number of monitoring points of the basic unit of the th basic unit; is the area of the basic unit of the th basic unit; is the area of the region; is the total number of monitoring points; s.t. is a constraint; is the high content area map of the basic unit of the th basic unit; is the number of monitoring points of the basic unit of the th basic unit; is a constraint proportion factor.

6. The method of claim 1, wherein, The expression of the objective function is as follows: ； wherein, is the objective function; is the initial number of monitoring points for the th basic cell; is the serial number of the monitoring point; is the Euclidean distance; is the th monitoring point in the basic cell; is the monitoring point in the basic cell closest to the th monitoring point.

7. The method of claim 1, wherein, Based on the objective function, the spatial simulated annealing algorithm is used to optimize the monitoring points in the initial layout in each basic unit, to obtain an optimized layout of the monitoring points, which specifically includes: Initial annealing parameters are initialized, including an initial temperature, a cooling rate, and a termination iteration condition; For each set of monitoring points in the initial layout in each basic unit, a set of perturbed monitoring points is generated in the basic unit in a perturbation manner under the constraint condition, and the objective function of the set of monitoring points and the objective function of the set of perturbed monitoring points are calculated respectively; Based on the objective function of the set of monitoring points, the objective function of the set of perturbed monitoring points, and the current iteration temperature, the Metropolis criterion is used to optimize the monitoring points in the initial layout in each basic unit, until the termination iteration condition is reached to complete the optimized layout of the monitoring points; the termination iteration condition is that the current iteration temperature is less than a temperature threshold; the current iteration temperature is determined according to the initial temperature, the cooling rate, and the number of iterations.

8. A monitoring well site layout optimization system based on regional scale nitrogen leaching, characterized in that, The monitoring well point layout optimization system based on regional scale nitrogen leaching includes: An acquisition module is configured to acquire a region area and region data; the region data includes digital elevation model data, groundwater depth data of known observation points in the region, and nitrogen contents of different soil layers at a preset depth of the known observation points in the region; A drawing module is configured to draw a slope grade map, a groundwater level map, and a nitrogen accumulation map of the region respectively based on the region data; A basic unit generation module is configured to generate a plurality of basic units by performing pixel alignment and combination calculation on the slope grade map, the groundwater level map, and the nitrogen accumulation map of the region; A monitoring point number generation module is configured to perform initial layout of monitoring points in each basic unit under a constraint condition according to the region area, an obstacle layer, and a preset monitoring point spacing; the constraint condition is that the nitrogen accumulation is greater than a nitrogen accumulation threshold and less than a groundwater depth threshold; the obstacle layer is a layer on which monitoring points cannot be set; A construction module is configured to construct an objective function of the monitoring points based on an average distance minimization criterion; An optimization module is configured to optimize the monitoring points in the initial layout in each basic unit based on the objective function by using the spatial simulated annealing algorithm, to obtain an optimized layout of the monitoring points. The slope grade map, the groundwater level grade map and the nitrogen accumulation map of the region are respectively drawn based on the region data, and the drawing specifically includes: The slope value of each grid unit in the region is calculated based on the digital elevation model data; The calculation formula of the slope value of each grid unit in the region is: ； wherein, is a slope value; is a rate of change of elevation of the digital elevation model data in direction; is a rate of change of elevation of the digital elevation model data in direction; The slope value is classified by using the natural breakpoint classification method, the slope grade is divided, and the slope grade map is drawn according to the slope grade; the slope grade map includes: flat slope map, inclined slope map and steep slope map; The groundwater level grade map and the nitrogen accumulation map are obtained by using the Kriging interpolation method based on the groundwater depth data of the known observation points in the region and the nitrogen content of different soil layers with a preset depth of the known observation points in the region; the groundwater level grade map includes: shallow buried area map, medium buried area map and deep buried area map; the nitrogen accumulation map includes: low content area map, medium content area map and high content area map.

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