Soil and water conservation online monitoring method and system based on internet of things

Abstract

The application provides a soil and water conservation online monitoring method and system based on the Internet of Things, and relates to the technical field of soil testing.The method comprises the following steps: acquiring satellite remote sensing vegetation images, terrain slope data of a region to be monitored, and soil moisture measured data collected through the water sensors arranged; dividing the region to be monitored based on the satellite remote sensing vegetation images, determining the candidate monitoring points based on the spatial distribution of the water sensors arranged in each vegetation block, the differences in vegetation coverage, and the terrain slope data; constructing the water region boundary based on the extreme data of the soil moisture measured data in the vegetation block, determining the soil moisture estimation data of the candidate monitoring points in combination with the vegetation gradient information of the vegetation block between the boundary; constructing the soil moisture spatial distribution model based on the soil moisture measured data and the soil moisture estimation data of the region to be monitored through the spatial interpolation algorithm, and determining the soil and water conservation state of the region to be monitored through the model.The application can accurately monitor the soil and water loss.

Description

Technical Field

[0001] This application relates to the field of soil testing technology, specifically to an online monitoring method and system for soil and water conservation based on the Internet of Things. Background Technology

[0002] In regional ecological management, soil and water conservation monitoring is not only an important foundation for assessing soil erosion and optimizing land use structure and vegetation configuration, but also a core basis for conducting regional dynamic assessments of soil erosion and formulating policies. It is of great significance for ensuring ecosystem stability and promoting ecological civilization.

[0003] The relevant technologies mainly achieve soil and water conservation monitoring through two main approaches: One approach involves deploying soil moisture sensors at selected sampling points to infer the overall soil and water conservation status of the region by collecting local soil moisture content data. However, due to limitations such as construction conditions and cost, the spacing between soil moisture sensors is usually large and the distribution is uneven, making it difficult to comprehensively cover areas with different terrains and ecological types. This results in monitoring data failing to reflect the spatial differences of the entire region, thus affecting the accuracy of soil erosion assessment. The other approach estimates vegetation cover through remote sensing imagery, indirectly reflecting the regional soil and water conservation level. However, this approach only focuses on the macroscopic characteristics of surface vegetation, lacking comprehensive analysis of key influencing factors such as soil moisture content and terrain slope. It cannot accurately capture the dynamic changes in soil and water conservation status and fails to meet the needs of refined monitoring. In summary, existing technologies suffer from incomplete coverage and a single analytical dimension, making it difficult to support refined and dynamic monitoring of regional soil and water conservation. Summary of the Invention

[0004] To address the shortcomings of existing technologies in comprehensively reflecting the actual soil erosion situation under different terrains and ecological types, and lacking a continuous and dynamic description of the overall soil and water conservation status of a region, this application provides an online soil and water conservation monitoring method based on the Internet of Things. The specific technical solution adopted is as follows:

[0005] Acquire satellite remote sensing images of vegetation, topographic slope data, and measured soil moisture data collected by moisture sensors already deployed in the area to be monitored;

[0006] Based on the satellite remote sensing vegetation image, the area to be monitored is divided into blocks, and the candidate monitoring points are determined based on the spatial distribution of the deployed moisture sensors, the difference in vegetation cover, and the terrain slope data within each vegetation block.

[0007] Based on the extreme value data of the measured soil moisture data within the vegetation block, a moisture region boundary is constructed, vegetation gradient information of the vegetation block between the moisture region boundaries is extracted, and soil moisture estimation data of each candidate monitoring point is determined based on the moisture region boundary and the vegetation gradient information.

[0008] Based on the measured soil moisture data and the estimated soil moisture data within the area to be monitored, a spatial distribution model of soil moisture in the area to be monitored is constructed using a spatial interpolation algorithm.

[0009] The soil and water conservation status of the area to be monitored is determined based on the aforementioned spatial distribution model of soil moisture.

[0010] For example, the step of dividing the area to be monitored into blocks based on the satellite remote sensing vegetation image includes: performing grayscale processing on the satellite remote sensing vegetation image to obtain a remote sensing vegetation grayscale image; performing edge detection operation on the remote sensing vegetation grayscale image, and dividing the area to be monitored into multiple vegetation blocks based on the detected edge contours.

[0011] For example, determining candidate monitoring points based on the spatial distribution of the deployed moisture sensors within a vegetation block, differences in vegetation cover, and the terrain slope data includes: obtaining the block area of ​​the vegetation block and the number of deployed moisture sensors within the block, and determining the sensor distribution density of the vegetation block based on the number of deployed moisture sensors and the block area; obtaining the maximum sensor distribution density in each vegetation block, and determining a target sensor density based on the maximum sensor distribution density; determining the number of candidate monitoring points in the corresponding vegetation block based on the target sensor density and the number of deployed moisture sensors in the vegetation block; and determining the number of candidate monitoring points in the corresponding vegetation block based on the spatial distribution of the deployed moisture sensors, differences in vegetation cover within the vegetation block, and the terrain slope data.

[0012] For example, determining the number of candidate monitoring points in the corresponding vegetation block based on the spatial distribution of the deployed moisture sensors, the vegetation cover differences of the vegetation block, and the terrain slope data includes: uniformly dividing the current vegetation block into multiple vegetation grids of equal area, and using the center point of the vegetation grid without deployed moisture sensors as a candidate point; for each candidate point, determining the target locations of the first preset number of deployed moisture sensors closest to it, and determining the distribution balance of the candidate points based on the distance between the candidate points and each target location; determining... The absolute difference between the average grayscale value of the current vegetation block and its neighboring vegetation blocks is denoted as the vegetation cover difference. The minimum distance between the candidate point and the edge of the current vegetation block is obtained. The location specificity of the candidate point is determined based on the minimum distance between the candidate point and the edge of the current vegetation block, the vegetation cover difference, and the terrain slope data of the vegetation grid where the candidate point is located. The selection priority of the candidate point is determined based on the distribution balance and the location specificity. The first number of candidate points are selected in descending order as the candidate monitoring points of the current vegetation block.

[0013] For example, constructing the moisture region boundary based on the extreme value data of the measured soil moisture data within the vegetation block includes: sorting the measured soil moisture data within the vegetation block in descending order; obtaining the first sorted second preset number of measured soil moisture data, and constructing a convex hull based on the spatial position of the corresponding deployed moisture sensors in spatial order to form a high moisture region boundary; obtaining the last sorted second preset number of measured soil moisture data, and constructing a convex hull based on the spatial position of the corresponding deployed moisture sensors in spatial order to form a low moisture region boundary.

[0014] For example, the step of extracting vegetation gradient information of the vegetation blocks between the boundaries of the water regions includes: determining the shortest connection path between the boundary of the high-moisture region and the boundary of the low-moisture region, and extracting green channel information of the vegetation blocks in the satellite remote sensing vegetation image; extracting the values ​​of the green channel information along the extension direction of the shortest connection path at preset intervals, calculating the value change of adjacent intervals, and obtaining a vegetation gradient value sequence.

[0015] For example, determining the soil moisture estimation data for each candidate monitoring point based on the water region boundary and the vegetation gradient information includes: for each candidate monitoring point, determining the nearest deployed water sensor on the high water region boundary and acquiring its corresponding measured soil moisture data, denoted as the high water boundary value; determining the nearest deployed water sensor on the low water region boundary and acquiring its corresponding measured soil moisture data, denoted as the low water boundary value; determining the projection point of the candidate monitoring point on the shortest connection path, and extracting the target vegetation gradient value corresponding to the projection point from the vegetation gradient value sequence; and determining the soil moisture estimation data for the candidate monitoring point based on the high water boundary value, the low water boundary value, and the target vegetation gradient value.

[0016] For example, determining the soil moisture estimation data of the candidate monitoring point based on the high moisture boundary value, the low moisture boundary value, and the target vegetation gradient value includes: determining the moisture change range based on the high moisture boundary value and the low moisture boundary value; determining the moisture change percentage of the candidate monitoring point based on the cumulative vegetation gradient value from the deployed moisture sensors to the projection point corresponding to the low moisture boundary value and the sum of vegetation gradient values ​​on the shortest connection path; and determining the soil moisture estimation data of the candidate monitoring point based on the low moisture boundary value, the moisture change range, and the moisture change percentage.

[0017] For example, the step of constructing a spatial distribution model of soil moisture in the area to be monitored using a spatial interpolation algorithm based on the measured soil moisture data and the estimated soil moisture data in the area to be monitored includes: uniformly dividing the area to be monitored into multiple monitoring grids of equal area; marking the monitoring grids corresponding to the measured soil moisture data or the estimated soil moisture data to obtain corresponding valuable monitoring grids, and recording the remaining monitoring grids in the area to be monitored after removing the valuable monitoring grids as valueless monitoring grids; for each valueless monitoring grid, determining a third preset number of reference valuable monitoring grids that are closest to the valueless monitoring grid, and using a spatial interpolation algorithm to determine the soil moisture data of the valueless monitoring grids based on the measured soil moisture data or the estimated soil moisture data of the reference valuable monitoring grids; and constructing the spatial distribution model of soil moisture based on the soil moisture data of all the monitoring grids.

[0018] Correspondingly, this application also provides an Internet of Things (IoT)-based online monitoring system for soil and water conservation, which includes the following steps for implementing an IoT-based online monitoring method for soil and water conservation:

[0019] The acquisition module is used to acquire satellite remote sensing vegetation images, topographic slope data, and measured soil moisture data collected by moisture sensors already deployed in the area to be monitored.

[0020] The processing module is used to divide the area to be monitored into blocks based on the satellite remote sensing vegetation image, and to determine the candidate monitoring points based on the spatial distribution of the deployed moisture sensors, the difference in vegetation cover and the terrain slope data within each vegetation block.

[0021] The processing module is further configured to construct a water area boundary based on the extreme value data of the measured soil moisture data within the vegetation block, extract vegetation gradient information of the vegetation blocks between the water area boundaries, and determine the estimated soil moisture data of each candidate monitoring point based on the water area boundaries and the vegetation gradient information.

[0022] The processing module is also used to construct a spatial distribution model of soil moisture in the area to be monitored by means of a spatial interpolation algorithm based on the measured soil moisture data and the estimated soil moisture data in the area to be monitored.

[0023] The monitoring module is used to determine the soil and water conservation status of the area to be monitored based on the spatial distribution model of soil moisture.

[0024] This application may have some or all of the following beneficial effects:

[0025] In the IoT-based online soil and water conservation monitoring method provided in this application, the monitoring area is divided into blocks using satellite remote sensing vegetation images. Candidate monitoring points are determined by combining the spatial distribution of pre-deployed moisture sensors, vegetation cover differences, and topographic slope data within each vegetation block. This ensures sufficient monitoring nodes under various vegetation and topographic conditions, enabling the monitoring data to comprehensively reflect the spatial differences in regional soil and water conservation. Furthermore, moisture zone boundaries are constructed using extreme values ​​of measured soil moisture data within the vegetation blocks. Soil moisture estimates for the candidate monitoring points are then calculated by combining vegetation gradient information between the boundaries, achieving a deep integration of sensor measured data and vegetation optical characteristics. The integration of soil moisture data makes the estimation of moisture data in areas without sensor deployment more accurate, laying a precise data foundation for subsequent full-area monitoring. Based on measured and estimated soil moisture data, a spatial distribution model of soil moisture covering the monitored area is constructed through spatial interpolation algorithms. By integrating soil moisture data from the entire grid into the model, the spatiotemporal continuity of soil and water monitoring is improved, enabling real-time capture of dynamic changes in the regional soil and water conservation status. The soil moisture spatial distribution model determines the soil and water conservation status of the monitored area, providing a basis for the formulation of ecological restoration and water and soil conservation measures, and improving the scientific nature and pertinence of regional soil and water conservation management.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0028] Figure 1 A flowchart of an Internet of Things-based online monitoring method for soil and water conservation according to an exemplary embodiment of this application is shown;

[0029] Figure 2 A schematic block diagram of an Internet of Things-based online monitoring system for soil and water conservation according to an exemplary embodiment of this application is shown. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the Internet of Things-based online monitoring method and system for soil and water conservation proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0032] The specific scheme of the Internet of Things-based online monitoring method and system for soil and water conservation provided in this application is described in detail below with reference to the accompanying drawings.

[0033] Please see Figure 1 It illustrates a flowchart of an Internet of Things-based online monitoring method for soil and water conservation provided in one embodiment of this application, as shown below. Figure 1 As shown, this IoT-based online water and soil conservation monitoring method specifically includes the following steps:

[0034] S110: Acquire satellite remote sensing images of vegetation, topographic slope data, and measured soil moisture data collected by moisture sensors already deployed in the area to be monitored.

[0035] S120: Based on satellite remote sensing vegetation images, the area to be monitored is divided into blocks, and the candidate monitoring points are determined based on the spatial distribution of moisture sensors already deployed in each vegetation block, differences in vegetation cover, and topographic slope data.

[0036] S130: Construct water area boundaries based on extreme value data of measured soil moisture within vegetation blocks, extract vegetation gradient information of vegetation blocks between water area boundaries, and determine soil moisture estimation data for each candidate monitoring point based on water area boundaries and vegetation gradient information.

[0037] S140: Based on the measured and estimated soil moisture data within the monitoring area, a spatial distribution model of soil moisture in the monitoring area is constructed using a spatial interpolation algorithm.

[0038] S150: Determine the soil and water conservation status of the area to be monitored based on the spatial distribution model of soil moisture.

[0039] The following is a detailed explanation of each step in the above-mentioned IoT-based online monitoring method for soil and water conservation:

[0040] In step S110, satellite remote sensing images of vegetation, topographic slope data, and measured soil moisture data of the area to be monitored are acquired.

[0041] In this embodiment of the application, the aforementioned satellite remote sensing vegetation image is surface image data of the area to be monitored obtained through satellite remote sensing technology, which includes information such as the spatial distribution, growth status and type of vegetation cover.

[0042] In this embodiment of the application, the above-mentioned terrain slope data is data that characterizes the degree of surface inclination at each location within the area to be monitored, and is used to reflect the terrain undulation characteristics.

[0043] In this embodiment of the application, the aforementioned pre-deployed moisture sensor is an Internet of Things (IoT) sensing device that is pre-deployed in the area to be monitored for real-time collection of soil moisture content. It has a geographic coordinate positioning function and can provide continuous measured soil moisture data.

[0044] In this embodiment of the application, the above-mentioned measured soil moisture data are raw soil moisture content data directly collected by deployed moisture sensors, including sensor number, collection time, geographical coordinates and corresponding moisture value.

[0045] Specifically, the acquisition of satellite remote sensing vegetation images, topographic slope data, and measured soil moisture data of the area to be monitored can be achieved as follows: Using the IoT-based soil and water conservation monitoring platform for the area to be monitored, the existing moisture sensors within the area are queried, and the sensor number, geographic coordinates, and historical moisture data for the past year of each sensor are obtained; the location distribution of each existing moisture sensor within the area to be monitored is clarified to ensure the integrity of subsequent data coverage; based on the geographic coordinates of the existing moisture sensors and the area to be monitored, satellite remote sensing vegetation images of the area to be monitored are downloaded, and the images undergo radiometric and geometric correction preprocessing; digital elevation model data of the area to be monitored is obtained through a geospatial data platform, and topographic slope data for various locations within the area is extracted.

[0046] In step S120, the area to be monitored is divided into blocks based on satellite remote sensing vegetation images, and candidate monitoring points are determined based on the spatial distribution of moisture sensors already deployed in each vegetation block, differences in vegetation cover, and terrain slope data.

[0047] In this embodiment, the above-mentioned vegetation blocks are continuous areas with relatively consistent vegetation cover type and growth status, obtained by performing grayscale and edge detection processing on satellite remote sensing vegetation images.

[0048] For example, the above-mentioned segmentation of the area to be monitored based on satellite remote sensing vegetation imagery can be achieved as follows: the satellite remote sensing vegetation imagery is processed into grayscale to obtain a remote sensing vegetation grayscale image; edge detection is performed on the remote sensing vegetation grayscale imagery, and the area to be monitored is divided into multiple vegetation blocks based on the detected edge contours.

[0049] In one specific implementation of this application, dividing the area to be monitored into multiple vegetation blocks can achieve the following: grayscale processing of satellite remote sensing vegetation images to convert them into single-channel grayscale images. Highlighting the differences in light and dark vegetation cover; employing the Canny edge detection algorithm on grayscale images. Edge extraction is performed to identify abrupt boundaries of vegetation cover features (such as the boundary between forest and grassland, or between cultivated land and wasteland); based on the detected edge contours, the area to be monitored is divided into multiple continuous vegetation blocks. Within each vegetation block, the vegetation cover type and growth are relatively consistent, exhibiting spatial homogeneity.

[0050] In this embodiment of the application, the above-mentioned candidate monitoring points are supplementary monitoring points selected based on vegetation block characteristics, sensor spatial distribution and terrain slope data, used to fill the monitoring blind spots of the original sensors, so that the monitoring network coverage is more comprehensive and the distribution is more uniform.

[0051] For example, the above-mentioned determination of candidate monitoring points based on the spatial distribution of water sensors already deployed in each vegetation block, vegetation cover differences, and terrain slope data can be achieved as follows: obtaining the block area of ​​the vegetation block and the number of water sensors already deployed in the block, and determining the sensor distribution density of the vegetation block based on the number of water sensors already deployed and the block area; obtaining the maximum sensor distribution density in each vegetation block, and determining the target sensor density based on the maximum sensor distribution density; determining the number of candidate monitoring points in the corresponding vegetation block based on the target sensor density and the number of water sensors already deployed in the vegetation block; and determining the number of candidate monitoring points in the corresponding vegetation block based on the spatial distribution of the deployed water sensors, vegetation cover differences in the vegetation blocks, and terrain slope data.

[0052] The process of determining the number of candidate monitoring points in a corresponding vegetation block based on the spatial distribution of deployed moisture sensors, vegetation cover differences in vegetation blocks, and topographic slope data can be achieved as follows: The current vegetation block is uniformly divided into multiple vegetation grids of equal area. The center point of the vegetation grid without deployed moisture sensors is used as a candidate monitoring point. For each candidate point, the first preset number of target locations of deployed moisture sensors are determined, and the distribution balance of the candidate points is determined based on the distance between the candidate points and each target location. The absolute difference between the average grayscale value of the current vegetation block and its adjacent vegetation blocks is determined and recorded as the vegetation cover difference. The minimum distance between the candidate point and the edge of the current vegetation block is obtained. The location specificity of the candidate point is determined based on the minimum distance between the candidate point and the edge of the current vegetation block, the vegetation cover difference, and the topographic slope data of the vegetation grid where the candidate point is located. The selection priority of the candidate points is determined based on the distribution balance and location specificity, and the top number of candidate points are selected in descending order as candidate monitoring points for the current vegetation block.

[0053] In one specific implementation of this application embodiment, the process of determining candidate monitoring points can be implemented as follows: for any vegetation block Calculate its segmented area and the number of moisture sensors already installed inside. And calculate the sensor distribution density. The sensor distribution density Used to reflect vegetation segmentation The density of existing monitoring points within the area: Screening for the highest sensor distribution density across all vegetation patches. With this maximum sensor distribution density Use twice the target sensor density (this can be adjusted according to the actual scenario, but monitoring accuracy must be ensured) to calculate vegetation patches. Number of candidate monitoring points Divide the vegetation into sections The area is uniformly divided into vegetation grids of equal area (grid size is adaptively adjusted according to the area of ​​each grid block). Based on the geographic coordinates of the already deployed moisture sensors, vegetation grids already covered by sensors are excluded. The center points of the uncovered vegetation grids are used as candidate monitoring points, thus initially forming a set of candidate points. For any candidate point... Obtain distance to candidate points Determine the distance between the candidate point and each of the N1 nearest deployed moisture sensors. And calculate the average distance. Candidate points are determined using the following formula. Distribution balance:

[0054]

[0055] in, Candidate points The distribution balance of the candidate points is such that the smaller the value, the better the distribution balance of the candidate points. The more evenly the distribution of moisture sensors is compared to the surrounding area; For candidate points The number of the nearest existing moisture sensors selected in the surrounding area is set to 5 in this specific implementation, and can be adaptively adjusted based on the density of existing moisture sensors and the size of vegetation blocks. Candidate points The distance between the i-th already deployed moisture sensor; Candidate points The average distance between the sensor and the N1 pre-deployed moisture sensors.

[0056] Further, candidate points are calculated. With current vegetation patches Minimum distance from the edge Get the current vegetation block average grayscale The average grayscale value of the nearest neighboring vegetation patch. Candidate points are determined using the following formula. Special location characteristics:

[0057]

[0058] in, Candidate points Due to the special nature of its location, the larger the value, the better it reflects the candidate point. The more critical the position; Candidate points With current vegetation patches The minimum distance to the edge; the smaller the value, the better it reflects the candidate point. The closer to the current vegetation patch The more likely the boundary is to be located in the vegetation transition zone across the sub-segments, the more critical its location becomes; Divide the current vegetation into blocks The average gray level; Divide the current vegetation into blocks The average grayscale value of the nearest neighboring vegetation patch; Used to characterize the current vegetation patch The difference in vegetation cover between the nearest adjacent vegetation patches; the larger the value, the more significant the difference in vegetation type and growth across patches. In the above formula, candidate points... The closer to the current vegetation patch The greater the difference in grayscale value between the boundary and the nearest neighboring block, the better. The larger the value, the more likely it is to be a candidate point. The higher the likelihood of a site being located in a neighboring cross-regional area and the greater the vegetation difference between it and neighboring regions, the higher the likelihood of it being identified as a candidate site. This is the minimum value, and its purpose is to avoid the denominator being 0; Candidate points The steeper the slope of the vegetation grid, the more critical the location (steep slope areas have a higher risk of soil erosion). This is a normalization function used to... and Mapping to the (0,1) interval ensures that the two weights can be directly added together.

[0059] Candidate points are determined through the above process. After considering the distribution balance and location specificity, candidate points are determined using the following formula. Candidate priority:

[0060]

[0061] in, Candidate points The higher the priority of the candidate points, the more likely they are to be selected. The higher the priority of a site selected as a candidate monitoring point; Candidate points The distribution balance of the candidate points is such that the smaller the value, the better the distribution balance of the candidate points. The more evenly the distribution of moisture sensors is compared to the surrounding area; Candidate points Due to the special nature of its location, the larger the value, the better it reflects the candidate point. The more critical the position.

[0062] After determining the priority of each candidate point through a method, the top candidates are selected in descending order of priority. (The number of candidate points determined through the above process) are used as the current vegetation block. The candidate monitoring points.

[0063] In step S130, the water area boundary is constructed based on the extreme value data of the measured soil moisture data within the vegetation block, the vegetation gradient information of the vegetation blocks between the water area boundary is extracted, and the soil moisture estimation data of each candidate monitoring point is determined based on the water area boundary and the vegetation gradient information.

[0064] In this embodiment, the aforementioned moisture region boundary is a high moisture region boundary formed by constructing a convex hull for the locations of deployed moisture sensors corresponding to high moisture extreme value data, and a low moisture region boundary formed by constructing a convex hull for the locations of deployed moisture sensors corresponding to low moisture extreme value data, used to define the extreme value range of moisture distribution within the block.

[0065] For example, the above-mentioned construction of moisture region boundaries based on extreme value data of soil moisture measured data within vegetation blocks can be achieved as follows: sort the soil moisture measured data within the vegetation blocks in descending order; obtain the first second preset number of soil moisture measured data, and construct a convex hull based on the spatial position of the corresponding deployed moisture sensors in spatial order to form a high moisture region boundary; obtain the last second preset number of soil moisture measured data, and construct a convex hull based on the spatial position of the corresponding deployed moisture sensors in spatial order to form a low moisture region boundary.

[0066] In one specific implementation of this application embodiment, the process of constructing the water region boundary described above can be achieved as follows: for the current vegetation block Collect all soil moisture measurement data from deployed moisture sensors and sort them in descending order. Select the top M1 data points (i.e., the second preset number mentioned above, which can be adjusted based on the total number of deployed moisture sensors within the vegetation block, the block area, and spatial heterogeneity, ensuring that the selected extreme values ​​accurately represent the boundaries of high / low moisture regions). The corresponding deployed moisture sensors form a high moisture set H. Select the bottom M1 data points, and the corresponding deployed moisture sensors form a low moisture set L. Construct a convex hull for the geographic coordinates of the deployed moisture sensors in the high moisture set H in spatial order to form the boundary of the high moisture region. Construct a convex hull for the geographic coordinates of the deployed moisture sensors in the low moisture set L to form the boundary of the low moisture region.

[0067] In this embodiment of the application, the above-mentioned vegetation gradient information is a sequence of vegetation green channel values ​​extracted along the shortest connection path between the boundary of the high-moisture area and the boundary of the low-moisture area. It reflects the gradual change characteristics of vegetation growth along the path. The vegetation growth is positively correlated with the soil moisture distribution (the more abundant the moisture, the higher the vegetation green channel value).

[0068] For example, the above-mentioned extraction of vegetation gradient information between vegetation blocks at the boundaries of water-rich areas can be achieved as follows: determine the shortest connection path between the boundaries of high-water-rich areas and low-water-rich areas, and extract the green channel information of vegetation blocks from satellite remote sensing vegetation images; extract the values ​​of green channel information along the extension direction of the shortest connection path at preset intervals, calculate the numerical change of adjacent intervals, and obtain a vegetation gradient value sequence.

[0069] In one specific implementation of this application embodiment, the above-mentioned extraction of vegetation gradient information can be achieved by: determining the shortest connection path between the boundary of the high-moisture region and the boundary of the low-moisture region using a spatial distance algorithm. Extracting current vegetation patches from satellite remote sensing vegetation images. Corresponding image area For image area Green channel extraction is performed to obtain single-channel data containing only vegetation green channel information (green channels are most sensitive to vegetation moisture status); along the shortest connection path In the direction of extension, extract the values ​​of green channel data at preset intervals (which can be adjusted based on satellite remote sensing vegetation image resolution, shortest connection path length, and sensitivity to vegetation gradient changes; it is necessary to ensure that the extracted vegetation gradient values ​​can truly reflect the correlation characteristics between water and vegetation). Calculate the change in values ​​between two adjacent extraction points (i.e., the value of the later extraction point minus the value of the earlier extraction point), and arrange all gradient values ​​in the extraction order to form a vegetation gradient value sequence. The sequence of vegetation gradient values ​​is the extracted vegetation gradient information.

[0070] In this embodiment of the application, the soil moisture estimation data mentioned above is the soil moisture content data of the candidate monitoring point calculated based on the measured data of the high / low moisture boundary sensors near the candidate monitoring point and the vegetation gradient information, which is used to supplement the moisture monitoring data of the area where no sensors are deployed.

[0071] For example, the above-mentioned determination of soil moisture estimation data for each candidate monitoring point based on water region boundary and vegetation gradient information can be achieved as follows: For each candidate monitoring point, the nearest deployed water sensor to the candidate monitoring point is determined on the boundary of the high water region, and its corresponding measured soil moisture data is obtained, which is recorded as the high water boundary value; the nearest deployed water sensor to the candidate monitoring point is determined on the boundary of the low water region, and its corresponding measured soil moisture data is obtained, which is recorded as the low water boundary value; the projection point of the candidate monitoring point on the shortest connection path is determined, and the target vegetation gradient value corresponding to the projection point is extracted from the vegetation gradient value sequence; the soil moisture estimation data of the candidate monitoring point is determined based on the high water boundary value, the low water boundary value, and the target vegetation gradient value.

[0072] Specifically, the soil moisture estimation data for the candidate monitoring points determined based on the high moisture boundary value, low moisture boundary value, and target vegetation gradient value can be achieved as follows: the moisture change range is determined based on the high moisture boundary value and the low moisture boundary value; the proportion of moisture change at the candidate monitoring points is determined based on the cumulative value of vegetation gradient values ​​from the deployed moisture sensors to the projection point corresponding to the low moisture boundary value and the sum of vegetation gradient values ​​on the shortest connection path; and the soil moisture estimation data for the candidate monitoring points is determined based on the low moisture boundary value, the moisture change range, and the proportion of moisture change.

[0073] In one specific implementation of this application embodiment, soil moisture estimation data for candidate monitoring points are determined by the following method: for any candidate monitoring point Based on geographic coordinates, the nearest deployed moisture sensor (belonging to the high moisture set H) on the boundary of the high moisture area is determined, and the measured soil moisture data of this sensor is obtained and recorded as the high moisture boundary value. Similarly, identify the nearest deployed moisture sensor (belonging to the low-moisture set L) on the boundary of the low-moisture area, obtain its measured soil moisture data, and record it as the low-moisture boundary value. ; Determine candidate monitoring points using spatial projection algorithms In the shortest connection path Projection point on According to the projection point In the shortest connection path The location of the monitoring point is determined by matching the corresponding cumulative vegetation gradient value from the vegetation gradient value sequence; the candidate monitoring point is determined by the following formula. Soil moisture estimation data:

[0074]

[0075] in, Candidate monitoring points Soil moisture estimation data; This is the low moisture boundary value; This represents the high moisture boundary value. The above-mentioned range of water variation reflects the maximum range of difference in water distribution within the current vegetation block and is the basic range for estimating the water value of the candidate points. Let i be the i-th vegetation gradient value in the vegetation gradient value sequence; Starting from the location of the existing moisture sensor corresponding to the low moisture set L, and proceeding to the candidate monitoring point In the shortest connection path Projection point on The cumulative value of all vegetation gradient values ​​up to this point reflects the selected monitoring points. The degree to which it is close to areas with high moisture content; For the shortest connection path The sum of all vegetation gradient values ​​reflects the total change in vegetation gradient between high and low moisture regions. The percentage of the aforementioned moisture change, i.e., the proportion of the cumulative gradient value to the total gradient value, is used to reflect the selected monitoring points. The closer the relative position of the monitoring point is to 1 along the gradient path from low to high moisture, the better it reflects the potential monitoring point. The closer to areas with high moisture content.

[0076] In step S140, based on the measured soil moisture data and estimated soil moisture data in the area to be monitored, a spatial distribution model of soil moisture in the area to be monitored is constructed using a spatial interpolation algorithm.

[0077] In this embodiment of the application, the above-mentioned spatial distribution model of soil moisture is a visualization model constructed based on soil moisture data of the entire area to be monitored. It can intuitively present the spatial distribution pattern of soil moisture content in the area to be monitored, and provide data support for the determination of soil and water conservation status.

[0078] For example, the above-mentioned construction of a spatial distribution model of soil moisture in the area to be monitored based on measured soil moisture data and estimated soil moisture data in the area to be monitored using a spatial interpolation algorithm can achieve the following: the area to be monitored is evenly divided into multiple monitoring grids of equal area; the monitoring grids corresponding to the measured soil moisture data or estimated soil moisture data are marked to obtain the corresponding value monitoring grids, and the remaining monitoring grids in the area to be monitored after removing the value monitoring grids are recorded as valueless monitoring grids; for each valueless monitoring grid, a third preset number of reference value monitoring grids that are closest to the valueless monitoring grid are determined, and the soil moisture data of the valueless monitoring grids is determined using a spatial interpolation algorithm based on the measured soil moisture data or estimated soil moisture data of the reference value monitoring grids; a spatial distribution model of soil moisture is constructed based on the soil moisture data of all monitoring grids.

[0079] In one specific implementation of this application, the above-mentioned spatial distribution model of soil moisture can be constructed by the following method: The area to be monitored is uniformly divided into multiple monitoring grids of equal area, where the size of the monitoring grid is set to 100m × 100m (adjustable according to monitoring accuracy requirements); the geographical coordinates of the deployed moisture sensors and candidate monitoring points are spatially correlated with the monitoring grids to determine the monitoring grid to which each deployed moisture sensor and candidate monitoring point belongs; the measured soil moisture data of all deployed moisture sensors are extracted and assigned to their respective monitoring grids; the estimated soil moisture data of all candidate monitoring points are extracted and assigned to their respective monitoring grids to obtain all valuable monitoring grids; the valuable monitoring grids are removed from all monitoring grids, and the remaining monitoring grids are recorded as non-valued monitoring grids; for any non-valued monitoring grid, the nearest n (i.e., the third preset number mentioned above, which can be adjusted based on interpolation accuracy and computational efficiency) reference valuable monitoring grids are selected using a spatial distance algorithm to form a reference valuable monitoring grid set I; the inverse distance weighting spatial interpolation algorithm is used. Interpolation Algorithm (IDW) identifies soil moisture data in monitoring grids with no values:

[0080]

[0081] in, Soil moisture data for the currently unmonitored grid is used to complete the moisture data for the entire grid in the area to be monitored. This represents the distance between the current unvalued monitoring grid and the i-th reference monitoring grid with a value. The smaller this value, the greater the contribution of the i-th reference monitoring grid with a value to the soil moisture data of the current unvalued monitoring grid. That is, the weight used to characterize the i-th reference value monitoring grid in the process of calculating the soil moisture data of the current valueless monitoring grid; It is the sum of the products of the weight of each reference value monitoring grid and its corresponding measured soil moisture data or estimated soil moisture data, reflecting the dominant role of the nearby reference value monitoring grid in the estimation results; This is the weighted sum of all reference value monitoring grids in the reference value monitoring grid set I, and its purpose is to ensure that the estimation result falls within a reasonable range.

[0082] After determining the soil moisture data of all monitoring grids without values ​​using the above method, the soil moisture data of all monitoring grids in the area to be monitored are integrated to construct a spatial distribution model of soil moisture.

[0083] In step S150, the soil and water conservation status of the area to be monitored is determined based on the spatial distribution model of soil moisture.

[0084] Furthermore, the soil and water conservation status of the monitored area can be determined based on the spatial distribution model of soil moisture. Specifically, this is achieved by extracting soil moisture data from all monitoring grids in the constructed spatial distribution model of soil moisture; and calculating the average water content W and standard deviation of the monitored area. The soil and water conservation status of the area to be monitored is determined according to the following rules: If If it is determined to be an arid area, If it is determined to be a normal area, The area was identified as excessively wet; a zoning map was created using GIS, and the arid, normal, and excessively wet areas were marked with different colors.

[0085] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] Correspondingly, embodiments of this application also provide an IoT-based online monitoring system for soil and water conservation, which is used to implement the steps of the IoT-based online monitoring method for soil and water conservation, as described in reference. Figure 2 As shown, the IoT-based online water and soil conservation monitoring system 200 may include an acquisition module 210, a processing module 220, and a monitoring module 230, wherein:

[0087] The acquisition module is used to acquire satellite remote sensing vegetation images, topographic slope data, and measured soil moisture data collected by moisture sensors already deployed in the area to be monitored.

[0088] The processing module is used to divide the area to be monitored into blocks based on satellite remote sensing vegetation images, and to determine the candidate monitoring points based on the spatial distribution of water sensors already deployed in each vegetation block, differences in vegetation cover, and topographic slope data.

[0089] The processing module is also used to construct water area boundaries based on extreme data of measured soil moisture data within vegetation blocks, extract vegetation gradient information of vegetation blocks between water area boundaries, and determine soil moisture estimation data for each candidate monitoring point based on water area boundaries and vegetation gradient information.

[0090] The processing module is also used to construct a spatial distribution model of soil moisture in the area to be monitored by using a spatial interpolation algorithm based on measured soil moisture data and estimated soil moisture data in the area to be monitored.

[0091] The monitoring module is used to determine the soil and water conservation status of the area to be monitored based on the spatial distribution model of soil moisture.

[0092] The specific implementation details of the IoT-based online monitoring system for soil and water conservation have been explained in detail in the corresponding section of the IoT-based online monitoring method for soil and water conservation, and will not be repeated here.

[0093] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A soil and water conservation online monitoring method based on the Internet of Things, characterized in that, The method includes: Acquire satellite remote sensing images of vegetation, topographic slope data, and measured soil moisture data collected by moisture sensors already deployed in the area to be monitored; Based on the satellite remote sensing vegetation image, the area to be monitored is divided into blocks, and the candidate monitoring points are determined based on the spatial distribution of the deployed moisture sensors, the difference in vegetation cover, and the terrain slope data within each vegetation block. Based on the extreme value data of the measured soil moisture data within the vegetation block, a moisture region boundary is constructed, vegetation gradient information of the vegetation block between the moisture region boundaries is extracted, and soil moisture estimation data of each candidate monitoring point is determined based on the moisture region boundary and the vegetation gradient information. Based on the measured soil moisture data and the estimated soil moisture data within the area to be monitored, a spatial distribution model of soil moisture in the area to be monitored is constructed using a spatial interpolation algorithm. The soil and water conservation status of the monitored area is determined based on the aforementioned spatial distribution model of soil moisture. The segmentation of the area to be monitored based on the satellite remote sensing vegetation image includes: The satellite remote sensing vegetation image is processed to obtain a remote sensing vegetation grayscale image; Edge detection is performed on the remote-sensed vegetation grayscale image, and the monitored area is divided into multiple vegetation blocks based on the detected edge contours. The process of determining candidate monitoring points based on the spatial distribution of the deployed moisture sensors within the vegetation blocks, differences in vegetation cover, and the terrain slope data includes: The area of ​​the vegetation block and the number of moisture sensors already deployed within the block are obtained, and the sensor distribution density of the vegetation block is determined based on the number of moisture sensors already deployed and the area of ​​the block. Obtain the maximum sensor distribution density in each of the vegetation blocks, and determine the target sensor density based on the maximum sensor distribution density; The number of candidate monitoring points in the vegetation block is determined based on the target sensor density and the number of moisture sensors already deployed in the vegetation block. The current vegetation is evenly divided into multiple vegetation grids of equal area, and the center point of the vegetation grid without moisture sensors is used as the candidate point of the monitoring point. For any candidate point , the distance between the candidate point and the nearest N1 locations of the installed moisture sensors is determined , and the average distance is calculated , and the distribution balance of the candidate point is determined by the following formula: in, Candidate points The distribution balance; For candidate points The number of the nearest existing moisture sensors selected from the surrounding area; Candidate points The distance between the i-th already deployed moisture sensor; Candidate points The average distance between the sensor and the N1 pre-deployed moisture sensors; Get the current vegetation block average grayscale The average grayscale value of the nearest neighboring vegetation patch. Candidate points are determined using the following formula. Special location characteristics: in, Candidate points The special nature of its location; Candidate points With current vegetation patches Minimum distance from the edge; Divide the current vegetation into blocks The average grayscale value; Divide the current vegetation into blocks The average grayscale value of the nearest neighboring vegetation patch; This is the minimum value, and its purpose is to avoid the denominator being 0; Candidate points The slope of the terrain within the vegetation grid, This is a normalization function used to... and Map to the interval (0,1); Candidate points are determined using the following formula. Candidate priority: in, Candidate points The priority of candidates; Based on the distribution balance and the location particularity, the candidate points are selected according to their priority. The first number of candidate points are selected in descending order as the candidate monitoring points for the current vegetation block.

2. The online water and soil conservation monitoring method based on the Internet of Things according to claim 1, characterized in that, The construction of water region boundaries based on extreme value data of measured soil moisture within the vegetation blocks includes: The measured soil moisture data within the vegetation blocks are sorted in descending order. Obtain the second preset number of soil moisture measured data points that are sorted first, and construct a convex hull based on the spatial location of the corresponding deployed moisture sensors in spatial order to form the boundary of the high moisture region. The second preset number of soil moisture measurement data are obtained in order, and a convex hull is constructed based on the spatial location of the corresponding deployed moisture sensors in spatial order to form the boundary of the low moisture region.

3. The online water and soil conservation monitoring method based on the Internet of Things according to claim 2, characterized in that, The extraction of vegetation gradient information of the vegetation blocks between the boundaries of the water region includes: Determine the shortest connection path between the boundary of the high-moisture area and the boundary of the low-moisture area, and extract the green channel information of the vegetation block from the satellite remote sensing vegetation image; The values ​​of the green channel information are extracted along the extension direction of the shortest connection path at preset intervals, and the value changes of adjacent intervals are calculated to obtain a vegetation gradient value sequence.

4. The online water and soil conservation monitoring method based on the Internet of Things according to claim 3, characterized in that, The process of determining soil moisture estimation data for each of the candidate monitoring points based on the water zone boundary and the vegetation gradient information includes: For each candidate monitoring point, the nearest deployed moisture sensor on the boundary of the high moisture area is determined and its corresponding measured soil moisture data is obtained, which is recorded as the high moisture boundary value. On the boundary of the low moisture area, determine the deployed moisture sensor that is closest to the candidate monitoring point and acquire its corresponding measured soil moisture data, which is recorded as the low moisture boundary value. Determine the projection point of the candidate monitoring point on the shortest connection path, and extract the target vegetation gradient value corresponding to the projection point from the vegetation gradient value sequence; The soil moisture estimation data for the candidate monitoring points are determined based on the high moisture boundary value, the low moisture boundary value, and the target vegetation gradient value.

5. The online water and soil conservation monitoring method based on the Internet of Things according to claim 4, characterized in that, The process of determining the soil moisture estimation data for the candidate monitoring points based on the high moisture boundary value, the low moisture boundary value, and the target vegetation gradient value includes: The magnitude of moisture variation is determined based on the high moisture boundary value and the low moisture boundary value. Based on the cumulative vegetation gradient values ​​from the deployed moisture sensors to the projection point corresponding to the low moisture boundary value and the sum of vegetation gradient values ​​on the shortest connection path, the percentage of moisture change at the candidate monitoring point is determined. The estimated soil moisture data for the candidate monitoring points are determined based on the low moisture boundary value, the magnitude of moisture change, and the percentage of moisture change.

6. The online water and soil conservation monitoring method based on the Internet of Things according to claim 1, characterized in that, The step of constructing a spatial distribution model of soil moisture in the monitored area based on the measured soil moisture data and the estimated soil moisture data within the monitored area, using a spatial interpolation algorithm, includes: The area to be monitored is evenly divided into multiple monitoring grids of equal area; The monitoring grids corresponding to the measured soil moisture data or the estimated soil moisture data are marked to obtain the corresponding value monitoring grids, and the remaining monitoring grids in the area to be monitored after removing the value monitoring grids are recorded as valueless monitoring grids; For each monitoring grid without a value, a third preset number of reference monitoring grids with values ​​that are closest to the monitoring grid without a value are determined, and a spatial interpolation algorithm is used to determine the soil moisture data of the monitoring grid without a value based on the measured soil moisture data or the estimated soil moisture data of the reference monitoring grids with values. The spatial distribution model of soil moisture is constructed based on soil moisture data from all the monitoring grids.

7. An Internet of Things-based online monitoring system for soil and water conservation, characterized in that, This system is used to implement the steps of the Internet of Things-based online monitoring method for soil and water conservation as described in any one of claims 1-6, the system comprising: The acquisition module is used to acquire satellite remote sensing vegetation images, topographic slope data, and measured soil moisture data collected by moisture sensors already deployed in the area to be monitored. The processing module is used to divide the area to be monitored into blocks based on the satellite remote sensing vegetation image, and to determine the candidate monitoring points based on the spatial distribution of the deployed moisture sensors, the difference in vegetation cover and the terrain slope data within each vegetation block. The processing module is further configured to construct a water area boundary based on the extreme value data of the measured soil moisture data within the vegetation block, extract vegetation gradient information of the vegetation block between the water area boundaries, and determine the estimated soil moisture data of each candidate monitoring point based on the water area boundaries and the vegetation gradient information. The processing module is also used to construct a spatial distribution model of soil moisture in the area to be monitored by means of a spatial interpolation algorithm based on the measured soil moisture data and the estimated soil moisture data in the area to be monitored. The monitoring module is used to determine the soil and water conservation status of the area to be monitored based on the spatial distribution model of soil moisture.

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