Soil sampling system and method

Abstract

The invention provides a soil sampling system and method. The method comprises the following steps: collecting the water content of each sensing node in a target wetland; determining a sudden change area of soil moisture in the target wetland according to the difference characteristics of the moisture content among the sensing nodes and the terrain gradient of the target wetland, and further determining a dynamic offset vector of an initial sampling point in the target wetland; determining layering characteristics of soil moisture in the target wetland, and determining fluctuation characteristics of soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses according to the layering characteristics and difference characteristics of the moisture content among the sensing nodes; and according to the dynamic offset vector and the fluctuation characteristic of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses, determining a credible interval of a sampling route when soil sampling is performed on the target wetland, and further controlling a sampler to sample the target wetland based on the credible interval of the sampling route. By adopting the scheme, the sampling point can be dynamically adjusted according to the real-time change condition of the soil moisture.

Description

Technical Field

[0001] This application relates to the field of sampling technology. More specifically, this application relates to a soil sampling system and method. Background Art

[0002] Sampling is a process of selecting a part of individuals or samples from a population to represent the entire population for analysis and research. Sampling has a wide range of applications in many fields such as scientific research, quality control, environmental monitoring, market research, and medical testing.

[0003] Soil moisture is one of the key parameters in fields such as agricultural production, ecological environment monitoring, and geological exploration. Accurately obtaining soil moisture information is of great significance for optimizing irrigation, increasing crop yields, preventing soil erosion, and evaluating groundwater recharge. Traditional soil sampling methods usually rely on random sampling at fixed sampling points. Although this method is simple and easy to implement, it often fails to comprehensively reflect the spatial variability and dynamic change characteristics of soil moisture. Especially in areas with complex terrain and uneven soil texture, fixed sampling points may not be able to capture the mutation areas and stratification characteristics of soil moisture, resulting in insufficient representativeness and accuracy of sampling results. Therefore, how to dynamically adjust sampling points according to the real-time changes of soil moisture has become a problem faced by the industry. Summary of the Invention

[0004] This application provides a soil sampling system and method that can dynamically adjust sampling points according to the real-time changes of soil moisture.

[0005] In a first aspect, this application provides a soil sampling method, including the following steps: Arrange sensor nodes in the target wetland, and collect the water content of each sensing node in the target wetland based on the water sensor; Determine the mutation area of soil moisture in the target wetland according to the difference characteristics of water content between each sensing node and the terrain slope of the target wetland, and dynamically adjust the preset initial sampling points in the target wetland based on the change amplitude of the water content in the mutation area, thereby obtaining the dynamic offset vector of the initial sampling points in the target wetland; Determine the stratification characteristics of soil moisture in the target wetland, and perform differential coupling on the stratification characteristics and the difference characteristics of water content between each sensing node, thereby obtaining the fluctuation characteristics of the soil unsaturated hydraulic conductivity when soil moisture diffuses in the target wetland; Determine the confidence interval of the sampling route for soil sampling in the target wetland according to the dynamic offset vector and the fluctuation characteristics of the soil unsaturated hydraulic conductivity when soil moisture diffuses in the target wetland, and then control the sampler to sample the target wetland based on the confidence interval of the sampling route.

[0006] In some embodiments, determining the mutation region of soil moisture in the target wetland according to the difference characteristics of water content between each sensing node and the terrain slope of the target wetland specifically includes: Determining the change trend of soil moisture in the target wetland according to the difference characteristics of water content between each sensing node and the terrain slope of the target wetland; Determining the soil texture distribution of the target wetland; Determining the retention difference amount of water between adjacent soil textures in the target wetland according to the soil texture distribution; Determining the mutation region of soil moisture in the target wetland according to the change trend of the soil moisture and all the retention difference amounts.

[0007] In some embodiments, determining the change trend of soil moisture in the target wetland according to the difference characteristics of water content between each sensing node and the terrain slope of the target wetland specifically includes: Determining the difference characteristics of water content between each sensing node; Obtaining the terrain slope of the target wetland; Determining the migration state of soil moisture in the target wetland according to the difference characteristics; Determining the change trend of soil moisture in the target wetland through the water migration state and the terrain slope.

[0008] In some embodiments, dynamically adjusting the preset initial sampling points in the target wetland according to the change amplitude of water content in the mutation region, and further obtaining the dynamic offset vector of the initial sampling points in the target wetland specifically includes: Determining the change amplitude of water content in the mutation region; Obtaining the preset initial sampling points in the target wetland; Determining multiple dynamic points of water content in the target wetland according to the change amplitude; Adjusting the initial sampling points through all the dynamic points to obtain the dynamic offset vector of the initial sampling points in the target wetland.

[0009] In some embodiments, determining the stratification characteristics of soil moisture in the target wetland specifically includes: Obtaining the historical water content data of each soil layer in the target wetland; Determining the stratification characteristics of soil moisture in the target wetland according to the historical water content data.

[0010] In some embodiments, performing difference coupling on the stratification characteristics and the difference characteristics of water content between each sensing node, and further obtaining the fluctuation characteristics of the unsaturated hydraulic conductivity of the soil when the soil moisture diffuses in the target wetland specifically includes: Perform correlation analysis on the difference characteristics of water content between the layered features and each sensing node to obtain the aggregation area of soil moisture in the target wetland; Determine the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses according to the aggregation area of the soil moisture.

[0011] In some embodiments, determining the confidence interval of the sampling route for soil sampling in the target wetland according to the dynamic offset vector and the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses specifically includes: Determine multiple retention areas of soil moisture in the target wetland according to the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses; Determine the confidence interval of the sampling route for soil sampling in the target wetland according to all the retention areas and the dynamic offset vector. In a second aspect, the present application provides a soil sampling system, including: A collection module, configured to collect the water content of each sensing node in the target wetland based on a moisture sensor after arranging sensor nodes in the target wetland; A processing module, configured to determine the mutation area of soil moisture in the target wetland according to the difference characteristics of water content between each sensing node and the terrain slope of the target wetland, and dynamically adjust the preset initial sampling points in the target wetland according to the change range of the water content in the mutation area, so as to obtain the dynamic offset vector of the initial sampling points in the target wetland; The processing module is further configured to determine the layered characteristics of soil moisture in the target wetland, and perform difference coupling on the layered characteristics and the difference characteristics of water content between each sensing node, so as to obtain the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses; An execution module, configured to determine the confidence interval of the sampling route for soil sampling in the target wetland according to the dynamic offset vector and the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses, and then control a sampler to sample the target wetland based on the confidence interval of the sampling route.

[0012] In a third aspect, the present application provides a computer device, the computer device includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned soil sampling method.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned soil sampling method is implemented.

[0014] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In the soil sampling system and method provided in this application, first, sensor nodes are arranged in the target wetland, and the water content of each sensor node in the target wetland is collected based on the water sensors; according to the difference characteristics of the water content between each sensor node and the terrain slope of the target wetland, the mutation area of the soil moisture in the target wetland is determined, and the initial sampling points preset in the target wetland are dynamically adjusted according to the change range of the water content in the mutation area, so as to obtain the dynamic offset vector of the initial sampling points in the target wetland; the stratification characteristics of the soil moisture in the target wetland are determined, and the stratification characteristics and the difference characteristics of the water content between each sensor node are differentially coupled, so as to obtain the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture diffuses in the target wetland; according to the dynamic offset vector and the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture diffuses in the target wetland, the credible interval of the sampling route for soil sampling in the target wetland is determined, and then the sampler is controlled to sample the target wetland based on the credible interval of the sampling route.

[0015] It can be seen that in the process of soil sampling in this application, first, by analyzing the difference characteristics of the water content and the terrain slope, the mutation area of the soil moisture can be effectively identified, and the sampling points can be dynamically adjusted according to the change range of the water content. This optimization strategy can capture the key change areas of the soil moisture distribution, avoid missing important information, and improve the scientificity and comprehensiveness of sampling; secondly, by analyzing the stratification characteristics of the soil moisture and the difference characteristics of the water content, the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture diffuses can be revealed. The fluctuation characteristics of the soil unsaturated hydraulic conductivity represent the characteristics of the fluctuation degree of the soil unsaturated hydraulic conductivity when the soil moisture diffuses in the target wetland. This coupling analysis method helps to deeply understand the migration law of the soil moisture and provides a scientific basis for the water management of the wetland ecosystem; then, according to the dynamic offset vector and the fluctuation characteristics of the soil moisture diffusion soil unsaturated hydraulic conductivity, the credible interval of the sampling route can be determined. This method not only ensures the rationality of the sampling route, but also can effectively control the operation of the sampler, avoid repeated sampling or missing key areas, thereby improving the sampling efficiency and reducing resource consumption; finally, the sampler is controlled to sample the target wetland based on the credible interval of the sampling route. By adopting the above scheme, the sampling points can be dynamically adjusted according to the real-time change of the soil moisture. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is an exemplary flowchart of a soil sampling method according to some embodiments of the present application; Figure 2 is an exemplary flowchart of determining a dynamic offset vector according to some embodiments of the present application; Figure 3is a structural diagram of the soil layer in the target wetland shown in some embodiments of the present application; Figure 4 is a schematic structural diagram of the soil sampling system shown in some embodiments of the present application; Figure 5 is a schematic structural diagram of a computer device for implementing the soil sampling method shown in some embodiments of the present application. Detailed implementation manners To better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0017] Reference Figure 1 , which is an exemplary flowchart of the soil sampling method shown in some embodiments of the present application. The soil sampling method 100 mainly includes the following steps: In step 101, sensor nodes are arranged in the target wetland, and the water content of each sensing node in the target wetland is collected based on the water sensors.

[0018] In specific implementation, after arranging multiple sensing nodes in the target wetland, the water content of each sensing node in the target wetland is collected through the water sensors. Among them, through the optimization algorithms in the prior art (such as genetic algorithms, particle swarm optimization) combined with the historical water content data analysis of the target wetland, each point that can represent the water content in the target wetland is analyzed, and each analyzed point is used as a sensing node, and the water sensors are corrected for zero-point offset by the soil type, temperature, salinity, etc. in the target wetland, that is: under the condition of no load or no stimulus, record the output signal of the sensor in the environment of the target wetland, and convert the value obtained from the output signal as the zero-point offset, which is used to correct the error of the water sensor when there is no input, so as to eliminate the influence of the soil type, temperature, salinity in the target wetland on the data collected by the water sensor; in other embodiments, other methods can also be used for collection, which is not limited here.

[0019] In step 102, according to the difference characteristics of the water content between each sensing node and the terrain slope of the target wetland, the mutation area of the soil moisture in the target wetland is determined, and the preset initial sampling points in the target wetland are dynamically adjusted according to the change range of the water content in the mutation area, so as to obtain the dynamic offset vector of the initial sampling points in the target wetland.

[0020] In some embodiments, determining the mutation area of the soil moisture in the target wetland according to the difference characteristics of the water content between each sensing node and the terrain slope of the target wetland can be implemented by the following steps: Determine the change trend of the soil moisture in the target wetland according to the difference characteristics of the water content between each sensing node and the terrain slope of the target wetland; Determine the soil texture distribution of the target wetland; Determine the amount of water retention difference between adjacent soil textures in the target wetland according to the soil texture distribution; Determine the mutation area of soil moisture in the target wetland according to the change trend of the soil moisture and all the water retention difference amounts.

[0021] In addition, in some embodiments, the change trend of soil moisture in the target wetland can be determined according to the difference characteristics of water content between each sensing node and the terrain slope of the target wetland by the following steps: Determine the difference characteristics of water content between each sensing node; Obtain the terrain slope of the target wetland; Determine the migration state of soil moisture in the target wetland according to the difference characteristics; Determine the change trend of soil moisture in the target wetland through the moisture migration state and the terrain slope.

[0022] When specifically implemented, the difference characteristics of water content between each sensing node can be determined in the following manner, that is: arrange all the water contents according to the positions of the corresponding sensing nodes in the target wetland to obtain a water content sequence, select a group of adjacent water contents in the water content sequence as the selected adjacent water contents, subtract the first water content from the second water content in the selected adjacent water contents, and use the obtained value as the difference amount between the selected adjacent water contents. Continue to determine the difference amounts between each group of adjacent water contents in the water content sequence, and use all the difference amounts as the difference characteristics between each water content, where the difference characteristics represent the characteristics of the difference degree of water content between each sensing node in the target wetland, and one difference value corresponds to two sensing nodes; the terrain slope of the target wetland can be obtained in the following manner, that is: measure the three-dimensional coordinate information of the target wetland by the slope measurement method in the prior art (such as GPS measurement method, lidar), where the three-dimensional coordinate information represents the three-dimensional coordinates of multiple detection points in the target wetland, and calculate the terrain slope of the target wetland through the three-dimensional coordinate information, where the terrain slope represents the slope of the terrain in the target wetland, and the terrain slope includes the slopes of all positions in the target wetland; in other embodiments, it can also be determined by other methods, which are not limited here.

[0023] In specific implementation, the migration state of soil moisture in the target wetland can be determined according to the difference features in the following way: judge the difference value in the difference features, that is, select a difference value in the difference features as the selected difference value. If the selected difference value is positive, define the moisture migration direction between the two sensing nodes corresponding to the selected difference value as the negative direction, that is, the moisture moves from the second sensing node to the first sensing node. If the selected difference value is negative, define the moisture migration direction between the two sensing nodes corresponding to the selected difference value as the positive direction, that is, the moisture moves from the first sensing node to the second sensing node. Then continue to determine the moisture migration direction between the two sensing nodes corresponding to the remaining difference values in the difference features, and take all the moisture migration directions as the migration state of soil moisture in the target wetland, where the migration state represents the state of moisture movement in the target soil; in other embodiments, it can also be determined by other methods, which are not limited here.

[0024] In specific implementation, the change trend of soil moisture in the target wetland can be determined according to the moisture migration state and the terrain slope in the following way: use machine learning algorithms (such as random forest, neural network) combined with the historical water content data in the target wetland to establish a prediction model for the change trend of soil moisture. The input features of this model include the migration state and the slope, and the output feature is the change trend of soil moisture. Input the moisture migration state and the terrain slope as input features into this change trend prediction model, and the change trend prediction model outputs the change trend of soil moisture in the target wetland; in other embodiments, it can also be determined by other methods, which are not limited here.

[0025] It should be noted that the change trend of soil moisture in this application represents the trend of the change of soil moisture in the target wetland. The change trend of soil moisture includes the flow trend of soil moisture, that is, the flow direction, which can be used to judge the change of soil moisture in the target wetland, so as to select a suitable sampling point from the target wetland.

[0026] In specific implementation, the soil texture distribution of the target wetland can be determined in the following way: obtain each soil texture and its location in the target wetland from the database of the corresponding monitoring department of the target wetland, and use the location distribution of each soil texture in the target wetland as the soil texture distribution of the target wetland. Among them, the soil textures include sandy soil, loam, clay, etc., and the soil texture distribution represents the distribution of each soil texture in the target wetland; determining the water retention difference amount between adjacent soil textures in the target wetland according to the soil texture distribution can be achieved in the following way: select a soil texture as the selected soil texture, extract the historical water content of each soil texture every day in a month from the database of the corresponding detection department of the target wetland, and use the average value of all historical water contents of each soil texture in a month as the water content threshold corresponding to the soil texture. Among them, the water content threshold represents the parameter value of the average water content of the soil geology. Select a group of adjacent soil geologies in the soil texture distribution as the selected adjacent soil geologies, subtract the water content threshold corresponding to the second soil geology from the water content threshold corresponding to the first soil geology in the selected adjacent soil geologies, and use the obtained value as the water retention difference amount between the selected adjacent soil textures. Continue to determine the water retention difference amounts between the remaining adjacent soil geologies. Among them, the retention difference amount represents the parameter value of the difference degree of the water retention situation between adjacent soil geologies; in other embodiments, it can also be determined in other ways, which are not limited here.

[0027] In specific implementation, determining the soil water mutation area in the target wetland according to the change trend of the soil water and all the retention difference amounts can be achieved in the following way: traverse and analyze the flow directions in the change trend of the soil water. If more than two flow directions point to a location, then take this location as the soil water mutation location. Among them, the mutation location represents the location where the soil water flows in multiple directions. Thus, multiple multi-source areas can be judged through the above method. Divide the maximum retention difference amount by the minimum retention difference amount, multiply the obtained value by the entropy of all the retention difference amounts, take the obtained value as a radius, draw a circle with this radius centered on each mutation location, and take the area included by the obtained circles in the target wetland as the soil water mutation area in the target wetland; in other embodiments, it can also be determined in other ways, which are not limited here.

[0028] It should be noted that the soil water mutation area in this application represents the area where the water content of the soil water in the target wetland suddenly changes during the flowing process; it can be used to judge the water in the target wetland and facilitate adjusting the sampling points of the target wetland.

[0029] In some embodiments, refer to Figure 2As shown, this figure is an exemplary flowchart for determining a dynamic offset vector in some embodiments of the present application. In this embodiment, the preset initial sampling points in the target wetland are dynamically adjusted according to the change range of the water content in the mutation area, and then the dynamic offset vector of the initial sampling points in the target wetland can be achieved by the following steps: First, in step 1021, determine the change range of the water content in the mutation area; Secondly, in step 1022, obtain the preset initial sampling points in the target wetland; Furthermore, in step 1023, determine multiple dynamic points of the water content in the target wetland according to the change range; Finally, in step 1024, adjust the initial sampling points through all the dynamic points to obtain the dynamic offset vector of the initial sampling points in the target wetland.

[0030] Specifically, the change range of the water content in the mutation area can be determined in the following way, that is: monitor the water content of each area in the mutation area, subtract the minimum water content from the maximum water content in each area, and take the obtained values as the change range of the water content in the mutation area. Among them, each value in the change range represents the parameter value of the change degree of the water content in the mutation area; obtain the preset initial sampling points in the target wetland from the database of the corresponding detection department of the target wetland; in other embodiments, other methods can also be used to determine, which are not limited here.

[0031] Specifically, multiple dynamic points of the water content in the target wetland can be determined according to the change range in the following way, that is: calculate the average value of all values in the change range, extract each value greater than the average value from the change range, and take the center points of the corresponding areas of each value as multiple dynamic points of the water content in the target wetland. Among them, the dynamic points represent the points with large fluctuations in the water content in the target wetland; adjusting the initial sampling points through all the dynamic points to obtain the dynamic offset vector of the initial sampling points in the target wetland can be achieved in the following way, that is: connect each adjacent dynamic point among all the dynamic points, connect the center point of the connected area with the initial sampling point, and take the horizontal distance and vertical distance between the center point of the connected line and the initial sampling point as the dynamic offset vector of the initial sampling points in the target wetland; in other embodiments, other methods can also be used to determine, which are not limited here.

[0032] It should be noted that the dynamic offset vector in the present application represents the vector of the offset degree when the initial sampling points in the target wetland are dynamically adjusted, and can be used for sampling the soil in the target wetland, so as to make the sampled samples better represent the soil conditions in the target wetland.

[0033] In step 103, determine the layered characteristics of soil moisture in the target wetland, perform differential coupling on the layered characteristics and the difference characteristics of water content between each sensing node, and then obtain the fluctuation characteristics of the soil unsaturated hydraulic conductivity when soil moisture diffuses in the target wetland.

[0034] In some embodiments, the determination of the layered characteristics of soil moisture in the target wetland can be achieved by the following steps: Obtain the historical water content data of each soil layer in the target wetland; Determine the layered characteristics of soil moisture in the target wetland according to the historical water content data.

[0035] Specifically, obtain the historical water content data of each soil layer in the target wetland from the database of the corresponding detection department of the target wetland. Among them, the historical water content data represents the water content of each layer of the target wetland every day in the past month. The soil layers in the target wetland include the soil surface layer, the upper mineral soil layer, the lower mineral soil layer, the deposition layer, and the bedrock layer. For example, in some embodiments, refer to Figure 3 As shown, this figure is the structural diagram of the soil layers in the target wetland in some embodiments of the present application; the determination of the layered characteristics of soil moisture in the target wetland according to the historical water content data can be achieved by the following method, that is: select a group of adjacent soil layers in the target wetland as the selected adjacent soil layers, extract all the water contents of the two soil layers in the selected adjacent soil layers from the historical water content data, calculate the average value of all the water contents of each extracted soil layer, subtract the average value of the water content corresponding to the second soil layer in the selected adjacent soil layer from the average value of the water content corresponding to the first soil layer, use the obtained subtracted value as the difference value of the water content of the selected adjacent soil layer, continue to determine the difference values of the water contents of the remaining adjacent soil layers, and use all the difference values as the layered characteristics of soil moisture in the target wetland; in other embodiments, other methods can also be used for determination, which are not limited here.

[0036] It should be noted that the layered characteristics in the present application represent the characteristics of the difference in water content of the soil in the target wetland in the vertical direction, which can be used to analyze the vertical change of soil water content in the target wetland and facilitate finding the sampling route in the target wetland.

[0037] In some embodiments, the differential coupling of the layered characteristics and the difference characteristics of water content between each sensing node to obtain the fluctuation characteristics of the soil unsaturated hydraulic conductivity when soil moisture diffuses in the target wetland can be achieved by the following steps: Perform correlation analysis on the layered characteristics and the difference characteristics of water content between each sensing node to obtain the aggregation area of soil moisture in the target wetland; Determine the fluctuation characteristics of the soil unsaturated hydraulic conductivity when soil moisture diffuses in the target wetland according to the aggregation area of the soil moisture.

[0038] In specific implementation, the correlation analysis of the layered features and the difference features of the water content between each sensing node is carried out to obtain the aggregation area of soil moisture in the target wetland, which can be implemented in the following way, that is: construct a correlation model of the layered features and the change trend based on the machine learning library, that is: train this correlation model through the historical layered features and historical change trend of soil moisture in the target wetland, and use the layered features and the difference features of the water content as the input features of this correlation model, and use the aggregation area of soil moisture as the output feature of this correlation model. Input the layered features and the difference features of the water content between each sensing node into this correlation model to update the layered features and the difference features of the water content, and output the aggregation area of soil moisture in the target wetland through this correlation model. Among them, the aggregation area represents the area of the aggregation degree of soil moisture in the target wetland, and the aggregation area includes multiple areas where soil moisture aggregates; in other embodiments, other methods can also be used to determine, which are not limited here.

[0039] In specific implementation, the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses can be determined according to the aggregation area of the soil moisture in the following way, that is: calculate the average water content of the water content of all sensing nodes, select an area in the aggregation area of the soil moisture as the selected area, multiply the area of the selected area by this average water content, perform a natural exponential operation on the obtained value, and use the opposite number of the value obtained by the natural exponential operation as the retention degree when the soil moisture in the selected area diffuses. Continue to determine the retention degree when the soil moisture in the remaining areas in the aggregation area of the soil moisture diffuses. Among them, the retention degree represents the parameter value of the retention degree of water when diffusing in the area. Use all the retention degrees as the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses; in other embodiments, other methods can also be used to determine, which are not limited here.

[0040] It should be noted that the fluctuation characteristics of the soil unsaturated hydraulic conductivity in this application represent the characteristics of the fluctuation degree of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses, and can be used to predict the retention situation of water in the target wetland, so as to judge the suitable sampling areas and routes.

[0041] In step 104, determine the confidence interval of the sampling route when sampling the soil of the target wetland according to the dynamic offset vector and the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture in the target wetland diffuses, and then control the sampler to sample the target wetland based on the confidence interval of the sampling route.

[0042] In some embodiments, the credible interval of the sampling route for soil sampling of the target wetland can be determined according to the dynamic offset vector and the fluctuation characteristics of the soil unsaturated hydraulic conductivity during soil moisture diffusion in the target wetland, which can be achieved by the following steps: Determine multiple retention areas of soil moisture in the target wetland according to the fluctuation characteristics of the soil unsaturated hydraulic conductivity during soil moisture diffusion in the target wetland; Determine the credible interval of the sampling route for soil sampling of the target wetland according to all the retention areas and the dynamic offset vector.

[0043] Specifically, when implemented, determining multiple retention areas of soil moisture in the target wetland according to the fluctuation characteristics of the soil unsaturated hydraulic conductivity during soil moisture diffusion in the target wetland can be achieved in the following way, that is: calculate the average value of all the retention degrees in the fluctuation characteristics of the soil unsaturated hydraulic conductivity during soil moisture diffusion in the target wetland, extract all the retention degrees greater than this average value from the fluctuation characteristics of the soil unsaturated hydraulic conductivity during soil moisture diffusion in the target wetland, and use the areas corresponding to each retention degree as the retention areas of soil moisture in the target wetland, where the retention area represents the area with a larger degree of water retention in the target wetland; determining the credible interval of the sampling route for soil sampling of the target wetland according to all the retention areas and the dynamic offset vector can be achieved in the following way, that is: take the horizontal distance in the dynamic offset vector as the length and the vertical distance in the dynamic offset vector as the width to obtain a rectangle, connect the center points of all the retention areas according to the principle of proximity, slide the center line of the above rectangle along the starting end of the connected curve until it slides to the end of the curve, and use the area where the above rectangle slides through as the credible interval of the sampling route for soil sampling of the target wetland; in other embodiments, other methods can be used to determine, which are not limited here.

[0044] It should be noted that the credible interval of the sampling route in this application represents the reliable area of the sampling route for soil sampling of the target wetland, and can be used to adjust the sampling route of the target wetland, so that the sampled soil can represent the soil conditions in the target wetland.

[0045] In some embodiments, controlling the sampler to sample the target wetland based on the credible interval of the sampling route can be achieved by the following steps: Determine the sampling plan when the sampler samples the target wetland; Adjust the sampling plan according to the credible interval of the sampling route to obtain an adjusted sampling plan; Control the sampler to sample the target wetland through the adjusted sampling plan.

[0046] In specific implementation, a sampling plan when the sampler samples the target wetland is obtained from the database of the sampler. The sampling plan includes sampling targets, sampling intervals, sampling routes, etc.; the sampling plan is adjusted according to the confidence interval of the sampling route, and the adjusted sampling plan can be implemented in the following manner, that is: the sampling route in the sampling plan is adjusted to the confidence interval of the sampling route through an adjustment model built in the sampler, and the replaced sampling plan is used as the adjusted sampling plan; and the adjusted sampling plan is input into the sampler, and the sampler is controlled to sample the target wetland according to the adjusted sampling plan; in other embodiments, it can also be implemented in other ways, which is not limited here.

[0047] In addition, on the other hand of the present application, in some embodiments, the present application provides a soil sampling system. Refer to Figure 4 , which is a schematic structural diagram of the soil sampling system shown in some embodiments of the present application. The soil sampling system 400 includes: a collection module 401, a processing module 402, and an execution module 403, which are described as follows: Collection module 401. In the present application, the collection module 401 is mainly used for arranging sensor nodes in the target wetland and then collecting the water content of each sensor node in the target wetland based on the water sensors. Processing module 402. In the present application, the processing module 402 is used to determine the mutation region of the soil moisture in the target wetland according to the difference characteristics of the water content between each sensor node and the terrain slope of the target wetland, and dynamically adjust the preset initial sampling points in the target wetland according to the change range of the water content in the mutation region, so as to obtain the dynamic offset vector of the initial sampling points in the target wetland. It should be noted that in the present application, the processing module 402 is also used to determine the stratification characteristics of the soil moisture in the target wetland, and perform differential coupling on the stratification characteristics and the difference characteristics of the water content between each sensor node, so as to obtain the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture diffuses in the target wetland. Execution module 403. In the present application, the execution module 403 is mainly used to determine the confidence interval of the sampling route when sampling the soil of the target wetland according to the dynamic offset vector and the fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture diffuses in the target wetland, and then control the sampler to sample the target wetland based on the confidence interval of the sampling route.

[0048] In addition, the present application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned soil sampling method.

[0049] In some embodiments, refer to Figure 5, which is a schematic structural diagram of a computer device for implementing the soil sampling method according to some embodiments of the present application. The soil sampling method in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0050] The processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0051] The communication bus 502 can be used to transmit information between the above components.

[0052] The memory 503 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0053] Among them, the memory 503 is used to store the program code for executing the solution of the present application and is controlled by the processor 501 to execute. The processor 501 is used to execute the program code stored in the memory 503. The program code can include one or more software modules. The method used in the above embodiments can be implemented by one or more software modules in the program code of the processor 501 and the memory 503.

[0054] A communication interface 504, using any device such as a transceiver, is used to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0055] In a specific implementation, as an example, a computer device may include multiple processors, and each of these processors may be a single-CPU processor or a multi-CPU processor. Here, the processor may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0056] The above computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0057] In addition, the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above soil sampling method is implemented.

[0058] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0059] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A soil sampling method, characterized in that: The steps include: Arrange sensor nodes in the target wetland, and collect the water content of each sensor node in the target wetland based on the moisture sensor; Determine the mutation area of ​​soil moisture in the target wetland according to the difference characteristics of the moisture content between each sensor node and the terrain slope of the target wetland, dynamically adjust the preset initial sampling point in the target wetland according to the change amplitude of the moisture content in the mutation area, and then obtain the dynamic offset vector of the initial sampling point in the target wetland; Determine the stratification characteristics of soil moisture in the target wetland, differentially couple the stratification characteristics with the differential characteristics of water content between the sensor nodes, and thereby obtain the fluctuation characteristics of soil unsaturated hydraulic conductivity when soil moisture diffuses in the target wetland; The credible interval of the sampling route when sampling soil in the target wetland is determined based on the dynamic offset vector and the fluctuation characteristics of the unsaturated hydraulic conductivity of the soil when the soil moisture diffuses in the target wetland, and then the sampler is controlled to sample the target wetland based on the credible interval of the sampling route.

2. The method according to claim 1, characterized in that According to the difference characteristics of the moisture content between each sensor node and the terrain slope of the target wetland, the mutation area of ​​soil moisture in the target wetland is determined, including: Determine the variation trend of soil moisture in the target wetland based on the difference characteristics of moisture content between each sensor node and the terrain slope of the target wetland; Determine soil texture distribution in target wetlands; Determining the amount of water retention difference between adjacent soil textures in the target wetland based on the soil texture distribution; The mutation area of ​​soil moisture in the target wetland is determined according to the variation trend of the soil moisture and all the retention differences.

3. The method according to claim 2, characterized in that The variation trend of soil moisture in the target wetland is determined based on the difference characteristics of moisture content between each sensor node and the terrain slope of the target wetland, including: Determine the differential characteristics of water content between various sensing nodes; Obtain the topographic slope of the target wetland; determining the migration state of soil moisture in the target wetland according to the difference characteristics; The change trend of soil moisture in the target wetland is determined by the moisture migration state and the terrain slope.

4. The method according to claim 1, characterized in that Dynamically adjusting the preset initial sampling point in the target wetland according to the change amplitude of the water content in the mutation area, and then obtaining the dynamic offset vector of the initial sampling point in the target wetland specifically includes: Determining the magnitude of the change in water content in the mutation region; Obtaining the preset initial sampling points in the target wetland; determining a plurality of dynamic points of water content in the target wetland according to the magnitude of the change; The initial sampling point is adjusted through all the dynamic points to obtain a dynamic offset vector of the initial sampling point in the target wetland.

5. The method according to claim 1, characterized in that Determine the stratified characteristics of soil moisture in the target wetland including: Obtain historical water content data for each soil layer in the target wetland; The stratified characteristics of soil moisture in the target wetland are determined based on the historical moisture content data.

6. The method according to claim 1, characterized in that The stratification feature and the difference feature of the water content between the sensor nodes are differentially coupled to obtain the fluctuation feature of the unsaturated hydraulic conductivity of the soil when the soil moisture diffuses in the target wetland, which specifically includes: Performing correlation analysis on the stratified features and the difference features of the water content between the sensor nodes to obtain the concentration area of ​​soil moisture in the target wetland; The fluctuation characteristics of the soil unsaturated hydraulic conductivity when the soil moisture diffuses in the target wetland are determined according to the soil moisture concentration area.

7. The method according to claim 1, characterized in that Determining the credible interval of the sampling route when sampling soil in the target wetland according to the dynamic offset vector and the fluctuation characteristics of the unsaturated hydraulic conductivity of the soil when the soil moisture in the target wetland diffuses specifically includes: Determining multiple retention areas of soil moisture in the target wetland according to the fluctuation characteristics of the unsaturated hydraulic conductivity of the soil when the soil moisture in the target wetland diffuses; A credible interval of a sampling route when soil sampling is performed on a target wetland is determined based on all retention areas and the dynamic offset vector.

8. A soil sampling system, characterized in that: include: A collection module is used to collect the water content of each sensor node in the target wetland based on the moisture sensor after the sensor nodes are arranged in the target wetland; A processing module is used to determine the mutation area of ​​soil moisture in the target wetland according to the difference characteristics of the moisture content between each sensor node and the terrain slope of the target wetland, and dynamically adjust the preset initial sampling point in the target wetland according to the change amplitude of the moisture content in the mutation area, so as to obtain the dynamic offset vector of the initial sampling point in the target wetland; The processing module is further used to determine the stratification characteristics of soil moisture in the target wetland, differentially couple the stratification characteristics with the differential characteristics of the moisture content between the sensor nodes, and thereby obtain the fluctuation characteristics of the unsaturated hydraulic conductivity of the soil when the soil moisture in the target wetland diffuses; An execution module is used to determine a credible interval of a sampling route when sampling soil in a target wetland based on the dynamic offset vector and the fluctuation characteristics of the unsaturated hydraulic conductivity of the soil when the soil moisture diffuses in the target wetland, and then control a sampler to sample the target wetland based on the credible interval of the sampling route.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the soil sampling method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the soil sampling method according to any one of claims 1 to 7 is implemented.

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