Soil moisture content monitoring method and system, program product and storage medium

Abstract

The invention discloses a soil moisture content monitoring method and system, a program product and a storage medium, and relates to the field of electrical digital data processing.The method comprises the steps that when the moisture content of a target area continuously decreases within a preset time period and the descending speed is smaller than a preset proportion of the moisture content change average value of surrounding areas, the target area is marked as a crack sensitive area; calculating a dominant water flow path based on a minimum path algorithm to obtain a water flow path distribution diagram, laying dominant flow monitoring points along the water flow path distribution diagram, calculating a moisture seepage velocity between the dominant flow monitoring points, and when the seepage velocity between every two adjacent dominant flow monitoring points exceeds a preset seepage velocity threshold, recording as a rapid seepage region; the rapid seepage areas are connected according to a moisture conduction time sequence to form a dominant seepage channel, and the dominant seepage channel represents a high-permeability area formed in the soil; and sending the geographic position of the dominant seepage channel to a target client. By implementing the method, the accuracy of soil moisture content monitoring can be improved.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to a soil moisture monitoring method, system, program product and storage medium. Background Art

[0002] Soil moisture is an important reference indicator for preventing debris flow, landslide and soil and water conservation. Its monitoring is of great significance for early warning of mountain torrent disasters and prevention of geological disasters. The characteristics of water movement in the soil not only reflect the water content of the soil, but also reveal the evolution of soil structure. It has important guiding value for predicting and preventing soil erosion and evaluating mountain stability.

[0003] Current soil moisture monitoring mainly uses linear or quasi-linear models to predict soil moisture movement patterns. These models are based on the assumption of homogeneous soil media, collect soil moisture data by evenly arranging sensors in the monitoring area, and use statistical methods to analyze the characteristics of water movement. In areas with stable geological conditions, this method can better reflect the overall soil moisture change trend in the region.

[0004] However, in areas prone to geological disasters such as mountains and hills, there may be natural cracks or structural surfaces in the soil. In addition, due to the influence of natural factors such as rainfall infiltration, water migration presents obvious nonlinear characteristics. In this case, the phenomenon of dominant flow will occur, which is manifested as a rapid change in the moisture content of a local area, while the moisture content of adjacent areas changes relatively slowly. The prediction accuracy of traditional monitoring schemes based on linear models is affected in this case, and it cannot well reflect the dynamic change process of soil structure, making it difficult to timely detect potential geological disaster risks. Summary of the invention

[0005] The present application provides a soil moisture monitoring method, system, program product and storage medium for improving the accuracy of soil moisture monitoring.

[0006] In a first aspect, the present application provides a soil moisture monitoring method, which is applied to a soil moisture monitoring system, and the method includes: obtaining moisture content data of multiple soil monitoring points and the moisture content change rate between adjacent soil monitoring points, and when the moisture content of a target area continues to decrease within a preset time period and the decrease rate is less than a preset proportion of the average moisture content change in the surrounding area, marking the target area as a fracture sensitive area; based on the spatial distribution of the fracture sensitive area, calculating the dominant water flow path based on the minimum path algorithm, and obtaining a water flow path distribution map, the water flow path distribution map includes multiple water flow channels connecting the fracture sensitive area; arranging dominant flow monitoring points along the water flow path distribution map, calculating the water infiltration rate between the dominant flow monitoring points, when the infiltration rate between two adjacent dominant flow monitoring points exceeds a preset infiltration rate threshold, recording the area of ​​the two adjacent dominant flow monitoring points as a fast infiltration area; connecting the fast infiltration area according to the water conduction timing to form a dominant infiltration channel, and the dominant infiltration channel characterizes the high permeability area formed in the soil; and sending the geographical location of the dominant infiltration channel to the target client.

[0007] By adopting the above technical solution, the moisture content data of soil monitoring points are obtained and the crack sensitive areas are identified. The dominant water flow path is calculated based on the minimum path algorithm to obtain the water flow path distribution map, and then the dominant flow monitoring points are arranged along the water flow path and the seepage rate is calculated to identify the fast seepage area. The fast seepage area is connected according to the water conduction time sequence to form a dominant seepage channel, so that the system can fully grasp the spatial distribution characteristics of high permeability areas in the soil, realize the precise positioning and tracking monitoring of soil water migration channels, and establish a complete monitoring chain from surface cracks to deep seepage, accurately reflecting the dominant flow development characteristics in the process of soil structure evolution, and providing an effective monitoring means for timely detection and prevention of soil structure degradation.

[0008] In combination with some embodiments of the first aspect, in some embodiments, based on the spatial distribution of the fracture sensitive area, the dominant water flow path is calculated based on the minimum path algorithm to obtain a water flow path distribution map, and the water flow path distribution map includes the steps of multiple water flow channels connecting the fracture sensitive area, specifically including: establishing a three-dimensional grid node based on the spatial position of the fracture sensitive area, calculating the distance weight between adjacent nodes in the three-dimensional grid node, and the distance weight is proportional to the actual distance and moisture content difference between the adjacent nodes; calculating the minimum weight path with the soil surface node as the starting point and the groundwater level node as the end point to obtain the initial water flow path; taking the nodes of the fracture sensitive area as the necessary points, calculating the minimum weight path between the necessary points according to the initial water flow path, and obtaining a potential channel connecting each of the fracture sensitive areas; generating a water flow path distribution map according to the spatial distribution of the potential channels, and the path width in the water flow path distribution map is inversely proportional to the weight value of the potential channel.

[0009] By adopting the above technical solution, three-dimensional grid nodes are established based on the spatial position of the fracture sensitive area and the distance weights between nodes are calculated. The minimum weight path is calculated with the soil surface node as the starting point and the groundwater level node as the end point to obtain the initial water flow path. The minimum weight path is calculated by taking the fracture sensitive area nodes as the necessary points to obtain the potential channels connecting the fracture sensitive areas, and a water flow path distribution map is generated based on the spatial distribution of the potential channels. This scheme establishes a complete water migration path prediction model, realizes the quantitative characterization of the dominant water flow channels, and forms a water flow network structure with the fracture sensitive area as the key node, making the spatial distribution characteristics of the dominant flow channels more intuitive and clear.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the geographical location of the dominant seepage channel to the target client, the method further includes: calculating the position fit based on the water flow path distribution map and the spatial distribution of the dominant seepage channel; when the position fit is lower than a preset threshold, adjusting the judgment parameter of the fracture sensitive area to a preset parameter threshold, the position fit characterizing the degree of overlap between the predicted path and the actual seepage channel.

[0011] By adopting the above technical solution, the position fit is calculated based on the water flow path distribution map and the spatial distribution of the dominant seepage channel, and the judgment parameters of the fracture sensitive area are adjusted when the position fit is lower than the preset threshold. A quantitative comparison mechanism between the prediction results and the actual monitoring data is established, and the prediction accuracy is continuously improved through parameter adaptive adjustment, and the dynamic optimization and self-correction of the prediction model are realized, so that the monitoring system can adjust the prediction parameters in time according to the actual monitoring results, and improve the accuracy and reliability of the identification of the dominant flow channel.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the position fit based on the water flow path distribution map and the spatial distribution of the dominant seepage channel specifically includes: converting the water flow path distribution map and the spatial distribution of the dominant seepage channel into three-dimensional grid data of the same resolution, wherein the value of the grid unit in the three-dimensional grid data is 0 or 1, and 1 indicates the existence of a channel; calculating the number of grid cells that overlap spatially between the water flow path distribution map and the dominant seepage channel to obtain the number of overlapping cells; respectively calculating the total number of grid cells occupied by the water flow path distribution map and the dominant seepage channel to obtain the number of path units and the number of channel units; calculating the position fit based on the number of overlapping units, the number of path units and the number of channel units, and the position fit is equal to the number of overlapping units divided by the minimum value of the number of path units and the number of channel units.

[0013] By adopting the above technical scheme, the water flow path distribution map and the spatial distribution of the dominant seepage channel are converted into three-dimensional grid data of the same resolution, the number of overlapping grid cells is calculated, and the position fit is calculated in combination with the number of path cells and the number of channel cells. A standardized evaluation index is established based on the ratio of the number of overlapping cells to the minimum number of cells. The similarity of spatial morphology is converted into an accurate numerical representation, eliminating the comparison deviation at different scales, and establishing a quantitative comparison standard between the prediction results and the actual monitoring data, making the prediction accuracy assessment more objective and reliable.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of adjusting the judgment parameters of the fracture sensitive zone to the preset parameter threshold when the position fit is lower than the preset threshold, the method further includes: when the regional area of ​​the fracture sensitive zone exceeds the first area threshold and the water content continues to increase within a preset time period, triggering a first-level warning; when the seepage velocity between the fast seepage areas exceeds the second seepage threshold, triggering a second-level warning; when the spatial connectivity of the dominant seepage channel exceeds the third connectivity threshold, triggering a third-level warning.

[0015] By adopting the above technical solutions, the first-level warning is triggered based on the changes in the area and moisture content of the fracture sensitive area, the second-level warning is triggered based on the seepage velocity between the fast seepage areas, and the third-level warning is triggered based on the spatial connectivity of the dominant seepage channel. The key characteristic indicators in the process of soil structure evolution are associated with the warning level, making the warning information more targeted and timely, ensuring that the monitoring system responds promptly to soil structure changes of varying degrees.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after comparing the water flow path distribution map with the spatial distribution of the dominant seepage channel, calculating the position fit, and when the position fit is lower than a preset threshold, adjusting the judgment parameters of the fracture sensitive area, wherein the position fit characterizes the degree of overlap between the predicted path and the actual seepage channel, the method further includes: collecting soil bulk density data, calculating the bulk density change rate of the soil area around the dominant seepage channel; when the bulk density change rate exceeds the preset bulk density threshold, sending a regional monitoring instruction to the target client, and the regional monitoring instruction includes setting settlement monitoring points in the surrounding soil area.

[0017] By adopting the above technical solution, soil bulk density data is collected and the bulk density change rate of the soil around the dominant seepage channel is calculated. When the change rate exceeds the preset threshold, a regional monitoring instruction is sent to set up settlement monitoring points. Taking soil bulk density as a quantitative indicator of structural stability, an evaluation mechanism for the impact of seepage channels on surrounding soil is established, and the monitoring extension from water migration characteristics to soil structure changes is realized, forming a three-dimensional monitoring network centered on the dominant seepage channel, so that the monitoring system can evaluate the overall stability of the soil structure more comprehensively and in-depth.

[0018] In combination with some embodiments of the first aspect, in some embodiments, when the bulk density change rate exceeds a preset bulk density threshold, a regional monitoring instruction is sent to the target client, and the regional monitoring instruction includes a step of deploying settlement monitoring points in the surrounding soil area. The method also includes: obtaining settlement data of the settlement monitoring points, and establishing a correspondence curve between the settlement data and soil moisture content; based on the correspondence curve, determining a safe threshold range of the soil moisture content; monitoring the changing trend of the soil moisture content in real time, and sending an alarm message to the target client when the soil moisture content is about to exceed the safe threshold range of moisture content.

[0019] By adopting the above technical solution, the settlement data of the settlement monitoring point is obtained and a corresponding relationship curve with the soil moisture content is established. Based on the curve, the safe threshold range of the soil moisture content is determined, and the moisture content change trend is monitored in real time and an alarm is issued when the safe threshold is about to be exceeded. A quantitative relationship model between settlement and moisture content is established, and soil deformation is associated with moisture content changes. A moisture content control standard based on structural stability is formed, and an intelligent judgment mechanism for inferring the moisture content safety range from soil deformation characteristics is realized.

[0020] In a second aspect, an embodiment of the present application provides a soil moisture monitoring system, which comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions to enable the soil moisture monitoring system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product is run on a soil moisture monitoring system, the above-mentioned soil moisture monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on a soil moisture monitoring system, the soil moisture monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It is understandable that the soil moisture monitoring system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application obtains moisture content data of soil monitoring points and identifies fissure sensitive areas, calculates the dominant water flow path based on the minimum path algorithm to obtain a water flow path distribution map, and then arranges dominant flow monitoring points along the water flow path and calculates the seepage rate, thereby identifying fast seepage areas. The fast seepage areas are connected according to the water conduction sequence to form dominant seepage channels, so that the system can fully grasp the spatial distribution characteristics of high permeability areas in the soil, realize accurate positioning and tracking monitoring of soil water migration channels, and establish a complete monitoring chain from surface cracks to deep seepage, accurately reflecting the dominant flow development characteristics in the process of soil structure evolution, and providing an effective monitoring means for timely detection and prevention of soil structure degradation.

[0025] 2. This application establishes three-dimensional grid nodes based on the spatial position of the fracture-sensitive area and calculates the distance weights between nodes, and calculates the minimum weight path with the soil surface node as the starting point and the groundwater level node as the end point to obtain the initial water flow path. The minimum weight path is calculated using the fracture-sensitive area nodes as the necessary points to obtain potential channels connecting the fracture-sensitive areas, and a water flow path distribution map is generated based on the spatial distribution of the potential channels. This scheme establishes a complete water migration path prediction model, realizes the quantitative characterization of the dominant water flow channels, and forms a water flow network structure with the fracture-sensitive area as the key node, making the spatial distribution characteristics of the dominant flow channels more intuitive and clear.

[0026] 3. This application converts the water flow path distribution map and the spatial distribution of the dominant seepage channel into three-dimensional grid data of the same resolution, calculates the number of overlapping grid cells, and calculates the position fit based on the number of path cells and the number of channel cells. A standardized evaluation index is established based on the ratio of the number of overlapping cells to the minimum number of cells, and the similarity of spatial morphology is converted into an accurate numerical representation, making the prediction accuracy assessment more objective and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the soil moisture monitoring method in the embodiment of the present application; Figure 2 is another flow chart of the soil moisture monitoring method in the embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the soil moisture monitoring system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] In a mountainous watershed covering an area of ​​50 square kilometers, the terrain is mainly steep slopes and terraces, with an altitude difference of more than 1,000 meters. The vegetation coverage includes shrubs and alpine meadows. The annual rainfall in the area reaches 1,500 mm, and 70% of it is concentrated in the flood season from June to September. There are also many faults and joint zones. In recent years, landslides and mudslides have occurred frequently in the area, posing a serious threat to downstream reservoirs and residential areas. Preliminary investigations found that these disasters are closely related to the rapid changes in soil moisture content. After a continuous rainfall, a large-scale landslide occurred on a hillside. Subsequent analysis showed that the soil moisture content around the landslide body was extremely unevenly distributed, and the moisture content difference between adjacent locations was more than 25%. Further investigations found that there were many hidden cracks in the mountain, forming a complex groundwater flow network. These dominant flow channels not only accelerated the infiltration process of rainwater, but also caused a significant reduction in the strength of the local soil. Traditional soil moisture monitoring methods are difficult to effectively identify and track the development process of these groundwater flow channels, and cannot timely warn of potential geological disaster risks.

[0032] A research institute used traditional soil moisture monitoring methods in a typical mountain test area. 250 soil moisture sensors were evenly arranged in a 20m×20m grid within a 10-hectare area, with monitoring depths of 0.5m, 1m, 2m and 3m below the surface. The monitoring system collected data every 30 minutes and used the Kriging interpolation algorithm to generate a soil moisture distribution map. During a rainfall process with a cumulative rainfall of 180 mm, the system showed that the average moisture content of the test area increased from 22% to 35%, showing an overall uniform upward trend. However, field investigations found that many parts of the mountain had water seepage and local deformation. Excavation inspections found that obvious cracks had appeared in the soil in some areas, and the moisture content of the soil around the cracks was as high as 45%, far exceeding the value shown by the monitoring data. The traditional monitoring method uses a linear interpolation model to average the data in the abnormal area, resulting in the masking of the dominant flow channel. At the same time, due to the fixed location of the monitoring points and limited sampling density, it is impossible to accurately capture the local rapid seepage phenomenon, and it is impossible to adjust the monitoring strategy in time according to the dynamic changes of the soil structure, so that the early signs of landslide disasters cannot be effectively identified.

[0033] A soil moisture monitoring system based on this solution was installed in a key protection area upstream of a reservoir. The area is about 20 square kilometers, including multiple geological fault zones, and the vegetation is mainly shrubs and meadows. The system first identified 15 areas with abnormally low water content through the initial deployment of 1,000 basic sensor networks, and marked them as fracture-sensitive areas. Most of these areas are distributed near faults and joints, and the average daily water content decline rate is more than three times that of the surrounding areas. Then the system calculated the possible water flow channels between these fracture-sensitive areas based on the improved minimum path algorithm and generated a preliminary water flow path distribution map. 200 high-precision monitoring points were added to the predicted water flow path, and the sampling frequency was increased to once every 5 minutes. By tracking the water migration speed in real time, 8 fast seepage areas were successfully captured, and the maximum seepage speed was measured to reach 15 cm / hour. The system connected these seepage areas according to the water conduction sequence, and accurately outlined the underground dominant seepage channel network with a total length of more than 2 kilometers. When the system detected that the bulk density of the soil around a seepage channel dropped by 8% within 7 days, it automatically set up 25 settlement monitoring points. Through continuous observation, a quantitative relationship model between water content and settlement was established. During a 72-hour rainfall process, the system predicted that the water content of three areas would exceed the safety threshold within 4 hours, triggering a level 3 warning in advance, allowing the flood control department to carry out the timely transfer of people and emergency disposal, successfully avoiding a major landslide disaster. The application of this system proves that the intelligent monitoring solution based on dominant flow identification can effectively improve the accuracy and timeliness of geological disaster warnings.

[0034] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1, is a flow chart of the soil moisture monitoring method in an embodiment of the present application.

[0035] S101. Acquire moisture content data of multiple soil monitoring points and the moisture content change rate between adjacent soil monitoring points. When the moisture content of a target area continues to decrease within a preset time period and the rate of decrease is less than a preset ratio of the average moisture content change of the surrounding area, mark the target area as a crack sensitive area.

[0036] Among them, soil monitoring points refer to the locations of sensors installed in the soil for monitoring moisture content; moisture content data refer to the measured value of moisture content per unit volume of soil; moisture content change rate refers to the amount of change in moisture content per unit time; target area refers to a continuous soil range with specific moisture content change characteristics; and fracture sensitive areas are used to indicate soil areas that may form dominant water flow channels.

[0037] Specifically, this step is performed after the soil moisture monitoring network is deployed. First, the moisture data of each monitoring point is collected in real time through the sensor network distributed in the monitoring area, and the moisture content change rate between adjacent monitoring points is calculated. Then, local areas where the moisture content continues to decrease and the rate of decrease is significantly lower than the average value of the surrounding area are screened out and marked as crack sensitive areas. These areas are the precursors of changes in soil structure.

[0038] In some embodiments, the identification of fracture-sensitive areas can be achieved in the following ways: Optionally, first divide the monitoring area into grids, calculate the average water content and its change rate of each grid, and establish a database of spatiotemporal changes in water content; then set a time window, and analyze the water content change trend of each grid within the time window; finally, mark the areas that meet the conditions according to the preset judgment rules. Optionally, a cluster analysis method can also be used to cluster areas with similar water content change characteristics, and identify abnormal change areas as fracture-sensitive areas. It is understandable that other data analysis methods can also be used to achieve the identification and marking of fracture-sensitive areas.

[0039] S102. Based on the spatial distribution of the fracture sensitive area, a dominant water flow path is calculated based on a minimum path algorithm to obtain a water flow path distribution map, where the water flow path distribution map includes multiple water flow channels connecting the fracture sensitive area.

[0040] Among them, the spatial distribution represents the positional relationship of the fracture sensitive area in three-dimensional space; the minimum path algorithm refers to the algorithm for finding the shortest path in a weighted network; the dominant water flow path represents the path where water is most likely to flow; the water flow path distribution map is used to represent the spatial distribution characteristics of multiple water flow channels.

[0041] Specifically, this step is performed after obtaining the distribution of the fracture sensitive area. First, a three-dimensional network model is established based on the spatial location of the fracture sensitive area, and the weights between the nodes in the network are calculated. Then, the minimum path algorithm is used to calculate the minimum weight path through the fracture sensitive area with the surface as the starting point and the groundwater level as the end point. Finally, the multiple calculated paths are integrated to generate a distribution map representing the possible paths of water migration.

[0042] In some embodiments, the calculation of the water flow path can be achieved in the following ways: Optionally, a soil moisture movement potential field is constructed based on monitoring data, and the moisture movement path is calculated using the steepest descent method to obtain a local dominant flow path; then multiple local paths are connected to form a complete water flow network. Optionally, a probabilistic graph model method can also be used to construct a transfer probability matrix between nodes, and calculate the maximum probability path as a water flow channel. It is understandable that other path optimization algorithms can also be used to calculate and connect water flow paths.

[0043] This step specifically includes: A three-dimensional grid node is established based on the spatial position of the fracture sensitive area, and a distance weight between adjacent nodes in the three-dimensional grid node is calculated, where the distance weight is proportional to the actual distance between the adjacent nodes and the water content difference; The minimum weight path is calculated with the soil surface node as the starting point and the groundwater level node as the end point to obtain the initial water flow path; Taking the nodes of the fracture sensitive area as the necessary points, the minimum weight path between the necessary points is calculated according to the initial water flow path to obtain the potential channel connecting each of the fracture sensitive areas; A water flow path distribution map is generated according to the spatial distribution of the potential channel, and the path width in the water flow path distribution map is inversely proportional to the weight value of the potential channel.

[0044] The three-dimensional grid node is the calculation unit after spatial discretization; the distance weight represents the strength of the spatial relationship between adjacent nodes; the minimum weight path refers to the path with the smallest total weight between two points; the necessary point refers to the key position that the water flow must pass through; the potential channel refers to the predicted possible path of the water flow; the water flow path distribution map refers to the spatial distribution map that describes the law of water flow migration. The soil moisture monitoring system first establishes a three-dimensional grid system in the monitoring area, with a grid size of 10cm×10cm×10cm. The system calculates the distance weight between each pair of adjacent nodes, and the weight value W = k×D×ΔH, where k is the proportional coefficient (value 1.0), D is the actual distance between nodes (unit: cm), and ΔH is the difference in water content between nodes (percentage). Subsequently, the system uses the Dijkstra algorithm to calculate the minimum weight path from the surface node to the groundwater level node as the initial water flow path. On this basis, the system sets the nodes in the fracture sensitive area as necessary points, and uses the segmented minimum weight path algorithm to calculate the connection path between the necessary points. The specific calculation process is: first determine the spatial coordinates and access order of the must-pass points, then calculate the minimum weight path between adjacent must-pass points in turn, and finally connect all segmented paths to form a complete potential channel. The system generates a water flow path distribution map based on the calculated potential channel, with a path width of B = C / W, where C is a proportional constant (valued at 100) and W is the cumulative weight value of the channel. For example, when the cumulative weight value of a potential channel is 500, the corresponding path width is 0.2 cm. In this way, the system completes the prediction process from the crack sensitive area to the water flow path distribution map.

[0045] S103, arranging dominant flow monitoring points along the water flow path distribution map, calculating the water infiltration rate between the dominant flow monitoring points, and when the infiltration rate between two adjacent dominant flow monitoring points exceeds a preset infiltration rate threshold, recording the area between the two adjacent dominant flow monitoring points as a fast infiltration area.

[0046] Among them, the dominant flow monitoring point refers to the position of high-precision sensors arranged on the water flow path; the water infiltration rate refers to the migration distance of water in the soil per unit time; the infiltration rate threshold represents the critical value of the velocity for determining rapid infiltration; the rapid infiltration area is used to indicate the soil section where water movement is abnormally active; adjacent monitoring points refer to two sensor measuring points that are adjacent in space.

[0047] This step is performed after obtaining the water flow path distribution map. Specifically, first determine the key locations based on the water flow path distribution map, and deploy high-precision soil moisture sensors at these locations as dominant flow monitoring points. By collecting the moisture content data of the dominant flow monitoring points in real time, calculate the water infiltration rate between adjacent monitoring points. When it is detected that the infiltration rate between two adjacent monitoring points is significantly higher than the preset threshold, it indicates that there may be a dominant flow channel in the area, and it is marked as a fast seepage area. These areas are often where the soil structure has changed.

[0048] In some embodiments, the identification and marking of fast seepage areas can be achieved in a variety of ways: Optionally, first establish a spatiotemporal database of the monitoring point network, perform time series analysis on the moisture content data of each monitoring point, and calculate the water transfer rate between the monitoring points; then set a multi-level velocity threshold, and grade and mark the area according to the size of the infiltration velocity; finally, combine the spatial distribution characteristics to determine the scope and boundaries of the fast seepage area. Optionally, a tracer test method can also be used to place a tracer at the dominant flow monitoring point, track and record the migration speed and diffusion range of the tracer; then calculate the migration speed of the tracer in different areas; finally, determine the fast seepage area based on the velocity distribution characteristics. It is understandable that other monitoring and analysis methods can also be used to achieve the identification of fast seepage areas, which are not limited here.

[0049] S104, connecting the fast seepage areas according to the water conduction sequence to form a dominant seepage channel, where the dominant seepage channel represents a high permeability area formed in the soil.

[0050] Among them, the water conduction sequence refers to the time sequence of water transmission in the soil; the dominant seepage channel refers to the preferential water flow channel formed in the soil; the high permeability area refers to the continuous area with a permeability coefficient significantly higher than that of the surrounding soil; and the spatial connectivity is used to indicate the integrity and continuity of the seepage channel.

[0051] This step is performed after multiple fast seepage areas are identified. Specifically, the water conduction relationship between the fast seepage areas is first analyzed to determine the order of water transmission. Then, the fast seepage areas that are spatially adjacent and have hydraulic connectivity are connected according to the temporal relationship to form a continuous dominant seepage channel. These channels often have a high permeability coefficient and are the main path for the rapid migration of water and solutes in the soil.

[0052] In some embodiments, the construction of the dominant seepage channel can be achieved in a variety of ways: Optionally, firstly, a spatiotemporal clustering analysis is performed on the fast seepage area to identify regional groups with similar water conduction characteristics; then the hydraulic gradient and connectivity index between the regional groups are calculated; finally, the spatial orientation of the dominant seepage channel is determined based on the principle of minimum resistance. Optionally, a hydrogeological modeling method can also be used to construct a three-dimensional seepage field model containing a fast seepage area; then the water conduction process between the regions is simulated and calculated; finally, the main seepage path is extracted as the dominant seepage channel. It is understandable that other analysis methods can also be used to achieve the construction of the dominant seepage channel, which is not limited here.

[0053] S105. Send the geographical location of the dominant seepage channel to the target client.

[0054] Among them, geographical location refers to the spatial distribution information of the dominant seepage channel in the geographic coordinate system; the target client refers to the terminal device or system that receives the monitoring data; the sending operation refers to the process of transmitting data through the communication network; the location information includes three-dimensional coordinate data such as longitude, latitude, and depth; the spatial distribution information is used to represent the morphological characteristics and extension range of the seepage channel.

[0055] This step is performed after determining the spatial distribution of the dominant seepage channel. Specifically, the spatial distribution data of the dominant seepage channel is first converted into a standard geographic information format, including the coordinate information of the starting point, end point, and key nodes of the seepage channel, as well as the width, depth and other geometric characteristic parameters of the seepage channel. Then, through the data transmission network, these spatial information are sent to target clients such as agricultural management departments and water conservancy departments in real time to provide a decision-making basis for subsequent irrigation regulation and soil structure maintenance. This information is of great guiding significance for preventing soil structure deterioration and optimizing irrigation plans.

[0056] In some embodiments, the sending and display of the location information of the dominant seepage channel can be achieved in a variety of ways: optionally, the spatial data of the seepage channel is first converted into a standard GIS format to generate a vector layer containing attribute information; then the layer is superimposed on the base map to form an intuitive distribution diagram; finally, the real-time updated seepage channel distribution information is pushed to the target client through the Web service interface. Optionally, a three-dimensional visualization method can also be used to first construct a three-dimensional model of the seepage channel, annotate key feature points and parameters; then generate a stereoscopic display interface containing multiple perspectives; finally, an interactive three-dimensional seepage channel distribution map is pushed to the target user through a mobile application. It is understandable that other data transmission and display methods can also be used to achieve the sending of the location information of the dominant seepage channel, which is not limited here.

[0057] The following is a more detailed description of the process of the method provided by this implementation. Figure 2, is another flow chart of the soil moisture monitoring method in the embodiment of the present application.

[0058] S201. Convert the water flow path distribution map and the spatial distribution of the dominant seepage channel into three-dimensional grid data of the same resolution, wherein the value of the grid unit in the three-dimensional grid data is 0 or 1, and 1 indicates the existence of a channel.

[0059] Among them, three-dimensional grid data refers to the data structure that discretizes continuous space into regular cubic units; resolution refers to the size of the grid unit; the grid unit is the basic unit of space division; the values ​​0 and 1 represent the binary representation method of the absence and presence of channels respectively. The soil moisture monitoring system first obtains the spatial coordinate data of the water flow path distribution map and the dominant seepage channel to determine the spatial range of the entire monitoring area. The monitoring area is divided into cubic grid units of equal size. The grid size is determined by the actual monitoring accuracy requirements and is usually set to 10cm×10cm×10cm. Each grid unit is binarized: when the grid unit intersects with the water flow channel or the seepage channel, it is assigned a value of 1, otherwise it is assigned a value of 0. In this way, two three-dimensional Boolean matrices with the same resolution are obtained, which represent the predicted water flow path and the actual seepage channel distribution, respectively.

[0060] S202, calculating the number of grid cells that overlap spatially between the water flow path distribution diagram and the dominant seepage channel, and obtaining the number of overlapping cells.

[0061] Among them, overlapping grid cells refer to grid cells that exist in both channels at the same spatial position; the number of overlapping cells is used to quantify the degree of overlap of the spatial distribution of the two channels. The soil moisture monitoring system compares the two converted three-dimensional Boolean matrices element by element. The specific calculation method is: multiply the two matrices element by element to obtain a new three-dimensional matrix, in which the element with a value of 1 indicates that the two channels overlap at this position. Count the total number of elements with a value of 1 in the new matrix, which is the number of overlapping cells. For example, if two 10×10×10 three-dimensional matrices have 100 and 80 elements with a value of 1 respectively, and the matrix obtained after element-by-element multiplication has 60 elements with a value of 1, then the number of overlapping cells is 60.

[0062] S203, respectively calculating the water flow path distribution diagram and the total number of grid cells occupied by the dominant seepage channel to obtain the number of path cells and the number of channel cells.

[0063] Among them, the number of path units refers to the total number of grid units with a value of 1 in the water flow path distribution map; the number of channel units refers to the total number of grid units occupied by the dominant seepage channel. The soil moisture monitoring system performs statistical calculations on two three-dimensional Boolean matrices respectively. For the matrix corresponding to the water flow path distribution map, the total number of elements with a value of 1 is counted to obtain the number of path units; for the matrix corresponding to the dominant seepage channel, the total number of elements with a value of 1 is counted to obtain the number of channel units. These two values ​​reflect the occupied volume of the two channels in space respectively. The statistical method uses matrix summation operation to add all element values ​​in the three-dimensional matrix to obtain the number of their respective units. For example, in a 100×100×100 monitoring area, if the water flow path occupies 5000 grid cells and the dominant seepage channel occupies 4000 grid cells, the number of path units is 5000 and the number of channel units is 4000.

[0064] S204, calculating the position matching degree according to the number of overlapping units, the number of path units and the number of channel units, where the position matching degree is equal to the number of overlapping units divided by the minimum value of the number of path units and the number of channel units.

[0065] Among them, the position fit refers to the degree of spatial overlap between the predicted water flow path and the actual seepage channel; the number of overlapping units indicates the number of grid units occupied by the two channels; the number of path units and the number of channel units respectively indicate the number of grid units occupied by the water flow path and the seepage channel. The soil moisture monitoring system uses a standardized calculation method to determine the position fit. First, the smaller value of the number of path units and the number of channel units is obtained as the normalized benchmark. Then the number of overlapping units is divided by the benchmark value to obtain the position fit between 0 and 1. For example, when the number of path units is 5000, the number of channel units is 4000, and the number of overlapping units is 3000, the position fit is equal to 3000 / 4000=0.75, indicating that 75% of the spatial positions overlap based on the smaller channel volume.

[0066] S205. When the position fit is lower than a preset threshold, adjust the determination parameter of the fracture sensitive area to a preset parameter threshold. The position fit represents the degree of overlap between the predicted path and the actual seepage channel.

[0067] Among them, the preset threshold represents the critical value for determining whether the prediction result is acceptable; the judgment parameters of the fracture sensitive area include control parameters such as the water content change rate threshold and the duration threshold; the preset parameter threshold refers to the parameter value range determined by experience. The soil moisture monitoring system calculates the position fit in real time and compares it with the preset threshold. When the position fit is lower than the preset threshold, it means that the predicted water flow path is quite different from the actual seepage channel formed, and the parameters of the prediction model need to be adjusted. The system modifies the judgment parameters of the fracture sensitive area in turn, including adjusting the water content change rate threshold to a value within the range of 80% to 120% of the original value, and adjusting the duration threshold to a value within the range of ±30% of the original value. Through multiple iterative calculations, until a parameter combination that makes the position fit reach the preset threshold is found. For example, when the position fit is 0.6, which is lower than the preset threshold of 0.8, the system adjusts the water content change rate threshold from 2mm / h to 1.8mm / h, and the duration threshold from 24h to 18h, and re-identifies and predicts the fracture sensitive area.

[0068] S206. When the area of ​​the fracture sensitive zone exceeds the first area threshold and the water content continues to increase within a preset time period, a first-level warning is triggered.

[0069] Among them, the first area threshold refers to the critical area for determining the scale of the fracture sensitive area; the preset time period represents a fixed time window for observing the change in moisture content; and the first-level warning represents the lowest level of risk warning. The soil moisture monitoring system conducts real-time monitoring and risk assessment of fracture sensitive areas. The system first calculates the area of ​​each fracture sensitive area, and marks the area with an area exceeding the first area threshold (such as 100 square meters) as a key focus object. At the same time, the moisture content change trend of these areas is monitored, and the moisture content data for 24 consecutive hours is recorded. When it is detected that the area of ​​a certain area exceeds the threshold, and the average moisture content of the area continues to rise within the preset time period (such as 24 hours), and the increase exceeds 20% of the initial value, the system automatically triggers the first-level warning. The warning information contains data such as the location coordinates, area value, and moisture content change curve of the fracture sensitive area, which are sent to relevant management personnel through the preset communication channel.

[0070] S207: When the seepage velocity between the fast seepage areas exceeds the second seepage threshold, a second level warning is triggered.

[0071] Among them, the rapid seepage area refers to the soil area with abnormal water migration speed; the seepage speed indicates the migration rate of water in the soil; the second seepage threshold refers to the speed critical value that triggers the second-level warning; the second-level warning indicates a moderate risk warning. The soil moisture monitoring system continuously monitors the water migration status through a sensor network deployed in the rapid seepage area. The system obtains the moisture content data between adjacent rapid seepage areas, calculates the water migration distance per unit time, and obtains the seepage speed value. When it is detected that the seepage speed exceeds the second seepage threshold (such as 5cm / h), the system immediately triggers the second-level warning. The second-level warning information includes specific data such as the spatial coordinates of the rapid seepage area, the measured seepage speed, and the number of times the standard is exceeded. For example, the seepage speed between two adjacent rapid seepage areas reaches 7.5cm / h, which exceeds the preset second seepage threshold of 5cm / h, and the system automatically issues a second-level warning information.

[0072] S208. When the spatial connectivity of the dominant seepage channel exceeds the third connectivity threshold, a third-level warning is triggered.

[0073] Among them, spatial connectivity refers to the integrity and degree of connectivity of the dominant seepage channel; the third connectivity threshold represents the critical value for determining the degree of networking of the seepage channel; and the third-level warning represents the highest level of risk warning. The soil moisture monitoring system calculates spatial connectivity based on the three-dimensional grid data of the dominant seepage channel. The connectivity calculation adopts the graph theory method to convert the seepage channel into a node-edge network and calculate the connectivity index of the network. The specific calculation process includes: counting the number of nodes and edges in the seepage channel network, calculating the ratio of the actual number of connections to the maximum possible number of connections, and obtaining a connectivity value between 0 and 1. When the spatial connectivity exceeds the third connectivity threshold (such as 0.8), it indicates that the dominant seepage channel has formed a highly connected network structure, and the system triggers a third-level warning. For example, the dominant seepage channel network in a certain area contains 100 nodes, the actual number of connections is 3960, the maximum possible number of connections is 4950, and the calculated connectivity is 0.85, which exceeds the preset third connectivity threshold of 0.8, and the system issues a third-level warning information.

[0074] S209, collecting soil bulk density data, and calculating the bulk density change rate of the soil area around the dominant seepage channel.

[0075] Among them, soil bulk density refers to the dry weight of soil per unit volume; the bulk density change rate indicates the speed of change of soil bulk density over time; and the surrounding soil area refers to the affected soil range outside the dominant seepage channel. The soil moisture monitoring system deploys bulk density sensors around the dominant seepage channel to collect soil bulk density data at predetermined time intervals. The collection process adopts the fixed-depth sampling method to obtain soil samples at different distances from the seepage channel (such as 20cm, 50cm, and 100cm) and measure their bulk density values. The system records the bulk density data at multiple consecutive time points and calculates the change in bulk density per unit time. The calculation of the bulk density change rate adopts the differential method: taking the initial bulk density value as the benchmark, the relative change in the bulk density value at each subsequent time point is calculated, and divided by the corresponding time interval to obtain the bulk density change rate. For example, if the initial bulk density of a measuring point is 1.35g / cm³, and the bulk density is measured to be 1.28g / cm³ after 30 days, the bulk density change rate is -0.0023g / (cm³·d).

[0076] S210. When the bulk density change rate exceeds a preset bulk density threshold, a regional monitoring instruction is sent to the target client, where the regional monitoring instruction includes setting settlement monitoring points in the surrounding soil area.

[0077] Among them, the preset bulk density threshold represents the critical value for determining significant changes in soil structure; the regional monitoring instruction refers to the monitoring deployment command issued by the system; and the settlement monitoring point represents a fixed measuring point used to observe the degree of surface subsidence. The soil moisture monitoring system compares the bulk density change rate with the preset threshold in real time. When the bulk density change rate is detected to exceed the preset bulk density threshold (such as -0.005g / (cm³·d)), the system automatically generates a regional monitoring instruction. The instruction includes a monitoring point layout plan, which clearly stipulates that settlement monitoring points are set up in a grid layout in the soil area around the seepage channel, with a spacing of 10 meters between monitoring points and monitoring depths of 0.5 meters, 1 meter and 2 meters below the surface, respectively. The system sends the monitoring instruction to the target client through the data network. The instruction contains technical parameters such as the specific coordinate position, installation depth, and sampling frequency of the monitoring point. For example, when the bulk density change rate in a certain area reaches -0.006g / (cm³·d), the system generates an instruction to lay out a 3×3 settlement monitoring grid in the area.

[0078] S211, obtaining the settlement data of the settlement monitoring point, and establishing a corresponding relationship curve between the settlement data and the soil moisture content.

[0079] Among them, settlement data represents the time series of soil compression deformation; soil moisture content refers to the water content in unit volume of soil; the corresponding relationship curve reflects the quantitative relationship between settlement and moisture content. The soil moisture monitoring system collects real-time data from settlement monitoring points and records settlement at different depths. At the same time, the soil moisture data at the corresponding position is obtained to establish the corresponding relationship between the two sets of data. The system uses the least squares method to fit the functional relationship between settlement and moisture content, and obtains a mathematical expression that describes the law of change of the two. The fitting process takes into account the data weights at different depths, and the deep data has a larger weight. For example, the system records data from a certain monitoring point for 30 consecutive days. When the moisture content increases from 15% to 25%, the surface sinks 15mm cumulatively. The quadratic function relationship is obtained through data fitting: settlement = 0.02×(moisture content-15%)²+0.5×(moisture content-15%).

[0080] S212: Determine a safe threshold range of the soil moisture content based on the corresponding relationship curve.

[0081] Among them, the safety threshold range refers to the allowable change range of soil moisture content; based on the corresponding relationship curve, the threshold is determined using the established mathematical model. The soil moisture monitoring system analyzes the corresponding relationship curve between settlement data and moisture content to determine the critical state of soil structural stability. By calculating the slope change of the curve, the inflection point where the influence of moisture content change on settlement increases significantly is identified. The system uses the moisture content value corresponding to the inflection point as the upper threshold and the minimum moisture content required to maintain the basic function of the soil as the lower threshold. For example, according to the analysis of the corresponding relationship curve, when the moisture content exceeds 30%, the settlement rate increases sharply, and the system sets 30% as the upper threshold; considering the basic functional requirements of the soil, 15% is set as the lower threshold, thereby determining that the safe range of moisture content is 15%-30%. This range serves as an important basis for irrigation control and prevention and control measures.

[0082] S213: monitor the change trend of the soil moisture content in real time, and send an alarm message to the target client when the soil moisture content is about to exceed the moisture content safety threshold range.

[0083] Among them, the change trend indicates the dynamic change law of soil moisture content over time; the alarm information refers to the notification data indicating abnormal soil moisture content; the target client refers to the terminal device that receives the monitoring warning information. The monitoring system collects soil moisture content data through a distributed sensor network, and the sensor sampling frequency is once an hour. The system processes the collected data in real time and uses the time series analysis method to calculate the change rate and predicted value of the moisture content. The specific processing process includes: first, filtering the original data to remove abnormal values; then using the exponential smoothing method to calculate the change rate of the moisture content; and finally predicting the moisture content value in the next 6 hours based on the current change rate. When the distance between the predicted value and the current safety threshold is less than the preset warning distance (such as 2%), the system automatically generates an alarm message. The alarm information contains specific contents such as the location of the monitoring point, the current moisture content value, the change rate, the predicted trend chart, and the expected time to exceed the threshold. For example, the moisture content of a certain monitoring point is 28.5% and continues to rise at a rate of 0.5% per hour. It is predicted that it will exceed the upper threshold of 30% in 4 hours. The system issues an alarm message in advance so that the management personnel can take preventive measures.

[0084] The soil moisture monitoring system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a soil moisture monitoring system in an embodiment of the present application.

[0085] It should be noted that Figure 3 The structure of the soil moisture monitoring system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0086] like Figure 3 As shown, the soil moisture monitoring system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0087] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0088] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0089] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0090] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0091] Specifically, the soil moisture monitoring system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the soil moisture monitoring method provided in the above embodiment is implemented.

[0092] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the soil moisture monitoring system described in the above embodiment; or may exist independently without being assembled into the soil moisture monitoring system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the soil moisture monitoring system, the soil moisture monitoring system implements the soil moisture monitoring method provided in the above embodiment.

[0093] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0094] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0095] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A soil moisture monitoring method, characterized in that: Applied to a soil moisture monitoring system, the method comprises: Acquire the moisture content data of multiple soil monitoring points and the moisture content change rate between adjacent soil monitoring points, and when the moisture content of a target area continues to decrease within a preset time period and the decrease rate is less than a preset ratio of the average moisture content change of the surrounding area, mark the target area as a crack sensitive area; Based on the spatial distribution of the fracture sensitive area, the dominant water flow path is calculated based on the minimum path algorithm to obtain a water flow path distribution map, wherein the water flow path distribution map includes a plurality of water flow channels connecting the fracture sensitive area; Arranging dominant flow monitoring points along the water flow path distribution map, calculating the water seepage rate between the dominant flow monitoring points, and when the seepage rate between two adjacent dominant flow monitoring points exceeds a preset seepage rate threshold, recording the area between the two adjacent dominant flow monitoring points as a fast seepage area; Connecting the fast seepage areas according to the water conduction time sequence to form a dominant seepage channel, wherein the dominant seepage channel represents a high permeability area formed in the soil; The geographic location of the dominant seepage channel is sent to a target client.

2. The method according to claim 1, characterized in that The step of calculating the dominant water flow path based on the spatial distribution of the fracture sensitive area and the minimum path algorithm to obtain the water flow path distribution map specifically includes: Establishing three-dimensional grid nodes based on the spatial position of the fracture sensitive area, and calculating the distance weights between adjacent nodes in the three-dimensional grid nodes, wherein the distance weights are proportional to the actual distances between the adjacent nodes and the difference in water content; The minimum weight path is calculated with the soil surface node as the starting point and the groundwater level node as the end point to obtain the initial water flow path; Taking the nodes of the fracture sensitive areas as necessary points, calculating the minimum weight path between the necessary points according to the initial water flow path, and obtaining a potential channel connecting each of the fracture sensitive areas; A water flow path distribution map is generated according to the spatial distribution of the potential channels, and the path width in the water flow path distribution map is inversely proportional to the weight value of the potential channels.

3. The method according to claim 1, characterized in that After the step of sending the geographical location of the dominant seepage channel to the target client, the method further includes: Calculating the position coincidence degree according to the water flow path distribution map and the spatial distribution of the dominant seepage channel; When the position fit is lower than a preset threshold, the determination parameter of the fracture sensitive area is adjusted to the preset parameter threshold, and the position fit represents the degree of overlap between the predicted path and the actual seepage channel.

4. The method according to claim 3, characterized in that The step of calculating the position coincidence degree according to the water flow path distribution map and the spatial distribution of the dominant seepage channel specifically includes: Converting the water flow path distribution map and the spatial distribution of the dominant seepage channel into three-dimensional grid data with the same resolution, wherein the value of the grid unit in the three-dimensional grid data is 0 or 1, and 1 indicates the existence of a channel; Calculating the number of grid cells that overlap spatially between the water flow path distribution diagram and the dominant seepage channel to obtain the number of overlapping cells; Calculate the total number of grid cells occupied by the water flow path distribution diagram and the dominant seepage channel respectively to obtain the number of path cells and the number of channel cells; The position matching degree is calculated according to the number of overlapping units, the number of path units and the number of channel units, and the position matching degree is equal to the number of overlapping units divided by the minimum value of the number of path units and the number of channel units.

5. The method according to claim 3, characterized in that: After the step of adjusting the determination parameter of the crack sensitive area to a preset parameter threshold when the position fit is lower than a preset threshold, the method further includes: When the area of ​​the crack sensitive zone exceeds the first area threshold and the water content continues to increase within a preset time period, a first-level warning is triggered; When the seepage velocity between the fast seepage areas exceeds the second seepage threshold, a secondary warning is triggered; When the spatial connectivity of the dominant seepage channel exceeds the third connectivity threshold, a third-level warning is triggered.

6. The method according to claim 4, characterized in that After the step of calculating the position fit according to the water flow path distribution map and the spatial distribution of the dominant seepage channel, and adjusting the determination parameter of the fracture sensitive area to the preset parameter threshold when the position fit is lower than the preset threshold, wherein the position fit represents the degree of overlap between the predicted path and the actual seepage channel, the method further comprises: Collecting soil bulk density data and calculating the bulk density change rate of the soil area around the dominant seepage channel; When the bulk density change rate exceeds a preset bulk density threshold, a regional monitoring instruction is sent to the target client, and the regional monitoring instruction includes setting settlement monitoring points in the surrounding soil area.

7. The method according to claim 6, characterized in that When the bulk density change rate exceeds a preset bulk density threshold, a regional monitoring instruction is sent to the target client, wherein the regional monitoring instruction is included in the step of arranging settlement monitoring points in the surrounding soil area, and the method further includes: Acquiring settlement data of the settlement monitoring point, and establishing a corresponding relationship curve between the settlement data and soil moisture content; Based on the corresponding relationship curve, determining a safe threshold range of the soil moisture content; The soil moisture content change trend is monitored in real time, and when the soil moisture content is about to exceed the moisture content safety threshold range, an alarm message is sent to the target client.

8. A soil moisture monitoring system, characterized in that: The soil moisture monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the soil moisture monitoring system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a soil moisture monitoring system, the soil moisture monitoring system is caused to perform the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on a soil moisture monitoring system, the soil moisture monitoring system is enabled to perform the method according to any one of claims 1 to 7.

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