A soil moisture monitoring method, system, program product and storage medium

Abstract

A soil moisture monitoring method, system, program product and storage medium, which relate to the field of electrical digital data processing. The method includes: when the moisture content of a target area continuously decreases within a preset time period and the decreasing speed is less than a preset ratio of the average moisture content change of the surrounding area, marking the target area as a fracture-sensitive area, calculating the dominant water flow path based on the minimum path algorithm to obtain a water flow path distribution map, arranging dominant flow monitoring points along the water flow path distribution map, calculating the moisture seepage velocity between the dominant flow monitoring points, and when the seepage velocity between two adjacent dominant flow monitoring points exceeds a preset seepage velocity threshold, recording it as a rapid seepage area; connecting the rapid seepage areas according to the moisture conduction time sequence to form a dominant seepage channel, and the dominant seepage channel represents a highly permeable area formed in the soil; sending the geographical location of the dominant seepage channel to a target client. Implementing this method can improve the accuracy of soil moisture monitoring.

Description

Technical Field

[0001] This 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 index for preventing debris flows, landslides and soil and water conservation. Its monitoring is of great significance for flash flood disaster warning and geological disaster prevention. The characteristics of water movement in soil not only reflect the soil water content, but also can reveal the evolution process of soil structure, which has important guiding value for predicting and preventing soil erosion and evaluating mountain stability.

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

[0004] However, in mountainous and hilly areas prone to geological disasters, there may be natural fissures or structural planes inside the soil. Coupled with the influence of natural factors such as rainfall infiltration, the water movement shows obvious non-linear characteristics. In this case, the preferential flow phenomenon will occur, which is manifested as a relatively rapid change in the water content in a local area, while the change in the water content in the adjacent area is relatively slow. The prediction accuracy of the traditional monitoring scheme 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 discover potential geological disaster hazards. Summary of the Invention

[0005] This 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. The method includes: obtaining the moisture content data of multiple soil monitoring points and the moisture content change rate between adjacent soil monitoring points; when there is a target area where the moisture content continuously decreases within a preset time period and the decrease rate is less than a preset ratio of the average moisture content change rate of 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 to obtain a water flow path distribution map, which includes multiple water flow channels connecting the fracture-sensitive area; arranging dominant flow monitoring points along the water flow path distribution map and calculating the moisture infiltration rate between the dominant flow monitoring points; when the infiltration 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 rapid seepage area; connecting the rapid seepage areas according to the moisture conduction time sequence to form a dominant seepage channel, which represents a highly permeable area formed in the soil; and sending the geographical location of the dominant seepage channel to a target client.

[0007] By adopting the above technical solution, the moisture content data of the soil monitoring points are obtained and the fracture-sensitive area is identified. Based on the minimum path algorithm, the dominant water flow path is calculated to obtain the water flow path distribution map. Then, the dominant flow monitoring points are arranged along the water flow path and the infiltration rate is calculated, so as to identify the rapid seepage area. The rapid seepage areas are connected according to the moisture conduction time sequence to form the dominant seepage channel, enabling the system to comprehensively grasp the spatial distribution characteristics of the highly permeable area in the soil, realizing the precise positioning and tracking monitoring of the soil moisture migration channel, establishing a complete monitoring chain from surface fractures to deep seepage, accurately reflecting the development characteristics of the dominant flow during the soil structure evolution process, and providing an effective monitoring means for timely discovering and preventing soil structure deterioration.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the dominant water flow path based on the minimum path algorithm according to the spatial distribution of the fracture-sensitive area to obtain a water flow path distribution map, which includes multiple water flow channels connecting the fracture-sensitive area, specifically includes: establishing three-dimensional grid nodes based on the spatial position of the fracture-sensitive area, calculating the distance weight between adjacent nodes in the three-dimensional grid nodes, and the distance weight is proportional to the actual distance and the 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 surface node as the ending point to obtain the initial water flow path; using the fracture-sensitive area nodes as the necessary passing points, and calculating the minimum weight path between the necessary passing points according to the initial water flow path to obtain the potential channels connecting each fracture-sensitive area; 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 positions of the fracture sensitive areas, and the distance weights between the nodes are calculated. Taking the soil surface nodes as the starting points and the groundwater level surface nodes as the ending points, the minimum weight path is calculated to obtain the initial water flow path. The nodes in the fracture sensitive areas are used as the necessary passing points to calculate the minimum weight path, and the potential channels connecting the fracture sensitive areas are obtained. According to the spatial distribution of the potential channels, a water flow path distribution map is generated. This solution establishes a complete water migration path prediction model, realizes the quantitative characterization of the preferential water flow channels, forms a water flow network structure with the fracture sensitive areas as the key nodes, and makes the spatial distribution characteristics of the preferential flow channels more intuitive and clear.

[0010] Combined with some embodiments of the first aspect, in some embodiments, after the step of sending the geographical location of the preferential seepage channel to the target client, the method further includes: calculating the position matching degree according to the water flow path distribution map and the spatial distribution of the preferential seepage channel; when the position matching degree is lower than a preset threshold, adjusting the determination parameter of the fracture sensitive area to a preset parameter threshold, where the position matching degree represents the coincidence degree between the predicted path and the actual seepage channel.

[0011] By adopting the above technical solution, the position matching degree is calculated according to the water flow path distribution map and the spatial distribution of the preferential seepage channel, and the determination parameter of the fracture sensitive area is adjusted when the position matching degree is lower than the preset threshold. A quantitative comparison mechanism between the prediction result and the actual monitoring data is established. By adaptively adjusting the parameters, the prediction accuracy is continuously improved, the dynamic optimization and self-correction of the prediction model are realized, enabling the monitoring system to adjust the prediction parameters in a timely manner according to the actual monitoring results, and enhancing the accuracy and reliability of the identification of the preferential flow channels.

[0012] Combined with some embodiments of the first aspect, in some embodiments, the step of calculating the position matching degree according to the water flow path distribution map and the spatial distribution of the preferential seepage channel specifically includes: converting the water flow path distribution map and the spatial distribution of the preferential seepage channel into three-dimensional grid data with the same resolution, where 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 units overlapping in space between the water flow path distribution map and the preferential seepage channel to obtain the number of overlapping units; respectively calculating the total number of grid units occupied by the water flow path distribution map and the preferential seepage channel to obtain the number of path units and the number of channel units; calculating the position matching degree 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.

[0013] By adopting the above technical solution, the water flow path distribution map and the spatial distribution of the dominant seepage channels are converted into three-dimensional grid data with the same resolution, the number of overlapping grid cells is calculated, and the position coincidence degree is calculated by combining 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, converting the similarity degree of the spatial form into an accurate numerical representation, eliminating the comparison deviation at different scales, establishing a quantitative comparison standard between the prediction result and the actual monitoring data, and making the prediction accuracy evaluation more objective and reliable.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after the step of adjusting the determination parameter of the fracture sensitive area to the preset parameter threshold when the position coincidence degree is lower than the preset threshold, the method further includes: when the area of the fracture sensitive area exceeds the first area threshold and the water content continuously increases within a preset time period, triggering a first-level warning; when the seepage velocity between the fast seepage regions exceeds the second seepage threshold, triggering a second-level warning; when the spatial connectivity of the dominant seepage channels exceeds the third connectivity threshold, triggering a third-level warning.

[0015] By adopting the above technical solution, a first-level warning is triggered based on the area of the fracture sensitive area and the change in water content, a second-level warning is triggered based on the seepage velocity between the fast seepage regions, and a third-level warning is triggered based on the spatial connectivity of the dominant seepage channels. The key characteristic indexes in the process of soil structure evolution are associated with the warning levels, making the warning information more targeted and timely, and ensuring that the monitoring system responds promptly to soil structure changes of different degrees.

[0016] Combined with some embodiments of the first aspect, in some embodiments, after the step of comparing the water flow path distribution map with the spatial distribution of the dominant seepage channels, calculating the position coincidence degree, and adjusting the determination parameter of the fracture sensitive area when the position coincidence degree is lower than the preset threshold, where the position coincidence degree represents the overlapping degree of the predicted path and the actual seepage channel, the method further includes: collecting soil bulk density data and calculating the bulk density change rate of the soil body 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 arranging settlement monitoring points in the surrounding soil body area.

[0017] By adopting the above technical solution, soil bulk density data is collected and the bulk density change rate of the soil body around the dominant seepage channel is calculated. When the change rate exceeds the preset threshold, a regional monitoring instruction is sent to arrange settlement monitoring points. Taking the soil bulk density as a quantitative index of structural stability, an evaluation mechanism for the influence of the seepage channel on the surrounding soil body is established, realizing the monitoring extension from the water movement characteristics to the soil body structure change, forming a three-dimensional monitoring network centered on the dominant seepage channel, and making the overall stability evaluation of the soil structure by the monitoring system more comprehensive and in-depth.

[0018] In some embodiments in combination with some embodiments of the first aspect, when the bulk density change rate exceeds a preset bulk density threshold, a regional monitoring instruction is sent to the target client. After the step of arranging settlement monitoring points in the surrounding soil body area included in the regional monitoring instruction, the method further includes: obtaining settlement data of the settlement monitoring points, and establishing a corresponding relationship curve between the settlement data and the soil moisture content; based on the corresponding relationship curve, determining a safe threshold range of the soil moisture content; and real-time monitoring the change trend of the soil moisture content, and when there is a situation that the soil moisture content is about to exceed the safe threshold range of the water content, sending an alarm message to the target client.

[0019] By adopting the above technical solution, settlement data of the settlement monitoring points is obtained and a corresponding relationship curve with the soil moisture content is established. Based on this curve, a safe threshold range of the soil moisture content is determined. The change trend of the moisture content is monitored in real time and an alarm is issued when it is about to exceed the safe threshold. A quantitative relationship model between the settlement amount and the moisture content is established, associating the soil body deformation with the change of the moisture content, forming a moisture content control standard based on structural stability, and realizing an intelligent determination mechanism for inferring the safe range of the moisture content from the soil body deformation characteristics.

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

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, and when the above computer program product runs on the soil moisture monitoring system, the above soil moisture monitoring system is enabled to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, and when the above instructions run on the soil moisture monitoring system, the above soil moisture monitoring system is enabled to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the soil moisture monitoring system provided in the second aspect above, 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 embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. By obtaining the water content data of soil monitoring points and identifying the fracture-sensitive areas, calculating the dominant water flow paths based on the minimum path algorithm to obtain the water flow path distribution map, and then arranging dominant flow monitoring points along the water flow paths and calculating the seepage velocity, the present application can identify the rapid seepage areas. Connecting the rapid seepage areas according to the water conduction time sequence to form the dominant seepage channels enables the system to comprehensively master the spatial distribution characteristics of highly permeable areas in the soil, realizes the precise positioning and tracking monitoring of the soil water migration channels, establishes a complete monitoring chain from surface fractures to deep seepage, accurately reflects the development characteristics of the dominant flow during the soil structure evolution process, and provides an effective monitoring means for timely discovering and preventing soil structure deterioration.

[0026] 2. By establishing three-dimensional grid nodes based on the spatial positions of the fracture-sensitive areas and calculating the distance weights between the nodes, and calculating the minimum weight path with the soil surface nodes as the starting point and the groundwater level surface nodes as the ending point to obtain the initial water flow path. Calculating the minimum weight path with the fracture-sensitive area nodes as the necessary passing points to obtain the potential channels connecting each fracture-sensitive area, and generating the water flow path distribution map according to the spatial distribution of the potential channels. This solution establishes a complete water migration path prediction model, realizes the quantitative characterization of the dominant water flow channels, forms a water flow network structure with the fracture-sensitive areas as the key nodes, and makes the spatial distribution characteristics of the dominant flow channels more intuitive and clear.

[0027] 3. By converting the water flow path distribution map and the spatial distribution of the dominant seepage channels into three-dimensional grid data with the same resolution, calculating the number of overlapping grid cells, and calculating the position coincidence degree in combination with the number of path cells and the number of channel cells, the present application establishes a standardized evaluation index based on the ratio of the number of overlapping cells to the minimum number of cells, converts the similarity degree of the spatial form into an accurate numerical representation, and makes the prediction accuracy evaluation more objective and reliable. Description of the Drawings

[0028] Figure 1 is a flowchart of the soil moisture monitoring method in the embodiments of the present application;

[0029] Figure 2 is another flowchart of the soil moisture monitoring method in the embodiments of the present application;

[0030] Figure 3 is a schematic structural diagram of an entity device of the soil moisture monitoring system in the embodiments of the present application. Detailed Embodiments

[0031] 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 limit the present application. As used in the specification of the present application, the singular forms "a", "an", "above-mentioned", "the" and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are only for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0033] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0034] In a mountainous watershed area of 50 square kilometers, the terrain is mainly dominated by steep slopes and terraced fields, with an altitude drop of more than 1000 meters. The vegetation cover includes shrub forests and alpine meadows. The annual rainfall in this area reaches 1500 millimeters, and 70% of it is concentrated in the flood season from June to September. At the same time, there are multiple faults and joint zones. In recent years, landslides and debris flow disasters have frequently occurred in this area, posing a serious threat to the downstream reservoir and residential areas. Preliminary investigations found that these disasters are closely related to the rapid change of soil moisture content. After a continuous rainfall, a large area of landslide occurred on a certain hillside. Post-event analysis showed that the soil moisture content distribution around the landslide body was extremely uneven, and the moisture content difference between adjacent positions was more than 25%. Further exploration found that there are multiple concealed fissures in the mountain body, forming a complex groundwater flow network. These preferential flow channels not only accelerate the infiltration process of rainwater but also cause a significant reduction in the strength of local soil masses. 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.

[0035] A certain research institution adopted traditional soil moisture monitoring methods in a typical mountainous test area. 250 soil moisture sensors were evenly arranged within a range of 10 hectares according to a grid of 20 meters × 20 meters, and the monitoring depths were 0.5 meters, 1 meter, 2 meters, and 3 meters below the ground surface respectively. The monitoring system collected data every 30 minutes and used the Kriging interpolation algorithm to generate soil moisture distribution maps. During a rainfall process with an accumulated rainfall of 180 millimeters, the system showed that the average moisture content in the test area increased from 22% to 35%, presenting an overall uniform upward trend. However, on-site investigations found that there were seepage and local deformation phenomena in many mountain areas. 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 values shown by the monitoring data. Due to the use of a linear interpolation model in the traditional monitoring method, the data in abnormal areas were averaged, resulting in the masking of preferential flow channels. At the same time, due to the fixed positions of the monitoring points and limited sampling density, it was impossible to accurately capture local rapid seepage phenomena, nor could the monitoring strategy be adjusted in a timely manner according to the dynamic changes of the soil structure, so that the early signs of landslide disasters were not effectively identified.

[0036] In a key protection area upstream of a reservoir, a soil moisture monitoring system based on this solution was installed. The area of this region 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 abnormal decreases in moisture content through the initially deployed network of 1000 basic sensors and marked them as crack-sensitive areas. These areas are mostly distributed near faults and joint zones, and the daily average rate of decrease in moisture content reaches more than 3 times that of the surrounding areas. Subsequently, based on the improved minimum path algorithm, the system calculated the possible water flow channels between these crack-sensitive areas and generated a preliminary water flow path distribution map. 200 high-precision monitoring points were added on the predicted water flow paths, and the sampling frequency was increased to once every 5 minutes. By real-time tracking the water movement speed, 8 rapid seepage areas were successfully captured, and the maximum seepage speed was measured to reach 15 centimeters per hour. The system connected these seepage areas according to the water conduction time sequence and accurately outlined an underground preferential seepage channel network with a total length of more than 2 kilometers. When it was monitored that the bulk density of the soil around a certain seepage channel decreased by 8% within 7 days, the system automatically deployed 25 settlement monitoring points. Through continuous observation, a quantitative relationship model between moisture content and settlement was established. During a rainfall process lasting 72 hours, the system predicted that the moisture content in 3 areas would exceed the safety threshold within 4 hours, triggering a level-3 early warning in advance, enabling the flood control department to promptly carry out the transfer of the masses and emergency response, and successfully avoiding a major landslide disaster. The application of this system proves that the intelligent monitoring solution based on preferential flow identification can effectively improve the accuracy and timeliness of geological disaster early warning.

[0037] For ease of understanding, the method provided in this implementation will be described in terms of its process in combination with the above scenarios. Please refer to Figure 1, which is a schematic flowchart of the soil moisture monitoring method in an embodiment of this application.

[0038] S101. Obtain the 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 continuously decreases within a preset time period and the decrease rate is less than a preset ratio of the average moisture content change rate of the surrounding area, mark this target area as a fissure-sensitive area.

[0039] Among them, the soil monitoring point represents the position of the sensor deployed in the soil for monitoring the moisture content; the moisture content data refers to the measured value of the moisture content in the soil per unit volume; the moisture content change rate represents the change amount of the moisture content per unit time; the target area refers to the continuous soil body range with specific moisture content change characteristics; the fissure-sensitive area is used to represent the soil area where a dominant water flow channel may be formed.

[0040] Specifically, this step is executed after deploying the soil moisture content monitoring network. First, through the sensor network distributed in the monitoring area, the moisture content data of each monitoring point is collected in real time, and the moisture content change rate between adjacent monitoring points is calculated. Then, local areas where the moisture content continuously decreases and the decrease rate is significantly lower than the average value of the surrounding area are screened out and marked as fissure-sensitive areas. These areas are the precursor positions where the soil structure changes.

[0041] In some embodiments, the identification of the fissure-sensitive area can be achieved in the following ways: Optionally, first divide the monitoring area into grids, calculate the average moisture content and its change rate of each grid, and establish a moisture content spatio-temporal change database; then set a time window and analyze the moisture content change trend of each grid within this time window; finally, mark the areas that meet the conditions according to the preset determination rules. Optionally, a clustering analysis method can also be used to cluster areas with similar moisture content change characteristics and identify the abnormal change areas as fissure-sensitive areas. It can be understood that other data analysis methods can also be used to achieve the identification and marking of the fissure-sensitive area.

[0042] S102. Based on the spatial distribution of this fissure-sensitive area, calculate the dominant water flow path based on the minimum path algorithm to obtain a water flow path distribution map, and this water flow path distribution map contains multiple water flow channels connecting this fissure-sensitive area.

[0043] Among them, the spatial distribution represents the positional relationship of the fissure-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.

[0044] Specifically, this step is executed after obtaining the distribution of fracture sensitive areas. First, a three-dimensional network model is established based on the spatial positions of the fracture sensitive areas, and the weights between nodes in the network are calculated. Then, using the minimum path algorithm, with the ground surface as the starting point and the groundwater level surface as the ending point, the minimum weight path passing through the fracture sensitive areas is calculated. Finally, the multiple calculated paths are integrated to generate a distribution map representing the possible paths of water migration.

[0045] In some embodiments, the calculation of the water flow path can be achieved in the following manner: Optionally, a soil water movement potential field is constructed based on the monitoring data, and the steepest descent method is used to calculate the water movement path to obtain the local preferential flow path; then, multiple local paths are connected to form a complete water flow network. Optionally, the probability graph model method can also be used to construct the transition probability matrix between nodes and calculate the maximum probability path as the water flow channel. It can be understood that other path optimization algorithms can also be used to achieve the calculation and connection of the water flow path.

[0046] This step specifically includes:

[0047] Three-dimensional grid nodes are established based on the spatial positions of the fracture sensitive areas, and the distance weights between adjacent nodes in the three-dimensional grid nodes are calculated. The distance weight is proportional to the actual distance and the difference in water content between the adjacent nodes;

[0048] Taking the soil surface node as the starting point and the groundwater level surface node as the ending point, the minimum weight path is calculated to obtain the initial water flow path;

[0049] Taking the fracture sensitive area nodes as the necessary passing points, the minimum weight path between the necessary passing points is calculated according to the initial water flow path to obtain the potential channels connecting each fracture sensitive area;

[0050] A water flow path distribution map is generated according to the spatial distribution of the potential channels. The path width in the water flow path distribution map is inversely proportional to the weight value of the potential channel.

[0051] A three-dimensional grid node is a computational 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 minimum total weight connecting two points; the necessary passing point refers to the key position through which water flow must pass; the potential channel refers to the predicted possible path of water flow; the water flow path distribution map refers to the spatial distribution map describing 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 10 cm × 10 cm × 10 cm. The system calculates the distance weight between each pair of adjacent nodes, and the weight value W = k×D×ΔH, where k is the proportionality coefficient (taking the value of 1.0), D is the actual distance between nodes (unit: centimeter), 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 passing points and uses the segmented minimum-weight path algorithm to calculate the connection paths between the necessary passing points. The specific calculation process is as follows: first, determine the spatial coordinates and access order of the necessary passing points, then calculate the minimum-weight paths between adjacent necessary passing points in sequence, and finally connect all the segmented paths to form a complete potential channel. The system generates a water flow path distribution map according to the calculated potential channel, and the path width B = C / W, where C is the proportionality constant (taking the value of 100) and W is the cumulative weight value of the channel. For example, when the cumulative weight value of a certain segment of the potential channel is 500, the corresponding path width is 0.2 cm. In this way, the system completes the prediction process from the fracture-sensitive area to the water flow path distribution map.

[0052] S103. Arrange preferential flow monitoring points along the water flow path distribution map, calculate the moisture infiltration velocity between these preferential flow monitoring points, and when the infiltration velocity between two adjacent preferential flow monitoring points exceeds the preset seepage velocity threshold, record the area between the two adjacent preferential flow monitoring points as the rapid seepage area.

[0053] Among them, the preferential flow monitoring point represents the position of a high-precision sensor arranged on the water flow path; the moisture infiltration velocity refers to the migration distance of moisture in the soil per unit time; the seepage velocity threshold represents the velocity critical value for determining rapid seepage; the rapid seepage area is used to represent the soil section where the moisture movement is extremely active; the adjacent monitoring points refer to two sensor measurement points that are adjacent in spatial position.

[0054] This step is executed after obtaining the water flow path distribution map. Specifically, first, key positions are determined based on the water flow path distribution map, and high-precision soil moisture sensors are deployed at these positions as preferential flow monitoring points. By collecting the water content data of the preferential flow monitoring points in real time, the water infiltration rate between adjacent monitoring points is calculated. 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 preferential flow channel in this area, and it is marked as a rapid infiltration area. These areas are often where the soil structure has changed.

[0055] In some embodiments, the identification and marking of the rapid infiltration area can be achieved in various ways: Optionally, first, a spatio-temporal database of the monitoring point network is established, and time series analysis is performed on the water content data of each monitoring point to calculate the water transfer rate between the monitoring points; then, multiple levels of speed thresholds are set, and the areas are classified and marked according to the magnitude of the infiltration rate; finally, in combination with the spatial distribution characteristics, the scope and boundary of the rapid infiltration area are determined. Optionally, the tracer test method can also be used. Tracer is released at the preferential flow monitoring points, and the migration speed and diffusion range of the tracer are tracked and recorded; then, the migration speed of the tracer in different areas is calculated; finally, the rapid infiltration area is determined according to the speed distribution characteristics. It can be understood that other monitoring and analysis methods can also be used to identify the rapid infiltration area, which is not limited here.

[0056] S104. Connect the rapid infiltration areas in the order of water conduction time series to form a preferential infiltration channel, which represents a highly permeable area formed in the soil.

[0057] Among them, the water conduction time series represents the time sequence of water transmission in the soil; the preferential infiltration channel refers to the preferential water flow channel formed in the soil; the highly permeable area represents a continuous area where the permeability coefficient is significantly higher than that of the surrounding soil mass; the spatial connectivity is used to represent the integrity and continuity of the infiltration channel.

[0058] This step is executed after identifying multiple rapid infiltration areas. Specifically, first, the water conduction relationship between the rapid infiltration areas is analyzed to determine the sequence of water transmission. Then, according to the time sequence relationship, the spatially adjacent and hydraulically connected rapid infiltration areas are connected to form a continuous preferential infiltration channel. These channels often have a high permeability coefficient and are the main paths for the rapid migration of water and solutes in the soil.

[0059] In some embodiments, the construction of the preferential seepage channels can be achieved in various ways: Optionally, first perform spatio-temporal clustering analysis on the rapid seepage areas to identify groups of areas with similar water conduction characteristics; then calculate the hydraulic gradient and connectivity index between each group of areas; finally, determine the spatial orientation of the preferential seepage channels based on the principle of minimum resistance. Optionally, a hydrogeological modeling method can also be used to construct a three-dimensional seepage field model including the rapid seepage areas; then simulate and calculate the water conduction process between each area; finally, extract the main seepage paths as the preferential seepage channels. It can be understood that other analysis methods can also be used to achieve the construction of the preferential seepage channels, which are not limited here.

[0060] S105. Send the geographical location of the preferential seepage channel to the target client.

[0061] Among them, the geographical location refers to the spatial distribution information of the preferential seepage channel in the geographic coordinate system; the target client represents 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.

[0062] This step is executed after determining the spatial distribution of the preferential seepage channel. Specifically, first convert the spatial distribution data of the preferential seepage channel into a standard geographic information format, including the coordinate information of the starting point, ending point, and key nodes of the seepage channel, as well as geometric feature parameters such as the width and depth of the seepage channel. Then, through the data transmission network, send this spatial information to the target clients such as the agricultural management department and the water conservancy department in real time, providing a decision-making basis for subsequent irrigation regulation and soil structure maintenance. These information is of great guiding significance for preventing soil structure deterioration and optimizing irrigation plans.

[0063] In some embodiments, the sending and display of the location information of the preferential seepage channel can be achieved in various ways: Optionally, first convert the spatial data of the seepage channel into a standard GIS format to generate a vector layer containing attribute information; then overlay the layer on the base map to form an intuitive distribution schematic diagram; finally, push the real-time updated seepage channel distribution information to the target client through the Web service interface. Optionally, a three-dimensional visualization method can also be used. First, construct a three-dimensional model of the seepage channel, mark the key feature points and parameters; then generate a three-dimensional display interface with multiple perspectives; finally, push an interactive three-dimensional seepage channel distribution map to the target user through a mobile application. It can be understood that other data transmission and display methods can also be used to send the location information of the preferential seepage channel, which are not limited here.

[0064] The following is a further and more specific process description of the method provided in this embodiment. Please refer to Figure 2, which is another schematic flowchart of the soil moisture monitoring method in the embodiments of this application.

[0065] S201. Convert the water flow path distribution map and the spatial distribution of the dominant seepage channels into three-dimensional grid data with the same resolution, where the value of each grid cell in the three-dimensional grid data is 0 or 1, and 1 indicates the existence of a channel.

[0066] Among them, the three-dimensional grid data refers to a data structure that discretizes a continuous space into regular cube cells; the resolution represents the size of the grid cell; the grid cell is the basic unit of spatial division; the values 0 and 1 represent the binary representation methods of the non-existence and existence of channels respectively. The soil moisture monitoring system first obtains the water flow path distribution map and the spatial coordinate data of the dominant seepage channels, and determines the spatial range of the entire monitoring area. The monitoring area is divided into cube grid cells of equal size, and the grid size is determined by the actual monitoring accuracy requirements, usually set to 10 cm × 10 cm × 10 cm. Binary processing is performed on each grid cell: when the grid cell 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, respectively representing the predicted water flow path and the actual seepage channel distribution.

[0067] S202. Calculate the number of grid cells where the water flow path distribution map and the dominant seepage channels overlap in space to obtain the number of overlapping cells.

[0068] Among them, the overlapping grid cells refer to the grid cells where both channels exist at the same spatial position; the number of overlapping cells is used to quantify the coincidence degree of the spatial distributions of the two channels. The soil moisture monitoring system makes an element-by-element comparison of the two converted three-dimensional Boolean matrices. The specific calculation method is: perform an element-by-element multiplication operation on the two matrices to obtain a new three-dimensional matrix, where the elements with a value of 1 indicate that the two channels overlap at that 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 there are 60 elements with a value of 1 in the matrix obtained after element-by-element multiplication, then the number of overlapping cells is 60.

[0069] S203. Calculate the total number of grid cells occupied by the water flow path distribution map and the dominant seepage channels respectively to obtain the number of path cells and the number of channel cells.

[0070] Among them, the number of path units represents the total number of grid units with a value of 1 in the water flow path distribution map; the number of channel units represents the total number of grid units occupied by the dominant seepage channels. The soil moisture monitoring system performs statistical calculations on the 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 channels, the total number of elements with a value of 1 is counted to obtain the number of channel units. These two values respectively reflect the occupied volumes of the two types of channels in space. The statistical method uses matrix summation operation, and adding all the element values in the three-dimensional matrix can obtain the respective number of units. For example, in a monitoring area of 100×100×100, if the water flow path occupies 5000 grid units and the dominant seepage channels occupy 4000 grid units, then the number of path units is 5000 and the number of channel units is 4000.

[0071] S204. Calculate the position coincidence degree according to the number of overlapping units, the number of path units, and the number of channel units. The position coincidence 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.

[0072] Among them, the position coincidence degree refers to the spatial coincidence degree between the predicted water flow path and the actual seepage channels; the number of overlapping units represents the number of grid units jointly occupied by the two channels; the number of path units and the number of channel units respectively represent the number of grid units occupied by the water flow path and the seepage channels. The soil moisture monitoring system uses a standardized calculation method to determine the position coincidence degree. First, obtain the smaller value of the number of path units and the number of channel units as the normalization benchmark. Then divide the number of overlapping units by this benchmark value to obtain the position coincidence degree 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 coincidence degree is equal to 3000 / 4000 = 0.75, indicating that on the basis of the smaller channel volume, 75% of the spatial positions coincide.

[0073] S205. When the position coincidence degree is lower than the preset threshold, adjust the determination parameter of the fissure sensitive area to the preset parameter threshold. The position coincidence degree characterizes the coincidence degree between the predicted path and the actual seepage channels.

[0074] Among them, the preset threshold represents the critical value for determining whether the prediction result is acceptable; the determination parameters for the fissure sensitive area include control parameters such as the threshold of the water content change rate and the threshold of the duration; the preset parameter threshold refers to the range of parameter values determined according to experience. The soil moisture monitoring system calculates the position coincidence degree in real time and compares it with the preset threshold. When the position coincidence degree is lower than the preset threshold, it indicates that there is a large difference between the predicted water flow path and the actually formed seepage channel, and the parameters of the prediction model need to be adjusted. The system sequentially modifies the determination parameters of the fissure sensitive area, including adjusting the threshold of the water content change rate to a value within the range of 80% to 120% of the original value, and adjusting the threshold of the duration to a value within the range of ±30% of the original value. Through multiple iterative calculations, until a parameter combination that makes the position coincidence degree reach the preset threshold is found. For example, when the position coincidence degree is 0.6, which is lower than the preset threshold of 0.8, the system adjusts the threshold of the water content change rate from 2 mm / h to 1.8 mm / h, and adjusts the threshold of the duration from 24 h to 18 h, and re-identifies and predicts the fissure sensitive area.

[0075] S206. When the area of the fissure sensitive area exceeds the first area threshold and the water content continuously increases within the preset time period, a first-level warning is triggered.

[0076] Among them, the first area threshold refers to the critical area for determining the scale of the fissure sensitive area; the preset time period represents a fixed time window for observing the change of the water content; the first-level warning represents the lowest-level risk prompt. The soil moisture monitoring system conducts real-time monitoring and risk assessment on the fissure sensitive area. The system first calculates the area of each fissure sensitive area, and marks the area whose area exceeds the first area threshold (such as 100 square meters) as the object of key concern. At the same time, it monitors the change trend of the water content in these areas and records the water content data for 24 consecutive hours. When it is detected that the area of a certain area exceeds the threshold, and the average water content in this area continuously rises within the preset time period (such as 24 hours), and the increase amplitude exceeds 20% of the initial value, the system automatically triggers a first-level warning. The warning information includes data such as the position coordinates, area value, and water content change curve of the fissure sensitive area, and is sent to the relevant management personnel through the preset communication channel.

[0077] S207. When the seepage velocity between the rapid seepage areas exceeds the second seepage threshold, a second-level warning is triggered.

[0078] Among them, the rapid seepage area refers to the soil area where the water migration speed is abnormal; the seepage speed represents the migration rate of water in the soil; the second seepage threshold is the speed critical value that triggers a secondary warning; the secondary warning represents a medium-level risk warning. The soil moisture monitoring system continuously monitors the water migration status through the sensor network deployed in the rapid seepage area. The system obtains the water 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 5 cm / h), the system immediately triggers a secondary warning. The secondary warning information includes specific data such as the spatial coordinates of the rapid seepage area, the measured seepage speed, and the over-standard multiple. For example, the seepage speed between two adjacent rapid seepage areas reaches 7.5 cm / h, exceeding the preset second seepage threshold of 5 cm / h, and the system automatically issues a secondary warning message.

[0079] S208. When the spatial connectivity of the dominant seepage channel exceeds the third connectivity threshold, a tertiary warning is triggered.

[0080] Among them, the spatial connectivity refers to the integrity and penetration degree of the dominant seepage channel; the third connectivity threshold represents the critical value for determining the degree of network formation of the seepage channel; the tertiary warning represents the highest level of risk warning. The soil moisture monitoring system calculates the spatial connectivity based on the three-dimensional grid data of the dominant seepage channel. The connectivity calculation uses graph theory methods 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 tertiary 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, exceeding the preset third connectivity threshold of 0.8, and the system issues a tertiary warning message.

[0081] S209. Collect soil bulk density data and calculate the bulk density change rate of the soil body area around the dominant seepage channel.

[0082] Among them, soil bulk density refers to the dry weight of unit volume of soil; the bulk density change rate represents the change speed of soil bulk density over time; the surrounding soil mass area refers to the affected soil range outside the dominant seepage channel. The soil moisture monitoring system arranges bulk density sensors around the dominant seepage channel and collects soil bulk density data at a predetermined time interval. The collection process uses the fixed-depth sampling method to obtain soil samples at different distances from the seepage channel (such as 20 cm, 50 cm, 100 cm) and measures their bulk density values. The system records the bulk density data at multiple consecutive time points and calculates the change amount of bulk density per unit time. The calculation of the bulk density change rate uses the difference method: taking the initial bulk density value as the benchmark, calculating the relative change amount of the bulk density values at subsequent time points, and dividing by the corresponding time interval to obtain the bulk density change rate. For example, if the initial bulk density of a measurement point is 1.35 g / cm³ and the bulk density measured after 30 days is 1.28 g / cm³, then the bulk density change rate is -0.0023 g / (cm³·d).

[0083] S210. When the bulk density change rate exceeds the preset bulk density threshold, send a regional monitoring instruction to the target client, and the regional monitoring instruction includes arranging settlement monitoring points in the surrounding soil mass area.

[0084] 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; the settlement monitoring point represents a fixed measurement 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 it detects that the bulk density change rate exceeds the preset bulk density threshold (such as -0.005 g / (cm³·d)), the system automatically generates a regional monitoring instruction. The instruction includes the monitoring point layout plan, clearly stipulating that settlement monitoring points are set in a grid layout in the surrounding soil mass area of the seepage channel, the spacing between monitoring points is 10 meters, and the monitoring depths are 0.5 meters, 1 meter, and 2 meters below the ground surface respectively. The system sends the monitoring instruction to the target client through the data network, and the instruction contains technical parameters such as the specific coordinate positions, installation depths, and sampling frequencies of the monitoring points. For example, when the bulk density change rate in a certain area reaches -0.006 g / (cm³·d), the system generates an instruction to arrange a 3×3 settlement monitoring grid in this area.

[0085] S211. Obtain the settlement data of the settlement monitoring point and establish a corresponding relationship curve between the settlement data and soil moisture content.

[0086] Among them, the settlement data represents the time series of the soil compression deformation; the soil moisture content refers to the water content in the soil per unit volume; the corresponding relationship curve reflects the quantitative relationship between the settlement amount and the moisture content. The soil moisture monitoring system collects the real-time data of the settlement monitoring points and records the settlement amounts at different depths. At the same time, it obtains the soil moisture content data at the corresponding positions and establishes the corresponding relationship between the two sets of data. The system uses the least squares method to fit the functional relationship between the settlement amount and the moisture content and obtains a mathematical expression describing the variation law of the two. The data weights at different depths are considered in the fitting process, and the weights of the deep-layer data are larger. For example, when the system records the data of a certain monitoring point for 30 consecutive days and the moisture content increases from 15% to 25%, the surface accumulatively sinks by 15 mm. Through data fitting, a quadratic function relationship is obtained: settlement amount = 0.02×(moisture content - 15%)² + 0.5×(moisture content - 15%).

[0087] S212. Based on this corresponding relationship curve, determine the safe threshold range of this soil moisture content.

[0088] Among them, the safe threshold range refers to the allowable change interval of the soil moisture content; based on the corresponding relationship curve means using the established mathematical model to determine the threshold. The soil moisture monitoring system analyzes the corresponding relationship curve between the settlement data and the moisture content to determine the critical state of the soil structure stability. By calculating the slope change of the curve, the inflection point where the influence of the moisture content change on the settlement amount increases significantly is identified. The system takes the moisture content value corresponding to this inflection point as the upper threshold and takes the lowest moisture content required to maintain the basic functions 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 function requirements of the soil, 15% is set as the lower threshold, so as to determine the safe range of the moisture content as 15% - 30%. This range is used as an important basis for irrigation control and prevention measures.

[0089] S213. Real-time monitor the change trend of this soil moisture content. When there is a situation that the soil moisture content is about to exceed the safe threshold range of the water content, send an alarm message to the target client.

[0090] Among them, the change trend represents the dynamic change law of soil moisture content over time; the alarm information refers to the notification data indicating the abnormality of soil moisture content; the target client refers to the terminal device that receives the monitoring and early warning information. The monitoring system collects soil moisture content data through a distributed sensor network, and the sensor sampling frequency is once per hour. The system processes the collected data in real time and uses time series analysis methods to calculate the change rate and predicted value of the moisture content. The specific processing process includes: first, filtering the original data to remove outliers; then using the exponential smoothing method to calculate the change rate of the moisture content; finally, predicting the moisture content value for 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 alarm information. The alarm information includes specific contents such as the monitoring point location, the current moisture content value, the change rate, the predicted trend graph, and the expected time to exceed the threshold. For example, when the moisture content of a monitoring point is 28.5% and continues to rise at a rate of 0.5% per hour, and it is predicted that it will exceed the upper threshold of 30% after 4 hours, the system issues an alarm information in advance so that the management personnel can take preventive measures.

[0091] The following describes the soil moisture monitoring system in the embodiments of the present invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the soil moisture monitoring system in the embodiments of the present application.

[0092] 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 limitations to the functions and usage scopes of the embodiments of the present invention.

[0093] As Figure 3 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 section 308 into the random access memory (RAM) 303, such as executing the methods described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0094] 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), 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. The drive 310 is also connected to the I / O interface 305 as required. 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 required so that a computer program read from it can be installed into the storage section 308 as required.

[0095] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. 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 the network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are executed.

[0096] It should be noted that specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or component.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the block may occur in a different order from that marked in the accompanying drawings.

[0098] Specifically, the soil moisture monitoring system of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, it implements the soil moisture monitoring method provided in the above embodiment.

[0099] On the other hand, the present invention also provides a computer-readable storage medium, which may be included in the soil moisture monitoring system described in the above embodiment; or it may exist separately without being assembled into the soil moisture monitoring system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the soil moisture monitoring system, the soil moisture monitoring system is enabled to implement the soil moisture monitoring method provided in the above embodiment.

[0100] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application.

[0101] As used in the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0102] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disk, or optical disk 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; 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, wherein the distance weight is proportional to the actual distance between the adjacent nodes and the water content difference, and the distance weight is k*D*ΔH, wherein k is a proportionality coefficient, the proportionality coefficient is 1.0, D is the actual distance between nodes, the unit of the actual distance is centimeters, and ΔH is the water content difference between nodes, the unit of the water content difference is percentage; 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 the necessary points, the minimum weight path between the necessary points is calculated according to the initial water flow path to obtain a potential channel connecting each of the fracture sensitive areas, and the process of calculating the minimum weight path between the necessary points includes: determining the spatial coordinates and the access order of the necessary points, calculating the minimum weight path between adjacent necessary points in sequence, and connecting all segmented paths to form a complete potential channel; Generate a water flow path distribution map according to the spatial distribution of the potential channel, wherein the path width in the water flow path distribution map is inversely proportional to the weight value of the potential channel, and the path width is C / W, wherein C is a proportional constant, the proportional constant is 100, and W is a cumulative weight value of the channel, and the water flow path distribution map includes a plurality of water flow channels connecting the fracture sensitive areas; 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; Sending the geographical location of the dominant seepage channel to a target client; 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; Calculating the position fit according to the number of overlapping units, the number of path units and the number of channel units, wherein 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; 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.

2. The method according to claim 1, 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.

3. The method according to claim 2, 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.

4. The method according to claim 3, 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.

5. 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 1-4.

6. 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 execute the method according to any one of claims 1 to 4.

7. 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 4.

Images