Soil erosion early warning method and device based on climate change

Abstract

The application provides a soil erosion early warning method and device based on climate change, and relates to the technical field of machine learning. The method comprises the following steps: collecting surface temperature data and freeze-thaw state data of a target area in real time, determining the freeze-thaw cycle state of the surface active layer based on a set freeze-thaw state criterion; calculating the meltwater runoff generation of the surface active layer based on the freeze-thaw cycle state, and calculating the comprehensive runoff yield by combining the rainfall forecast data; dynamically calculating the surface unit scouring force based on the comprehensive runoff yield, and determining whether the surface unit scouring force reaches the erosion starting state based on the soil instability limit shear threshold value under the freeze-thaw interface; when the surface freeze-thaw conversion state superimposes the effective meltwater runoff and reaches the preset percentage, and the surface unit scouring force exceeds the soil instability limit shear threshold value, the soil erosion early warning is triggered. The application can early warn the soil erosion in the alpine region according to the climate change.

Description

Technical Field

[0001] This application relates to the technical field of machine learning, specifically to a method and device for early warning of soil erosion based on climate change. Background Technology

[0002] Soil and water loss early warning refers to a dynamic management activity that uses continuous monitoring and dynamic analysis of surface environmental conditions, hydrological and meteorological changes, and soil erosion mechanisms, employing methods such as physical models, statistical inference, or machine learning, to assess the potential risks of soil erosion in a region in real time, and to issue risk warnings in advance when soil structure damage and sediment loss are predicted to reach or are about to reach a set threshold, and to coordinate response measures to prevent or mitigate the degradation of soil and water resources, loss of ecological functions, and secondary disasters.

[0003] Early warning systems for soil erosion in high-altitude and cold regions based on climate change are of great significance in addressing the deep loosening of soil structure and thermal thaw collapse caused by permafrost degradation due to global warming. By monitoring the dynamics of surface freeze-thaw cycles, the evolution of effective meltwater runoff, and changes in surface mechanical stability in real time, these systems can identify high-risk states for large-scale erosion and debris flows in surface soil in freeze-thaw zones under the influence of extreme summer rainfall events. This effectively prevents ecological damage, infrastructure destruction, and the spread of secondary disasters, providing scientific, efficient, and forward-looking technical support for ecological protection, engineering safety, and climate adaptation management in high-altitude and cold regions. Summary of the Invention

[0004] This application provides a method and device for early warning of soil erosion based on climate change, which can provide early warning of soil erosion in high-altitude and cold regions based on climate change.

[0005] The first aspect of this application provides a method for early warning of soil erosion based on climate change, the method comprising:

[0006] Real-time acquisition of surface temperature and freeze-thaw status data of the target area; determination of freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria.

[0007] Based on the freeze-thaw cycle status, geothermal gradient data, frozen soil thickness data, surface roughness data, and initial pore water saturation data, the meltwater runoff generation of the active surface layer is estimated, and the comprehensive runoff production is estimated in combination with rainfall forecast data.

[0008] Based on the comprehensive runoff, the surface scour force is dynamically calculated, and the soil instability limit shear threshold at the freeze-thaw interface is used to determine whether the surface scour force has reached the erosion initiation state.

[0009] When the surface freeze-thaw transition state combined with the effective meltwater runoff reaches a preset percentile, and the surface unit scour force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered.

[0010] Based on the above technical solutions, preferably, the real-time acquisition of surface temperature data and freeze-thaw status data of the target area, and the determination of the freeze-thaw cycle status of the active surface layer based on a set freeze-thaw status criterion, specifically includes:

[0011] By deploying a surface temperature sensor array and a geothermal depth gradient probe in the target area, and combining it with satellite remote sensing observation data, a joint acquisition system for multi-source surface temperature data and freeze-thaw status data is formed. The acquisition frequency of the surface temperature data is the same as the acquisition frequency of the freeze-thaw status data.

[0012] According to the surface temperature data, when the surface temperature remains below a preset first temperature for a preset period of time and the rate of change of the ground temperature gradient is in a downward trend, the active surface layer is determined to be in a frozen state.

[0013] When the surface temperature is consistently higher than the preset second temperature and the rate of change of the ground temperature gradient changes from negative to positive, and the daily average surface temperature rise reaches the preset threshold, and the freeze-thaw state data detects that the position of the surface freeze-thaw interface has moved up beyond the preset thickness change value, the active surface layer is determined to be in a melting state.

[0014] When the surface temperature is between the preset second temperature and the preset first temperature, and the daily amplitude of the ground temperature gradient exceeds the preset amplitude, the active surface layer is determined to be in a critical transition state.

[0015] Alternatively, if the upward movement rate of the surface freeze-thaw interface exhibits unstable fluctuations, and the freeze-thaw state data contains records of high-frequency alternating changes between frozen and thawed states, then the active surface layer is determined to be in a critical transition state.

[0016] Based on the above technical solutions, preferably, the calculation of meltwater runoff generation in the active surface layer based on the freeze-thaw cycle status and geothermal gradient data, permafrost thickness data, surface roughness data, and initial pore water saturation data specifically includes:

[0017] Based on the freeze-thaw cycle state, it is determined whether the active surface layer is currently in the frozen state, the thawing state, or the critical transition state, wherein the frozen state corresponds to zero meltwater production, and the thawing state and the critical transition state trigger meltwater runoff generation calculation.

[0018] Based on the geothermal gradient data, the heat flux variation trend from the surface to the freezing interface is calculated, the daily movement rate of the freeze-thaw interface is dynamically calculated using the heat diffusion formula, and the effective thawing thickness of the active surface layer is determined in combination with the frozen soil thickness data.

[0019] Based on the effective melting thickness, combined with the surface roughness data and the initial pore water saturation data, an active layer meltwater runoff model is constructed, wherein the initial effective porosity, capillary suction and permeability coefficient inside the active surface layer are dynamically adjusted according to the initial pore water saturation data and the surface roughness data.

[0020] Within each time step, the potential meltwater generation per unit area caused by the movement of the freeze-thaw interface is first calculated according to the active layer meltwater generation model. The soil moisture replenishment status is determined based on the initial pore water saturation. If the soil pores are not saturated, some meltwater is converted into soil moisture storage. If the soil pores are saturated, the remaining meltwater is converted into surface runoff.

[0021] The surface resistance coefficient is calculated based on the surface roughness data, the runoff rate per unit area is corrected, the amount of meltwater runoff generated in the active layer per unit time is accumulated, and the amount of meltwater runoff generated is continuously updated in real time based on the latest collected data.

[0022] Based on the above technical solutions, preferably, the step of calculating the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle status and geothermal gradient data, permafrost thickness data, surface roughness data, and initial pore water saturation data, and combining this with rainfall forecast data to calculate the comprehensive runoff yield, specifically includes:

[0023] Based on the rainfall forecast data, obtain the time series of rainfall intensity corresponding to the target area;

[0024] Based on the surface roughness data and the initial pore water saturation data, the initial runoff curve number of the target area is dynamically corrected to obtain the corrected runoff curve number.

[0025] Based on the corrected runoff curve number, the initial rainfall loss is calculated, wherein the initial rainfall loss is a preset proportion of the parameter corresponding to the corrected runoff curve number;

[0026] Based on the rainfall intensity time series and the initial rainfall loss, it is determined whether the rainfall intensity generates effective runoff. When the rainfall intensity is greater than the initial rainfall loss, the rainfall runoff is calculated. The rainfall runoff is the result of dividing the square of the remaining part after deducting the initial rainfall loss from the rainfall intensity by the rainfall intensity and the initial rainfall loss, and then adding the difference between the parameters corresponding to the number of corrected runoff curves.

[0027] The meltwater runoff generation is used as the basic meltwater runoff input and linearly superimposed with the rainfall runoff to obtain the comprehensive runoff generation.

[0028] Based on the above technical solutions, preferably, the step of dynamically calculating the surface scour force based on the comprehensive runoff yield, and determining whether the surface scour force has reached the erosion initiation state based on the soil instability ultimate shear threshold at the freeze-thaw interface, specifically includes:

[0029] Based on the comprehensive runoff volume, the surface free flow thickness is calculated, and the surface free flow thickness is determined according to the functional relationship between the comprehensive runoff volume and the local surface slope;

[0030] Based on the surface free flow thickness, combined with surface slope data and surface roughness data, the surface flow velocity is calculated using the Manning formula, and the surface flow velocity is dynamically updated as the surface free flow thickness changes.

[0031] Based on the surface free flow thickness and surface flow velocity, combined with water density, gravitational acceleration, and surface slope tangent, the surface hydrodynamic shear force is calculated.

[0032] The surface scour force per unit area is obtained based on the surface hydrodynamic shear force and the surface area of ​​the active surface layer.

[0033] The surface scour force per unit area is compared with the soil instability limit shear threshold in real time. The soil instability limit shear threshold is preset or dynamically adjusted based on soil structure characteristics, water content, temperature state and porosity.

[0034] When the surface unit scour force is greater than or equal to the soil instability limit shear threshold, it is determined that the active surface layer has reached the erosion initiation state.

[0035] When the surface scour force is less than the soil instability limit shear threshold, it is determined that the active surface layer has not reached the erosion initiation state.

[0036] Based on the above technical solutions, preferably, when the surface freeze-thaw transformation state combined with the effective meltwater runoff reaches a preset percentile, and the surface unit scour force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered, specifically including:

[0037] The surface freeze-thaw transformation state is monitored in real time. When the active surface layer changes from the frozen state to the thawed state or the critical transition state and remains continuously for the preset duration, it is determined that the surface freeze-thaw transformation state is completed, and the start time point of the completion of the surface freeze-thaw transformation state is recorded.

[0038] Starting from the initial time point, the amount of meltwater runoff generated in the active surface layer is continuously accumulated and superimposed with the rainfall runoff in the corresponding time step to obtain the effective meltwater runoff. The effective meltwater runoff is the linear superposition result of the amount of meltwater runoff generated in the active layer and the rainfall runoff.

[0039] Based on historical statistical samples, the percentile distribution of effective meltwater runoff in the target area is set, and a preset percentile corresponding to a specific risk level is selected. When the effective meltwater runoff reaches the flow value corresponding to the preset percentile, it is determined that the first triggering condition is met.

[0040] Within each time step, when the unit scour force on the surface is greater than the soil instability limit shear threshold, it is determined that the second triggering condition is met.

[0041] When the first triggering condition and the second triggering condition are met simultaneously, a soil erosion warning is triggered.

[0042] Based on the above technical solutions, preferably, after triggering a soil erosion early warning when the effective meltwater runoff in the local surface freeze-thaw transition state reaches a preset percentile and the surface scour force exceeds the soil instability limit shear threshold, the method further includes:

[0043] An uncertainty management mechanism is established, which is used to receive various monitoring data of the target area in real time and to uniformly quantify the measurement error, estimation error and environmental disturbance error of the monitoring data.

[0044] Based on the uncertainty quantification results, multiple sets of erosion process scenario samples are generated using particle filtering or sequential Monte Carlo simulation methods. The comparison results of the unit surface scour force and the soil instability limit shear threshold under each scenario are calculated, as well as the probability of erosion initiation, forming a dynamic distribution curve of erosion probability.

[0045] The probability of soil erosion risk at the current moment is calculated in real time. The probability of soil erosion risk represents the likelihood that the active surface layer will enter an erosion state under the current environmental and data uncertainty conditions.

[0046] Set thresholds for multiple levels of soil erosion warnings, including normal state, Level 1 warning state, Level 2 warning state and Level 3 warning state, with each warning state corresponding to an erosion probability interval;

[0047] When the probability of soil erosion risk enters different erosion probability ranges, the corresponding soil erosion early warning level is dynamically adjusted.

[0048] The probability of soil erosion risk is continuously and dynamically updated, and the soil erosion warning level and response measures are adjusted until the environmental data stabilizes and falls back to a safe range and the erosion probability is lower than the warning cancellation threshold, at which point the current soil erosion warning status is lifted.

[0049] A second aspect of this application provides a climate change-based soil erosion early warning device, the device being used to execute a climate change-based soil erosion early warning method as described in any of the above-described embodiments, the device comprising an acquisition module, a processing module, and an output module, wherein:

[0050] The acquisition module is used to collect surface temperature data and freeze-thaw status data of the target area in real time, and determine the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria.

[0051] The processing module is used to calculate the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle status, geothermal gradient data, frozen soil thickness data, surface roughness data, and initial pore water saturation data, and to calculate the comprehensive runoff production in combination with rainfall forecast data.

[0052] The processing module is used to dynamically calculate the surface scour force based on the comprehensive runoff output, and to determine whether the surface scour force has reached the erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface.

[0053] The output module is used to trigger a soil erosion warning when the effective meltwater runoff, which is superimposed on the surface freeze-thaw transformation state, reaches a preset percentile and the surface scour force exceeds the soil instability limit shear threshold.

[0054] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any of the foregoing.

[0055] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the preceding descriptions.

[0056] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0057] 1. This application dynamically determines the freeze-thaw cycle changes of the active surface layer by collecting real-time surface temperature data and freeze-thaw status data. It calculates the amount of meltwater runoff generated in the active layer by combining geothermal gradient data, frozen soil thickness data, surface roughness data, and initial pore water saturation data. It also calculates the comprehensive runoff yield by integrating rainfall forecast data. Furthermore, it dynamically calculates the unit scour force of the surface based on the comprehensive runoff yield and determines in real time whether it exceeds the soil instability limit shear threshold at the freeze-thaw interface. Thus, when the effective meltwater runoff reaches a risk level due to the surface freeze-thaw transformation, it can trigger soil erosion early warning in advance. It can accurately capture the conditions for the occurrence of soil erosion disasters in high-altitude and cold regions under the combined effects of permafrost degradation and extreme rainfall caused by climate warming, and realize continuous dynamic response and forward-looking risk warning for climate change processes.

[0058] 2. By acquiring high-frequency multi-source surface temperature data and geothermal depth gradient data, and combining them with established freeze-thaw state criteria, the system can dynamically identify the freezing, thawing, and critical transition states of the active surface layer with high precision. This ensures that the freeze-thaw cycle state determination has fine-grained spatiotemporal resolution, laying an accurate foundation for subsequent meltwater runoff estimation and scour risk analysis.

[0059] 3. Based on the freeze-thaw cycle and geothermal gradient data, the effective thaw thickness is calculated, and the active layer meltwater runoff process is dynamically calculated. This can accurately characterize the meltwater release and runoff behavior of surface soil under freeze-thaw alternation, and improve the physical accuracy and dynamic response capability of meltwater runoff generation estimation.

[0060] 4. By introducing rainfall forecast data and combining it with dynamically corrected runoff curves, the linear superposition results of rainfall runoff and meltwater runoff are calculated, enabling real-time dynamic prediction of comprehensive runoff under extreme rainfall superimposed with freeze-thaw meltwater scenarios, significantly improving the completeness and timeliness of comprehensive hydrodynamic input.

[0061] 5. Based on the comprehensive runoff yield, the surface free flow thickness is estimated, and the surface velocity is estimated using the Manning formula. The unit scour force of the surface is further calculated and compared with the soil instability limit shear threshold in real time. This enables dynamic identification of the erosion initiation state of the active surface layer, effectively capturing the critical conditions for soil instability and erosion, and improving the sensitivity and reliability of erosion risk monitoring.

[0062] 6. By monitoring the surface freeze-thaw transformation status in real time and accumulating the effective meltwater runoff, combined with the simultaneous determination of the surface unit scour force and the soil instability limit shear threshold, it is possible to trigger soil erosion early warning in two ways, effectively avoid misjudgment by a single indicator, and achieve accurate identification and risk control of complex erosion processes in high-altitude freeze-thaw zones.

[0063] 7. After the soil erosion warning is triggered, an uncertainty management mechanism is introduced to quantify the monitoring data error in real time and dynamically update the probability of soil erosion risk based on particle filtering or sequential Monte Carlo simulation methods. Through the linkage adjustment of multi-level warning levels and response measures, the adaptability, robustness and response accuracy of the overall risk management system under extreme climate change conditions can be improved, and full-cycle closed-loop dynamic risk control can be achieved. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating a method for early warning of soil erosion based on climate change, as disclosed in an embodiment of this application.

[0065] Figure 2 This is a schematic diagram of a soil erosion early warning device based on climate change disclosed in an embodiment of this application;

[0066] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0067] Explanation of reference numerals in the attached drawings: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0068] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0069] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0070] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0071] Soil and water loss early warning is conducted by continuously monitoring the surface environment, hydrological and meteorological changes, and soil erosion mechanisms. Based on physical models, statistical inference, or machine learning methods, it assesses soil erosion risks in real time and issues warnings and coordinated responses before reaching thresholds, aiming to prevent soil and water resource degradation and ecological disasters. In high-altitude and cold regions, soil and water loss early warning based on climate change targets permafrost degradation and thermo-thaw collapse caused by global warming. By monitoring the dynamics of freeze-thaw cycles, effective meltwater runoff, and changes in surface mechanics, it identifies high-risk states of erosion and debris flows induced by extreme rainfall in advance, providing efficient scientific support for ecological protection, engineering safety, and climate adaptation management.

[0072] This embodiment discloses a method for early warning of soil erosion based on climate change, referring to... Figure 1 This includes the following steps S110-S140:

[0073] S110 collects surface temperature data and freeze-thaw status data of the target area in real time, and determines the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria.

[0074] This application discloses a method for early warning of soil erosion based on climate change, which is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablets, wearable devices, and PCs (Personal Computers), and can also be a backend server running the method for early warning of soil erosion based on climate change. The server can be implemented using a standalone server or a server cluster composed of multiple servers.

[0075] In one possible implementation, surface temperature data and freeze-thaw status data of the target area are collected in real time. Based on a set freeze-thaw status criterion, the freeze-thaw cycle status of the active surface layer is determined. Specifically, this includes: deploying a surface temperature sensor array and a geothermal depth gradient probe within the target area, and combining this with satellite remote sensing observation data to form a joint acquisition system for multi-source surface temperature data and freeze-thaw status data. The acquisition frequency of surface temperature data is the same as that of freeze-thaw status data. According to the surface temperature data, when the surface temperature remains below a preset first temperature for a preset duration and the geothermal gradient change rate is decreasing, the active surface layer is determined to be in a frozen state. When the surface temperature remains above a preset second temperature and the rate of change of the geothermal gradient changes from negative to positive, and the daily average surface temperature rise reaches a preset threshold, and the upward shift of the surface freeze-thaw interface is detected by freeze-thaw status data exceeding a preset thickness change value, the active surface layer is determined to be in a melting state; when the surface temperature is between a preset second temperature and a preset first temperature, and the daily amplitude of the geothermal gradient exceeds a preset amplitude, the active surface layer is determined to be in a critical transition state; or, when the upward shift rate of the surface freeze-thaw interface exhibits unstable fluctuations, and the freeze-thaw status data contains records of high-frequency alternation between frozen and thawing states, the active surface layer is determined to be in a critical transition state.

[0076] Specifically, firstly, a surface temperature sensor array and a geothermal depth gradient probe are deployed within the target area. The surface temperature sensor array is used to collect surface temperature data of the target area, and the geothermal depth gradient probe is used to measure soil temperature at different depths, forming a data curve of ground temperature variation with depth. Simultaneously, combined with satellite remote sensing observation data, including but not limited to microwave remote sensing freeze-thaw discrimination products and surface temperature remote sensing inversion data, a joint acquisition system of multi-source surface temperature data and freeze-thaw status data is established to ensure that the acquired data has consistency in spatial range and temporal resolution. The acquisition frequency of surface temperature data and the acquisition frequency of freeze-thaw status data are set to the same value, preferably once per hour, to achieve high-timeliness tracking of the freeze-thaw cycle dynamics.

[0077] Subsequently, based on the real-time collected surface temperature data, the continuous time series was analyzed. When the surface temperature was detected to be continuously lower than the preset first temperature within a preset time period, and the rate of change of the ground temperature gradient showed a decreasing trend, it indicated that the downward conduction of surface heat was reduced and freezing was intensified. At this time, the active surface layer was determined to be in a frozen state according to the set freeze-thaw state criteria. In the frozen state, the soil structure strength is improved and the water migration is restricted, providing a dynamic monitoring basis for the subsequent erosion risk accumulation stage of the thawing stage.

[0078] Subsequently, when the surface temperature continues to exceed the preset second temperature and the rate of change of the geothermal gradient changes from negative to positive, that is, the geothermal gradient from the surface to the shallow layer reverses, and the daily average surface temperature rise reaches the set temperature rise threshold, and at the same time, combined with the freeze-thaw status data, it is detected that the position of the surface freeze-thaw interface has moved upward and exceeded the preset thickness change value, and it is comprehensively determined that the large-scale melting of the surface permafrost has started. According to the set freeze-thaw status criteria, the active surface layer is determined to be in a melting state. At this time, the soil strength decreases rapidly, the release of water accelerates, and the surface becomes extremely susceptible to erosion.

[0079] When the surface temperature is between a preset second temperature and a preset first temperature, where the preset second temperature is lower than the preset first temperature, meaning the surface temperature is fluctuating around zero degrees Celsius, and the daily amplitude of the ground temperature gradient exceeds the set amplitude threshold, it indicates that the daytime thawing and nighttime freezing alternate repeatedly and are accompanied by strong thermal dynamic disturbances. Based on the set freeze-thaw state criteria, the active surface layer is determined to be in a critical transition state. In the critical transition state, the soil structure stability is extremely low, and micro-cracks expand rapidly, making it very easy to become the starting point for subsequent concentrated erosion.

[0080] Alternatively, during monitoring, if the rate of upward movement of the surface freeze-thaw interface shows obvious unstable fluctuations, that is, if the freeze-thaw interface shows abnormal rise and fall changes in the time series, and if the freeze-thaw state data identifies a phenomenon of high-frequency alternation between the frozen state and the thawing state in a short time scale, then based on the above information and the established freeze-thaw state criteria, the active surface layer is also determined to be in a critical transition state. Under such circumstances, the surface physical structure is extremely fragile, which is a sensitive window period for extreme weather to trigger erosion disasters.

[0081] S120, based on freeze-thaw cycle status and geothermal gradient data, frozen soil thickness data, surface roughness data and initial pore water saturation data, calculates the meltwater runoff generation in the active surface layer, and combines rainfall forecast data to calculate the comprehensive runoff output.

[0082] In one possible implementation, based on freeze-thaw cycle status and geothermal gradient data, permafrost thickness data, surface roughness data, and initial pore water saturation data, the meltwater runoff generation of the active surface layer is estimated. Specifically, this includes: determining whether the active surface layer is currently in a frozen, thawing, or critical transition state based on the freeze-thaw cycle status, where the frozen state corresponds to zero meltwater production, and the thawing and critical transition states trigger meltwater runoff generation estimation; calculating the heat flux change trend from the surface to the frozen interface based on geothermal gradient data, dynamically calculating the daily movement rate of the freeze-thaw interface using the heat diffusion formula, and determining the effective thawing thickness of the active surface layer based on permafrost thickness data; and based on the effective thawing thickness, combining surface roughness data and initial pore water saturation data... Data was used to construct an active layer meltwater runoff model, in which the initial effective porosity, capillary suction, and permeability coefficient within the active layer were dynamically adjusted based on initial pore water saturation data and surface roughness data. At each time step, the potential meltwater generation per unit area due to freeze-thaw interface movement was first calculated based on the active layer meltwater runoff model. The soil moisture replenishment status was determined based on the initial pore water saturation; if the soil pores were unsaturated, some meltwater was converted into soil moisture storage; if the soil pores were saturated, the remaining meltwater was converted into surface runoff. The surface resistance coefficient was calculated based on surface roughness data to correct the runoff rate per unit area, accumulating the active layer meltwater runoff generation per unit time, and continuously updating the meltwater runoff generation in real time based on the latest collected data.

[0083] Specifically, geothermal gradient data refers to the gradient information of soil temperature change with depth from the surface to a certain depth, reflecting the direction and intensity of surface heat transfer, and is a fundamental parameter for determining the dynamic evolution of the frozen interface and the rate of freeze-thaw processes. Permafrost thickness data describes the vertical distance from the surface to the bottom boundary of permafrost or seasonally frozen soil, used to define the effective melting volume range of the active layer. Surface roughness data is a quantitative description of the hindering effect of surface micro-topography, vegetation cover, and soil surface structure characteristics on water flow, and is an important factor affecting surface runoff formation and velocity. Initial pore water saturation data indicates the proportion of soil pore space filled with water at the beginning of the freeze-thaw process, determining whether the soil can continue to absorb meltwater or directly form runoff. Meltwater runoff generation from the active surface layer refers to the unit area yield of frozen water released from the soil due to the upward movement of the freeze-thaw interface and surface temperature rise, which is converted into surface runoff after soil pores become saturated or supersaturated; it is the direct driving force for energy and material transport in the soil-water erosion process.

[0084] First, based on the real-time freeze-thaw cycle status, it is determined whether the active surface layer is currently in a frozen state, a thawing state, or a critical transition state. When the active surface layer is in a frozen state, the soil pore structure is filled with ice crystals, water is consolidated, and permeability is extremely low, so the meltwater runoff is determined to be zero. When the active surface layer is in a thawing state or a critical transition state, the permafrost structure is loosened, pore connectivity is enhanced, water release and migration are intensified, so it is determined that meltwater runoff generation calculation needs to be performed to provide initial input for subsequent hydrodynamic evolution.

[0085] Subsequently, based on the collected geothermal gradient data, the trend of heat flux variation between the surface and the freezing interface was deduced. A one-dimensional heat diffusion model was used, combined with geothermal gradient and soil thermal conductivity parameters, to dynamically calculate the vertical movement rate of the freeze-thaw interface on a daily scale. Specifically, the calculation was performed using the following formula:

[0086]

[0087] Among them, v interface ρ represents the daily vertical movement rate of the freeze-thaw interface, measured in meters per day. It describes the speed at which the frozen or thawed interface advances or retreats over time in the vertical space from the surface to the subsurface. λ represents soil thermal conductivity, measured in watts per meter per Kelvin. It measures the soil's ability to conduct heat during freezing or thawing; soil moisture content, porosity, and soil texture significantly affect this parameter value. w L represents the density of water, measured in kilograms per cubic meter, usually taken as 1,000 kilograms per cubic meter. It represents the mass of a unit volume of water and is a fundamental physical constant in hydrodynamic and thermodynamic calculations. f The latent heat of water is expressed in joules per kilogram. It represents the energy absorbed or released when one kilogram of water changes from a solid to a liquid state or vice versa. The standard value is approximately 334,000 joules per kilogram. It represents the geothermal gradient, measured in degrees Celsius per meter, and indicates the rate of temperature change per unit depth. It is a key parameter describing the direction and intensity of thermal driving at the surface to shallow freeze-thaw interface.

[0088] Simultaneously, by combining the frozen soil layer thickness data, the effective thawing thickness of the active layer is determined. The effective thawing thickness refers to the thickness of the soil layer between the surface and the frozen interface that has reached the water state of thawing and has the ability to generate flow, which serves as an important basis for subsequent calculation of meltwater volume.

[0089] Next, based on the effective melt thickness and combined with surface roughness data and initial pore water saturation data, an active layer meltwater runoff model was constructed. The meltwater runoff model is based on the modified Green-Ampt model. On the framework of the traditional Green-Ampt infiltration model, a real-time adjustment mechanism for parameters such as initial effective porosity, capillary suction, and permeability coefficient of the soil is dynamically introduced. Among them, the initial effective porosity is determined by mapping based on the initial pore water saturation data, and the capillary suction and permeability coefficient are corrected based on the surface roughness data to ensure that the runoff process can reflect the non-steady-state evolution characteristics of soil hydraulic properties caused by freeze-thaw cycles.

[0090] Within each time step, the potential meltwater generation per unit area is calculated based on the freeze-thaw interface movement rate. The potential meltwater generation is the product of the water released by the effective thaw thickness change and the amount of water generated per unit area, specifically calculated using the following formula:

[0091] M potential =Δh melt ×θ i

[0092] Among them, M potential Δh represents the potential meltwater generation per unit area, expressed in meters. It is used to quantify the ratio of the volume of liquid water released during the thawing of frozen soil to the surface area. melt θ represents the effective change in thaw thickness, measured in meters. It refers to the net change in the thickness of the thawed area caused by the upward shift of the freeze-thaw interface within a specific time step. i It represents the initial soil ice content, with the unit being a dimensionless ratio value. It refers to the ratio of the volume of ice contained in a unit volume of soil to the total soil volume when the soil is frozen. It is a direct indicator of the potential meltwater storage in frozen soil.

[0093] Subsequently, the soil moisture replenishment status is determined based on the initial pore water saturation. When the soil pores are not fully saturated, the potential meltwater is used first to replenish the soil moisture reserves without generating surface runoff. When the soil pores reach saturation or supersaturation, the remaining potential meltwater is converted into surface runoff, providing actual flow for the hydrodynamic scouring process.

[0094] In the process of estimating the formation of surface runoff, the surface resistance coefficient is further calculated based on surface roughness data. The surface resistance coefficient describes the degree of obstruction to surface water flow and has a direct impact on free flow velocity and runoff generation rate. The runoff generation rate per unit area is corrected by combining surface roughness and water flow characteristics. Finally, the amount of active layer meltwater runoff generated per unit time is accumulated. At each time step, the amount of active layer meltwater runoff generated is continuously and dynamically updated based on the latest collected ground temperature, surface condition and hydraulic data to ensure that the calculation results reflect changes in the surface environment in real time.

[0095] First, based on rainfall forecast data, the rainfall intensity time series corresponding to the target area is obtained. The rainfall intensity time series is a continuous rainfall intensity dataset collected or forecast at fixed time intervals, with the unit being millimeters per hour. It can reflect the rainfall change trend in the target area within a specific future time period and is one of the core inputs for estimating rainfall runoff and comprehensive runoff.

[0096] Subsequently, based on surface roughness data and initial pore water saturation data, the initial runoff curve number of the target area was dynamically corrected. The initial runoff curve number is used to characterize the surface runoff characteristics. The standard runoff curve number is preset according to land use type, soil type and slope classification. During the dynamic correction process, by analyzing the influence of the current surface roughness level and the initial pore water saturation level on the runoff potential, the standard runoff curve number is weighted and adjusted by applying roughness correction factor and saturation correction factor respectively, so as to obtain the corrected runoff curve number that reflects the actual surface hydrodynamic conditions.

[0097] After obtaining the corrected runoff curve number, the initial rainfall loss is calculated based on the formula relating the runoff curve number and the initial loss. The initial rainfall loss represents the sum of rainfall interception and infiltration that the surface must meet before runoff is generated. Its value is equal to the parameter corresponding to the corrected runoff curve number after a certain proportional conversion. The specific conversion ratio is determined based on empirical formulas in the runoff curve method to ensure dynamic consistency between the initial rainfall loss and the surface runoff response characteristics. The initial rainfall loss is specifically calculated using the following formula:

[0098]

[0099] Among them, I a This represents the initial loss of rainfall, expressed in millimeters. It quantifies the amount of rainfall that must be intercepted, infiltrated, or retained by the soil before it can flow onto the surface, and is a fundamental condition for determining whether effective rainfall runoff occurs. adj This represents the corrected runoff generation curve number, expressed as a dimensionless proportional value. It is obtained by dynamically adjusting the standard runoff generation curve number based on surface roughness and initial pore water saturation data, and is used to comprehensively reflect the actual surface runoff response characteristics. The value 0.2 is derived from an empirical setting, indicating that under normal circumstances, the initial rainfall loss is approximately 20% of the maximum possible water storage. This proportion is determined based on statistical analysis of a large amount of measured runoff data, reflecting the rainfall required for the main non-runoff generation processes such as interception, initial infiltration, and surface retention that occur at the beginning of rainfall under natural surface conditions.

[0100] The values ​​25400 and 254 both originate from the standard runoff curve number theory. The value 25400 is a unit conversion factor, with its unit being millimeters multiplied by a dimensionless number. It is used to convert the dimensionless corrected runoff curve number into a maximum water storage value with actual physical dimensions. The value 254 is an empirical correction constant, used to standardize and correct the initial loss characteristics of different land uses and soil types during the formula derivation process. These two values ​​ensure that the Curve Number value can correspond to the actual soil water capacity range that can generate runoff within a reasonable physical range (usually between 40 and 100).

[0101] Subsequently, effective rainfall is determined based on the rainfall intensity time series and initial rainfall loss. At each time step, if the current rainfall intensity is less than or equal to the initial rainfall loss, no effective rainfall runoff is generated, and the rainfall runoff volume is zero. If the current rainfall intensity is greater than the initial rainfall loss, the rainfall runoff volume is calculated. The rainfall runoff volume is the square of the remaining portion after deducting the initial rainfall loss from the rainfall intensity, divided by the sum of the differences between the rainfall intensity after deducting the initial rainfall loss and the corresponding parameters of the corrected runoff generation curve. This ensures that the rainfall runoff volume calculation conforms to the nonlinear runoff generation characteristics. Specifically, it is calculated using the following formula:

[0102]

[0103] Among them, Q rain P represents rainfall runoff, expressed in millimeters, and is used to quantify the surface runoff per unit area under the influence of effective rainfall intensity after considering initial loss. rain Indicates rainfall intensity, measured in millimeters per hour. It originates from rainfall forecast or measured data and represents the amount of rainfall in a target area per unit time. It is a direct driving parameter for estimating rainfall runoff. a This indicates the initial loss due to rainfall. (CN) adj This indicates the number of corrected runoff curves. If the rainfall intensity P... rain Less than or equal to the initial rainfall loss I a Then determine the rainfall runoff Q. rain A value of zero indicates that no effective rainfall runoff is generated.

[0104] Finally, within each time step, the calculated active layer meltwater runoff generation is used as the basic meltwater runoff input and linearly superimposed with the rainfall runoff. Linear superposition is a direct addition of the two values ​​to obtain the comprehensive runoff generation. The comprehensive runoff generation can simultaneously reflect the combined contribution of the meltwater process and the rainfall process to the total surface runoff, providing continuous dynamic input for subsequent dynamic calculation of surface unit scour force and determination of erosion initiation status.

[0105] S130 dynamically calculates the unit scour force on the surface based on the comprehensive runoff output, and determines whether the unit scour force on the surface has reached the erosion initiation state based on the soil instability limit shear threshold under the freeze-thaw interface.

[0106] In one possible implementation, the surface scour force is dynamically calculated based on the comprehensive runoff yield, and the erosion initiation state is determined based on the soil instability limit shear threshold at the freeze-thaw interface. Specifically, this includes: calculating the surface free flow thickness based on the comprehensive runoff yield, which is determined by a functional relationship between the comprehensive runoff yield and the local surface slope; calculating the surface velocity using the Manning formula based on the surface free flow thickness, combined with surface slope and surface roughness data, with the surface velocity dynamically updated as the surface free flow thickness changes; and calculating the surface velocity based on the surface free flow thickness and surface velocity. By combining water density, gravitational acceleration, and the tangent of surface slope, the surface hydrodynamic shear force is calculated. Based on the surface hydrodynamic shear force and the surface area of ​​the active surface layer, the unit erosion force is obtained. The unit erosion force is compared in real time with the soil instability limit shear threshold, which is preset or dynamically adjusted based on soil structure characteristics, water content, temperature, and porosity. When the unit erosion force is greater than or equal to the soil instability limit shear threshold, the active surface layer is determined to have reached the erosion initiation state; when the unit erosion force is less than the soil instability limit shear threshold, the active surface layer is determined not to have reached the erosion initiation state.

[0107] Specifically, surface erosion force refers to the shear force generated on a unit area of ​​the land surface by surface water flow due to gravity and slope action during the flow process. Its physical essence is the tangential pushing force exerted by water dynamics on soil particles along the surface direction. It is the core hydraulic parameter that drives the detachment and movement of surface soil particles and forms the erosion process. The magnitude of surface erosion force mainly depends on the thickness of the free water flow, the surface water velocity, the water density, the surface slope, and the gravitational acceleration. The larger the value, the easier it is for the surface soil to be damaged and enter the erosion state. Therefore, surface erosion force is not only a direct basis for judging the initiation of erosion, but also a key control variable for establishing soil and water loss dynamic models and implementing early warning responses.

[0108] First, the surface free flow thickness is estimated based on the comprehensive runoff yield. The surface free flow thickness refers to the thickness of the water body formed by surface runoff per unit area, measured in meters. The estimation takes into account the functional relationship between the comprehensive runoff yield and the local surface slope. Specifically, it is based on the assumption that the surface runoff is in a shallow, uniform flow state. The comprehensive runoff yield is converted into the surface free flow thickness within the corresponding time step using empirical formulas. Local surface slope data is used to adjust the free flow accumulation and outflow rate, thereby accurately determining the instantaneous surface free flow thickness at each location.

[0109] Subsequently, based on the surface free flow thickness, combined with surface slope and surface roughness data, the Manning formula was applied to estimate the surface velocity. The Manning formula is a classic formula for shallow open channel flow, which can estimate the flow velocity when the surface free flow thickness, surface slope, and surface roughness parameters are known. During the estimation process, the surface velocity is dynamically updated in real time with the change of surface free flow thickness, reflecting the continuous response characteristics of the surface hydrodynamic state to changes in comprehensive runoff and surface physical properties.

[0110] Next, based on the calculated thickness of the free surface water flow and the surface velocity, combined with the water density, gravitational acceleration, and surface slope tangent, the surface hydrodynamic shear force is calculated. The surface hydrodynamic shear force is defined as the tangential force exerted by the water flow on a unit area of ​​the surface. In the calculation formula, the water density is 1,000 kg / m³, the gravitational acceleration is 9.8 m / s², and the surface slope tangent is calculated in real time based on the surface elevation data to ensure that the hydrodynamic shear force can accurately reflect the actual stress state of the surface.

[0111] After obtaining the surface hydrodynamic shear force, the surface scour force per unit area is calculated based on the surface hydrodynamic shear force and the surface area of ​​the active layer. The surface scour force per unit area refers to the intensity of hydrodynamic shear force on the surface per unit area, and the unit is Pascal. In the calculation process, the surface hydrodynamic shear force is divided by the surface area to ensure that the scour force value has spatial standardization characteristics and can be directly used for erosion initiation judgment.

[0112] The surface unit scour force is compared with the soil instability limit shear threshold in real time. The soil instability limit shear threshold is preset based on soil structure characteristics, water content, temperature state and porosity or dynamically adjusted based on real-time data. When the soil structure is loose, the pore water is saturated or the temperature rises, the soil instability limit shear threshold decreases and the soil's scour resistance weakens. Dynamic adjustment ensures that the judgment criteria match the current surface physical environment.

[0113] When the real-time calculated surface erosion force per unit is greater than or equal to the soil instability limit shear threshold, the active surface layer is determined to have reached the erosion initiation state, meaning that the surface soil has lost sufficient erosion resistance and will rapidly erode and destroy under hydrodynamic action; when the surface erosion force per unit is less than the soil instability limit shear threshold, the active surface layer is determined not to have reached the erosion initiation state, and the comparison results of erosion force and instability threshold continue to be monitored in real time and dynamically updated in order to continuously capture the evolution of erosion risk.

[0114] S140: When the surface freeze-thaw transformation state combined with the effective meltwater runoff reaches the preset percentile, and the surface unit scour force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered.

[0115] In one possible implementation, when the effective meltwater runoff in the surface freeze-thaw transition state reaches a preset percentile, and the unit scour force on the surface exceeds the soil instability limit shear threshold, a soil erosion warning is triggered. Specifically, this includes: real-time monitoring of the surface freeze-thaw transition state; determining that the surface freeze-thaw transition is complete when the active surface layer changes from a frozen state to a thawing state or a critical transition state and remains so for a preset duration; recording the start time point of the completion of the surface freeze-thaw transition state; and continuously accumulating the meltwater runoff generation in the active surface layer from the start time point, and comparing it with the rainfall runoff within the corresponding time step. The effective meltwater runoff is obtained by superimposing the data, which is the linear superposition of the active layer meltwater runoff generation and the rainfall runoff. Based on historical statistical samples, the percentile distribution of the effective meltwater runoff in the target area is set, and a preset percentile corresponding to a specific risk level is selected. When the effective meltwater runoff reaches the flow value corresponding to the preset percentile, it is determined that the first trigger condition is met. In each time step, when the surface unit scour force is greater than the soil instability limit shear threshold, it is determined that the second trigger condition is met. When the first and second trigger conditions are met simultaneously, a soil erosion warning is triggered.

[0116] Specifically, firstly, the surface freeze-thaw transformation state is monitored in real time. The surface freeze-thaw transformation state is the complete process of the active layer of the surface changing from a frozen state to a thawing state or a critical transition state. Specifically, the evolution of the active layer state is judged by surface temperature data and geothermal gradient data. When the thawing state criterion or the critical transition state criterion is met for a continuous period of time, and the continuous maintenance time exceeds a preset duration threshold, such as twelve hours or twenty-four hours, the surface freeze-thaw transformation state is determined to be completed. At the same time, the completion time point is recorded as the starting time marker for the subsequent accumulation of dynamic indicators.

[0117] Subsequently, starting from the initial time point when the surface freeze-thaw transition is completed, the amount of meltwater runoff generated in the active surface layer is continuously calculated in each time step. This amount of meltwater runoff generated is then numerically added to the rainfall runoff in the same time step to form the effective meltwater runoff. The effective meltwater runoff is the linear superposition result of the active layer meltwater runoff generated and the rainfall runoff, with the unit being millimeters. It reflects that the surface runoff process is simultaneously influenced by both freeze-thaw and rainfall, and is a core hydrological indicator for characterizing the intensity of soil erosion conditions.

[0118] Next, a percentile distribution model of effective meltwater runoff in the target area is constructed based on historical statistical sample data. The percentile distribution model is generated by statistically sorting the effective meltwater runoff data measured or simulated over many years, reflecting the probability of occurrence of hydrological processes of different intensities in historical samples. A preset percentile (e.g., the 90th, 95th, or 99th percentile) is selected as a high-risk threshold for soil erosion according to the set risk management level. When the real-time accumulated effective meltwater runoff reaches or exceeds the flow value corresponding to the preset percentile at any time step, it is determined that the first triggering condition is met.

[0119] Simultaneously, within each time step, the surface erosion force is dynamically calculated and compared in real time with the soil instability limit shear threshold corresponding to the current moment. When the surface erosion force is greater than or equal to the soil instability limit shear threshold, it indicates that the shearing effect of hydrodynamics on the soil structure has reached the critical level that triggers erosion and damage, and it is determined that the second triggering condition is met. The comparison process is based on the real-time updated surface hydrodynamic and soil physical state parameters to ensure that the threshold judgment is always accurate and effective in complex environmental changes.

[0120] When the first and second triggering conditions are met simultaneously at any time step, i.e. the effective meltwater runoff exceeds the corresponding value of the preset percentile and the unit scour force on the surface exceeds the soil instability limit shear threshold, a soil erosion warning is immediately triggered, and the high-risk state response process is initiated. The warning result will be linked to the downstream response mechanism for subsequent risk notification, graded treatment or emergency control.

[0121] Furthermore, after triggering a soil erosion warning when the effective meltwater runoff in the local freeze-thaw transition state reaches a preset percentile and the surface scour force exceeds the soil instability limit shear threshold, the method also includes: establishing an uncertainty management mechanism, which is used to receive various monitoring data of the target area in real time and uniformly quantify the measurement error, estimation error, and environmental disturbance error of the monitoring data; based on the uncertainty quantification results, using particle filtering or sequential Monte Carlo simulation methods, generating multiple sets of erosion process scenario samples, calculating the comparison results of surface scour force and soil instability limit shear threshold under each scenario, and the probability of erosion initiation, forming a dynamic probability distribution of erosion. The system employs a curve calculation method; it calculates the probability of soil erosion risk in real time, representing the likelihood of the active surface layer entering an erosion state under current environmental and data uncertainties; it sets multi-level warning thresholds for soil erosion, including normal state, Level 1 warning state, Level 2 warning state, and Level 3 warning state, with each warning state corresponding to an erosion probability range; it dynamically adjusts the corresponding soil erosion warning level when the soil erosion risk probability falls into different erosion probability ranges; it continuously and dynamically updates the soil erosion risk probability and adjusts the soil erosion warning level and response measures until the environmental data stabilizes and falls back to a safe range and the erosion probability is lower than the warning cancellation threshold, at which point the current soil erosion warning state is lifted.

[0122] Specifically, firstly, after triggering a soil erosion early warning, an uncertainty management mechanism is established. This mechanism is used to centrally receive various monitoring data from the target area, including but not limited to surface temperature data, geothermal gradient data, frozen soil thickness data, surface roughness data, initial pore water saturation data, rainfall intensity data, meltwater runoff generation data, rainfall runoff data, comprehensive runoff generation data, surface free flow thickness data, surface velocity data, surface unit scour force data, and soil instability limit shear threshold data. Furthermore, a unified quantitative model is performed on the measurement errors, model calculation errors, and external environmental disturbance errors in the above data to form an error covariance structure that can be used for dynamic prediction.

[0123] Subsequently, based on the established uncertainty quantification model, a large number of erosion process scenario samples are generated at each time step using particle filtering or sequential Monte Carlo simulation. The scenario samples are potential surface response trajectories that evolve under different disturbance conditions. Each sample corresponds to a set of comparison results between the unit surface scour force and the soil instability limit shear threshold. By statistically analyzing the proportion of erosion initiation states triggered in all scenario samples, the probability of erosion initiation states occurring at the current moment is estimated, thereby constructing a dynamic distribution curve of surface erosion probability and forming the basis for risk situation awareness.

[0124] Within each real-time calculation cycle, based on the dynamic distribution curve of the erosion probability, the statistical expectation or confidence interval mean is extracted to calculate the soil erosion risk probability at the current moment. The soil erosion risk probability represents the probability that the active surface layer will enter an erosion state under the current environmental and data uncertainty conditions. It is a probabilistic representation of the stability of the scour force-threshold structure under disturbance conditions, and the unit is percentage.

[0125] Based on long-term experience and regional prevention and control needs, multiple levels of soil erosion warning thresholds are pre-set. Typical settings include four levels: normal state, Level 1 warning state, Level 2 warning state, and Level 3 warning state. Each soil erosion warning level corresponds to a soil erosion risk probability range. For example, the normal state corresponds to 0% to 20%, the Level 1 warning state corresponds to 20% to 40%, the Level 2 warning state corresponds to 40% to 70%, and the Level 3 warning state corresponds to more than 70%.

[0126] When the probability of soil erosion risk enters any preset range, the current soil erosion warning level will be dynamically adjusted immediately. Changes in the warning level will trigger corresponding response instruction modules, including differentiated response measures such as increasing monitoring frequency, issuing risk notices, activating drainage facilities, closing specific areas, or initiating personnel evacuation, to ensure that emergency response resources match the current risk level.

[0127] The system continuously performs uncertain sampling, erosion probability updates, and early warning level determination at fixed time intervals, dynamically correcting the risk assessment results as environmental parameters change continuously. When the real-time collected data indicates that all core parameters have stably fallen back to the historical safe range and the current soil erosion risk probability is lower than the preset cancellation threshold (e.g., 10%), the system automatically cancels the current soil erosion early warning status, restores to normal monitoring mode, and completes a full closed-loop response cycle.

[0128] This embodiment also discloses a soil erosion early warning device based on climate change, referring to... Figure 2 The device includes an acquisition module 201, a processing module 202, and an output module 203. It is used to execute any of the above-described methods for early warning of soil erosion based on climate change, wherein:

[0129] The acquisition module 201 is used to collect surface temperature data and freeze-thaw status data of the target area in real time, and determine the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria.

[0130] The processing module 202 is used to calculate the meltwater runoff generation of the active surface layer based on freeze-thaw cycle status and geothermal gradient data, frozen soil thickness data, surface roughness data and initial pore water saturation data, and to calculate the comprehensive runoff production in combination with rainfall forecast data.

[0131] The processing module 202 is used to dynamically calculate the unit scour force of the surface based on the comprehensive runoff output, and to determine whether the unit scour force of the surface has reached the erosion initiation state based on the soil instability limit shear threshold under the freeze-thaw interface.

[0132] The output module 203 is used to trigger a soil and water loss warning when the effective meltwater runoff reaches a preset percentile under the superimposed effective meltwater runoff of the surface freeze-thaw conversion state, and the unit scour force of the surface exceeds the soil instability limit shear threshold.

[0133] In one possible implementation, the acquisition module 201 is used to form a joint acquisition system of multi-source surface temperature data and freeze-thaw status data by deploying a surface temperature sensor array and a geothermal depth gradient probe in the target area, combined with satellite remote sensing observation data. The acquisition frequency of the surface temperature data is the same as that of the freeze-thaw status data.

[0134] The processing module 202 is used to determine that the active surface layer is in a frozen state when the surface temperature is continuously lower than a preset first temperature within a preset time period and the ground temperature gradient change rate is in a downward trend, based on the surface temperature data.

[0135] The processing module 202 is used to determine that the active surface layer is in a melting state when the surface temperature is continuously higher than the preset second temperature and the rate of change of the ground temperature gradient changes from negative to positive, and the daily average surface temperature rise reaches the preset threshold, and the surface freeze-thaw interface position is detected to have moved up more than the preset thickness change value based on the freeze-thaw status data.

[0136] The processing module 202 is used to determine that the active surface layer is in a critical transition state when the surface temperature is between a preset second temperature and a preset first temperature and the daily amplitude of the geothermal gradient exceeds a preset amplitude.

[0137] Alternatively, if the rate of upward movement of the surface freeze-thaw interface exhibits unstable fluctuations, and the freeze-thaw state data contains records of high-frequency alternation between frozen and thawed states, the active surface layer is determined to be in a critical transitional state.

[0138] In one possible implementation, the processing module 202 is used to determine whether the active surface layer is currently in a frozen state, a thawing state, or a critical transition state based on the freeze-thaw cycle state, wherein the frozen state corresponds to zero meltwater production, and the thawing state and the critical transition state trigger the calculation of meltwater runoff generation.

[0139] The processing module 202 is used to calculate the heat flux change trend from the surface to the freezing interface based on the geothermal gradient data, dynamically calculate the daily movement rate of the freeze-thaw interface using the heat diffusion formula, and determine the effective melting thickness of the active surface layer by combining the frozen soil thickness data.

[0140] Processing module 202 is used to construct a meltwater runoff model of the active layer based on the effective melt thickness, combined with surface roughness data and initial pore water saturation data. The initial effective porosity, capillary suction and permeability coefficient inside the active layer are dynamically adjusted according to the initial pore water saturation data and surface roughness data.

[0141] The processing module 202 is used to calculate the potential meltwater generation per unit area caused by the movement of the freeze-thaw interface in each time step according to the active layer meltwater generation model, and to determine the soil moisture replenishment status based on the initial pore water saturation. If the soil pores are not saturated, some meltwater is converted into soil moisture storage. If the soil pores are saturated, the remaining meltwater is converted into surface runoff.

[0142] The processing module 202 is used to calculate the surface resistance coefficient based on the surface roughness data, correct the runoff rate per unit area, accumulate the active layer runoff generation per unit time, and continuously update the runoff generation in real time based on the latest collected data.

[0143] In one possible implementation, the processing module 202 is used to obtain the time series of rainfall intensity corresponding to the target area based on rainfall forecast data.

[0144] The processing module 202 is used to dynamically correct the number of initial runoff curves in the target area based on surface roughness data and initial pore water saturation data, and obtain the corrected number of runoff curves.

[0145] The processing module 202 is used to calculate the initial rainfall loss based on the number of corrected runoff curves, wherein the initial rainfall loss is a preset ratio of the parameters corresponding to the number of corrected runoff curves.

[0146] The processing module 202 is used to determine whether the rainfall intensity generates effective runoff based on the rainfall intensity time series and the initial rainfall loss. When the rainfall intensity is greater than the initial rainfall loss, the rainfall runoff is calculated. The rainfall runoff is the result obtained by dividing the square of the remaining part after deducting the initial rainfall loss from the rainfall intensity by the rainfall intensity and the initial rainfall loss, and then adding the difference between the parameters corresponding to the corrected runoff generation curve.

[0147] The processing module 202 is used to linearly superimpose the meltwater runoff generation as the basic meltwater runoff input with the rainfall runoff to obtain the comprehensive runoff generation.

[0148] In one possible implementation, the processing module 202 is used to calculate the surface free flow thickness based on the comprehensive runoff output, wherein the surface free flow thickness is determined according to the functional relationship between the comprehensive runoff output and the local surface slope.

[0149] The processing module 202 is used to calculate the surface flow velocity based on the surface free flow thickness, combined with surface slope data and surface roughness data, using the Manning formula. The surface flow velocity is dynamically updated as the surface free flow thickness changes.

[0150] The processing module 202 is used to calculate the surface hydrodynamic shear force based on the surface free flow thickness and surface flow velocity, combined with the water density, gravitational acceleration and surface slope tangent.

[0151] Processing module 202 is used to obtain the unit scour force of the surface based on the surface hydrodynamic shear force and the surface area of ​​the active surface layer.

[0152] The processing module 202 is used to compare the unit scour force on the ground surface with the soil instability limit shear threshold in real time. The soil instability limit shear threshold is preset or dynamically adjusted based on soil structure characteristics, water content, temperature state and porosity.

[0153] Processing module 202 is used to determine that the active surface layer has reached the erosion initiation state when the unit scour force of the surface is greater than or equal to the soil instability limit shear threshold.

[0154] Processing module 202 is used to determine that the active surface layer has not reached the erosion initiation state when the unit scour force of the surface is less than the soil instability limit shear threshold.

[0155] In one possible implementation, the acquisition module 201 is used to monitor the surface freeze-thaw conversion state in real time. When the active surface layer changes from a frozen state to a thawing state or a critical transition state and remains continuously for a preset duration, it is determined that the surface freeze-thaw conversion state is completed, and the starting time point of the completion of the surface freeze-thaw conversion state is recorded.

[0156] The processing module 202 is used to continuously accumulate the amount of meltwater runoff generated in the active layer of the surface from the starting time point, and superimpose it with the rainfall runoff in the corresponding time step to obtain the effective meltwater runoff. The effective meltwater runoff is the linear superposition result of the amount of meltwater runoff generated in the active layer and the rainfall runoff.

[0157] The processing module 202 is used to set the percentile distribution of the effective meltwater runoff in the target area based on historical statistical samples, select the preset percentile corresponding to a specific risk level, and determine that the first triggering condition is met when the effective meltwater runoff reaches the flow value corresponding to the preset percentile.

[0158] The processing module 202 is used to determine that the second triggering condition is met when the unit scour force on the ground is greater than the soil instability limit shear threshold at each time step.

[0159] The output module 203 is used to trigger a soil erosion warning when the first triggering condition and the second triggering condition are met simultaneously.

[0160] In one possible implementation, the processing module 202 is used to establish an uncertainty management mechanism, which is used to receive various monitoring data of the target area in real time and to uniformly quantify the measurement error, estimation error and environmental disturbance error of the monitoring data.

[0161] The processing module 202 is used to generate multiple sets of erosion process scenario samples based on the uncertainty quantification results, using particle filtering or sequential Monte Carlo simulation methods. It calculates the comparison results of the unit surface scour force and the soil instability limit shear threshold under each scenario, as well as the probability of erosion initiation, and forms a dynamic distribution curve of erosion probability.

[0162] The processing module 202 is used to calculate the probability of soil erosion risk at the current moment in real time. The probability of soil erosion risk represents the likelihood that the active surface layer will enter an erosion state under the current environmental and data uncertainty conditions.

[0163] The processing module 202 is used to set the warning level thresholds for multiple levels of soil erosion, including normal state, first-level warning state, second-level warning state and third-level warning state, and each warning state corresponds to an erosion probability interval.

[0164] The output module 203 is used to dynamically adjust the corresponding soil and water loss warning level when the probability of soil and water loss enters different erosion probability ranges.

[0165] The output module 203 is used to continuously and dynamically update the probability of soil erosion risk and adjust the soil erosion warning level and response measures until the environmental data stabilizes and falls back to a safe range and the erosion probability is lower than the warning cancellation threshold, and then the current soil erosion warning status is lifted.

[0166] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0167] This embodiment also discloses an electronic device, as shown in the reference. Figure 3 The electronic device may include: at least one processor 301, at least one communication bus 302, user interface 303, network interface 304, and at least one memory 305.

[0168] The communication bus 302 is used to enable communication between these components.

[0169] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0170] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0171] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles operations, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0172] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing operations, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operation module, a network communication module, a user interface 303 module, and an application program for a method for early warning of soil erosion based on climate change.

[0173] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call an application stored in the memory 305 for a method of early warning of soil erosion based on climate change. When executed by one or more processors 301, the electronic device performs one or more methods as described in the above embodiments.

[0174] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0175] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0177] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0179] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 305 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0180] This application also discloses a computer-readable storage medium storing instructions. When executed by one or more processors 301, these instructions cause an electronic device to perform one or more methods as described in the above embodiments.

[0181] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for early warning of soil erosion based on climate change, characterized in that, The method includes: Real-time acquisition of surface temperature and freeze-thaw status data of the target area; determination of freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria. Based on the freeze-thaw cycle status, geothermal gradient data, frozen soil thickness data, surface roughness data, and initial pore water saturation data, the meltwater runoff generation of the active surface layer is estimated, and the comprehensive runoff production is estimated in combination with rainfall forecast data. Based on the comprehensive runoff, the surface scour force is dynamically calculated, and the soil instability limit shear threshold at the freeze-thaw interface is used to determine whether the surface scour force has reached the erosion initiation state. When the surface freeze-thaw transition state combined with the effective meltwater runoff reaches a preset percentile, and the surface unit scour force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered. The calculation of meltwater runoff generation in the active surface layer based on the freeze-thaw cycle status, geothermal gradient data, permafrost thickness data, surface roughness data, and initial pore water saturation data specifically includes: Based on the freeze-thaw cycle state, it is determined whether the active surface layer is currently in a frozen state, a thawing state, or a critical transition state. The frozen state corresponds to zero meltwater production, while the thawing state and the critical transition state trigger the calculation of meltwater runoff generation. Based on the geothermal gradient data, the heat flux variation trend from the surface to the freezing interface is calculated, the daily movement rate of the freeze-thaw interface is dynamically calculated using the heat diffusion formula, and the effective thawing thickness of the active surface layer is determined in combination with the frozen soil thickness data. Based on the effective melting thickness, combined with the surface roughness data and the initial pore water saturation data, an active layer meltwater runoff model is constructed, wherein the initial effective porosity, capillary suction and permeability coefficient inside the active surface layer are dynamically adjusted according to the initial pore water saturation data and the surface roughness data. Within each time step, the potential meltwater generation per unit area caused by the movement of the freeze-thaw interface is first calculated according to the active layer meltwater generation model. The soil moisture replenishment status is determined based on the initial pore water saturation. If the soil pores are not saturated, some meltwater is converted into soil moisture storage. If the soil pores are saturated, the remaining meltwater is converted into surface runoff. The surface resistance coefficient is calculated based on the surface roughness data, the runoff rate per unit area is corrected, the amount of meltwater runoff generated in the active layer per unit time is accumulated, and the amount of meltwater runoff generated is continuously updated in real time based on the latest collected data. Based on the freeze-thaw cycle status, geothermal gradient data, frozen soil thickness data, surface roughness data, and initial pore water saturation data, the meltwater runoff generation of the active surface layer is calculated, and the comprehensive runoff yield is calculated in conjunction with rainfall forecast data, specifically including: Based on the rainfall forecast data, obtain the time series of rainfall intensity corresponding to the target area; Based on the surface roughness data and the initial pore water saturation data, the initial runoff curve number of the target area is dynamically corrected to obtain the corrected runoff curve number. Based on the corrected runoff curve number, the initial rainfall loss is calculated, wherein the initial rainfall loss is a preset proportion of the parameter corresponding to the corrected runoff curve number; Based on the rainfall intensity time series and the initial rainfall loss, it is determined whether the rainfall intensity generates effective runoff. When the rainfall intensity is greater than the initial rainfall loss, the rainfall runoff is calculated. The rainfall runoff is the result of dividing the square of the remaining part after deducting the initial rainfall loss from the rainfall intensity by the rainfall intensity and the initial rainfall loss, and then adding the difference between the parameters corresponding to the number of corrected runoff curves. The meltwater runoff generation is used as the basic meltwater runoff input and linearly superimposed with the rainfall runoff to obtain the comprehensive runoff generation.

2. The method for early warning of soil erosion based on climate change according to claim 1, characterized in that, The real-time acquisition of surface temperature data and freeze-thaw status data of the target area, based on a set freeze-thaw status criterion, determines the freeze-thaw cycle status of the active surface layer, specifically including: By deploying a surface temperature sensor array and a geothermal depth gradient probe in the target area, and combining it with satellite remote sensing observation data, a joint acquisition system for multi-source surface temperature data and freeze-thaw status data is formed. The acquisition frequency of the surface temperature data is the same as the acquisition frequency of the freeze-thaw status data. According to the surface temperature data, when the surface temperature remains below a preset first temperature for a preset duration and the rate of change of the ground temperature gradient is in a downward trend, the active surface layer is determined to be in a frozen state. When the surface temperature is consistently higher than the preset second temperature and the rate of change of the ground temperature gradient changes from negative to positive, and the daily average surface temperature rise reaches the preset threshold, and the freeze-thaw state data detects that the position of the surface freeze-thaw interface has moved up beyond the preset thickness change value, the active surface layer is determined to be in a melting state. When the surface temperature is between the preset second temperature and the preset first temperature, and the daily amplitude of the ground temperature gradient exceeds the preset amplitude, the active surface layer is determined to be in a critical transition state. Alternatively, if the upward movement rate of the surface freeze-thaw interface exhibits unstable fluctuations, and the freeze-thaw state data contains records of high-frequency alternating changes between frozen and thawed states, then the active surface layer is determined to be in a critical transition state.

3. The method for early warning of soil erosion based on climate change according to claim 1, characterized in that, The process of dynamically calculating the surface scour force based on the comprehensive runoff yield, and determining whether the surface scour force has reached the erosion initiation state based on the soil instability ultimate shear threshold at the freeze-thaw interface, specifically includes: Based on the comprehensive runoff volume, the surface free flow thickness is calculated, and the surface free flow thickness is determined according to the functional relationship between the comprehensive runoff volume and the local surface slope; Based on the surface free flow thickness, combined with surface slope data and surface roughness data, the surface flow velocity is calculated using the Manning formula, and the surface flow velocity is dynamically updated as the surface free flow thickness changes. Based on the surface free flow thickness and surface flow velocity, combined with water density, gravitational acceleration, and surface slope tangent, the surface hydrodynamic shear force is calculated. The surface scour force per unit area is obtained based on the surface hydrodynamic shear force and the surface area of ​​the active surface layer. The surface scour force per unit area is compared with the soil instability limit shear threshold in real time. The soil instability limit shear threshold is preset or dynamically adjusted based on soil structure characteristics, water content, temperature state and porosity. When the surface unit scour force is greater than or equal to the soil instability limit shear threshold, it is determined that the active surface layer has reached the erosion initiation state. When the surface scouring force is less than the soil instability limit shear threshold, it is determined that the active surface layer has not reached the erosion initiation state.

4. The method for early warning of soil erosion based on climate change according to claim 2, characterized in that, When the combined effective meltwater runoff from the surface freeze-thaw transition reaches a preset percentile, and the surface scour force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered, specifically including: The surface freeze-thaw transformation state is monitored in real time. When the active surface layer changes from the frozen state to the thawed state or the critical transition state and remains continuously for the preset duration, it is determined that the surface freeze-thaw transformation state is completed, and the start time point of the completion of the surface freeze-thaw transformation state is recorded. Starting from the initial time point, the amount of meltwater runoff generated in the active surface layer is continuously accumulated and superimposed with the rainfall runoff in the corresponding time step to obtain the effective meltwater runoff. The effective meltwater runoff is the linear superposition result of the amount of meltwater runoff generated in the active layer and the rainfall runoff. Based on historical statistical samples, the percentile distribution of effective meltwater runoff in the target area is set, and a preset percentile corresponding to a specific risk level is selected. When the effective meltwater runoff reaches the flow value corresponding to the preset percentile, it is determined that the first triggering condition is met. Within each time step, when the unit scour force on the surface is greater than the soil instability limit shear threshold, it is determined that the second triggering condition is met. When the first triggering condition and the second triggering condition are met simultaneously, a soil erosion warning is triggered.

5. A method for early warning of soil erosion based on climate change according to claim 1, characterized in that, After triggering a soil erosion warning when the effective meltwater runoff in the local freeze-thaw transition state reaches a preset percentile and the unit scour force on the surface exceeds the soil instability limit shear threshold, the method further includes: An uncertainty management mechanism is established, which is used to receive various monitoring data of the target area in real time and to uniformly quantify the measurement error, estimation error and environmental disturbance error of the monitoring data. Based on the uncertainty quantification results, multiple sets of erosion process scenario samples are generated using particle filtering or sequential Monte Carlo simulation methods. The comparison results of the unit surface scour force and the soil instability limit shear threshold under each scenario are calculated, as well as the probability of erosion initiation, forming a dynamic distribution curve of erosion probability. The probability of soil erosion risk at the current moment is calculated in real time. The probability of soil erosion risk represents the likelihood that the active surface layer will enter an erosion state under the current environmental and data uncertainty conditions. Set thresholds for multiple levels of soil erosion warnings, including normal state, Level 1 warning state, Level 2 warning state and Level 3 warning state, with each warning state corresponding to an erosion probability interval; When the probability of soil erosion risk enters different erosion probability ranges, the corresponding soil erosion early warning level is dynamically adjusted. The probability of soil erosion risk is continuously and dynamically updated, and the soil erosion warning level and response measures are adjusted until the environmental data stabilizes and falls back to a safe range and the erosion probability is lower than the warning cancellation threshold, at which point the current soil erosion warning status is lifted.

6. A soil erosion early warning device based on climate change, characterized in that, The device is used to execute a method for early warning of soil erosion based on climate change as described in any one of claims 1-5. The device includes an acquisition module (201), a processing module (202), and an output module (203), wherein: The acquisition module (201) is used to collect surface temperature data and freeze-thaw status data of the target area in real time, and determine the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria. The processing module (202) is used to calculate the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle status and geothermal gradient data, frozen soil thickness data, surface roughness data and initial pore water saturation data, and to calculate the comprehensive runoff production in combination with rainfall forecast data. The processing module (202) is used to dynamically calculate the surface scour force based on the comprehensive runoff output, and determine whether the surface scour force has reached the erosion initiation state based on the soil instability limit shear threshold under the freeze-thaw interface. The output module (203) is used to trigger a soil and water loss warning when the effective meltwater runoff in the surface freeze-thaw transformation state reaches a preset percentile and the surface unit scour force exceeds the soil instability limit shear threshold.

7. An electronic device, characterized in that, The device includes a processor (301), a communication bus (302), a user interface (303), a network interface (304), and a memory (305). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are both used to communicate with other devices. The communication bus (302) is used to realize the connection and communication between the components within the electronic device. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device performs the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-5.

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