A landslide surge physical and numerical hybrid model construction method, system and application

Abstract

The present invention relates to the technical fields of water conservancy engineering, rock and soil mechanics, and geological disaster prevention and control engineering, and discloses a method, system, and application for constructing a hybrid physical and numerical model of landslide surge waves. The method comprises analyzing the landslide instability evolution movement process, the height of the first wave entering the landslide reservoir, and the surge propagation process of the landslide surge waves based on the first result of the landslide surge wave physical model test; conducting a numerical simulation test based on the second result of the landslide surge wave numerical model test; adjusting the particle size and kernel function in the landslide surge wave numerical simulation test so that the first wave height in the landslide surge wave numerical simulation test and the landslide surge wave physical model test reaches the opposite bank height and the surge pressure distribution is consistent; and using the adjusted landslide surge wave numerical model as the obtained landslide surge wave hybrid model. The system comprises a first result acquisition module, a second result acquisition module, a test parameter adjustment module, and a hybrid model acquisition module. The present invention conveniently analyzes different calculation conditions.

Description

Technical Field

[0001] The present invention relates to the technical fields of water conservancy engineering, rock and soil mechanics and geological disaster prevention and control engineering, and in particular to a method, system and application for constructing a landslide surge physical and numerical hybrid model. Background Art

[0002] Landslide surge, a massive wave phenomenon caused by landslides entering water bodies, poses significant risks. These hazards include the enormous impact force generated by landslides, which can severely damage buildings, infrastructure, and the ecological environment along the coast. The resulting waves can rapidly inundate low-lying coastal areas, impacting shipping and causing property damage and casualties. To accurately characterize landslide surge, physical model tests have been conducted to construct a landslide surge model tailored to engineering geological conditions. The results provide valuable guidance for landslide surge prevention and control. However, due to limitations in monitoring equipment and terrain accuracy, the results of these physical model tests primarily capture the macroscopic characteristics of landslide surge hazards and struggle to accurately capture their specific details. Numerical simulation methods offer advantages in efficiency and intuitive results, allowing for a clearer understanding of the details of landslide surge hazards. However, the results of these numerical simulations are significantly affected by computational parameters, some of which are often difficult to determine, resulting in relatively low reliability.

[0003] Prior art 1, Chinese patent application number: 202410623079.7, discloses a landslide surge test device and method for overall slope adjustment. The device comprises a movable lift test platform, an angle-adjustable slope, and a speed-measuring pulley. The movable lift test platform comprises a movable frame, a hydraulic lift test platform, and a load-bearing support plate. The angle-adjustable slope comprises a slope, an angle-adjustable cylinder, a rotating support seat, an angle measuring plate, and a buffer block. The speed-measuring pulley comprises a slide box and a speed-measuring device. The device also relates to a test method, comprising the steps of: constructing a water model; simulating the landslide surge process; and collecting and processing experimental data. The device conducts landslide surge physical model tests by varying test conditions, adapting to test pools of varying heights, and adapting to landslide models with various sliding angles. The device also adjusts the impact velocity of the sliding body. By replacing the slide box, the device can be adapted to landslide surge tests involving different sliding body materials. While this reduces testing time and can be reused multiple times in different test models, it places extremely high demands on the performance and model of the testing equipment, increasing testing costs.

[0004] Prior art two, Chinese patent application number: 202210105273.7, discloses a three-dimensional normal physical model for landslide surge testing in a river-type reservoir. The model includes a wave-breaking zone, as well as a landslide zone, a river channel zone, and a dam zone, which are configured based on engineering geological map information and hydraulic structure design drawings. The dam zone includes a hydraulic structure with a drainage gate. The landslide zone includes materials similar to the landslide body and a landslide initiation device. A water circulation system is provided between the wave-breaking zone, the landslide zone, the river channel zone, and the dam zone to simulate real-world water flow conditions. The model also includes a data monitoring system for monitoring the entire process of landslide surge generation and propagation. Although the landslide surge test physical model is a large-scale physical model, the overall normal model, designed based on engineering geological map data and hydraulic structure design data, can more comprehensively reflect the entire process of the combined effects of landslide, river channel, and high dam, and can more accurately reflect the disaster phenomena and data patterns of real projects. It can be used to establish large-scale three-dimensional landslide surge physical model tests for real projects. However, it mainly reflects the macro characteristics of landslide surge disasters and is difficult to accurately capture the various details of landslide surge disasters, which increases the difficulty of accurately capturing the various details of landslide surge disasters.

[0005] Prior art three, Chinese patent application number: 202410555040.6 discloses a method and system for numerical simulation of landslide surges based on nested grids, including obtaining terrain data of the area to be simulated and establishing a three-dimensional geometric model based on the terrain data; dividing the three-dimensional geometric model into grid nested areas and non-nested areas based on the terrain information of the area to be simulated; performing a first grid division on the grid nested areas and non-nested areas of the three-dimensional geometric model, and then performing a second grid division on the grid nested areas; setting simulation model parameters and initial conditions and performing simulation to obtain simulated values ​​of parameters in landslide surges. Although numerical simulation calculations are achieved by dividing the area to be simulated into regions and nesting grids based on regions, the number of grids is greatly reduced, the consumption of computing resources is reduced, and computing efficiency is improved; and nesting grids based on regions can set more refined computing grids to adapt to complex terrain conditions and ensure the accuracy of the calculation results. However, the calculation results are too dependent on the calculation parameters, resulting in relatively low credibility and accuracy of the numerical simulation results.

[0006] Currently, existing technologies 1, 2, and 3 have difficulty accurately capturing the various details of landslide surge hazards, and the reliability of numerical simulation results is relatively low. Therefore, the present invention provides a method, system, and application for constructing a hybrid physical and numerical model of landslide surge. This system combines the advantages of physical model testing and numerical simulation. Based on large-scale physical model testing, the system uses physical experimental results to iteratively modify the numerical model, establishing a physical-numerical hybrid model for comprehensive assessment of landslide surge hazard data. Summary of the Invention

[0007] The main purpose of the present invention is to provide a method, system and application for constructing a landslide surge physical and numerical hybrid model to solve the problem in the prior art that it is difficult to accurately capture various details of landslide surge disasters and the credibility of numerical simulation results is relatively low.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for constructing a landslide surge physical and numerical hybrid model, comprising:

[0010] Obtain the target area's contour geological information, the height of the sliding mass's center of gravity, and the water level; construct a landslide surge physical model test based on this information. The landslide surge physical model test includes constructing the terrain based on the contour information and constructing the sliding mass's position and volume based on the sliding mass's volume and center of gravity; and analyze the landslide surge's instability evolution, the height of the first wave entering the reservoir, and the surge propagation process based on the initial results of the landslide surge physical model test.

[0011] Constructing a numerical model test of landslide surge based on the information. The numerical model test of landslide surge includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body. Conducting a numerical simulation test based on the second result of the numerical model test of landslide surge;

[0012] Taking the first wave height, the height reaching the opposite bank and the surge pressure distribution as the evaluation criteria, the particle size and kernel function in the numerical simulation test of landslide surge were adjusted to ensure that the first wave height, the height reaching the opposite bank and the surge pressure distribution in the numerical simulation test of landslide surge and the physical model test of landslide surge were consistent.

[0013] The adjusted landslide surge numerical model is used as the obtained landslide surge hybrid model to analyze the working conditions of different landslide velocities, landslide volumes and terrain conditions.

[0014] As a further improvement of the present invention, the process of adjusting the particle size and kernel function in the landslide surge numerical simulation test includes:

[0015] Analyze the experimental data obtained, including extracting specific values ​​for the head wave height, the height reaching the opposite shore, and the surge pressure distribution, which will serve as evaluation criteria; establish a relationship diagram between the target value and the current numerical simulation results, and identify the gap between the numerical simulation and the physical experimental results;

[0016] Analyze the particle distribution characteristics in the landslide surge physical model and compare the impact of different particle sizes on the numerical results. Perform numerical simulations by varying the particle diameter, continuously refining the particle size until it matches the results of the landslide surge physical model. Based on the analysis of surge behavior in the landslide surge physical model experiment, determine the kernel function type and adjust the kernel function's support domain and decay rate.

[0017] After completing the adjustment of particle size and kernel function, a new round of numerical simulation is carried out: the adjusted numerical model is comprehensively compared with the physical experimental results, including the verification of the first wave height, the detection of the height reaching the opposite shore, and the fitting of the surge pressure distribution.

[0018] As a further improvement of the present invention, the process of establishing a relationship diagram between the target value and the current numerical simulation result includes:

[0019] Obtain key parameters such as the first wave height, arrival height at the opposite shore, and surge pressure distribution to set target values. Simultaneously, obtain corresponding results from numerical simulations and record the first wave height, arrival height, and corresponding surge pressure distribution data obtained under the same simulated conditions.

[0020] The collected landslide surge physical model experimental data and the second result of the numerical simulation were matched with the time point and spatial position of the collection, and smoothed; the height of the first wave and the height reaching the opposite bank were normalized according to the maximum value, generating a standardized value between 0 and 1;

[0021] A two-dimensional coordinate system is used, with the X-axis representing the simulation results and the Y-axis representing the experimental target value. A scatter plot is made to show the relationship between the simulation results and the target values. The distance between each point is calculated to quantify the gap and obtain the deviation between the numerical simulation results and the target values.

[0022] As a further improvement of the present invention, there are n pairs of simulation results and target values ​​of key parameters, which are respectively expressed as: numerical simulation results S i , where i = 1, 2, ..., n, target value T i ;

[0023] Absolute error refers to the direct difference between the numerical simulation result and the target value, and the calculation formula is:

[0024] E abs,i =|S i -T i |

[0025] Among them, E abs,i represents the absolute error of the i-th key parameter;

[0026] The relative error takes into account the influence of the target value and uses the ratio of the target value to standardize the error. The calculation formula is:

[0027]

[0028] Among them, E rel,i Represents the relative error of the i-th key parameter;

[0029] The root mean square error (RMSE) is used to comprehensively evaluate the deviations of multiple parameters. The calculation formula is:

[0030]

[0031] The average of the squared errors of all key parameters was calculated and then the square root was taken, providing a more comprehensive error measure;

[0032] The comprehensive deviation evaluation evaluates each key parameter by combining absolute error, relative error, and root mean square error to form a new indicator:

[0033]

[0034] Among them, α, β, and γ are weight coefficients, reflecting the importance of different deviation measures in the analysis.

[0035] As a further improvement of the present invention, the process of changing the particle diameter and adjusting the support domain and decay rate of the kernel function includes:

[0036] The current particle distribution characteristics and diameters were extracted, and a numerical landslide surge model was used to perform regression analysis on the relationship between the existing particle distribution and surge behavior to obtain preliminary results. The particle size variation range was set, and multiple particle diameters were selected for simulation experiments. In the numerical simulation, the corresponding particle sizes were replaced one by one, and the simulation results focused on the first wave height, the wave height reaching the shore, and the pressure distribution area.

[0037] After each adjustment of the particle diameter, numerical simulations were repeated and the results were compared with the experimental target values. By collecting the simulated data, the accuracy of the corresponding wave height, arrival altitude, and pressure distribution was evaluated, and the particle size was reduced until it matched the target value.

[0038] After the particle size is determined, the support domain of the kernel function is set according to the particle's action distance; according to the actual fluctuations in the simulation results, the decay rate of the kernel function is adjusted, the initial decay rate is set, and fine-tuning is performed by comparing the simulation results.

[0039] As a further improvement of the present invention, the process of performing regression analysis on the relationship between the existing particle distribution and surge behavior using the landslide surge numerical model includes:

[0040] The system acquires various data on particle trajectory, position, diameter, and water surface fluctuations in the surge, analyzes the acquired data, and automatically marks and identifies particle boundaries. A tree-based model assesses the impact of each variable on surge behavior and selects the most influential features. A cross-feature construction is performed on particle characteristics and wave behavior to generate a new feature combination, namely the wave-particle coupling feature.

[0041] Construct a deep learning-based neural network, using an adaptive network structure and hierarchical feature extraction mechanism to fit the complex nonlinear relationship between particle characteristics and surges, analyze the characteristics of fluctuating data series, and capture dynamic changes in time series. Combine fluid dynamics equations with deep learning models to guide neural network learning through physical constraints.

[0042] The performance of the neural network model is evaluated through k-fold cross-validation, and the hyperparameters are adjusted using Bayesian optimization to achieve the optimal configuration.

[0043] As a further improvement of the present invention, the process of guiding the learning of the neural network by physical constraints includes:

[0044] Collect particle dynamics and water surface fluctuation data, annotate particles to identify the boundaries, shapes, and positions of different particles; extract particle characteristics and wave parameters; create time window features to convert all particle characteristics and wave parameters into a distribution with mean 0 and variance 1;

[0045] The particle dynamics and water surface fluctuation data were divided into training and validation sets. The loss and accuracy curves of the training and validation sets were plotted to observe convergence and stability. The effectiveness of the neural network model was evaluated through scatter plots of actual wave data and model prediction results.

[0046] Establish a set of model equations to describe fluid dynamics, and calculate losses by comparing the residuals between the neural network model output and the equations. After the neural network model is trained, use physical consistency testing to check whether the wave and particle behaviors output by the model meet physical expectations.

[0047] in,

[0048]

[0049] Where ρ is the fluid density, u is the velocity field, p is the pressure, μ is the viscosity coefficient, and f is the body force;

[0050] Calculate the loss function expression:

[0051] Loss = L data +λ·L physics

[0052] Among them, Ldata is the data loss, L physics is the loss of the physical model, and λ is the importance coefficient used to balance the loss.

[0053] As a further improvement of the present invention, the process of reducing the particle size until it matches the target value includes:

[0054] The output results of each numerical simulation are collected. The output results include three parameters: wave height, arrival height, and pressure distribution. The mean square error and absolute error are used as standardized evaluation indicators.

[0055] Set an accuracy threshold and use it to determine the deviations of the three parameters: wave height, arrival height, and pressure distribution. Compare the simulation results with the target values ​​and generate an error analysis chart to obtain the deviations and trends of the model output under different particle sizes, thereby determining whether the effect of the current particle size on surge behavior meets the experimental objectives.

[0056] If the simulation results fail to meet the target value, the particle size will be reduced proportionally for the next round of simulation;

[0057] The process of proportionally reducing the particle size for the next round of simulation includes:

[0058] After comparing the simulation results with the target values, the deviation of each parameter is identified;

[0059] Definition symbol: Y sim =(h sim ,z sim ,P sim ) represents the current simulation result vector, h sim represents the simulated wave height, z sim Indicates the simulated arrival height, P sim represents the simulated pressure distribution, Y target =(h target ,z target ,P target ) represents the target value vector, h target Indicates the target wave height, z target Indicates the target arrival height, P target represents the target pressure distribution, D i Represents the deviation of the i-th parameter, calculated as:

[0060] where i∈h,z,P

[0061] i = h represents the deviation for wave height, i = z represents the deviation for arrival height, and i = P represents the deviation for pressure distribution;

[0062] Set the comprehensive deviation D to;

[0063] Definition symbol: N represents the number of data points; calculation formula:

[0064]

[0065] ∈ the set accuracy threshold, indicating the maximum allowed deviation);

[0066] Reduce the particle size proportionally and adjust the particle size after identifying that the deviation D exceeds the accuracy threshold;

[0067] Definition symbol: D current Indicates the current particle size value, D new Represents the updated new particle size value, r represents the reduction ratio, and the range is r∈(0,1);

[0068] The formula for updating particle size is: D new =D current ×(1-r);

[0069] For example: If the current particle size (D current =0.05) meters, reduce the ratio (r = 0.1), then: D new =0.05×(1-0.1)=0.045 m;

[0070] After adjusting the particle size, perform a new numerical simulation to generate a new output result Y sim,new ; Return to re-collect data for evaluation.

[0071] To achieve the above object, the present invention also provides the following technical solutions:

[0072] A landslide surge physical and numerical hybrid model construction system is applied to the landslide surge physical and numerical hybrid model construction method. The landslide surge physical and numerical hybrid model construction system includes:

[0073] The first result acquisition module is used to obtain the contour geological information of the target area, the height of the center of gravity of the sliding body, and the water level information; construct a landslide surge physical model test based on this information, which includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body; based on the first result of the landslide surge physical model test, analyze the landslide instability evolution movement process, the height of the first wave entering the landslide reservoir, and the surge propagation process;

[0074] The second result acquisition module is used to construct a landslide surge numerical model test based on the information. The landslide surge numerical model test includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body; and conducting a numerical simulation test based on the second result of the landslide surge numerical model test;

[0075] The test parameter adjustment module is used to use the first wave height, the height reaching the opposite bank, and the surge pressure distribution as evaluation criteria to adjust the particle size and kernel function in the landslide surge numerical simulation test to ensure that the first wave height, the height reaching the opposite bank, and the surge pressure distribution in the landslide surge numerical simulation test and the landslide surge physical model test are consistent;

[0076] The hybrid model acquisition module is used to use the adjusted landslide surge numerical model as the obtained landslide surge hybrid model to analyze the working conditions of different landslide velocities, landslide volumes and terrain conditions.

[0077] To achieve the above object, the present invention also provides the following technical solutions:

[0078] An application of a landslide surge hybrid model, the landslide surge hybrid model is obtained by the landslide surge physical and numerical hybrid model construction method, and the landslide surge hybrid model analyzes working conditions of different landslide velocities, landslide volumes and terrain conditions.

[0079] The construction and preliminary analysis of the physical model test of the present invention provide basic data for the test and model construction by collecting contour geological information, sliding body volume center of gravity height and water level information; based on the collected information, a physical model reflecting the actual terrain and sliding body characteristics is constructed to make the test results have higher practical significance; physical model tests are carried out to analyze the landslide instability evolution process, the first wave height and the surge propagation process, and obtain a preliminary intuitive understanding of the motion characteristics and mechanical behavior. Significance: Through the physical model, the motion laws and influencing factors of the landslide surge can be understood more intuitively, providing a reference for numerical simulation; providing a physical experimental basis for the establishment of the numerical model, helping to predict and optimize the parameters and complexity of the numerical simulation. The construction and simulation of the numerical model test, constructing a numerical model based on the contour lines and sliding body characteristics, provides an efficient calculation tool for the simulation of complex landslide surge processes; conducting experiments through the numerical model, simulating the dynamic process of the landslide surge, and providing quantitative data support. Significance: Numerical simulation technology can be applied in a wider range of situations (such as extreme weather and varying terrain), improving the accuracy of landslide surge predictions. Compared to physical experiments, numerical simulation offers significant advantages in terms of time and cost, enabling rapid simulation and analysis of a variety of scenarios. Model results were calibrated and optimized by adjusting the particle size and kernel function to optimize the numerical model, ensuring consistency between the simulation results and the physical experimental results. Evaluation criteria for head wave height, height at the opposite bank, and surge pressure distribution were established to ensure the model's applicability under various conditions. Significance: The calibrated landslide surge numerical model showed improved agreement with physical experiments, significantly enhancing the model's reliability and accuracy in practical applications. The calibration and optimization process not only improved the accuracy of the existing model but also provided a methodological basis for future similar studies. The hybrid model was applied and analyzed, utilizing the adjusted hybrid model to analyze landslide surges under different operating conditions, evaluating their response to landslide velocity, volume, and terrain conditions. This model was able to simulate surge behavior under different operating conditions, resulting in a comprehensive study of the landslide surge phenomenon. Significance: The analysis results of different working conditions can provide a scientific basis and reference for disaster prevention and mitigation, guide the design of infrastructure and landslide monitoring and early warning; promote the development of research technology on the dynamic behavior of landslide surges, and lay the foundation for the proposal and application of new methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic flow chart of steps in one embodiment of a method for constructing a landslide surge physical and numerical hybrid model of the present invention;

[0081] Figure 2 A schematic diagram of an embodiment of a method for constructing a landslide surge physical and numerical hybrid model of the present invention;

[0082] Figure 3This is a schematic flow chart of the steps of one embodiment of a method for constructing a landslide surge physical and numerical hybrid model according to a first result of a landslide surge physical model test;

[0083] Figure 4 This is a schematic flow chart of the steps of one embodiment of the method for constructing a landslide surge physical and numerical hybrid model according to the second result of the landslide surge numerical model test;

[0084] Figure 5 This is a schematic flow chart of the steps for adjusting the particle size and kernel function in a landslide surge numerical simulation test according to one embodiment of the method for constructing a hybrid landslide surge physical and numerical model of the present invention;

[0085] Figure 6 A schematic flow chart of the steps of obtaining a landslide surge hybrid model according to an embodiment of the method for constructing a landslide surge physical and numerical hybrid model of the present invention;

[0086] Figure 7 A schematic diagram of functional modules of an embodiment of a landslide surge physical and numerical hybrid model construction system of the present invention;

[0087] Figure 8 This is a schematic structural diagram of an embodiment of an electronic device of the present invention;

[0088] Figure 9 This is a schematic structural diagram of an embodiment of a storage medium of the present invention. DETAILED DESCRIPTION

[0089] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0090] The terms "first", "second" and "third" in the present invention are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present invention (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0091] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0092] like Figure 1 As shown, this embodiment provides an embodiment of a method for constructing a landslide surge physical and numerical hybrid model. In this embodiment, the method for constructing a landslide surge physical and numerical hybrid model specifically includes the following steps:

[0093] Step S1: Acquire contour geological information of the target area, information on the height of the center of gravity of the sliding mass, and water level; construct a landslide surge physical model test based on this information. The landslide surge physical model test includes constructing the terrain based on the contour information and constructing the position and volume of the sliding mass based on the volume and center of gravity of the sliding mass; and analyze the landslide instability evolution movement process, the height of the first wave entering the landslide reservoir, and the surge propagation process of the landslide surge based on the first result of the landslide surge physical model test.

[0094] Step S2: constructing a landslide surge numerical model test based on the information, the landslide surge numerical model test including constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body; performing a numerical simulation test based on the second result of the landslide surge numerical model test;

[0095] Step S3: Using the first wave height, the height reaching the opposite bank, and the surge pressure distribution as evaluation criteria, the particle size and kernel function in the numerical simulation test of landslide surge waves are adjusted to ensure that the first wave height, the height reaching the opposite bank, and the surge pressure distribution in the numerical simulation test of landslide surge waves and the physical model test of landslide surge waves are consistent;

[0096] Step S4: Using the adjusted landslide surge numerical model as the obtained landslide surge hybrid model, the working conditions of different landslide velocities, landslide volumes and terrain conditions are analyzed.

[0097] Preferably, in step S1 of this embodiment, the construction and preliminary analysis of the physical model test provides basic data for the test and model construction by collecting contour geological information, the height of the center of gravity of the sliding body, and water level information; based on the collected information, a physical model reflecting the actual terrain and sliding body characteristics is constructed, so that the test results have higher practical significance; physical model tests are conducted to analyze the evolution process of landslide instability, the height of the first wave, and the propagation process of the surge, and obtain a preliminary intuitive understanding of the motion characteristics and mechanical behavior. Significance: Through the physical model, the motion laws and influencing factors of the landslide surge can be more intuitively understood, providing a reference for numerical simulation; providing a physical experimental basis for the establishment of the numerical model, helping to predict and optimize the parameters and complexity of the numerical simulation. Step S2 is the construction and simulation of the numerical model test. The numerical model is constructed based on the contour lines and sliding body characteristics, providing an efficient computational tool for simulating complex landslide surge processes; experiments are conducted through the numerical model to simulate the dynamic process of the landslide surge and provide quantitative data support. Significance: Numerical simulation technology can be applied in a wider range of situations (such as extreme weather and varying terrain), improving the accuracy of landslide surge predictions. Compared to physical experiments, numerical simulation offers significant advantages in terms of time and cost, enabling rapid simulation and analysis of a variety of scenarios. Step S3 involves calibration and optimization of model results. By adjusting particle size and kernel function, the numerical model is optimized to ensure consistency between numerical simulation results and physical experimental results. By setting evaluation criteria for head wave height, height at the opposite bank, and surge pressure distribution, the model's applicability under various conditions is ensured. Significance: The calibrated landslide surge numerical model shows improved agreement with physical experiments, significantly enhancing the model's reliability and accuracy in practical applications. The calibration and optimization process not only improves the accuracy of the existing model but also provides a methodological basis for future similar studies. Step S4 involves application and analysis of the hybrid model. Using the adjusted hybrid model, landslide surges under different operating conditions are analyzed to evaluate their response to landslide velocity, volume, and terrain conditions. This model can simulate surge behavior under different operating conditions, forming a comprehensive study of landslide surge phenomena. Significance: The analysis results of different working conditions can provide scientific basis and reference for disaster prevention and mitigation, guide the design of infrastructure and landslide monitoring and early warning; promote the development of landslide surge dynamic behavior research technology, and lay the foundation for the proposal and application of new methods (for specific principles, please refer to the attached Figure 2 ).

[0098] In summary, this embodiment optimizes the accuracy, efficiency and application scope of research related to landslide surges by combining physical models with numerical models, and is of great value to both theoretical research and practical engineering applications. At the same time, this embodiment constructs a landslide surge physical model based on the landslide topographic and geomorphological characteristics, and conducts landslide surge physical model tests based on landslide characteristics such as landslide volume, center of gravity height, and other factors; analyzes the landslide instability evolution movement process, the height of the first wave entering the landslide reservoir, and the surge propagation process of the landslide surge. Based on the landslide surge physical model test, a numerical simulation test is conducted to simulate the landslide surge conditions under the same geological conditions, and analyzes the landslide instability evolution movement process, the height of the first wave entering the landslide reservoir, the surge propagation process, and the impact on important residential areas upstream and downstream. Based on the results of the landslide surge physical model test, the relevant parameters of the landslide surge numerical simulation test are optimized, and multi-angle and multi-variable test results are obtained from the optimized landslide surge numerical simulation test to obtain a landslide surge hybrid model. This embodiment can improve simulation accuracy, enhance calculation efficiency, and expand the calculation range; the experimental cost is low, and different calculation conditions can be easily analyzed, such as different landslide speeds, landslide volumes, and terrain conditions, providing a scientific basis for disaster prevention and mitigation.

[0099] Furthermore, if Figure 3 As shown, the process in step S1 according to the first result of the landslide surge physical model test specifically includes the following steps:

[0100] Step S11: Acquire contour data of the target area, including information such as altitude, slope, soil type, and rock properties, and assess the volume and center of gravity of the potential landslide. The volume of the landslide includes cross-sectional area, volume, and shape, and the center of gravity height includes the geometric center of the landslide and its position on the slope.

[0101] Step S12: constructing a landform model based on the collected contour data; placing the simulated sliding body on the landform model; selecting a fluid medium (such as clean water, salt water, etc.) to simulate the hydrodynamic effects of landslide surges; and configuring experimental equipment;

[0102] The experimental equipment includes a water tank, a wave generating device, sensors, and a data acquisition system. The sensors can be used to monitor parameters such as wave height, pressure changes, and flow rate.

[0103] Step S13: Set the initial water level and the initial state of the sliding body, and use high-speed cameras, pressure sensors, wave height meters and other equipment to record in real time the first result of the landslide entering the water, including the evolution of landslide instability, the height of the first wave, the propagation speed of the surge wave, and the wave pressure distribution at each point. Process the wave height change diagram and pressure distribution diagram of the first result to display the propagation process of the landslide surge wave.

[0104] Preferably, in step S11 of this embodiment, data acquisition and landslide assessment, by acquiring contour data of the target area, can fully understand the characteristics of the terrain, including altitude, slope, etc., which is crucial for determining the shape and movement trajectory of the landslide body; assessing the potential volume of the landslide body and the position of its center of gravity helps to establish a more accurate physical model, ensuring that the landslide body reflects the actual landslide phenomenon in the model. Significance: Ensure that the constructed physical model is scientific and accurate; by analyzing the volume and center of gravity of the landslide body, predict the stability and potential risks of the landslide, and provide a basis for preventing landslide accidents; at the same time, ensure that the model results can be effectively transformed in practical applications (such as disaster assessment, engineering design, etc.). Step S12, geomorphic model construction and experimental equipment configuration, constructs a geomorphic model based on the contour data, ensures that the geometric characteristics of the model are consistent with the actual terrain, and enhances the realism and accuracy of the model; configures a water tank, a wave generating device, a sensor, and a data acquisition system to form an integrated experimental platform that can conduct a comprehensive analysis of landslide surge events. Significance: Through practical experimental settings, the hydrodynamic effects caused by landslides can be simulated in a controllable environment, allowing for a more intuitive study of the generation and propagation of surge waves. The configured experimental equipment allows for real-time monitoring and recording of multiple parameters (wave height, pressure, flow velocity, etc.), providing a rich source of information for subsequent data analysis, helping to improve the accuracy and depth of research. Step S13 sets initial conditions and records them in real time, setting the initial water level and the initial state of the sliding body to ensure that the experiment is conducted in a controllable environment, making the simulation results more valuable for reference. Using specialized equipment such as high-speed cameras, pressure sensors, and wave height meters, it is possible to capture in real time the dynamic changes of the landslide during water entry, including the evolution of landslide instability and the propagation speed of surge waves, and obtain corresponding wave height change maps and pressure distribution maps. Significance: Through real-time recording and data processing, the generation mechanism, propagation characteristics and impact of landslide surges on the surrounding environment are observed and analyzed, providing empirical support for understanding complex hydrodynamic phenomena. The visualization of the results (such as wave height variation maps and pressure distribution maps) provides an important basis for theoretical analysis and model verification, helps to identify the patterns of landslide and surge occurrence, and provides a scientific basis and suggestions for future disaster prevention and mitigation projects.

[0105] In summary, this example constitutes the core process of the landslide surge physical model experiment, laying a solid foundation for subsequent numerical simulation, verification, and analysis. Through data acquisition, precise model construction, and meticulous experimental observation, we can not only gain a deeper understanding of landslides and the resulting surge phenomena, but also provide reliable theoretical support and practical guidance for related fields (such as water conservancy engineering, environmental protection, and disaster management).

[0106] Furthermore, the process of evaluating the volume and center of gravity of the potential landslide body in step S11 specifically includes the following steps:

[0107] Step S111: Calculate the slope by using the elevation difference and horizontal distance between adjacent contour lines, perform section analysis on the contour lines, and calculate the cross-sectional area of ​​the landslide according to the contours of the landslide at different heights; and calculate the volume of the entire landslide according to the area of ​​each cross-section.

[0108] The slope calculation formula is:

[0109]

[0110] Where S represents the slope, h i and h j is the altitude of two adjacent contour lines, d ij is the horizontal distance between two adjacent contour lines;

[0111] For each level of cross-section, define a cross-sectional area:

[0112] A(z)=∫ x (y upper -y lower ),dx

[0113] Where A is the cross-sectional area, z is the height, and y is the upper and y lower are functions of the upper and lower boundaries respectively, and x is the horizontal coordinate;

[0114] The total volume of the sliding body is obtained by adding up the cross-sectional areas along the height direction:

[0115]

[0116] Where V represents the total volume of the sliding body, z1 and z2 are the heights of the lowest and highest points of the sliding body respectively;

[0117] Step S112: Divide the slider into multiple basic geometric shapes, such as triangular prisms, cuboids, and cones, based on their geometric shape, and calculate the center of gravity of each part. For each divided small volume, calculate the weighted average of the center of gravity of all parts to comprehensively determine the center of gravity of the entire slider.

[0118]

[0119] Among them, x c ,y c , z c They represent the coordinates of the center of gravity of the sliding body, V is the overall volume, and x, y, and z are the coordinates of each point in the body;

[0120]

[0121] Among them, the center of gravity position of the sliding body is x g ,y g,z g , V i is the volume of the ith body, is the coordinate of the center of gravity of the i-th body;

[0122] Step S113: converting the height of the center of gravity of the slider into the slope coordinate system;

[0123] The conversion formula is: r =z g -(S·D)

[0124] Among them, z r is the height of the center of gravity on the slope, S is the slope, and D is the horizontal distance from the slope baseline to the center of gravity.

[0125] Preferably, step S111 of this embodiment calculates the slope, cross-sectional area, and total volume to determine the slope characteristics of the terrain within the landslide area. The slope is calculated using the elevation difference and horizontal distance between adjacent contour lines, providing basic data for subsequent analysis. By sectioning the contour lines, the outline of the landslide body at different heights is obtained, thereby calculating the cross-sectional area at each height level, ensuring an accurate description of the geometric shape of the sliding body. The total volume of the sliding body is obtained by adding up the various cross-sectional areas along the height, forming the basis for quantitative analysis of the hydrodynamic impact that the landslide body may cause. Significance: Accurate slope and cross-sectional data provide key parameters for assessing the stability and sliding risk of the landslide body. The estimation of the total volume can provide an important reference for subsequent hydrodynamic surge calculations. After a landslide occurs, the degree of impact that the water flow may have is closely related to the volume of the sliding body, helping to assess potential catastrophic consequences. Step S112: Geometric segmentation and center of gravity calculation. The sliding body is divided into multiple basic geometric bodies (such as triangular prisms, rectangular parallelepipeds, cones, etc.), which facilitates the calculation of the center of gravity of each part one by one, helping to simplify the calculation of the center of gravity of objects with complex shapes. The center of gravity of each small body is calculated, and the weighted average of the centers of gravity of all parts is taken to obtain the overall center of gravity position of the sliding body, which can more accurately reflect the mass distribution characteristics of the sliding body. Significance: The determination of the center of gravity position provides an important basis for evaluating the stability of the sliding body on the slope. A high center of gravity or deviation from the support surface may cause the sliding body to lose its center of gravity, thereby increasing the risk of landslides. Through the precise calculation of the sliding body's geometric shape and its center of gravity, a more accurate data foundation is provided for the subsequent hydrodynamic model, improving the authenticity and reliability of the landslide surge simulation. Step S113: Center of gravity height conversion to the slope coordinate system. The center of gravity of the sliding body is transformed from the standard coordinate system to the slope coordinate system through the center of gravity height conversion formula, ensuring the center of gravity position evaluation after considering the influence of the slope. Significance: Converting the center of gravity position to the slope coordinate system makes the assessment results more realistic and can be directly used to judge the stability of the landslide body under specific terrain conditions. It helps to accurately assess the potential risk of landslides based on the existing slope and height conditions, guide the monitoring and management of potential landslide areas, and reduce the probability of landslide disasters.

[0126] In summary, this example establishes a systematic and scientific process for assessing landslide volume and center of gravity. This not only provides solid data support for the physical model of landslide surges, but also provides a scientific basis for practical landslide risk assessment and the development of preventive measures, helping to improve safety and reduce disaster risks in engineering practice.

[0127] Furthermore, the construction process of the landform model in step S12 specifically includes the following steps:

[0128] Step S121: Grid lines are arranged in the target area to form a grid structure. The vertex coordinates of each grid unit are adjusted according to the contour data. The heights of all vertices in the grid corresponding to the contour lines are calculated and obtained. The coordinates and height data of each grid are stored in the database.

[0129] Step S122: Input the elevation and its corresponding position coordinates into the interpolation model, determine the four corner points of a given grid as input, and perform interpolation calculation on any point in the given grid based on their height values; generate a set of three-dimensional point cloud data through interpolation calculation;

[0130] in:

[0131]

[0132] In the formula, the four corner points of the grid (P1(x1, y1), P2(x2, y3), P3(x3, y3), P4(x4, y4)) are used as input, and according to their height values ​​(Z1, Z2, Z3, Z4), any point (x, y) within the given grid is interpolated.

[0133] Step S123: Use the generated point cloud data to perform triangulation, connect the point cloud data into a triangular mesh, form non-overlapping triangles, and form an irregular mesh; select a point from the point cloud data as the starting point, select the two nearest adjacent points to form the first triangle; continue to add adjacent points to the unconnected points, and determine whether the newly added points form a triangle, until all points are connected, and display the triangular mesh as a continuous three-dimensional surface.

[0134] Preferably, in step S121 of this embodiment, grid division and data storage are performed. The grid lines arranged within the target area form a regular grid structure that can subdivide the area so that each grid cell can reflect the terrain characteristics of the area. The coordinates of the vertices of each grid cell and their corresponding elevation values ​​are calculated and obtained. These data are stored in a database, providing the necessary data foundation for subsequent processing. The significance achieved: By storing data in a database, the consistency and integrity of the data are guaranteed. The subdivided area enables each grid cell to reflect subtle changes in the terrain, which facilitates the subsequent interpolation and modeling process and improves the accuracy of the model. Step S122 is an interpolation calculation. The elevation data is input into the interpolation model and the elevation value of any point within each grid cell is calculated using an interpolation formula (e.g., bilinear interpolation), thereby generating functional three-dimensional point cloud data. The significance achieved: Through interpolation technology, smoother values ​​can be created between known data points, which not only fills the gaps in the elevation data but also more accurately reflects the continuity and changing characteristics of the terrain. The generated three-dimensional point cloud data lays a solid foundation for subsequent three-dimensional reconstruction and visualization, ensuring that the model can truly present the three-dimensional structure of the terrain. Step S123 involves triangulation and model reconstruction. The generated point cloud data is triangulated, and the non-overlapping triangular mesh formed by connecting the points ensures the connectivity and seamlessness of the model. The significance achieved: The irregular mesh created using triangulation can better adapt to complex terrain and express topographic details. The resulting triangular mesh can be represented as a continuous three-dimensional surface, making the terrain model more visually realistic and facilitating subsequent analysis and research. The triangular mesh not only provides a foundation for subsequent visual display, but also effectively displays the ups and downs, steep slopes, and other important landform features of different regions.

[0135] In summary, each step of this embodiment can ultimately construct an accurate, continuous, and realistic three-dimensional landform model. These models not only help understand and analyze actual geographical phenomena, but also provide important basic data and support for related research and engineering applications.

[0136] Furthermore, the propagation process of the landslide surge shown in step S13 specifically includes the following steps:

[0137] Step S131: The wave height data obtained from the wave height meter are organized into a time series, and a wave height change curve is plotted to analyze the wave height changes before and after the landslide enters the water. The pressure sensor data are organized to create a time series record of the pressure changes of each sensor during the experiment. The motion trajectory of the sliding body and the relevant data at the moment of entry into the water, including the contact between the sliding body position and the water surface, are extracted from the video recorded by the high-speed camera.

[0138] Step S132: Import the recorded wave height data and generate a wave height variation graph using a graphical tool; with time represented on the X-axis and wave height represented on the Y-axis, a wave height variation curve is generated to show how the wave height jumps at the moment the landslide enters the water, and the changing trend over time; obtain the reading of each pressure sensor, construct a data set of the pressure at each point on the water surface that changes with time, and map the pressure data onto a fictitious two-dimensional plane to generate a pressure distribution graph to show the changes in wave impact force at different locations;

[0139] Step S133: Import the original video obtained by the high-speed camera into the video processing software, extract the key frames related to the natural landslide, water entry and wave formation from the video, and annotate them in combination with the wave height and pressure data; combine the wave height change map and the pressure distribution map, superimpose them on the video, generate a visual animation of the surge propagation process, and demonstrate the dynamic characteristics of the landslide surge.

[0140] Preferably, step S131 of this embodiment involves data collation and trajectory extraction, which organizes the continuous wave height data from the wave height meter into a time series for comparison and analysis, facilitating observation of wave height changes over time. The pressure sensor readings are converted into a time-varying pressure dataset, identifying the pressure fluctuations of the waves recorded by each sensor during the experiment. Images provided by the high-speed camera are used to extract the trajectory of the landslide entering the water, such as the changes in the landslide's position and the moment of contact with the water surface, providing essential dynamic information for subsequent analysis. Significance: By comprehensively collcating and examining the data, a preliminary understanding of the landslide and its interaction with the water medium can be obtained, providing the necessary foundation for subsequent analysis and laying a good data foundation for subsequent dynamic analysis and visualization. Step S132 involves graphing and data mapping, using graphical tools to present the time series wave height data in graphical form, allowing viewers to intuitively see the changes in wave height over time. This graph can quickly convey the changing trends of wave height and the impact of the landslide event. The data recorded by the pressure sensors is mapped onto a two-dimensional plane to form a pressure distribution map, allowing for a spatial understanding of the wave impact force at different locations. For example, the intensity and impact range of waves at different locations can be observed. Significance: Through the visualization of wave height change maps and pressure distribution maps, the key dynamic changes of landslide surges can be quickly identified and interpreted. This not only improves the comprehensibility of the data, but also provides obvious and easy-to-understand visual data support for further demonstration, report writing or scientific communication. Step S133 video processing and animation generation extracts key frames related to landslide, water entry and wave formation from the original video to provide visual presentation for making dynamic demonstrations; the wave height change map and pressure distribution map are superimposed on the video to form a dynamic visualization including data and real scenes, allowing the audience to see the real dynamics of the landslide event and related data changes at the same time. Significance: The generated visualization animation not only vividly shows the propagation process of the landslide surge, making the dynamic characteristics clearly visible, but also provides a powerful display method for teaching, publicity, and scientific research; while intuitively understanding, it will also have a deeper understanding of the complex engineering background and hydrodynamic behavior of the landslide surge, which is of great significance to promoting the formulation and implementation of corresponding disaster prevention and mitigation measures.

[0141] In summary, each step of this example, through the integration, visualization, and animation of landslide surge data, not only improves data analysis efficiency and comprehensibility, but also provides key support for understanding the dynamic characteristics of landslide surges. This not only facilitates scientific research but also provides an important foundation and material for academic exchange and practical application in related fields.

[0142] Furthermore, if Figure 4 As shown, the process in step S2 according to the second result of the landslide surge numerical model test specifically includes the following steps:

[0143] Step S21: Obtaining contour geological information of the target area, analyzing and determining the volume, center of gravity height, and impact water level of the sliding mass based on geological survey data at the source of the landslide; constructing a corresponding contour terrain model based on the obtained contour information, and constructing and placing the sliding mass in the contour model according to the calculated center of gravity position and volume;

[0144] Step S22: Based on the same geological data and center of gravity characteristics, the sliding body is simulated in the landslide surge numerical model to present the sliding body movement process;

[0145] Step S23: In the numerical model test of the landslide surge, the height of the first wave formed after the landslide enters the reservoir is accurately measured using an altimeter and recorded as a reference value; a numerical simulation is run to automatically extract the height of the first wave, the height reaching the opposite bank, and the surge pressure distribution value.

[0146] Preferably, step S21 of this embodiment involves obtaining contour geological information and constructing a model. High-precision geological surveys are used to obtain contour lines and related geological information for the target area, providing a topographic foundation for landslide simulation and ensuring the model's authenticity. Based on the survey data, the volume, center of gravity height, and impact water level of the sliding body are determined, providing the necessary parameters for the sliding body's representation in the model. The obtained contour information is used to construct an accurate contour terrain model, precisely mapping the topography and landforms to ensure the simulation scenario is consistent with the actual environment. This ensures that the constructed numerical model possesses realistic geological characteristics. By combining accurate geological information with sliding body characteristics, the formation and evolution of landslide surges can be more scientifically simulated and analyzed. Step S22 simulates the motion of the sliding body in the numerical model based on the previously obtained volume, center of gravity position, and other parameters. This process may utilize a fluid dynamics model to calculate the sliding body's trajectory and water entry moment under the influence of gravity and water flow. During the simulation, the system tracks the sliding body's motion in real time and dynamically updates the interaction between the sliding body and the water surface through numerical algorithms, providing basic data for subsequent wave height and pressure changes. Significance: This method provides a deep understanding of the complex process of a landslide entering a water body, thereby predicting the initial formation of landslide surge waves and their impact on the surrounding environment. Accurate simulation of the landslide's motion is a prerequisite for subsequent analysis of surge characteristics, improving the model's effectiveness and reliability. Step S23 involves measuring and extracting wave height and pressure distribution. Using precision measurement tools such as altimeters, the first wave height after the landslide enters the reservoir is accurately recorded in the physical model. This data serves as a benchmark for subsequent analysis. Through numerical simulation, the first wave height, the height at the opposite bank, and the surge pressure distribution values ​​after the model is run are automatically extracted. Specific numerical model analysis software allows for rapid processing, ensuring efficient and accurate results. Significance: This method not only provides data support for quantifying the initial formation of landslide surge waves, but also establishes a direct basis for comparison between numerical and physical models. By recording and extracting various parameters in detail, the accuracy of the numerical model can be effectively verified, and necessary corrections and optimization can be made to the model. This helps to enhance the credibility of the results and provide a theoretical basis for practical disaster prevention, early warning, and engineering design.

[0147] In summary, this example is an indispensable component of the landslide surge numerical modeling process. By gradually constructing a terrain model, simulating landslide motion, and accurately measuring surge parameters, it provides a scientific foundation and reliable data support for the study of the dynamic characteristics of landslide surge. This not only enhances the model's applicability but also provides rich experience and data references for future related research and practice.

[0148] Furthermore, the process of automatically extracting the first wave height, the height reaching the opposite shore, and the surge pressure distribution value in step S23 specifically includes the following steps:

[0149] Step S231: Meshing is performed, with the mesh size being adapted to key areas such as the interface between the sliding body and the water surface. The sliding body is injected into the water under the action of gravity, forming an initial wave to simulate the surge generated by the interaction between the sliding body and the water. The sliding body motion is simulated by considering the fluid dynamics equations.

[0150] Among them, the grid generation standard is set, and the gradient change of the flow field is greater than a certain threshold Δx adaptive When the mesh is refined, the following standards are expressed: is the adaptation function according to the change of velocity field, Δx min Indicates the minimum grid size;

[0151] The form of the equation in the initial wave simulation is:

[0152]

[0153] Where h0 is the still water depth and η is the wave height, which is used for high-order correction;

[0154] Step S232: The numerical model continuously calculates the motion state of the sliding body in the water and optimizes the flow field within each time step. The water surface height changes are monitored and recorded in real time. Monitoring points are set in the downstream waters and the opposite bank area where the sliding body enters the water to capture the propagation of the first wave. The first wave height of the ripple is extracted and recorded as the baseline value.

[0155] Among them, the complex interaction between the water-entering slider and the water surface is tracked, and the relevant calculation format is as follows: Where φ is the interface function, which identifies the interface between water and air;

[0156] Step S233: After the sliding body enters the water, the landslide surge numerical simulation model will continue to track the surge propagation process and extract the first wave height, the height reaching the opposite bank, and the surge pressure distribution at the end of the run;

[0157] The first wave height is recorded as the water surface height when the wave reaches the highest point; the arrival height at the opposite bank measures the change in water level when the first wave reaches the opposite bank and is recorded at a specific point on the opposite bank; the surge pressure distribution is automatically calculated by monitoring the change in water depth, fluid velocity and water surface height, combined with the fluid dynamics model; the expression of the surge propagation process is:

[0158]

[0159] f i represents the distribution function of the particle, τ is the relaxation time e i is the direction of particle flow.

[0160] Preferably, in step S231 of this embodiment, meshing and initial wave simulation utilize adaptive meshing technology to finely segment key areas of the flow field (such as the interface between the sliding body and the water surface), ensuring computational accuracy and numerical stability. Dynamically adjusting the mesh size improves computational efficiency, particularly by using smaller meshes in areas with drastic flow field changes. The introduction of high-order corrections to the equations more realistically reflects wave characteristics and their propagation patterns, providing a more scientific basis for setting initial conditions. Significance: Precise meshing and the application of high-order equations ensure the reliability of surge simulation, effectively capturing the waves caused by the sliding body entering the water and contributing to a deeper understanding of surge characteristics. In step S232, motion state calculation and real-time monitoring, the model continuously calculates the motion state of the sliding body in the water and updates the flow field state in real time, enabling the dynamic evolution of the surge to be rapidly reflected in the simulation and improving responsiveness. The use of interface functions to track the water-air interface ensures that the complex interactions between the sliding body and the water surface are accurately captured, providing the necessary information support for subsequent wave height extraction. Significance: Real-time monitoring of changes in water surface height enables the capture of the propagation process of the head wave, improves the extraction accuracy of the head wave height, and is of great significance for understanding the propagation mechanism of surge waves; it provides forward-looking warning information for actual disaster prevention and mitigation work, and can predict possible impacts in advance. Step S233 surge propagation tracking and parameter extraction, the model can continuously monitor the dynamic evolution of surge waves, and the use of relevant expressions makes the calculation of surge wave behavior more accurate. At the same time, through the particle distribution function, it can better simulate complex fluid behavior; by combining multiple fluid dynamics models and monitoring results, it can automatically calculate the pressure distribution caused by surge waves, providing a basis for quantifying the potential threat of surge waves. Significance: In the final parameter extraction, the head wave height, the height reaching the opposite bank, and the surge pressure distribution can be clearly recorded, enabling a comprehensive assessment of the surge effect; it has important application value in the fields of engineering design, facility safety assessment, etc., and can provide a scientific basis for actual flood control, facility design and emergency response, thereby reducing potential risks.

[0161] In summary, through the complete execution of the above steps in this embodiment, the model can not only efficiently extract key parameters related to surge waves, but also plays an important positive role in optimizing hydrodynamic simulation models, improving the accuracy of parameter extraction, and supporting practical applications and decision-making processes; the improvement in the degree of automation not only makes the prediction of surge behavior more accurate, but also provides strong support for predicting the impact of surge waves on structures in actual engineering, which helps to enhance society's ability to respond to natural disasters and the safety of engineering facilities.

[0162] Furthermore, if Figure 5 As shown, the process of adjusting the particle size and kernel function in the landslide surge numerical simulation test in step S3 specifically includes the following steps:

[0163] Step S31: Analyze the experimental data obtained, including extracting the specific values ​​of the first wave height, the height reaching the opposite shore, and the surge pressure distribution. The specific values ​​will be used as evaluation criteria; establish a relationship diagram between the target value and the current numerical simulation results, and identify the gap between the numerical simulation and the physical experimental results;

[0164] Step S32: Analyze the particle distribution characteristics in the landslide surge physical model and compare the effects of different particle sizes on the numerical results; perform numerical simulations by varying the particle diameter and continuously refine the particle size until it matches the results of the landslide surge physical model; based on the analysis of surge behavior in the landslide surge physical model experiment, determine the kernel function type and adjust the kernel function's support domain and decay rate;

[0165] Step S33: After the particle size and kernel function are adjusted, a new round of numerical simulation is performed: the adjusted numerical model is comprehensively compared with the physical experimental results, including the verification of the first wave height, the detection of the height reaching the opposite shore, and the fitting of the surge pressure distribution;

[0166] Among them, the first wave height verification: observe whether the height of the first wave reaches the peak of the physical experiment, record and evaluate the difference between the two; the arrival height detection on the opposite bank: check the time and height of the surge reaching the opposite bank in the new simulation results to ensure the consistency of the two in time and space; surge pressure distribution fitting: analyze the pressure changes at different positions to ensure that the pressure distribution of the surge is consistent with the physical experiment, especially the pressure characteristics in key areas (such as the junction of the water body and the shore).

[0167] Preferably, step S31 of this embodiment analyzes experimental data and establishes initial evaluation criteria. By extracting specific values ​​for head wave height, height to shore, and surge pressure distribution, this provides a clear quantitative basis for subsequent numerical model adjustments. By establishing a relationship diagram between target values ​​and current numerical simulation results, the gap between numerical analysis and physical experimental results can be intuitively identified, helping engineers and researchers understand the current performance of the model and narrow the gap between the model and actual conditions. Significance achieved: It sets clear, quantifiable goals for subsequent model adjustments and verification, ensuring the repeatability and reliability of numerical simulations. It also facilitates a deeper understanding of specific error sources within the model, providing a practical basis for parameter adjustment through quantitative analysis, and thus forming a strategy for iterative improvement during model development. Step S32 involves adjusting particle size and kernel function. Experiments have shown that different particle sizes significantly affect fluid flow and wave characteristics. By comparing numerical simulation results for different particle diameters, particle settings can be optimized to ensure the accuracy of fluid momentum and energy transfer. The appropriate kernel function type, support domain, and decay rate are identified and adjusted to ensure the model better simulates surge behavior and fluid interactions. Significance achieved: Reasonable adjustment of particle size and kernel function characteristics can help enhance the performance of the numerical model under complex flow conditions and improve the model's ability to predict actual phenomena; by adjusting specific model parameters (such as kernel function configuration), higher flexibility and personalization can be achieved, so that the model can adapt to different landslide surge scenarios and improve comprehensive analysis capabilities. Step S33 is a new round of numerical simulation and result verification. After comparing the adjusted model with the physical experimental results, the improvement effect of the numerical model can be evaluated, and key projects such as first wave height verification, arrival height detection on the opposite bank, and surge pressure distribution fitting can be completed; through consistency analysis of key parameters (such as wave height, time, and pressure), comprehensive feedback on the accuracy and reliability of the model is provided. Significance achieved: Through systematic comparative verification, not only can the current numerical model be effectively adjusted, but it can also help to confirm which parameters are crucial for future surge predictions; it ensures that the iteratively adjusted numerical model has good applicability, and can provide a scientific basis for safety assessment and engineering design under similar landslide or water wave conditions, and provide support for subsequent practical applications.

[0168] In summary, this embodiment, through the above steps, not only provides a systematic methodology for optimizing and calibrating the landslide surge numerical model, but also ensures the model's validity and usability through quantitative experimental data and cross-validation. This has important practical significance for improving the safety assessment of water conservancy projects and reducing potential losses caused by landslides.

[0169] Furthermore, the process of establishing the relationship diagram between the target value and the current numerical simulation result in step S31 specifically includes the following steps:

[0170] Step S311: Acquire multiple key parameters such as the head wave height, the height reaching the opposite shore, and the surge pressure distribution for setting target values; at the same time, obtain corresponding results from the numerical simulation, and record the head wave height, the height reaching the opposite shore, and the corresponding surge pressure distribution data obtained under the same conditions;

[0171] Step S312: The collected landslide surge physical model experimental data and the second result of the numerical simulation are aligned with the time point and spatial position of collection and smoothed; the first wave height and the height reaching the opposite bank are normalized according to the maximum value respectively, to generate a standardized value between 0 and 1;

[0172] Step S313: Using a two-dimensional coordinate system, with the X-axis representing the simulation results and the Y-axis representing the experimental target value, a scatter plot is created to show the relationship between the simulation results and the target values. The deviation between the simulation results and the target values ​​is obtained by calculating the distance between each point and quantifying the difference.

[0173] Among them, there are n pairs of simulation results and target values ​​of key parameters, which are expressed as follows: numerical simulation results S i , where i = 1, 2, ..., n, target value T i ;

[0174] Absolute error refers to the direct difference between the numerical simulation result and the target value, and the calculation formula is:

[0175] E abs,i =|S i -T i |

[0176] Among them, E abs,i represents the absolute error of the i-th key parameter;

[0177] The relative error takes into account the influence of the target value and uses the ratio of the target value to standardize the error. The calculation formula is:

[0178]

[0179] Among them, E rel,i represents the relative error of the i-th key parameter (expressed in percentage);

[0180] The root mean square error (RMSE) is used to comprehensively evaluate the deviations of multiple parameters. The calculation formula is:

[0181]

[0182] The average of the squared errors of all key parameters was calculated and then the square root was taken, providing a more comprehensive error measure;

[0183] The comprehensive deviation evaluation evaluates each key parameter by combining absolute error, relative error, and root mean square error to form a new indicator:

[0184]

[0185] Among them, α, β, and γ are weight coefficients, reflecting the importance of different deviation measures in the analysis.

[0186] Preferably, step S311 of this embodiment involves data acquisition and recording. By collecting key parameters from the physical experiment, such as head wave height, shore-reaching height, and surge pressure distribution, a comprehensive data foundation is established. This data serves as an important basis for subsequent evaluation and comparison. Corresponding parameters are obtained from the numerical simulation to ensure consistency between the two data sets in terms of conditions, time, and space, ensuring the scientific and effective nature of subsequent comparisons. The significance achieved is that clear criteria are set for accurate model evaluation, ensuring that the comparison between physical experimental data and numerical simulation results is feasible and effective. Raw data is provided for subsequent relationship diagram construction, ensuring comprehensiveness and systematicity of the analysis. Step S312 involves data collation and normalization. By aligning the acquisition time points and spatial locations of the experimental and numerical simulation data, data consistency is established, eliminating errors caused by time delays or spatial offsets. Input data is smoothed to remove noise and outliers, improving data stability and reliability and enhancing the accuracy of the analysis results. Head wave height and shore-reaching height are normalized to their maximum values, ensuring that the data range is between 0 and 1, avoiding distortion of the analysis results due to differences in orders of magnitude. Significance Achieved: Normalization facilitates comparison of parameters of different categories and scales in the same coordinate system, improving the visualization and comprehensibility of the results. Smoothing and normalizing data processing makes comparative analysis clearer, facilitating identification and quantification of deviations between the model and actual results, and providing an effective basis for subsequent adjustments. Step S313 establishes a relationship diagram and gap quantification. By constructing a two-dimensional coordinate system with the X-axis representing the numerical simulation results and the Y-axis representing the target value, this provides an intuitive way to display the relationship between the data. A scatter plot is used to display the relationship between the simulation results and the target value. Through intuitive graphical display, the correspondence and differences between the two can be clearly observed. By calculating the distance between each point, the deviation between the simulation results and the target value can be quantitatively identified. Significance Achieved: The scatter plot can quickly determine the relationship between the numerical simulation results and the experimental target value, thus clearly identifying where there are significant deviations. Quantified deviation analysis provides a scientific basis for subsequent model adjustments, allowing researchers to perform targeted parameter optimization and improve the model's predictive ability. It provides visual support for model validation and can intuitively display analysis results, facilitating scientific decision-making and further research discussions.

[0187] In summary, this embodiment plays an important role in the overall goal of establishing a relationship diagram between the target value and the current numerical simulation results. It not only helps to accurately evaluate the accuracy of the numerical model, but also provides a solid data foundation and clear path guidance for the optimization and improvement of the model. Through the above steps, establishing a relationship diagram between the target value and the current numerical simulation results is not only a scientific and technological analysis process, but also an important tool for effectively identifying and evaluating the accuracy of the model. Steps such as data organization, normalization, coordinate system construction and error identification ensure the accuracy and reliability of the model, and lay a solid foundation for subsequent adjustment and optimization. Through such quantitative analysis, the numerical model can be improved in a more targeted manner, thereby improving the accuracy of landslide surge simulation and providing support for research in related fields.

[0188] Furthermore, the process of changing the particle diameter and adjusting the support domain and decay rate of the kernel function in step S32 specifically includes the following steps:

[0189] Step S321: Extract the current particle distribution characteristics and diameters, and use the landslide surge numerical model to perform regression analysis on the relationship between the current particle distribution and surge behavior to obtain preliminary results; set the range of particle size variation and select multiple particle diameters for simulation experiments; in the numerical simulation, replace the corresponding particle sizes one by one, and focus on the first wave height, the wave height reaching the shore, and the pressure distribution area in the simulation results;

[0190] Step S322: After each adjustment of the particle diameter, numerical simulations are repeated and the simulation results are compared with the experimental target values. By collecting the simulated data, the accuracy of the corresponding wave height, arrival height, and pressure distribution is evaluated, and the particle size is reduced until it matches the target value.

[0191] Step S323: After the particle size is determined, the support domain of the kernel function is set according to the particle's action distance; the decay rate of the kernel function is adjusted according to the actual fluctuations in the simulation results, and the initial decay rate is set. Fine adjustments are made by comparing the simulation results.

[0192] Preferably, step S321 of this embodiment is particle distribution feature extraction and preliminary simulation experiment, which extracts the diameter and distribution characteristics of the particles to obtain a quantitative understanding of the particle system, such as average particle size, particle concentration, particle spacing, etc.; establishes a mathematical model between the current particle distribution and surge behavior, and identifies the relationship between the two, which may involve linear regression or other regression techniques to reveal the dependence of particle characteristics on wave characteristics (such as wave height, pressure); on the basis of setting the particle size variation range, selects multiple particle diameters for subsequent simulation experiments. This ensures the coverage analysis of surge behavior by different particle diameters. Significance achieved: This step provides a baseline understanding of the existing numerical model, and provides a basis for theoretically analyzing how particles affect the formation and propagation of waves; through regression analysis, it can guide subsequent particle size adjustments to more effectively explore the impact of particle size changes on surge behavior. Step S322: Particle diameter adjustment and simulation verification. By replacing particles of different diameters one by one, multiple sets of numerical simulations are performed to systematically evaluate the impact of different particle sizes on wave characteristics, including key data collection for multiple indicators such as the first wave height, the wave height reaching the shore, and the pressure distribution. The simulation results are compared with the target values, and relevant error indicators (for example, the root mean square error) are calculated to evaluate the model matching degree at each particle size to ensure the accuracy of the data and the effectiveness of the model. After repeated simulations, the particle size range is gradually narrowed, and an attempt is made to find the actual particle size closest to the experimental target value. Significance achieved: This step ensures that the matching degree between the numerical simulation results and the physical experiment is gradually improved, and the accuracy and credibility of the model are improved through gradual debugging. Through comparison and feedback, the optimal particle size range can be identified, thereby providing accurate parameter configuration for subsequent research and reducing unnecessary waste of experimental resources. Step S323 adjusts the kernel function support domain and decay rate. Based on the effective interaction distance of the particles, the kernel function support domain is appropriately set to accurately capture interactions, resulting in higher accuracy. The kernel function decay rate is initially set and fine-tuned. Through feedback from numerical simulations, the impact of the decay rate on the simulation results is quantified, aiming to achieve outputs closer to experimental values. The results achieved are: The model can effectively capture interactions between particles, improving simulation accuracy, especially when dealing with complex flow conditions. By adjusting the decay rate, the model demonstrates greater adaptability to varying wave conditions and particle characteristics, thereby enhancing the reliability and application value of the simulation results.

[0193] In summary, the three steps of this example form an effective feedback loop, from particle feature extraction and preliminary verification to the optimization and adjustment of specific particle size and kernel functions, forming a systematic methodology. This approach can be applied not only to the optimization of landslide surge models but also to the study of other complex fluid dynamics phenomena, enhancing the potential for the application of numerical models in practical engineering and scientific research.

[0194] Furthermore, the process of performing regression analysis on the relationship between the existing particle distribution and surge behavior using the landslide surge numerical model in step S321 includes the following steps:

[0195] Step S3211: Obtain various data on the particle's trajectory, position, diameter, and water surface fluctuations in the surge, analyze the acquired data, and automatically mark and identify the particle boundaries; use a tree-based model to evaluate the impact of each variable on surge behavior and select the most influential features; construct cross-features between particle characteristics (diameter, number, shape) and wave behavior (wave height, wave energy, and wave period) to generate a new feature combination, namely the wave-particle coupling feature;

[0196] Step S3212: Construct a deep learning-based neural network. Utilizing an adaptive network structure and hierarchical feature extraction mechanism, the system fits the complex nonlinear relationship between particle characteristics and surge waves, analyzes the characteristics of the fluctuating data series, and captures dynamic changes in the time series. Combining fluid dynamics equations with the deep learning model, the neural network's learning is guided by physical constraints.

[0197] Step S3213: Evaluate the performance of the neural network model through k-fold cross-validation, and use Bayesian optimization to adjust the hyperparameters to achieve the optimal configuration.

[0198] Preferably, step S3211 of this embodiment involves data acquisition and feature construction, which captures particle motion trajectories, positions, diameters, and other characteristics, as well as water surface fluctuation data. High-frequency, accurate data acquired through sensor technology facilitates a comprehensive understanding of the interaction between particles and waves. Computer vision techniques, such as image processing and machine learning algorithms (e.g., convolutional neural networks in deep learning), are applied to automatically extract particle boundaries. This reduces the need for manual labeling and improves efficiency and accuracy. By assessing the impact of each variable on surge behavior through tree models (e.g., random forests), a preliminary relationship between particle characteristics and surge behavior can be obtained, helping to identify the most influential features and reduce noise in subsequent analysis. Wave-particle coupling features are generated by combining particle characteristics with wave behavior to produce new feature combinations, which are expected to reflect more complex physical phenomena and improve the output and accuracy of the model. The significance achieved is: Through systematic data acquisition and feature construction, a solid data foundation is laid, providing useful features for subsequent modeling. Automatic labeling and boundary identification enable efficient and accurate particle analysis, helping to reduce human error and improve data processing efficiency. By quantifying the influence of variables, a direction is provided for model selection and feature engineering, thereby establishing a more accurate prediction model. Step S3212 constructs a deep learning neural network. The constructed neural network can capture the complex nonlinear relationship between particle characteristics and wave behavior. Through an adaptive network structure, it achieves automated feature extraction. The application of time series models such as LSTM can analyze the data sequence characteristics of surge fluctuations and capture dynamic changes in the time series, allowing the model to consider the influence of historical data, which helps improve the accuracy of predictions. The basic equations of fluid dynamics are combined with the deep learning model to make the output results reasonable in terms of physical laws. Constraints help limit the learning scope of the model, avoid overfitting, and improve the model's interpretability. Significance achieved: Through deep learning methods, the model's ability to capture complex nonlinear relationships is enhanced, improving the accuracy of predictions. Combined with time series modeling, the model can accurately reflect dynamic behavior that changes over time, providing a more appropriate simulation of landslide surge phenomena. Physical constraints improve the rationality and interpretability of the model, ensuring that the prediction results are not only mathematically valid but also physically understandable. Step S3213: Model evaluation and optimization. Using k-fold cross-validation, we can comprehensively evaluate model performance, ensuring the robustness of the model across different segmentations and preventing overfitting and underfitting. Effective validation results can increase confidence in model performance. We also use Bayesian optimization to automatically adjust model hyperparameters, such as the learning rate and number of layers, so that the model can find a more optimal parameter configuration during training, improving the overall performance of the model. Significance: Cross-validation improves the robustness of the model, ensuring its feasibility and reliability in practical applications. Hyperparameter adjustment strategies using Bayesian optimization are more efficient than traditional grid search methods, finding the optimal hyperparameter configuration in a shorter time and improving model performance.

[0199] In summary, this example, through a systematic process from data acquisition and feature engineering to model construction and optimization, achieves a deep understanding and precise modeling of the relationship between particle distribution and surge behavior in a numerical landslide surge model. Each step provides a scientific basis and decision-making support for the management and prediction of landslide surges. This not only improves the accuracy and effectiveness of research but also lays a foundation for practical application, thereby advancing the theoretical and practical development of fluid dynamics and related fields.

[0200] Furthermore, the process of guiding the learning of the neural network by physical constraints in step S3212 specifically includes the following steps:

[0201] Step S32121: Collect particle dynamics and water surface fluctuation data, annotate particles to identify the boundaries, shapes, and positions of different particles; extract particle characteristics (such as particle size, shape, velocity vector in the fluid, etc.) and wave parameters (such as wave height, energy spectrum, frequency, wavelength, etc.); create a time window feature to convert all particle characteristics and wave parameters into a distribution with a mean of 0 and a variance of 1;

[0202] Among them, the neural network architecture includes:

[0203] Input layer: The input dimension should be consistent with the number of features. For example, for data containing particle characteristics and wave characteristics at 5 time points, the input dimension of each sample is 15 (assuming 3 indicators for each characteristic);

[0204] LSTM layer: Construct three LSTM layers with increasing numbers of hidden units (e.g., 100, 150, and 200) to capture temporal features at different levels. A dropout layer is applied after each LSTM layer with a dropout ratio of 0.2 to prevent overfitting.

[0205] Fully connected layer: The LSTM layer is followed by a fully connected layer. The number of nodes in the output layer should be the same as the surge characteristics to be predicted (e.g., wave height, wave speed), and a linear activation function should be used.

[0206] Kuang Design: Use an adaptive learning rate algorithm, such as the Adam optimizer, with an initial learning rate of 0.001, which is further optimized using a learning rate decay strategy; the output layer is a surge parameter;

[0207] Step S32122: Particle dynamics and water surface fluctuation data are divided to obtain training and validation sets. Loss and accuracy curves of the training and validation sets are plotted to observe convergence and stability. The effectiveness of the neural network model is evaluated through a scatter plot of actual wave data and model prediction results.

[0208] Step S32123: Establish a model equation system to describe the fluid dynamics, and calculate the loss by the residual between the neural network model output and the equations; after the neural network model training is completed, use physical consistency testing to check whether the wave and particle behaviors output by the model meet the physical expectations;

[0209] in,

[0210]

[0211] Where ρ is the fluid density, u is the velocity field, p is the pressure, μ is the viscosity coefficient, and f is the body force;

[0212] Calculate the loss function expression:

[0213] Loss = L data +λ·L physics

[0214] Among them, L data is the data loss, L physics is the loss of the physical model, and λ is the importance coefficient used to balance the loss.

[0215] Preferably, step S32121 of this embodiment performs data acquisition and preprocessing, collects dynamic information of particles and water surface fluctuation data, and labels the particles, thereby identifying the boundaries, shapes, and positions of different particles. This labeling lays the foundation for subsequent data processing and feature extraction; extracts particle characteristics (such as particle size, shape, velocity vector) and wave parameters (such as wave height, energy spectrum, frequency, wavelength), enabling the model to be trained based on rich features. Features provide the model with the necessary information to capture the interaction between particles and waves; converts all features into a distribution with a mean of 0 and a variance of 1, which can ensure that the model converges faster during training and is less sensitive to differences in feature scales, helping to improve the learning efficiency and accuracy of the model; designs a neural network architecture including an input layer, an LSTM layer, a fully connected layer, etc., which can capture complex dependencies in time series data; sets the number of hidden units in the LSTM layer and the dropout ratio of the Dropout layer, which helps prevent overfitting and further enhances the generalization ability of the model. The significance achieved is: providing rich and high-quality data features for model input. This is a key prerequisite for ensuring the effectiveness of subsequent model learning; the network structure has been carefully designed to fully exploit the time series information in the data and improve the accuracy and reliability of predictions. Step S32122 Dataset Division and Model Evaluation divides the particle dynamics and wave data into training sets and validation sets, providing independent data sources for model training and evaluation, allowing the model to perform performance tests on unseen data; observing the loss and accuracy curves of the training set and validation set can effectively monitor the model's training process, including convergence and stability, and help adjust the training strategy early or in a timely manner to avoid overfitting; comparing the actual wave data with the model prediction results through scatter plots can intuitively evaluate the model's performance and generalization ability, which is an intuitive test of the model's output results and can discover the limitations of the model. Significance achieved: By evaluating model performance, verification information on the model's learning effect is provided, which improves the transparency and credibility of model development; the comparison of actual wave data with model prediction results provides direction for subsequent model improvements and can better guide model optimization. Step S32123: Physical consistency testing and equation establishment. Basic equations of fluid dynamics (such as the continuity equation and momentum equation) are used to describe the dynamic characteristics of the fluid, providing physical constraints for the neural network output. This ensures that the model not only has a good fit to the data but also adheres to the laws of physics. By calculating the residual between the neural network model output and the physical equations, the two parts of the loss function (data loss and physical loss) are formed. The design of the loss function can effectively integrate data-driven learning with physics-based learning. After the model training is completed, a physical consistency test is performed to ensure that the wave and particle behavior output by the model conforms to theoretical expectations, avoiding the model only conforming to the data without losing its physical interpretation ability.Significance achieved: Incorporating physical constraints improves the scientific nature of the model, ensuring that predictions are not only based on data features but also guided by physical laws; making the model output more interpretable and trustworthy; and optimizing the model's applicability in real-world scenarios through the balanced design of the loss function, which is beneficial for practical engineering and scientific research needs.

[0216] In summary, through the synergistic effect of these three steps, this embodiment achieves the effective construction and application of a deep learning model based on physical constraints. Data acquisition and processing provide a solid foundation for the model, the rational design of the network structure ensures the effectiveness of learning, and physical consistency testing ensures that the model can achieve both accurate predictions and adhere to the laws of physics. This will promote the in-depth development of landslide surge research and provide solutions to more complex fluid dynamics problems.

[0217] Furthermore, the process of reducing the particle size until it matches the target value in step S322 specifically includes the following steps:

[0218] Step S3221: Collect the output results of each numerical simulation. The output results include three parameters: wave height, arrival height, and pressure distribution. The mean square error and absolute error are used as standardized evaluation indicators.

[0219] Step S3222: Set an accuracy threshold and judge the deviation of the three parameters of wave height, arrival height, and pressure distribution based on the accuracy threshold; compare the simulation results with the target values, generate an error analysis chart, obtain the deviation and trend of the model output under different particle sizes, and judge whether the effect of the current particle size on surge behavior meets the experimental objectives;

[0220] Step S3223: If the simulation results fail to meet the target value, the particle size is proportionally reduced for the next round of simulation.

[0221] Preferably, step S3221 of this embodiment collects simulation output results. By systematically collecting simulation results, including key parameters such as wave height, arrival height, and pressure distribution, a comprehensive understanding of the simulated behavior is ensured. Mean square error (MSE) and absolute error (MAE) are used as standardized evaluation indicators to provide a data basis for accuracy judgment. Statistics of these indicators can quantify the gap between simulation results and experimental target values, thereby effectively comparing parameter performance under different circumstances. The significance achieved: providing basic data for analysis, ensuring that all simulations are evaluable, and providing clear quantitative standards so that decisions during the optimization process are based on measurable results. By collecting multiple simulation results, the impact trend of particle size adjustment on surge behavior can be identified, providing a reference for subsequent iterations. Step S3222 sets an accuracy threshold. By setting the accuracy threshold, the deviation of wave height, arrival height, and pressure distribution is effectively judged, ensuring that each simulation has a reasonable success standard. The analysis results are visualized, making the output deviation under different particle size settings clear at a glance, facilitating analysis and identification of the specific circumstances of particle size influence. Significance achieved: Setting the accuracy threshold can provide a limit for the model output, ensuring that the research direction is clear and that the focus can be fully on finding the appropriate particle size range; error analysis can clearly point out the problem, guide the next particle size adjustment decision, avoid invalid parameter attempts, and improve the efficiency of the entire process. Step S3223 proportionally reduces the particle size for the next round of simulation. Based on the results of the previous step, the particle size is systematically adjusted, and a new test is performed by proportionally reducing this parameter; the gradual adjustment not only reduces the simulation range, but also ensures that the target value is gradually approached; each adjustment is made based on the feedback of the results to ensure that the model parameters are always within a reasonable range, effectively promoting the improvement of accuracy. Significance achieved: By comparing the simulation results with the target values, an effective feedback mechanism can be formed, so that the particle size adjustment is no longer arbitrary, but a scientific decision based on actual data; each fine adjustment can make the model closer to the target, thereby improving the simulation accuracy of the actual surge phenomenon, so that the results can not only achieve the experimental goals, but also have good physical meaning.

[0222] In summary, this example demonstrates a closed loop from data collection, error assessment, and scientific optimization through these three steps. This makes the entire parameter adjustment process more efficient and targeted, gradually achieving the preset goals. This not only effectively improves the output accuracy of the simulation model but also enhances our understanding of the nature of surge behavior, ultimately promoting research progress in this field.

[0223] Furthermore, the process of proportionally reducing the particle size for the next round of simulation in step S3223 specifically includes the following steps:

[0224] Step S32231: After comparing the simulation results with the target values, identify the deviation of each parameter;

[0225] Definition symbol: Y sim =(h sim ,z sim ,P sim ) represents the current simulation result vector, h sim represents the simulated wave height, z sim Indicates the simulated arrival height, P sim represents the simulated pressure distribution, Y target =(h target ,z target ,P target ) represents the target value vector, h target Indicates the target wave height, z target Indicates the target arrival height, P target represents the target pressure distribution, D i Represents the deviation of the i-th parameter, calculated as:

[0226] where i∈h,z,P

[0227] i = h represents the deviation for wave height, i = z represents the deviation for arrival height, and i = P represents the deviation for pressure distribution;

[0228] Step S32232: Set the comprehensive deviation D to;

[0229] Definition symbol: N represents the number of data points; calculation formula:

[0230]

[0231] ∈The set accuracy threshold, which indicates the maximum allowed deviation (for example, the maximum deviation of wave height is 0.1 m);

[0232] Step S32233: Reduce the particle size proportionally, and adjust the particle size after recognizing that the deviation D exceeds the accuracy threshold;

[0233] Definition symbol: D current Indicates the current particle size value, D new Represents the updated new particle size value, r represents the reduction ratio, and the range is r∈(0,1);

[0234] The formula for updating particle size is: D new =D current ×(1-r);

[0235] For example: If the current particle size (D curren t=0.05) meters, reduce the ratio (r=0.1), then: D new =0.05×(1-0.1)=0.045 m;

[0236] Step S32234: After adjusting the particle size, perform a new numerical simulation to generate a new output result Y sim,new ; Return to step S3221 to collect data again for evaluation.

[0237] Preferably, step S32231 of this embodiment identifies the deviation of each parameter, and by calculating the deviation, accurately quantifies the gap between the simulation results and the target value of each parameter (wave height, arrival height, pressure distribution), providing a clear basis for particle size adjustment. The significance achieved: It can clearly identify which specific parameters of the simulation results deviate from the target, thereby pointing to the direction that needs to be focused on and optimized. It is crucial in the optimization process to ensure that the improvement measures are targeted at actual problems. Step S32232 sets the comprehensive deviation. By calculating the comprehensive deviation, the deviations of multiple parameters are incorporated into a unified metric, making the evaluation of the model output quality more comprehensive and scientific. The significance achieved: It provides a comprehensive evaluation standard for the entire model output, which is easy to compare and judge the model performance; it helps to efficiently decide when the next step is needed and when the expected goal can be achieved when the particle size is adjusted. Step S32233 reduces the particle size proportionally by setting a specific strategy for proportionally reducing the particle size, ensuring that each particle size adjustment is carried out within a scientific and reasonable range. Fine control is achieved through formulas to ensure that the new particle size setting is more suitable for actual simulation needs. Significance achieved: It avoids the simulation distortion that may be caused by over-dramatic adjustments, ensures that the particle size gradually approaches the optimal range, and makes the simulation results gradually approach the target value during the adjustment process, thereby improving the accuracy and reliability of the model. Step S32234: Execute a new round of simulation and collect data. After adjusting the particle size, execute a new round of numerical simulation, generate new output results, and then use the new data for feedback. This forms an effective iterative process that can continuously optimize the model parameters. Significance achieved: Through continuous simulation and data collection, it is possible to quickly obtain results and adjust strategies to form a closed-loop feedback mechanism. The ultimate goal is to make the model output continuously close to the experimental goal, greatly improve the effectiveness and scientificity of the simulation, and promote research progress.

[0238] In summary, this embodiment can accurately measure and adjust parameters, ensuring that the accuracy of simulation results is gradually improved. This not only helps improve model performance, but also significantly improves research efficiency, ensuring that experimental goals are achieved in a shorter time.

[0239] Furthermore, the process of adjusting the decay rate of the kernel function in step S323 specifically includes the following steps:

[0240] Step S3231: Estimate the diameter and density of the particle, examine the basic principles of fluid dynamics, and determine the particle's range of influence under fluid conditions. The range of influence, or the particle's range of action, refers to the range of influence exerted by the particle on nearby media (such as surges, fluid motion, etc.). Based on the range, determine the support domain of the function.

[0241] Step S3232: Based on historical experimental data, an empirical decay rate is selected as the basis to set the effect at the beginning of the simulation; during the simulation, the accuracy of the simulation output is evaluated by comparing it with actual observations (such as wave height and pressure changes measured in the field);

[0242] Step S3233: By repeating the simulation experiments multiple times and performing various tests according to different environmental parameters and conditions, the sensitivity of the kernel function to the simulation results under different decay rates is evaluated, and the optimal parameter settings are identified.

[0243] Preferably, step S3231 of this embodiment determines the impact range and sets the support domain. The particle's range of action is estimated based on its diameter and density, thereby clarifying the particle's ability and range of influence on the surrounding fluid medium. This assessment can be based on the basic principles of fluid dynamics, combined with equations related to particle motion (such as the Navier-Stokes equations). By properly setting the kernel function's support domain to coordinate with the particle's range of action, the simulation accurately reflects the particle's impact on the surrounding environment. Significance: By appropriately setting the support domain, the numerical values ​​within this region approximate the real-world impact of particles on the fluid. Numerical calculations provide a more scientific basis, resulting in more accurate simulation results. In theory, limiting the support domain can reduce the computational area, thereby reducing computational complexity and time, allowing for efficient multiple simulations. Step S3232 sets and evaluates the empirical decay rate. Based on historical experimental data, an appropriate empirical decay rate is selected to reflect the particle's impact in the current fluid environment. The basic parameter settings provide a starting point for the simulation. During the simulation, the accuracy of the model output is evaluated by comparing it with field-measured wave height and pressure changes to test the accuracy of the simulation. Significance: By comparing with the actual observation values, the deviations and inaccuracies in the model can be identified in time, providing a basis for further adjustments and enhancing the adaptability of the model to complex fluid environments; the empirical decay rate setting reflects the actual physical situation to a certain extent. Through this combination, the model has greater practical significance and applicability in theory. Step S3233 simulates and tests parameters multiple times. Through repeated simulations, the influence of the kernel function on the simulation results under different decay rates is tested for different environmental variables (such as flow rate, particle size, density, etc.), and its sensitivity to the simulation output results is clarified; through continuous experimental analysis and data comparison, the optimal decay rate parameter setting is identified to ensure the best simulation effect. Significance: By finding the optimal parameter setting, it provides guidance for future experimental design and numerical models, helping to obtain more ideal results under similar conditions; through the simulation of different environmental parameters, not only can the decay rate be adjusted, but also a more comprehensive understanding of the performance of the kernel function itself is gained, which greatly enhances the adaptability of the model under uncertain conditions in the future.

[0244] In summary, the decay rate of the kernel function in this embodiment can not only be reasonably set and continuously adjusted, but also provides reproducible experimental conditions for numerical simulations, while ensuring high accuracy and reliability of simulation results. This is a key step in ensuring the effective performance of kernel functions in complex fluid environments (such as surges), and is crucial for understanding the interaction between fluid and particle behavior and its ability to predict in real-world environments.

[0245] Furthermore, if Figure 6 As shown, the process of obtaining the landslide surge hybrid model in step S4 specifically includes the following steps:

[0246] Step S41: combining the modified landslide surge numerical model with the landslide surge physical model to form a hybrid landslide surge model;

[0247] Step S42: Using the obtained hybrid model, analyze various working conditions with different landslide velocities, volumes, and terrain conditions; including comparing the transformation dynamics and surge characteristics under actual working conditions to derive the surge generation mechanism and its influencing factors;

[0248] Step S43: By simulating different parameter scenarios, the hybrid model's adaptability to changing conditions is evaluated, and the possible consequences of landslide surges under different circumstances are predicted, including wave height, wave width, and potential dangers caused by surges.

[0249] Preferably, step S41 of this embodiment forms a hybrid landslide surge model, integrating the revised numerical model with the physical model to create a comprehensive hybrid model. This ensures that the model possesses both the depth of theoretical analysis and the reliability of experimental verification. By parameterizing the numerical and physical models, the consistency and complementarity between the two are verified, thereby enhancing understanding of the landslide surge process. Significance: The hybrid model leverages the real-world data of the physical model and the computational advantages of the numerical model, enabling more accurate predictions of landslide surge dynamics. By integrating different models, researchers can more comprehensively analyze landslide surge phenomena within a theoretical framework, providing stronger support for practical applications. Step S42 analyzes multiple operating conditions with different landslide parameters. Using the hybrid model, a systematic numerical simulation of surge behavior under varying landslide velocities, volumes, and terrain conditions is performed to obtain a large amount of simulation data. By comparing the simulation results with actual operating conditions, the dynamic transformations and surge characteristics of the landslide process are explored, revealing the surge mechanism and influencing factors, such as landslide angle, volume change, and water level. Significance: It can deeply analyze the specific conditions and environmental factors that cause landslide surges, thereby providing a scientific basis for a better understanding of the laws of landslide surges. Understanding the characteristics and mechanisms of surges will help formulate corresponding monitoring and early warning strategies, improve the effectiveness of natural disaster responses, and provide a reference for environmental protection and urban planning. Step S43 evaluates the resilience of the hybrid model and predicts potential outcomes. By inserting different parameters (such as landslide velocity, volume, and terrain changes), a variety of scenario simulations are conducted to observe the response of the hybrid model under changing conditions; identify and predict the surge characteristics that may be triggered under different landslide conditions, such as wave height, wave width, and other potential hazards. Significance: Through the prediction of various scenarios, researchers can assess the potential risks of landslide surges, provide a scientific basis for local governments and relevant agencies, and promote the efficiency of emergency management. The stability and applicability of the model in variable and unpredictable environments are enhanced, making it widely applicable to various landslide surge research and practices.

[0250] In summary, the hybrid landslide surge model developed through three steps in this example not only strengthens theoretical research on landslide surge phenomena but also provides strong support for practical applications. The establishment of this comprehensive model enables the scientific community to analyze the interaction between water bodies and landslides from a more comprehensive and systematic perspective, providing a strong theoretical basis and practical guidance for fields such as natural disaster prevention and control, urban planning, and ecological and environmental protection.

[0251] like Figure 7 As shown, this embodiment also provides an embodiment of a landslide surge physical and numerical hybrid model construction system. In this embodiment, the landslide surge physical and numerical hybrid model construction system is applied to the landslide surge physical and numerical hybrid model construction method in the above embodiment. The landslide surge physical and numerical hybrid model construction system includes a first result acquisition module 1, a second result acquisition module 2, a test parameter adjustment module 3, and a hybrid model acquisition module 4 that are electrically connected in sequence.

[0252] Among them, the first result acquisition module 1 is used to obtain the contour geological information of the target area, the height of the center of gravity of the sliding body and the water level information; construct a landslide surge physical model test based on the information, and the landslide surge physical model test includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body; according to the first result of the landslide surge physical model test, analyze the landslide instability evolution movement process, the height of the first wave entering the landslide reservoir and the surge propagation process of the landslide surge; the second result acquisition module 2 is used to construct a landslide surge numerical model test based on the information, and the landslide surge numerical model test includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body. The position and volume of the sliding body are constructed based on the volume and center of gravity; a numerical simulation test is carried out according to the second result of the landslide surge numerical model test; the test parameter adjustment module 3 is used to use the first wave height, the height reaching the opposite bank and the surge pressure distribution as evaluation criteria, and adjust the particle size and kernel function in the landslide surge numerical simulation test to make the first wave height, the height reaching the opposite bank and the surge pressure distribution in the landslide surge numerical simulation test and the landslide surge physical model test consistent; the hybrid model acquisition module 4 is used to use the adjusted landslide surge numerical model as the obtained landslide surge hybrid model to analyze the working conditions of different landslide velocities, landslide volumes and terrain conditions.

[0253] Preferably, the first result acquisition module 1 of this embodiment obtains the contour geological information of the target area, the height of the center of gravity of the sliding body and the water level information, which provides important basic information for model construction; based on the information obtained, a physical model of landslide surge is created, which can conduct intuitive experiments and observations on landslide phenomena. Significance: It provides the necessary basic data for model tests to ensure the authenticity and relevance of the model; through the testing of the physical model, it helps to deeply understand the dynamic process of landslide instability and its consequences, and provide a basis for analysis. The second result acquisition module 2 constructs a numerical model of landslide surge based on the information, conducts quantitative analysis and simulation; and carries out numerical simulation experiments to obtain specific numerical data of the landslide surge process. Significance: The numerical model can be theoretically extended to multiple different cases, and more accurate predictions of landslide surge behavior can be obtained through calculation; compared with physical model experiments, numerical simulation can quickly obtain results under different working conditions, saving time and resources. Experimental Parameter Adjustment Module 3 uses head wave height, arrival height at the opposite shore, and surge pressure distribution as evaluation criteria to adjust the particle size and kernel function in the numerical model to ensure consistency between the numerical simulation and the physical experiment. This systematic adjustment of model parameters ensures that the numerical results are more consistent with actual observations. Significance: This calibration process ensures the reliability of the numerical model, enabling it to more realistically reflect the landslide surge process and provide higher simulation accuracy. The adjusted model can be better applied in real-world projects, providing a scientific basis for disaster prevention and mitigation. Hybrid Model Acquisition Module 4 combines the adjusted numerical model with the physical model to form a comprehensive hybrid landslide surge model. It conducts a comprehensive analysis of different landslide velocities, volumes, and terrain conditions, encompassing a wider range of application scenarios and conditions. Significance: The establishment of this hybrid model provides a deeper understanding of landslide and surge behavior, allowing for comprehensive consideration of the influence of multiple factors. The results of the model analysis provide a scientific basis for relevant engineering design, safety assessments, and emergency response plans, promoting practical application.

[0254] In summary, through the collaborative work of various modules, this embodiment of the landslide surge physical and numerical hybrid model construction system implements a complete process from data acquisition, model establishment, parameter adjustment, to results analysis. This makes landslide surge research not only more scientific but also more practical, helping to improve landslide monitoring and early warning capabilities, reduce potential risks, and protect life and property.

[0255] The landslide surge physical model test of this embodiment constructs a landslide surge physical model that meets the engineering geological characteristics, which can accurately reflect the characteristics of landslide surge, and its results have important guiding significance for the prevention and control decision-making of landslide surge. However, due to the limitation of the number of monitoring equipment and the influence of terrain accuracy, its results mainly reflect the macro characteristics of landslide surge disasters, and it is difficult to accurately capture the various details of landslide surge disasters. The use of numerical simulation methods to study landslide surge problems has the advantages of high efficiency and intuitive results, and can clearly depict the details of landslide surge disasters. The calculation results of the numerical simulation method are greatly affected by the calculation parameters, and some parameters are often difficult to determine, resulting in relatively low credibility of the numerical simulation results. To this end, this embodiment combines the advantages of physical model tests and numerical simulations, based on large-scale physical model tests, uses the results of physical model experiments to iteratively correct the numerical model, establishes a physical-numerical hybrid model, and comprehensively evaluates landslide surge disasters.

[0256] like Figure 8 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0257] The memory 42 stores program instructions for implementing the method for constructing a landslide surge physical and numerical hybrid model according to any of the above embodiments.

[0258] The processor 41 is used to execute program instructions stored in the memory 42 to construct a landslide surge physical and numerical hybrid model.

[0259] The processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having signal processing capabilities. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor or any conventional processor.

[0260] Furthermore, Figure 9This is a schematic diagram of the structure of a storage medium in an embodiment of the present application. The storage medium 5 in the embodiment of the present application stores program instructions 51 that can implement all the above methods, wherein the program instructions 51 can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, server, mobile phone, and tablet.

[0261] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0262] In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units. The above is only an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0263] The above detailed description of the specific embodiments of the invention is intended to be illustrative only, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of the present invention. Therefore, equivalent changes, modifications, and improvements made without departing from the spirit and scope of the present invention are also encompassed within the scope of the present invention.

Claims

1. A method for constructing a landslide surge physical and numerical hybrid model, characterized in that: The method for constructing a landslide surge physical and numerical hybrid model includes: Obtain the target area's contour geological information, the height of the sliding mass's center of gravity, and the water level; construct a landslide surge physical model test based on this information. The landslide surge physical model test includes constructing the terrain based on the contour information and constructing the sliding mass's position and volume based on the sliding mass's volume and center of gravity; and analyze the landslide surge's instability evolution, the height of the first wave entering the reservoir, and the surge propagation process based on the initial results of the landslide surge physical model test. Constructing a numerical model test of landslide surge based on the information. The numerical model test of landslide surge includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body. Conducting a numerical simulation test based on the second result of the numerical model test of landslide surge; Taking the first wave height, the height reaching the opposite bank and the surge pressure distribution as the evaluation criteria, the particle size and kernel function in the numerical simulation test of landslide surge were adjusted to ensure that the first wave height, the height reaching the opposite bank and the surge pressure distribution in the numerical simulation test of landslide surge and the physical model test of landslide surge were consistent. The adjusted landslide surge numerical model is used as the obtained landslide surge hybrid model to analyze the working conditions of different landslide velocities, landslide volumes and terrain conditions. The process of adjusting the particle size and kernel function in the numerical simulation of landslide surge includes: Analyze the experimental data obtained, including extracting specific values ​​for the head wave height, the height reaching the opposite shore, and the surge pressure distribution, which will serve as evaluation criteria; establish a relationship diagram between the target value and the current numerical simulation results, and identify the gap between the numerical simulation and the physical experimental results; Analyze the particle distribution characteristics in the landslide surge physical model and compare the impact of different particle sizes on the numerical results. Perform numerical simulations by varying the particle diameter, continuously refining the particle size until it matches the results of the landslide surge physical model. Based on the analysis of surge behavior in the landslide surge physical model experiment, determine the kernel function type and adjust the kernel function's support domain and decay rate. After completing the adjustment of particle size and kernel function, a new round of numerical simulation is carried out: the adjusted numerical model is comprehensively compared with the physical experimental results, including the verification of the first wave height, the detection of the height reaching the opposite shore, and the fitting of the surge pressure distribution.

2. The method for constructing a landslide surge physical and numerical hybrid model according to claim 1, characterized in that: The process of establishing a relationship diagram between the target value and the current numerical simulation results includes: Obtain key parameters such as the first wave height, arrival height at the opposite shore, and surge pressure distribution to set target values. Simultaneously, obtain corresponding results from numerical simulations and record the first wave height, arrival height, and corresponding surge pressure distribution data obtained under the same simulated conditions. The collected landslide surge physical model experimental data and the second result of the numerical simulation were matched with the time point and spatial position of the collection, and smoothed; the height of the first wave and the height reaching the opposite bank were normalized according to the maximum value, generating a standardized value between 0 and 1; A two-dimensional coordinate system is used, with the X-axis representing the simulation results and the Y-axis representing the experimental target value. A scatter plot is made to show the relationship between the simulation results and the target values. The distance between each point is calculated to quantify the gap and obtain the deviation between the numerical simulation results and the target values.

3. The method for constructing a landslide surge physical and numerical hybrid model according to claim 2, characterized in that: in, There are n pairs of simulation results and target values ​​of key parameters, which are expressed as follows: numerical simulation results S i , where i = 1, 2, ..., n, target value T i ; Absolute error refers to the direct difference between the numerical simulation result and the target value, and the calculation formula is: E abs,i =|S i -T i | Among them, E abs,i represents the absolute error of the i-th key parameter; The relative error takes into account the influence of the target value and uses the ratio of the target value to standardize the error. The calculation formula is: Among them, E rel,i Represents the relative error of the i-th key parameter; The root mean square error (RMSE) is used to comprehensively evaluate the deviations of multiple parameters. The calculation formula is: The average of the squared errors of all key parameters was calculated and then the square root was taken, providing a more comprehensive error measure; The comprehensive deviation evaluation evaluates each key parameter by combining absolute error, relative error, and root mean square error to form a new indicator: Among them, α, β, and γ are weight coefficients, reflecting the importance of different deviation measures in the analysis.

4. The method for constructing a landslide surge physical and numerical hybrid model according to claim 1, characterized in that: The process of changing the particle diameter and adjusting the support domain and decay rate of the kernel function includes: The current particle distribution characteristics and diameters were extracted, and a numerical landslide surge model was used to perform regression analysis on the relationship between the existing particle distribution and surge behavior to obtain preliminary results. The particle size variation range was set, and multiple particle diameters were selected for simulation experiments. In the numerical simulation, the corresponding particle sizes were replaced one by one, and the simulation results focused on the first wave height, the wave height reaching the shore, and the pressure distribution area. After each adjustment of the particle diameter, numerical simulations were repeated and the results were compared with the experimental target values. By collecting the simulated data, the accuracy of the corresponding wave height, arrival altitude, and pressure distribution was evaluated, and the particle size was reduced until it matched the target value. After the particle size is determined, the support domain of the kernel function is set according to the particle's action distance; according to the actual fluctuations in the simulation results, the decay rate of the kernel function is adjusted, the initial decay rate is set, and fine-tuning is performed by comparing the simulation results.

5. The method for constructing a landslide surge physical and numerical hybrid model according to claim 4, characterized in that: The process of using the landslide surge numerical model to conduct a regression analysis of the relationship between the existing particle distribution and surge behavior includes: The system acquires various data on particle trajectory, position, diameter, and water surface fluctuations in the surge, analyzes the acquired data, and automatically marks and identifies particle boundaries. A tree-based model assesses the impact of each variable on surge behavior and selects the most influential features. A cross-feature construction is performed on particle characteristics and wave behavior to generate a new feature combination, namely the wave-particle coupling feature. Construct a deep learning-based neural network, using an adaptive network structure and hierarchical feature extraction mechanism to fit the complex nonlinear relationship between particle characteristics and surges, analyze the characteristics of fluctuating data series, and capture dynamic changes in time series. Combine fluid dynamics equations with deep learning models to guide neural network learning through physical constraints. The performance of the neural network model is evaluated through k-fold cross-validation, and the hyperparameters are adjusted using Bayesian optimization to achieve the optimal configuration.

6. The method for constructing a landslide surge physical and numerical hybrid model according to claim 5, characterized in that: The process of guiding the learning of neural networks through physical constraints includes: Collect particle dynamics and water surface fluctuation data, annotate particles to identify the boundaries, shapes, and positions of different particles; extract particle characteristics and wave parameters; create time window features to convert all particle characteristics and wave parameters into a distribution with mean 0 and variance 1; The particle dynamics and water surface fluctuation data were divided into training and validation sets. The loss and accuracy curves of the training and validation sets were plotted to observe convergence and stability. The effectiveness of the neural network model was evaluated through scatter plots of actual wave data and model prediction results. Establish a set of model equations to describe fluid dynamics, and calculate losses by comparing the residuals between the neural network model output and the equations. After the neural network model is trained, use physical consistency testing to check whether the wave and particle behaviors output by the model meet physical expectations. in, Where ρ is the fluid density, u is the velocity field, p is the pressure, μ is the viscosity coefficient, and f is the body force; Calculate the loss function expression: Loss=L data +λ·L physics Among them, L data is the data loss, L physics is the loss of the physical model, and λ is the importance coefficient used to balance the loss.

7. The method for constructing a landslide surge physical and numerical hybrid model according to claim 4, characterized in that: The process of reducing the particle size until it matches the target value includes: The output results of each numerical simulation are collected. The output results include three parameters: wave height, arrival height, and pressure distribution. The mean square error and absolute error are used as standardized evaluation indicators. Set an accuracy threshold and use it to determine the deviations of the three parameters: wave height, arrival height, and pressure distribution. Compare the simulation results with the target values ​​and generate an error analysis chart to obtain the deviations and trends of the model output under different particle sizes, thereby determining whether the effect of the current particle size on surge behavior meets the experimental objectives. If the simulation results fail to meet the target value, the particle size will be reduced proportionally for the next round of simulation; The process of proportionally reducing the particle size for the next round of simulation includes: After comparing the simulation results with the target values, the deviation of each parameter is identified; Definition symbol: Y sim =(h sim , z sim , P sim ) represents the current simulation result vector, h sim represents the simulated wave height, z sim Indicates the simulated arrival height, P sim represents the simulated pressure distribution, Y target =(h target , z target , P target ) represents the target value vector, h target Indicates the target wave height, z target Indicates the target arrival height, P target represents the target pressure distribution, D i Represents the deviation of the i-th parameter, calculated as: where i∈h,z,P i = h represents the deviation for wave height, i = z represents the deviation for arrival height, and i = P represents the deviation for pressure distribution; Set the comprehensive deviation D to; Definition symbol: N represents the number of data points; calculation formula: ∈ the set accuracy threshold, indicating the maximum allowed deviation); Reduce the particle size proportionally and adjust the particle size after identifying that the deviation D exceeds the accuracy threshold; Definition symbol: D current Indicates the current particle size value, D new Represents the updated new particle size value, r represents the reduction ratio, and the range is r∈(0,1); The formula for updating particle size is: D new =D current ×(1-r); For example: If the current particle size (D current =0.05) meters, reduce the ratio (r = 0.1), then: D new =0.05×(1-0.1)=0.045 m; After adjusting the particle size, perform a new numerical simulation to generate a new output result Y sim,new ; Return to collect data again for evaluation.

8. A landslide surge physical and numerical hybrid model construction system, which is applied to the landslide surge physical and numerical hybrid model construction method according to any one of claims 1 to 7, characterized in that: The landslide surge physical and numerical hybrid model construction system includes: The first result acquisition module is used to obtain the contour geological information of the target area, the height of the center of gravity of the sliding body, and the water level information; construct a landslide surge physical model test based on this information, which includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body; based on the first result of the landslide surge physical model test, analyze the landslide instability evolution movement process, the height of the first wave entering the landslide reservoir, and the surge propagation process; The second result acquisition module is used to construct a landslide surge numerical model test based on the information. The landslide surge numerical model test includes constructing the terrain based on the contour information and constructing the position and volume of the sliding body based on the volume and center of gravity of the sliding body; and conducting a numerical simulation test based on the second result of the landslide surge numerical model test; The test parameter adjustment module is used to use the first wave height, the height reaching the opposite bank, and the surge pressure distribution as evaluation criteria to adjust the particle size and kernel function in the landslide surge numerical simulation test to ensure that the first wave height, the height reaching the opposite bank, and the surge pressure distribution in the landslide surge numerical simulation test and the landslide surge physical model test are consistent; The hybrid model acquisition module is used to use the adjusted landslide surge numerical model as the obtained landslide surge hybrid model to analyze the working conditions of different landslide velocities, landslide volumes and terrain conditions.

9. An application of a landslide surge hybrid model, characterized in that: The landslide surge hybrid model is obtained by the landslide surge physical and numerical hybrid model construction method according to any one of claims 1 to 7. The landslide surge hybrid model analyzes working conditions of different landslide velocities, landslide volumes and terrain conditions.

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