Landslide surge attenuation method and system based on ecological vegetation configuration

Abstract

The invention relates to the technical field of ecological vegetation information configuration, and discloses a landslide surge attenuation method and system based on biological vegetation configuration, and the method comprises the steps: constructing a landslide surge physical model, and determining a vegetation configuration range according to basic features; calculating a flood position and a normal water level according to the vegetation configuration range to determine an elevation area of vegetation configuration; determining the vegetation type of each elevation area of vegetation configuration; preprocessing data obtained by the landslide surge physical model to form a data set; calculating an average value and a standard deviation of the data set to obtain a standard data set; constructing a vegetation configuration model, inputting the standard data set into the vegetation configuration model, and simulating the landslide surge to obtain an influence mechanism of vegetation on the surge; and analyzing the influence mechanism of the vegetation on the surge to obtain a plurality of vegetation configuration schemes. The system comprises an acquisition module, a processing module and a vegetation configuration module. High-water-level protection is provided, rain wash can be effectively prevented, and the shallow slope is stable.

Description

Technical Field

[0001] The invention relates to the technical field of ecological vegetation information configuration, and in particular to a landslide surge attenuation method and system based on ecological vegetation configuration. Background Art

[0002] Globally, more than half of the large river systems have evolved into river-type reservoir systems due to the construction of dams. However, during the operation of the dam, the large fluctuations in the reservoir water level often become a potential factor inducing slope deformation in the reservoir area. In extreme cases, it may even trigger landslide geological disasters and secondary surge disasters. Such surge activities not only directly threaten the navigation safety and berthing stability of ships in the reservoir waters, but also pose a severe challenge to the sustained and stable operation of reservoir infrastructure. What is particularly serious is that once the surge reaches the land, the lives and safety of coastal residents will face immeasurable risks. Compared with the direct damage caused by landslide disasters, the harm and losses caused by the secondary surge disasters are often more far-reaching and difficult to estimate. Therefore, there is an urgent need to develop effective strategies to mitigate the negative impacts of such wave disasters.

[0003] Coastal wetland ecosystems, especially salt marshes and mangroves, are not only of great interest for their unique ecological value, but are also well known for their excellent wave buffering capacity. Vegetation communities in these wetlands play a pivotal role in dissipating wave energy, effectively alleviating the pressure on coastal defense systems. Incorporating such natural habitats into coastal protection strategies has become a hot topic of ongoing discussion in academia and practice. In this context, many studies have been devoted to exploring the mechanisms of the influence of vegetation on wave attenuation through a variety of research methods. Theoretical research, field experiments and numerical simulations complement each other and jointly promote the continuous deepening of cognition in this field. Studies have clearly shown that factors such as increased vegetation density, reduced flooding depth and increased plant stem rigidity generally promote more efficient dissipation of wave energy. These valuable insights from coastal environments provide new dimensions for thinking about the attenuation mechanism of landslide-induced waves and their damage control in reservoir environments. It is worth noting that the dissipation effect of vegetation on wave energy is deeply affected by the complex interaction between canopy characteristics and incident wave conditions. Therefore, the attenuation characteristics of landslide-induced waves will be significantly different from those previously reported in other environments. Nevertheless, to date, research on the impact of vegetation on landslide surges in river-type reservoir environments is relatively scarce, which further highlights the urgency and importance of deepening research in this area to elucidate the complex dynamics and provide information for the development of strong ecological protection strategies. Summary of the invention

[0004] The main purpose of the present invention is to provide a landslide surge attenuation method and system based on biological vegetation configuration, so as to solve the problem that the research on the influence of vegetation on landslide-induced waves in river reservoirs in the prior art is very limited.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A landslide surge attenuation method based on biological vegetation configuration:

[0007] Construct a landslide surge physical model, collect the basic characteristics of the landslide area, and determine the vegetation configuration range based on the basic characteristics; calculate the flood position and the normal water level based on the vegetation configuration range to determine the elevation area of ​​the vegetation configuration; determine the vegetation types in each elevation area of ​​the vegetation configuration based on the main function of the landslide area;

[0008] Among them, the basic characteristics of the landslide area include soil type, slope, and water discharge pattern;

[0009] Preprocess the data obtained from the landslide surge physical model, clean the obtained data, normalize it, and fill in missing values ​​to form a data set; standardize the data set, calculate the mean value and standard deviation of the data set to obtain a standard data set;

[0010] A vegetation configuration model was constructed, and the standard data set was input into the vegetation configuration model to simulate landslide surges and derive the mechanism of the influence of vegetation on surges. The mechanism of the influence of vegetation on surges was analyzed to derive a variety of vegetation configuration schemes.

[0011] As a further improvement of the present invention, determining the vegetation type of each elevation area of ​​the vegetation configuration includes:

[0012] Construct a physical model of landslide surge, collect soil types in the landslide area of ​​the physical landslide surge model, calculate the slope distribution of the landslide area, analyze the distribution of rivers, gullies and drainage equipment, and determine the water discharge pattern;

[0013] The acquired data is normalized and preprocessed, and the vegetation coverage of the landslide area is evaluated according to the normalized preprocessing results; the vegetation types in different areas are determined according to the vegetation coverage and soil type and the growth habits of the vegetation;

[0014] The flood location in the landslide area is predicted through historical flood data and terrain models. The normal water level in the landslide area is determined based on historical hydrological data and terrain characteristics. The landslide area is divided into different elevation areas based on the flood and normal water levels. Each elevation area corresponds to different vegetation configuration requirements.

[0015] As a further improvement of the present invention, a standard data set is obtained, including:

[0016] Fill missing values, outliers and duplicate values ​​in the data obtained from the landslide surge physical model; delete outliers and duplicate values; and clean the data;

[0017] Scale the cleaned data to a specific range; for numerical data, fill it with the average value of the class to which the missing value belongs; for non-numerical data, fill it with the mode of the class to which the missing value belongs; the filled data form a data set;

[0018] Perform data standardization on the data set, calculate the average value of the data set, and standardize the data set according to the calculated average value and standard deviation. The standardized data set satisfies the mean value of 0 and the standard deviation of 1.

[0019] As a further improvement of the present invention, a variety of vegetation configuration schemes are obtained, including:

[0020] According to the historical vegetation type data, the initial parameters of the vegetation configuration model are set; according to the elevation area parameters of the landslide surge physical model, the terrain factor weights of the vegetation configuration model are set; the vegetation configuration model is trained using a standard data set;

[0021] The standard data set is integrated with the historical data and preprocessed; the standard data is input into the vegetation configuration model for simulation calculation to derive the mechanism of the influence of vegetation on surge;

[0022] The mechanism of vegetation's impact on surge is analyzed to derive different vegetation configuration schemes; historical vegetation configuration schemes are retrieved and compared with real-time vegetation configuration schemes; a comparison threshold is set, and if it is greater than the threshold, the vegetation configuration model is adjusted and iterated in sequence until the final vegetation configuration scheme is obtained.

[0023] As a further improvement of the present invention, a vegetation configuration model is trained using a standard data set, including:

[0024] The elevation area parameters of the landslide surge physical model are divided into target layer, factor layer and index layer; the influence of each terrain factor on different vegetation types and populations is analyzed; and the contribution of each terrain factor to the vegetation type is calculated;

[0025] The criterion layer includes vegetation type and population distribution characteristics;

[0026] Construct a judgment matrix to quantify the contribution of each terrain factor to vegetation distribution and obtain the relative importance of each factor; set an impact standard and select terrain factors whose contribution to vegetation distribution is greater than the impact standard;

[0027] Weights are assigned according to the actual contribution of each terrain factor to the distribution; terrain factors that are greater than the impact standard are given the highest authority.

[0028] As a further improvement of the present invention, the terrain factors whose contribution to vegetation distribution is greater than the influence standard are selected, including:

[0029] Construct a judgment matrix, extract comparative terrain factors, set the influence standard of each terrain factor on vegetation distribution, construct the first judgment matrix, the second judgment matrix and the third judgment matrix; standardize the first judgment matrix, the second judgment matrix and the third judgment matrix;

[0030] Among them, the first judgment matrix represents the proportion of the importance of the criterion layer to the target layer, and the second judgment matrix and the third judgment matrix represent the proportion of the importance of the factor layer to the criterion layer;

[0031] Calculate the consistency index and evaluate the consistency of the judgment matrix; set the consistency index threshold, and when the consistency index is greater than the consistency index, adjust the judgment matrix; extract the principal components of the factors affecting vegetation distribution and calculate the contribution values ​​of various terrain factors;

[0032] Set the impact standard of each terrain factor on vegetation distribution, select terrain factors whose contribution to vegetation distribution is greater than the impact standard, and grant them the highest authority; optimize the weight distribution of each terrain factor through multiple iterations and adjustments.

[0033] As a further improvement of the present invention, the influence mechanism of vegetation on surge is obtained, including:

[0034] The real-time collected landslide surge physical model data is integrated with historical data to remove noise and outliers in the real-time data, normalize data of different scales, and fill them with interpolation; extract key features from the pre-processed data and input the extracted key features into the vegetation configuration model;

[0035] The vegetation configuration model is simulated, three indicators are used to determine the nonlinearity of the waves, and the surge types are divided according to the nonlinearity;

[0036] The height of the surge on the slope is calculated according to the classified surge types, and the attenuation characteristics of the surge in the vegetation area are obtained; based on the attenuation characteristics of the surge in the vegetation area, the surge amplitude and wave height attenuation amplitude in the vegetation area are predicted.

[0037] As a further improvement of the present invention, the amplitude and wave height attenuation amplitude of the swell in the vegetation area are predicted according to the attenuation characteristics of the swell in the vegetation area, including:

[0038] According to the classification of swell types, the amplitude a is defined 1cref , the amplitude of the incident surge ain and the maximum height of the surge rp , define the amplitude a 1cref , the amplitude of the incident surge a in and the maximum height of the surge rp Calculations are made to form a relationship diagram between the wave run-up height on the slope and the wave momentum flux parameter M;

[0039] Analyze the relationship diagram and calculate the amplitude attenuation coefficient Δa v and wave height attenuation coefficient ΔH v Define it; calculate the defined amplitude attenuation coefficient and wave height attenuation coefficient to obtain the overall attenuation change diagram of the amplitude and wave height from the time when the wave passes through the vegetation area;

[0040] The attenuation of the surge wave amplitude and wave height in the vegetation area is predicted based on the overall attenuation change diagram of the wave amplitude and wave height from the time when the wave passes through the vegetation area; the predicted results are compared with the measured results, and a comparison threshold is set. If the comparison threshold is higher than the threshold, the vegetation allocation model is readjusted;

[0041] Define the amplitude a 1cref is the amplitude of the leading wave before the surge reaches the vegetation area, and the amplitude of the incident surge a in is the amplitude of the incident surge leader wave, and the highest surge height a rp It is the maximum vertical height of the leader wave crest climbing along the slope;

[0042] The amplitude of the leader wave before the surge reaches the vegetation area, the amplitude of the incident surge leader wave, and the maximum vertical height of the wave leader wave crest climbing along the slope are calculated to obtain the maximum relative climbing height a of the surge at different vegetation coverage densities. rp / d0 with relative parameter amplitude a 1cref / D0 change diagram;

[0043] According to the relative amplitude a of the incident wave in / d0 to obtain the wave climbing height on the slope and the wave momentum flux parameter M, and analyze the change diagram with the wave climbing height on the slope and the wave momentum flux parameter M, a in / d0 and M get the relationship diagram;

[0044] As a further improvement of the present invention, the prediction of the surge amplitude and wave height attenuation amplitude in the vegetation area includes:

[0045] The geometric form of the vegetation area is obtained, and the canopy density in the geometric form of the vegetation area is quantified to obtain the frontal area of ​​the canopy volume; the plane unit area is obtained by calculating according to the frontal area of ​​the canopy; the plane unit area is analyzed to obtain the plane area occupied by the canopy elements;

[0046] The overall wave attenuation variation diagram was analyzed, and the vegetation inundation degree was defined as the ratio of the still water depth to the vegetation inundation height, the modified head-on face value index was defined as the product of the head-on face value index and the inundation degree, and the dimensionless canopy density was numerically defined as the canopy density;

[0047] Calculate the defined data to obtain the change process of the relative amplitude attenuation coefficient with the relative initial amplitude; make predictions based on the change process, compare the prediction results with the actual measurements, set a comparison threshold, and recalculate if it is greater than the comparison threshold;

[0048] The wave attenuation variation diagram was analyzed, and the vegetation submergence was defined as the ratio of the still water depth to the vegetation submergence height, the modified headward face value index was defined as the product of the headward face value index and the submergence, and the dimensionless canopy density was numerically defined as the canopy density;

[0049] The amplitude attenuation characteristics are analyzed. The non-flooded state of vegetation corresponds to a still water depth of less than or equal to 0.16 meters, while the non-flooded state of vegetation corresponds to a still water depth of greater than or equal to 0.2 meters. The wave height attenuation characteristics are analyzed. The non-flooded state of vegetation corresponds to a still water depth of less than or equal to 0.16 meters, while the non-flooded state of vegetation corresponds to a still water depth of greater than or equal to 0.24 meters.

[0050] Based on the analysis of the wave amplitude attenuation characteristics and wave height attenuation characteristics, the non-flooded and flooded states of vegetation are obtained;

[0051] Make predictions based on empirical formulas and get results; set a comparison threshold, compare the predicted value with the measured value, and if it is greater than the threshold, readjust the threshold; if it is less than the threshold, analyze the comparison value;

[0052] Based on the analysis results, the variation trajectory of the relative wave height attenuation coefficient with the relative initial wave height in the non-flooded and flooded states of vegetation is obtained.

[0053] To achieve the above object, the present invention also provides the following technical solutions:

[0054] A landslide surge attenuation system configured with biological vegetation:

[0055] Acquisition module: construct a landslide surge physical model, collect the basic characteristics of the landslide area, and determine the vegetation configuration range based on the basic characteristics; calculate the flood position and the normal water level based on the vegetation configuration range to determine the elevation area of ​​the vegetation configuration; based on the main function of the landslide area, determine the vegetation type of each elevation area of ​​the vegetation configuration;

[0056] Among them, the basic characteristics of the landslide area include soil type, slope, water discharge pattern, etc.

[0057] Processing module: pre-process the data obtained from the landslide surge physical model, perform data cleaning, normalization and missing value filling on the obtained data to form a data set; perform data standardization on the data set, calculate the mean value and standard deviation of the data set to obtain a standard data set;

[0058] Vegetation configuration module: Construct a vegetation configuration model, input the standard data set into the vegetation configuration model, simulate the landslide surge, and derive the mechanism of vegetation's influence on surge; analyze the mechanism of vegetation's influence on surge and derive a variety of vegetation configuration schemes.

[0059] The present invention simulates the movement of landslide bodies and the formation process of surge waves through physical simulation methods, such as simplified model tests and prototype model tests. According to the basic characteristics of the landslide area and the influencing factors of the landslide surge waves, the range and elevation area of ​​vegetation configuration are determined. Through the physical model test, the surge phenomenon can be studied intuitively and reliably, and the simulation accuracy can be improved. Based on the analysis of basic characteristics and influencing factors, the vegetation configuration is optimized to reduce the impact of landslide surge waves on the environment and infrastructure. Data cleaning, normalization processing and missing value filling ensure the integrity and consistency of the data set. By calculating the mean value and standard deviation of the data set, data standardization processing is performed to improve the accuracy of data analysis. Through data preprocessing and standardization processing, the quality and availability of data are improved. The standardized data set can more accurately reflect the actual situation of landslide surge waves and improve the accuracy of subsequent analysis. Based on the standard data set, a vegetation configuration model is constructed to simulate the influence mechanism of vegetation on landslide surge waves. Through model analysis, the specific influence mechanism of vegetation on surge waves is obtained, and multiple vegetation configuration schemes are proposed. Through model analysis, the specific influence mechanism of vegetation on landslide surge waves is clarified, which provides a scientific basis for vegetation configuration. This paper aims to elucidate the characteristics of landslide-induced shock waves in a rigid vegetation environment, with a particular focus on shallow water scenarios where nonlinearities become prominent during wave evolution. To achieve this goal, a series of experiments with rigid simulated vegetation were conducted in a hydro wave tank. Multiple shock wave conditions with different degrees of nonlinearity were generated by varying the initial landslide position and the still water depth. Four different vegetation layout patterns, each containing three different diameters, were tested under both out-of-water and submerged conditions. For the waves generated by the landslide, the prediction of the maximum amplitude and wave height were investigated, and a nonlinear dispersion relation governing the wave evolution was established. In addition, a comprehensive analysis and discussion were performed to systematically examine the effect of vegetation on wave attenuation, considering both amplitude and wave height and their influence on wave run-up. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of the steps of an embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0061] Figure 2It is a schematic flow chart of the steps of determining the vegetation types of each elevation area of ​​the vegetation configuration in one embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0062] Figure 3 A schematic flow chart of steps for obtaining a standard data set according to an embodiment of a landslide surge method based on biological vegetation configuration of the present invention;

[0063] Figure 4 A schematic flow chart of the steps of obtaining various vegetation configuration schemes according to an embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0064] Figure 5 A schematic flow chart of the steps of using a standard data set to train a vegetation configuration model in one embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0065] Figure 6 A schematic flow chart of the steps of selecting a terrain factor whose contribution to vegetation distribution is greater than the influence standard in one embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0066] Figure 7 A schematic flow chart of the steps of deriving the mechanism of the influence of vegetation on surge in one embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0067] Figure 8 It is a schematic flow chart of the steps of predicting the amplitude and wave height attenuation amplitude of the surge wave in the vegetation area according to the attenuation characteristics of the surge wave in the vegetation area according to one embodiment of the landslide surge wave method based on biological vegetation configuration of the present invention;

[0068] Fig. 9 A schematic flow chart of the steps of forming a relationship diagram between the wave climbing height on a slope and the wave momentum flux parameter M in one embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0069] Fig.10 A schematic flow chart of the steps of obtaining an overall attenuation change diagram of wave amplitude and wave height from the time when the wave passes through the vegetation area to the time when the wave passes through the vegetation area according to an embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0070] Fig.11 It is a schematic flow chart of the steps of predicting the amplitude and wave height attenuation amplitude of the surge in the vegetation area according to one embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0071] Fig.12 The dimensionless canopy density of one embodiment of the landslide surge method based on biological vegetation configuration of the present invention is a schematic flow chart of the canopy density steps;

[0072] Fig.13A schematic flow chart of the steps of obtaining a process in which a relative amplitude attenuation coefficient varies with a relative initial amplitude in one embodiment of a landslide surge method based on biological vegetation configuration according to the present invention;

[0073] Fig.14 A schematic flow chart of steps for obtaining a final vegetation configuration scheme in accordance with an embodiment of the landslide surge method based on biological vegetation configuration of the present invention;

[0074] Fig.15 A block diagram of an embodiment of a landslide surge system based on biological vegetation configuration according to the present invention;

[0075] Fig.16 It is an experimental diagram of the experimental model arrangement of the landslide surge method experiment based on biological vegetation configuration of the present invention;

[0076] Fig.17 It is a schematic diagram of an experimental model of the landslide surge method experiment based on biological vegetation configuration of the present invention;

[0077] Fig.18 It is a schematic diagram of vegetation arrangement for the landslide surge method experiment based on biological vegetation configuration of the present invention;

[0078] Fig.19 It is a graph showing the variation of the maximum climbing height of the surge slope with the amplitude of the surge wave before the vegetation area in the experiment of the landslide surge method based on the biological vegetation configuration of the present invention;

[0079] Fig. 20 A graph showing the variation of the maximum climbing height of the surge slope with the wave momentum flux in the landslide surge method experiment based on the biological vegetation configuration of the present invention;

[0080] Fig.21 This is a diagram showing the changes in the amplitude and wave height attenuation coefficient of the BV20S2 series of landslide surge method experiments based on biological vegetation configuration of the present invention;

[0081] Fig. 22 It is a diagram of the error range of the landslide surge method experiment based on biological vegetation configuration of the present invention;

[0082] Fig.23 It is a comparison chart of the measured value and the predicted value of the landslide surge method experiment based on the biological vegetation configuration of the present invention;

[0083] Fig.24 This is a diagram showing changes in the landslide surge method experiment based on biological vegetation configuration of the present invention;

[0084] Fig.25 This is a comparison diagram of the landslide surge method experiment based on biological vegetation configuration of the present invention;

[0085] Fig.26 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0086] Fig. 27 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can 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, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention 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 also changes accordingly. In addition, the terms "including" and "having" and any of their variations 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 optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0089] The reference to "embodiment" in this embodiment means that the specific features, structures or characteristics described in conjunction with the embodiment 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 is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiment described in this embodiment can be combined with other embodiments.

[0090] like Figure 1 As shown, this embodiment provides an embodiment of a landslide surge attenuation method based on ecological vegetation configuration. In this embodiment, the landslide surge attenuation method based on ecological vegetation configuration specifically includes the following steps:

[0091] Step S1: construct a landslide surge physical model, collect basic characteristics of the landslide area, and determine the vegetation configuration range based on the basic characteristics; calculate the flood position and the normal water level based on the vegetation configuration range to determine the elevation area of ​​the vegetation configuration; based on the main function of the landslide area, determine the vegetation type of each elevation area of ​​the vegetation configuration;

[0092] Among them, the basic characteristics of the landslide area include soil type, slope, water discharge pattern, etc.

[0093] Step S2: preprocessing the data obtained from the landslide surge physical model, performing data cleaning, normalization and missing value filling on the obtained data to form a data set; performing data standardization on the data set, calculating the mean value and standard deviation of the data set to obtain a standard data set;

[0094] Step S3: construct a vegetation configuration model, input the standard data set into the vegetation configuration model, simulate the landslide surge, and obtain the mechanism of vegetation influence on surge; analyze the mechanism of vegetation influence on surge, and obtain multiple vegetation configuration schemes.

[0095] Preferably, step S1 of this embodiment simulates the movement of landslide body and the process of surge formation by physical simulation method, such as simplified model test and prototype model test. According to the basic characteristics of landslide area, combined with the influencing factors of landslide surge, the range and elevation area of ​​vegetation configuration are determined. Through physical model test, surge phenomenon can be studied intuitively and reliably, and simulation accuracy can be improved. Based on the analysis of basic characteristics and influencing factors, vegetation configuration is optimized to reduce the impact of landslide surge on environment and infrastructure. Step S2 data cleaning, normalization and missing value filling ensure the integrity and consistency of data set. By calculating the mean value and standard deviation of the data set, data standardization is performed to improve the accuracy of data analysis. Through data preprocessing and standardization, the quality and availability of data are improved. The standardized data set can more accurately reflect the actual situation of landslide surge and improve the accuracy of subsequent analysis. Step S3 constructs a vegetation configuration model based on the standard data set to simulate the influence mechanism of vegetation on landslide surge. Through model analysis, the specific influence mechanism of vegetation on surge is obtained, and a variety of vegetation configuration schemes are proposed. Through model analysis, the specific impact mechanism of vegetation on landslide surge was clarified, providing a scientific basis for vegetation configuration. Based on the analysis of the impact mechanism, a variety of vegetation configuration schemes were proposed, providing diverse options for practical applications.

[0096] Further, if Figure 2 As shown, the process of determining the vegetation type of each elevation area of ​​the vegetation configuration in step S1 specifically includes the following steps:

[0097] Step S11: construct a landslide surge physical model, collect soil types in the landslide area of ​​the physical landslide surge model, calculate the slope distribution of the landslide area, analyze the distribution of rivers, gullies and drainage equipment, and determine the water discharge pattern;

[0098] Among them, soil types include but are not limited to humus soil, earthy soil and calcareous soil;

[0099] Step S12: performing normalization preprocessing on the acquired data, and evaluating the vegetation coverage of the landslide area according to the normalization preprocessing result; determining the vegetation types of different areas according to the vegetation coverage and soil type and analyzing the growth habits of the vegetation;

[0100] Among them, the growth habits of vegetation include drought tolerance and barrenness tolerance;

[0101]

[0102] Where V c Vegetation Coverage, which indicates the vegetation coverage of the landslide area. i Represents the surface area of ​​the i-th vegetation type (Surface Area), in square meters (m 2 ), G i The growth factor of the i-th vegetation type indicates the growth potential of the vegetation type, with a value range of 0 to 1. i The Drought Resistance Factor represents the survivability of the vegetation type under drought conditions, with a value range of 0 to 1. i The Poor Soil Resistance Factor represents the survival ability of the vegetation type under poor soil conditions, with a value range of 0 to 1. i The ecological niche factor of the i-th vegetation type indicates the adaptability and competitiveness of the vegetation type in the ecosystem, with a value range of 0 to 1. i The Temperature Adaptation Factor represents the temperature adaptability of the i-th vegetation type, which indicates the adaptability of the vegetation type to temperature changes. The value range is 0 to 1. i The root depth factor (Root Depth Factor) of the i-th vegetation type indicates the root depth of the vegetation type, which affects its ability to absorb soil moisture and nutrients. The value range is 0 to 1, and n represents the total number of different vegetation types in the landslide area;

[0103] Step S13: predict the flood position in the landslide area through historical flood data and terrain model; determine the normal water level in the landslide area based on historical hydrological data and terrain characteristics; divide the landslide area into different elevation areas based on the flood and normal water levels; each elevation area corresponds to different vegetation configuration requirements.

[0104] Preferably, step S11 of this embodiment collects soil types in the landslide area, including humus soil, earthy soil and calcareous soil, etc. These soil types have an important influence on the stability of the landslide. Analyze the distribution of rivers, gullies and drainage equipment to determine the water discharge pattern, which is crucial for predicting flood locations and normal water levels. Through physical models and soil type analysis, the formation and propagation path of landslide surges can be more accurately predicted, thereby improving the accuracy of early warning. By analyzing the distribution of rivers and drainage equipment, the design of the drainage system can be optimized to reduce flood disasters caused by landslides. Step S12 normalizes the collected data for subsequent analysis; the vegetation coverage of the landslide area is evaluated using the normalized difference vegetation index and divided into different levels. According to vegetation coverage, soil type and vegetation growth habits (such as drought resistance and barrenness resistance), the vegetation types in different areas are determined. By evaluating vegetation coverage and determining vegetation types in different areas, vegetation management and restoration work can be more effectively carried out, soil fixation can be enhanced, and landslides can be prevented again. Selecting appropriate vegetation types according to vegetation growth habits can improve the effect of ecological restoration and enhance the ecological stability of the region. Step S13 uses historical flood data and terrain models to predict the flood position in the landslide area. Determine the normal water level in the landslide area based on historical hydrological data and terrain characteristics. Combined with floods and normal water levels, the landslide area is divided into different elevation areas, and different vegetation types are configured for each elevation area. Through elevation area division, high-risk areas in the landslide area can be accurately identified, providing a scientific basis for subsequent prevention and control measures. Configuring appropriate vegetation types according to the different characteristics of the elevation areas can effectively prevent landslides from happening again and improve the ecological stability of the region.

[0105] Furthermore, if Figure 3 As shown, the process of obtaining the standard data set in step S2 specifically includes the following steps:

[0106] Step S21: missing values, abnormal values ​​and repeated values ​​in the data obtained by the landslide surge physical model are filled, abnormal values ​​are deleted, and repeated values ​​are deleted; and the data is cleaned by removing noise, etc.;

[0107] Step S22: scaling the cleaned data to a specific range; for numerical data, filling with the average value of the class to which the missing value belongs; for non-numerical data, filling with the mode of the class to which the missing value belongs; the filled data forms a data set;

[0108] Step S23: performing data standardization processing on the data set, calculating the average value of the data set, and standardizing the data set according to the calculated average value and standard deviation, wherein the standardized data set satisfies the mean value of 0 and the standard deviation of 1;

[0109] Among them, the expression of standardization is:

[0110]

[0111] In the formula, Z i represents the standardized data value (Standardized Value), which represents the standardized result of the jth data point, X j represents the original data value (Original Value), which represents the original value of the jth data point, μ represents the mean value of the data (Mean), which represents the mean value of the data set, σ represents the standard deviation of the data (Standard Deviation), which represents the standard deviation of the data set, S k Indicates the skewness of the data, indicating the degree of asymmetry of the data distribution, K u It represents the kurtosis of the data, which indicates the sharpness of the data distribution. α represents the skewness adjustment factor, which is used to adjust the effect of skewness on the standardized results, and the value range is usually 0 to 1. β represents the kurtosis adjustment factor, which is used to adjust the effect of kurtosis on the standardized results, and the value range is usually 0 to 1. Through this complex formula, the data can be standardized more comprehensively, so as to better capture the distribution characteristics of the data and improve the accuracy and reliability of data analysis.

[0112] Preferably, step S21 of this embodiment fills the missing values ​​in the data, and the mean value (for numerical data) or mode (for non-numerical data) of the class to which the missing value belongs can be used for filling. By filling the missing values, the integrity of the data set is ensured, and the analysis deviation or model training failure caused by missing values ​​is avoided. The outliers in the data are deleted or replaced, and the outliers can be identified and processed using methods such as Z-score method, quantile method or autoencoder method. By deleting or replacing outliers, the impact of outliers on data analysis and modeling results is reduced, and the accuracy and reliability of the model are improved. The duplicate values ​​in the data are deleted, and the accuracy and reliability of the data set are improved by deleting the duplicate values, and the impact of duplicate data on the analysis results is avoided. In step S22, the cleaned data is scaled to a specific range. By scaling the data, data with different characteristics are made comparable, which is convenient for subsequent data analysis and modeling. In step S23, the data set is standardized, the mean value and standard deviation of the data set are calculated, and then the data set is standardized according to the calculated mean value and standard deviation, so that the standardized data set satisfies the mean value of 0 and the standard deviation of 1. Through data standardization, the unit and scale differences of the data set are eliminated, making the data distribution more concentrated, facilitating comparison and weighting, and improving the accuracy and interpretability of data analysis. By calculating the mean and standard deviation, basic statistics are provided for subsequent data standardization to ensure the consistency and accuracy of data processing.

[0113] Furthermore, in step S21, the process of filling missing values, deleting abnormal values, and deleting duplicate values ​​specifically includes the following steps:

[0114] Step S211: traverse the time series data, and when a missing value is encountered, use the previous non-missing value to fill it; traverse the time series data, and when a missing value is encountered, use the next non-missing value to fill it; determine the non-missing values ​​before and after the missing value; calculate the missing value using the linear interpolation formula: interpolation value = previous value + (latter value - previous value) * (missing position - previous position) / (latter position - previous position);

[0115] Step S212: Calculate the distance between the missing value and all data points, select the K data points with the closest distance, use the features of these K data points for weighted averaging, and calculate the missing value;

[0116] Step S213: construct multiple isolated trees, calculate the average path length of each data point, and evaluate the degree of abnormality of the data point based on the path length; calculate the K nearest neighbor density of each data point, calculate the local abnormality factor of each data point, and evaluate the degree of abnormality of the data point based on the local abnormality factor; calculate the mean and standard deviation of the data set, calculate the Z-score of each data point: Z-score = (data point-mean) / standard deviation, and evaluate the degree of abnormality of the data point based on the Z-score; mark the outliers based on the anomaly detection results, and record the location and characteristics of the outliers; map high-dimensional data to low-dimensional space through hash functions to efficiently identify duplicate data.

[0117] Preferably, in step S211 of this embodiment, missing value filling ensures the continuity of time series data at missing values ​​and avoids data interruption; the filled data remains smooth and reduces data fluctuations; it is simple and efficient and suitable for large-scale data processing. Backward filling ensures the continuity of time series data at missing values ​​and avoids data interruption; the filled data remains smooth and reduces data fluctuations; it is simple and efficient and suitable for large-scale data processing; linear interpolation ensures the continuity of time series data at missing values ​​and avoids data interruption; the filled data remains smooth and reduces data fluctuations; it is relatively efficient and suitable for data with obvious linear trends. Step S212 adaptive filling, the filled data maintains the consistency of features and avoids feature mutations, fills according to the features of surrounding data points, and improves the accuracy of filling; it is relatively efficient and suitable for data with obvious feature similarity. Step S213 outlier processing and repeated value processing, efficiently identifies outliers in high-dimensional data and avoids the influence of outliers on analysis results; it is suitable for large-scale data processing and has high computational efficiency; efficiently identifies local outliers and avoids the influence of local outliers on analysis results; it is suitable for data with obvious local anomalies and has high computational efficiency. The Z-score method efficiently identifies outliers in normally distributed data, avoiding the impact of abnormal data on the analysis results; it is applicable to normally distributed data and has high computational efficiency. Outliers mark outliers to ensure the traceability and verifiability of data; marking rather than directly deleting them retains the possibility of further processing or verification. Locally sensitive hashing efficiently identifies duplicate data in high-dimensional data and ensures the uniqueness of the data set; it is applicable to high-dimensional data and has high computational efficiency.

[0118] In summary, the data set of this embodiment will be fully processed with high quality to ensure the accuracy and reliability of data analysis. Each step of processing is hierarchical, and the duplication rate reaches more than 40%, ensuring the quality and consistency of the data set. Specifically, it includes: ensuring the continuity of time series data to avoid data interruption; keeping the data smooth after filling to reduce data fluctuations; keeping the consistency of features after filling to avoid feature mutations; efficiently identifying outliers to avoid the impact of abnormal data on analysis results; marking outliers to ensure data traceability and verifiability; efficiently identifying duplicate data in high-dimensional data to ensure the uniqueness of the data set. Through these technical effects, the data set will be fully processed with high quality to ensure the accuracy and reliability of data analysis.

[0119] Furthermore, the process of calculating the average path length of each data point in step S213 specifically includes the following steps:

[0120] Step S2131: During the construction of each isolated tree, a feature and feature value are randomly selected to divide the data set into two subsets; each subset is randomly divided until each subset contains only one data point or reaches a predetermined tree depth;

[0121] Step S2132: The path length is the number of edges from the root node to the leaf node, indicating the isolation depth of the data point in the isolation tree;

[0122] Step S2133: For each data point, calculate its path length in all isolated trees and take the average.

[0123] Preferably, in step S2131 of this embodiment, the construction of the isolated tree is carried out by randomly selecting features and feature values ​​to ensure that the construction process of each isolated tree is random and avoids data deviation; through recursive partitioning, the data points are gradually isolated to form a tree structure; the shallower the depth of the tree, the easier the data point is to be isolated, indicating that it may be an outlier; the structure of each isolated tree reflects the isolation path of the data point, the shallower the depth of the tree, the easier the data point is to be isolated, indicating that it may be an outlier; through random and recursive partitioning, the data points are gradually isolated to form a tree structure, which is convenient for the subsequent calculation of the path length; the shallower the depth of the tree, the easier the data point is to be isolated, which helps to identify potential outliers. Step S2132 Calculation of path length, the path length is the number of edges from the root node to the leaf node, indicating the isolation depth of the data point in the isolated tree; by calculating the path length, the degree of isolation of the data point in the isolated tree is quantified; the shorter the path length, the easier the data point is to be isolated, indicating that it may be an outlier; the path length provides a quantitative basis for subsequent abnormal evaluation. Step S2133 calculates the average path length. For each data point, calculate its path length in all isolated trees and take the average value. The shorter the average path length, the more likely the data point is an outlier. By calculating the average path length, the degree of isolation of the data point in multiple isolated trees is comprehensively evaluated to improve the accuracy of anomaly detection. The average path length provides a comprehensive quantitative indicator for anomaly detection, which helps to identify potential anomalies.

[0124] In summary, this embodiment constructs multiple isolated trees, randomly divides the data set and recursively constructs a tree structure, calculates the average path length of each data point, and evaluates its degree of abnormality. Random division and recursive division form a tree structure, which is convenient for data isolation and anomaly detection; calculates the path length, quantifies the degree of isolation of data points, and provides a basis for anomaly evaluation; calculates the average path length, comprehensively evaluates the degree of isolation of data points, and improves the accuracy of anomaly detection. The entire process ensures the comprehensiveness and accuracy of data processing through hierarchy and randomness, and provides reliable technical support for anomaly detection.

[0125] Furthermore, if Figure 4 As shown, the process of obtaining multiple vegetation configuration schemes in step S3 specifically includes the following steps:

[0126] Step S31: setting initial parameters of the vegetation configuration model according to historical vegetation type data; setting terrain factor weights of the vegetation configuration model according to elevation area parameters of the landslide surge physical model; and training the vegetation configuration model using a standard data set;

[0127] Step S32: Fusing the standard data set with the historical data and preprocessing them; inputting the standard data into the vegetation configuration model, performing simulation calculations, and obtaining the mechanism of the influence of vegetation on surge;

[0128] Among them, preprocessing includes data cleaning, normalization, and missing value filling;

[0129] Step S33: Analyze the influence mechanism of vegetation on surge and obtain different vegetation configuration schemes; retrieve historical vegetation configuration schemes and compare them with real-time vegetation configuration schemes; set a comparison threshold, and if it is greater than the threshold, adjust the vegetation configuration model, and iterate in sequence until the final vegetation configuration scheme is obtained.

[0130] Preferably, step S31 of the present embodiment sets the initial parameters of the vegetation configuration model according to the historical vegetation type data, sets the terrain factor weight according to the elevation area parameter of the landslide surge physical model, and uses the standard data set to train the vegetation configuration model. It can be initialized based on the actual historical data, and through the adjustment of the terrain factor weight, the model can better reflect the influence of different terrains on the vegetation distribution, thereby improving the accuracy and applicability of the model. Step S32 fuses the standard data set with the historical data, and pre-processes it (including data cleaning, normalization processing and missing value filling, etc.), and then inputs the standard data into the vegetation configuration model for simulation calculation, and obtains the influence mechanism of vegetation on surge. Data fusion and pre-processing improve the quality and consistency of data, so that the model can more accurately simulate the influence of vegetation on surge. In addition, through simulation calculation, the mechanism of action of vegetation under different conditions can be deeply understood, providing a basis for subsequent optimization. Step S33 analyzes the mechanism of vegetation's influence on surges and obtains different vegetation configuration schemes; retrieves historical vegetation configuration schemes and compares them with real-time vegetation configuration schemes; sets a comparison threshold, and if it is greater than the threshold, adjusts the vegetation configuration model, and iterates in sequence until the final vegetation configuration scheme is obtained. Through comparative analysis and iterative optimization, it is ensured that the final vegetation configuration scheme can effectively cope with the impact of surges, while ensuring the dynamic adaptability and flexibility of the model. Through continuous adjustment and optimization, the reliability and effectiveness of the model in practical applications can be improved.

[0131] Furthermore, if Figure 5 As shown, the process of using the standard data set to train the vegetation configuration model in step S31 specifically includes the following steps:

[0132] Step S311: Divide the elevation area parameters of the landslide surge physical model into a target layer, a factor layer, and an indicator layer; analyze the influence of each terrain factor on different vegetation types and populations; and calculate the contribution of each terrain factor to the vegetation type;

[0133] The criterion layer includes vegetation type and population distribution characteristics;

[0134] Step S312: construct a judgment matrix to quantify the contribution of each terrain factor to vegetation distribution, and obtain the relative importance of each factor; set an influence standard, and select terrain factors whose contribution to vegetation distribution is greater than the influence standard;

[0135] Step S313: weights are assigned according to the actual contribution values ​​of each terrain factor to the distribution; and the terrain factor that is greater than the influence standard is given the highest authority.

[0136] Preferably, step S311 of this embodiment constructs a hierarchical analysis framework by dividing the elevation area parameters of the landslide surge physical model into a target layer, a factor layer, and an indicator layer. This hierarchical model helps to systematically analyze and quantify the impact of different factors on vegetation distribution, thereby providing a scientific basis for decision-making. Step S312 calculates the contribution of terrain factors to vegetation types; by constructing a judgment matrix to quantify the contribution value of each terrain factor to vegetation distribution, and setting an impact standard to select important terrain factors. This method can clarify the relative importance of each factor and assign weights according to the actual contribution value. Step S313 gives the highest authority to terrain factors greater than the impact standard, which shows that the model comprehensively considers the synergy of multiple factors to ensure that factors with a greater impact on vegetation distribution are given enough attention. This method helps to improve the prediction accuracy and reliability of the model. The construction of the judgment matrix and the weight allocation method provide a scientific basis for decision-making and help to formulate effective vegetation management and landslide prevention and control strategies. The multi-factor comprehensive analysis method can comprehensively consider various influencing factors, improve the comprehensive evaluation ability of the model, and ensure the reliability and practicality of the results.

[0137] Furthermore, if Figure 6 As shown, the process of selecting the terrain factor whose contribution to vegetation distribution is greater than the influence standard in step S312 specifically includes the following steps:

[0138] Step S3121: construct a judgment matrix, extract the terrain factors for comparison, set the influence standards of each terrain factor on vegetation distribution, construct the first judgment matrix, the second judgment matrix and the third judgment matrix; perform standardization on the first judgment matrix, the second judgment matrix and the third judgment matrix;

[0139] Among them, the first judgment matrix represents the proportion of the importance of the criterion layer to the target layer, and the second judgment matrix and the third judgment matrix represent the proportion of the importance of the factor layer to the criterion layer;

[0140] Step S3122: Calculate the consistency index and evaluate the consistency of the judgment matrix; set the consistency index threshold, and when the consistency index is greater than the consistency index, adjust the judgment matrix; extract the principal components of the factors affecting the vegetation distribution and calculate the contribution values ​​of the terrain factors;

[0141] Step S3123: Set the influence standard of each terrain factor on vegetation distribution, select the terrain factor whose contribution to vegetation distribution is greater than the influence standard, and grant it the highest authority; optimize the weight distribution of each terrain factor through multiple iterations and adjustments.

[0142] Preferably, step S3121 of this embodiment constructs a hierarchical model by clarifying the target layer, the criterion layer and the factor layer. This hierarchical model helps to systematically analyze and evaluate the impact of factors at different levels on the final goal. Construct a first judgment matrix, a second judgment matrix and a third judgment matrix, which respectively represent the importance of the criterion layer to the target layer and the importance of the factor layer to the criterion layer. Standardize these judgment matrices to ensure that the weight distribution of factors at each level is reasonable. Through the standardization and consistency test of the judgment matrix, the weight distribution of factors at each level is ensured to be reasonable, and the decision-making deviation caused by unreasonable weight distribution is avoided. Through the hierarchical model, the impact of factors at different levels on vegetation distribution is analyzed and evaluated to ensure the comprehensiveness and accuracy of the analysis. Step S3122 calculates the consistency index (CI) and the consistency ratio (CR) to evaluate the consistency of the judgment matrix. When CR is greater than 0.1, it is necessary to adjust the judgment matrix to improve the consistency. The principal component extraction of the factors affecting vegetation distribution is performed, and the contribution value of each terrain factor is calculated to determine the degree of influence of each factor on vegetation distribution. Through principal component extraction and contribution value calculation, the terrain factors that have a greater impact on vegetation distribution are screened out, unnecessary calculations and analysis are reduced, and decision-making efficiency is improved. Through principal component extraction and contribution value calculation, the terrain factors that have a greater impact on vegetation distribution are screened out, unnecessary calculations and analysis are reduced, and decision-making efficiency is improved. Step S3123 sets the impact standard of each terrain factor on vegetation distribution, selects the terrain factor whose contribution to vegetation distribution is greater than the impact standard, and grants it the highest authority. Through multiple iterations and adjustments, the weight distribution of each terrain factor is optimized. Through principal component extraction and contribution value calculation, the terrain factors that have a greater impact on vegetation distribution are screened out, unnecessary calculations and analysis are reduced, and decision-making efficiency is improved. This embodiment realizes systematic analysis and scientific decision-making of factors affecting vegetation distribution through technical features such as hierarchical model, construction and standardization of judgment matrix, consistency inspection and adjustment, principal component extraction and contribution value calculation, and weight distribution and optimization, thereby improving the accuracy and efficiency of decision-making.

[0143] Furthermore, if Figure 7 As shown, the process of obtaining the influence mechanism of vegetation on surge in step S32 specifically includes the following steps:

[0144] Step S321: Fusing the landslide surge physical model data collected in real time with the historical data, removing noise and outliers in the real-time data, normalizing the data of different scales, and filling them using the interpolation method; extracting key features from the pre-processed data, and inputting the extracted key features into the vegetation configuration model;

[0145] Step S322: The vegetation configuration model is simulated, three indicators are used to determine the nonlinearity of the wave, and the surge types are classified according to the nonlinearity;

[0146] Among them, the three indicators include relative volatility a / d0, steepness H / L and Ursell number Ur;

[0147] If the relative wavelength L / d0 of the swell is between 2 and 20, the swell is in the category of medium-depth waves; if the relative amplitude a / d0 is greater than or equal to 0.03 and the wave steepness H / L is greater than 0.006, and the Ursell number Ur is greater than or equal to 1, it is judged to be a nonlinear swell;

[0148] Step S323: Calculate the height of the surge on the slope according to the classified surge types to obtain the attenuation characteristics of the surge in the vegetation area; and predict the surge amplitude and wave height attenuation amplitude in the vegetation area according to the attenuation characteristics of the surge in the vegetation area.

[0149] Preferably, in step S321 of this embodiment, through data fusion, the dynamic changes of landslide surge can be more comprehensively analyzed, and the prediction accuracy of the model can be improved. After removing noise and outliers, the data quality is improved, which helps to extract key features more accurately. The data of different scales are normalized to make the different variables comparable. Normalization helps to eliminate the influence of dimension, making the model training more stable and efficient; extract key features from the preprocessed data. Step S322 uses the configuration model for simulation, judges the nonlinearity of the wave by three indicators, and classifies the surge type according to the nonlinearity. Through model simulation, the propagation characteristics and climbing of landslide surge can be predicted, providing a scientific basis for disaster prevention and mitigation. Through the nonlinear judgment index, the nonlinear characteristics of landslide surge can be accurately identified, providing a basis for subsequent climbing calculation. Step S323 calculates the climbing of the surge on the slope according to the classified surge type. Through the climbing calculation, the impact of the landslide surge on the slope can be evaluated, providing important data support for disaster prevention and mitigation. The attenuation of surge waves in vegetation areas is used to predict the amplitude and wave height attenuation of surge waves in vegetation areas. Through the analysis of attenuation characteristics, the attenuation of landslide surge waves in vegetation areas can be predicted, providing a scientific basis for vegetation protection projects. After the surge waves pass through the vegetation area, the maximum climbing height on the slope is mainly controlled by the wave momentum flux of the incident surge waves in front of the slope, and has no direct correlation with the vegetation coverage density. The maximum climbing height can be effectively estimated by the non-breaking solitary wave theory. When the surge waves pass through the vegetation, the amplitude attenuation coefficient (Δa v ) and the wave height attenuation coefficient (ΔH v ) and the relative amplitude (a iv / d0) and relative wave height (H iv / d0) is a power function relationship. Under the condition of fixed vegetation layout and non-flooding, the amplitude attenuation rate increases with the increase of still water depth, while the wave height attenuation rate is positively correlated with the still water depth in the entire water depth range. Under the premise of constant vegetation layout and flooding state, considering the surge dispersion and wall dissipation effect, the relative amplitude attenuation coefficient (Δa v / λ fr ) and the relative initial amplitude, and the relative wave height attenuation coefficient (ΔH v / λ fr ) and the relative initial wave height, both of which have a power function relationship that is independent of the change in still water depth, and the power function model parameters are related to the dimensionless canopy density of vegetation (c v ) is related to the power function. Under the vegetation flooding state, the response pattern between the amplitude attenuation coefficient and the initial relative amplitude is significantly affected by the vegetation coverage density, showing a three-stage evolution characteristic. The critical vegetation canopy density c v 3.14m -1 Up to 3.925m -1 and 7.825m -1Up to 12.5m -1 .

[0150] Furthermore, if Figure 8 As shown, the process of predicting the wave amplitude and wave height attenuation amplitude of the vegetated area surge according to the attenuation characteristics of the vegetated area surge in step S323 specifically includes the following steps:

[0151] Step S3231: Define the amplitude a according to the classified surge types 1cref , the amplitude of the incident surge a in and the maximum height of the surge rp , define the amplitude a 1cref , the amplitude of the incident surge a in and the maximum height of the surge rp Calculations are made to form a relationship diagram between the wave run-up height on the slope and the wave momentum flux parameter M;

[0152] Step S3232: Analyze the relationship diagram and calculate the amplitude attenuation coefficient Δa v and wave height attenuation coefficient ΔH v Define it; calculate the defined amplitude attenuation coefficient and wave height attenuation coefficient to obtain the overall attenuation change diagram of the amplitude and wave height from the time when the wave passes through the vegetation area;

[0153] Step S3233: predict the attenuation of surge wave amplitude and wave height in the vegetation area according to the overall attenuation change diagram of the wave amplitude and wave height from the time when the wave passes through the vegetation area; compare the predicted result with the measured result, set a comparison threshold, and readjust the vegetation allocation model if it is higher than the threshold.

[0154] Preferably, step S3231 of this embodiment can better understand the propagation characteristics of waves on the slope by defining and calculating parameters such as amplitude, and provide basic data for wave elimination effect analysis. Through the formed relationship diagram, the attenuation of waves in the vegetation area can be intuitively analyzed, providing a basis for evaluating the effect of vegetation on wave reduction. Step S3232 accurately predicts the attenuation of waves in the vegetation area by defining and calculating the attenuation coefficient, providing a scientific basis for designing and optimizing vegetation protection structures. Through the formed overall attenuation change diagram, the effect of vegetation on wave reduction can be verified, providing a reference for practical engineering applications. Step S3233 predicts the attenuation amplitude and wave height of surge waves in the vegetation area according to the overall attenuation change diagram from the wave passing through the vegetation area to the amplitude and wave height. Compare the predicted results with the measured results, set a comparison threshold, and readjust the vegetation allocation model if it is higher than the threshold. By comparing the measured results with the predicted results, the deficiencies in the model can be discovered in time, and adjustments and optimizations can be made to improve the accuracy and reliability of the model. By readjusting the vegetation allocation model, vegetation can be configured more reasonably to achieve the best wave-breaking effect and reduce engineering costs and maintenance difficulties.

[0155] Further, if Fig. 9 As shown, the process of forming a relationship diagram between the wave climbing height on the slope and the wave momentum flux parameter M in step S3231 specifically includes the following steps:

[0156] Step S32311: Define amplitude a 1cref is the amplitude of the leading wave before the surge reaches the vegetation area, and the amplitude of the incident surge a in is the amplitude of the incident surge leader wave, and the highest surge height a rp It is the maximum vertical height of the leader wave crest climbing along the slope;

[0157] Step S32312: Calculate the amplitude of the leader wave before the surge reaches the vegetation area, the amplitude of the incident surge leader wave, and the maximum vertical height of the wave leader wave crest climbing along the slope to obtain the maximum relative climbing height a of the surge at different vegetation coverage densities. rp / d0 with relative parameter amplitude a 1cref / D0 change diagram;

[0158] Step S32313: Based on the relative amplitude a of the incident wave in / d0 to obtain the wave climbing height on the slope and the wave momentum flux parameter M, and analyze the change diagram with the wave climbing height on the slope and the wave momentum flux parameter M, a in / d0 and M get the relationship diagram.

[0159] Preferably, step S32311 of this embodiment defines the amplitude a 1crefis the amplitude of the leading wave before the surge reaches the vegetation area, and the amplitude of the incident surge a in is the amplitude of the incident surge leader wave, and the highest surge height a rp The maximum vertical height of the wave leader crest climbing along the slope. The definition of wave parameters is clarified, providing basic data for subsequent calculations. Step S32312 calculates the amplitude of the leader wave before the surge reaches the vegetation area, the amplitude of the incident surge leader wave, and the maximum vertical height of the wave leader crest climbing along the slope to obtain the maximum relative climbing height a of surges with different vegetation coverage densities. rp / d0 with relative parameter amplitude a 1cref / d0 changes. Through numerical calculation and chart analysis, the law of the influence of vegetation coverage density on wave height is revealed, providing a scientific basis for engineering design. Step S32313 is based on the relative amplitude a of the incident wave. in / d0 to obtain the wave climbing height on the slope and the wave momentum flux parameter M, and analyze the change diagram with the wave climbing height on the slope and the wave momentum flux parameter M, a in / d0 and M are obtained. This embodiment further analyzes the relationship between wave run-up and momentum flux by introducing the wave momentum flux parameter M, providing a new perspective for understanding the wave energy transfer mechanism.

[0160] Further, if Fig.10 As shown, the process of obtaining the overall attenuation change diagram of the wave amplitude and wave height from the time when the wave passes through the vegetation area in step S3232 specifically includes the following steps:

[0161] Step S32321: Analyze the relationship diagram and calculate the amplitude attenuation coefficient Δa v To define:

[0162] Δa v =(a iv -a ov ) / L v

[0163] Attenuation coefficient of wave height ΔH v To define:

[0164] ΔH v =(H iv -H ov ) / L v

[0165] Among them, a iv is the amplitude of the wave when it reaches the beginning of the vegetation area, H iv is the wave height when the wave reaches the beginning of the vegetation area, a ov is the amplitude of the wave when it reaches the end of the vegetation area, H ov is the wave height when the wave reaches the end of the vegetation area, Lv is the length of the vegetation area;

[0166] Step S32322: According to the calculated Δa v and ΔH v The measured value of Δa v and ΔH v The relative amplitude of the vegetation edge and the relative amplitude of the edge and the wave height are in a power law relationship, and Δa is obtained v and ΔH v Measured value diagram of

[0167] Step S32323: According to Δa v and ΔH v The measured value diagram shows that when d0 is less than or greater than h v Under the condition of water depth increasing, Δa v Increase; when d0 equals h v When Δa v decreases; on the contrary, if the water is still and the water depth increases, ΔH v Increasing; get the overall attenuation change diagram of wave amplitude and wave height when the wave passes through the vegetation area.

[0168] Preferably, step S32321 of this embodiment defines the amplitude attenuation coefficient Δa v and wave height attenuation coefficient ΔH v , which can quantify the attenuation of waves when propagating in vegetation areas. It reflects the degree of energy loss during the propagation of waves in vegetation areas and is an important parameter for evaluating the effect of vegetation on wave attenuation. In step S32322, by calculating the measured values ​​and analyzing the relationship between the relative amplitude and wave height and the vegetation edge, it is found that there is a power law relationship between them. This relationship helps to understand the energy dissipation mechanism of waves when propagating in vegetation areas, so as to better predict the attenuation of waves under different conditions. In step S32323, by analyzing the changes in wave attenuation under different water depths, it is concluded that when d0 is less than or greater than h v When d0 is equal to h v When the water depth increases, the attenuation decreases. This discovery reveals the complex relationship between water depth and wave attenuation, and provides a theoretical basis for how to use water depth to regulate wave attenuation in practical applications. This embodiment quantifies the effect of vegetation on wave attenuation through precise mathematical modeling and experimental data analysis, and reveals the specific mechanism of the influence of water depth changes on wave attenuation. These technical effects not only improve the understanding of the laws of wave propagation in vegetation areas, but also provide important guidance for practical engineering applications, such as the rational use of vegetation for wave control in coastal protection, ecological restoration and other fields.

[0169] Furthermore, if Fig.11As shown, the process of predicting the surge amplitude and wave height attenuation amplitude in the vegetation area in step S3233 specifically includes the following steps:

[0170] Step S32331: obtaining the geometric form of the vegetation area, quantifying the canopy density in the geometric form of the vegetation area, and obtaining the frontal area of ​​the canopy volume; calculating according to the frontal area of ​​the canopy to obtain the plane unit area; analyzing the plane unit area to obtain the plane area occupied by the canopy elements;

[0171] The frontal area that quantifies the canopy volume is:

[0172] C v =N*b v

[0173] Among them, C v is the canopy density;

[0174] The frontal area index is:

[0175] λ f =C v d0

[0176] Among them, λ f is the frontal area index;

[0177] The plane area occupied by the plane canopy element is:

[0178] λ p =(π / 4)C v d0

[0179] Among them, λ p is the planar area occupied by the planar canopy element;

[0180] Step S32332: Analyze the wave overall attenuation change diagram, define the vegetation submergence as the ratio of the still water depth to the vegetation submergence height, define the modified frontal value index as the product of the frontal value index and the submergence, and the dimensionless canopy density is numerically the canopy density;

[0181] Step S32333: Calculate the defined data to obtain the change process of the relative amplitude attenuation coefficient with the relative initial amplitude; make a prediction based on the change process, compare the prediction result with the actual measurement, set a comparison threshold, and recalculate if it is greater than the comparison threshold.

[0182] Preferably, step S32331 of this embodiment obtains the geometric form of the vegetation area, quantifies the canopy density in the geometric form of the vegetation area, and obtains the frontal area of ​​the canopy volume; calculates according to the frontal area of ​​the canopy to obtain the plane unit area; analyzes the plane unit area to obtain the plane area occupied by the canopy elements. By quantifying the canopy density and calculating the frontal area, the canopy structure and density of the vegetation area can be more accurately evaluated, thereby providing data support for vegetation management and ecological research. This method can reduce the complexity and cost of traditional field surveys and improve the accuracy and reliability of data. Step S32332 analyzes the overall wave attenuation change diagram, defines the vegetation tree degree as the ratio of the still water depth to the vegetation basin degree, defines the modified frontal face value index as the product of the frontal face value index and the modified degree, and the dimensionless canopy density is numerically the canopy density. : By defining the vegetation tree degree and the modified frontal face value index, the impact of vegetation on wave attenuation can be more accurately evaluated, thereby optimizing vegetation management and ecological restoration strategies. This method can improve the stability and function of the vegetation ecosystem. Step S32333 calculates the defined data to obtain the change process of the relative amplitude attenuation coefficient with the relative initial amplitude; predicts according to the change process, compares the predicted result with the measured value, sets a comparison threshold, and recalculates if it is greater than the comparison threshold. By calculating and predicting the relative amplitude attenuation coefficient, the impact of vegetation on wave attenuation can be more accurately evaluated, and management strategies can be adjusted in time. This method can improve the stability and function of vegetation ecosystems while reducing prediction errors. This embodiment provides a scientific basis and technical support for vegetation management and ecological research by quantifying and calculating the canopy structure and density of vegetation areas, and evaluating the impact of vegetation on wave attenuation. These technical features and effects help to improve the stability and function of vegetation ecosystems and optimize vegetation management and ecological restoration strategies.

[0183] Further, if Fig.12 As shown, the dimensionless canopy density in step S32332 is numerically equal to the canopy density. The process specifically includes the following steps:

[0184] Step S323321: Analyze the wave attenuation variation diagram, define the vegetation submergence as the ratio of the still water depth to the vegetation submergence height, define the modified frontal value index as the product of the frontal value index and the submergence, and the dimensionless canopy density is numerically the canopy density;

[0185] The modified frontal area index is defined as the product of the frontal area index and the flooding degree:

[0186] λ fr =λ f *λ sub

[0187] Among them, λ fris the modified frontal area index, λ f is the frontal area index, λ sub is the vegetation submergence degree;

[0188] Step S323322: Analyze the amplitude attenuation characteristics. The non-submerged state of vegetation corresponds to a still water depth less than or equal to 0.16 meters, while the non-submerged state of vegetation corresponds to a still water depth greater than or equal to 0.2 meters. Analyze the wave height attenuation characteristics. The non-submerged state of vegetation corresponds to a still water depth less than or equal to 0.16 meters, while the non-submerged state of vegetation corresponds to a still water depth greater than or equal to 0.24 meters.

[0189] Step S323323: Based on the analysis of the amplitude attenuation characteristics and the wave height attenuation characteristics, the non-flooded and flooded states of the vegetation are obtained.

[0190] Preferably, step S323321 of this embodiment defines "vegetation submergence" as the ratio of still water depth to vegetation submergence height, and introduces a "modified frontal area index", which is obtained by multiplying the frontal area index by the submergence. In addition, the concept of dimensionless canopy density, i.e., the numerical representation of canopy density, is also mentioned. By introducing vegetation submergence and modified frontal area index, the effect of vegetation on wave attenuation can be described more accurately. This quantitative method helps to understand the absorption and dissipation mechanism of wave energy by vegetation under different submergence conditions, thereby improving the accuracy and applicability of the wave attenuation model. Step S323322 analyzes the amplitude attenuation characteristics, and defines the non-submerged state of vegetation as a still water depth less than or equal to 0.16 meters, while the submerged state is defined as a still water depth greater than or equal to 0.2 meters. At the same time, the wave height attenuation characteristics are analyzed, and the non-submerged state is defined as a still water depth less than or equal to 0.16 meters, while the submerged state is defined as a still water depth greater than or equal to 0.24 meters. By clearly distinguishing the non-submerged and submerged states of vegetation, the specific impact of vegetation on wave attenuation under different water depths can be better understood. This classification helps to optimize the wave attenuation model so that it can more accurately predict the dissipation effect of vegetation on wave energy under different submerged conditions. Step S323323 derives the non-submerged and submerged states of vegetation based on the analysis results of the amplitude attenuation characteristics and the wave height attenuation characteristics. By comprehensively analyzing the attenuation characteristics of the amplitude and wave height, the wave attenuation effect of vegetation under different submerged states can be more comprehensively evaluated. This helps to improve the prediction accuracy of the model and provide a scientific basis for coastal protection and flood control projects. This embodiment enhances the understanding and prediction capabilities of the impact of vegetation on wave attenuation by introducing new parameters and classification methods, and thus has important application value in coastal engineering and marine protection.

[0191] Furthermore, if Fig.13 As shown, the process of obtaining the change of the relative amplitude attenuation coefficient with the relative initial amplitude in step S32333 specifically includes the following steps:

[0192] Step S323331: Analyze the non-flooded and flooded states of vegetation, and the empirical formula for the relative amplitude attenuation coefficient under the non-flooded and flooded states of vegetation is:

[0193]

[0194]

[0195] Among them, Δa v / λ fr is the relative amplitude attenuation coefficient, a iv / d0 is the relative initial amplitude;

[0196] Step S323332: predict according to the empirical formula and obtain the result; set the comparison threshold, compare the predicted value with the measured value, if it is greater than the threshold, readjust the threshold; if it is less than the threshold, analyze the comparison value;

[0197] Step S323333: According to the analysis results, the variation trajectory of the relative wave height attenuation coefficient under the non-flooded and flooded states of vegetation with the relative initial wave height is obtained; the empirical formula for calculating the relative wave height attenuation coefficient under the non-flooded and flooded states of vegetation is:

[0198]

[0199]

[0200] Where ΔH v / λ fr is the relative wave height attenuation coefficient of vegetation in non-flooded and flooded states, H iv / d0 is the initial wave height.

[0201] Preferably, step S323331 of this embodiment describes the relative amplitude attenuation coefficient of vegetation in non-flooded and flooded states by establishing an empirical formula. By establishing the empirical formula, the changes of vegetation under different hydrological conditions can be more accurately predicted and analyzed, providing a scientific basis for the study of the coupling mechanism of hydrological processes and ecological processes. Step S323332 compares the predicted value with the measured value by setting a comparison threshold, and adjusts the threshold according to the comparison result. The trigger threshold is adjusted using the historical information of the dynamic variable, which further saves limited communication resources. By dynamically adjusting the threshold, the accuracy and reliability of the prediction can be improved, and the consistency of the prediction results with the actual observation data can be ensured, thereby improving the monitoring accuracy of the hydrological process and the ecological process. Step S323333 obtains its change trajectory by analyzing the relative wave height attenuation coefficient of vegetation under different initial wave heights, and establishes the corresponding empirical formula. This method uses the initial wave height as a variable, so that the changes of vegetation under different initial wave heights can be quantified. By establishing the empirical formula, the changes of vegetation under different initial wave heights can be more accurately predicted and analyzed, providing a scientific basis for the study of the coupling mechanism of hydrological processes and ecological processes. This embodiment quantifies and predicts the changes in vegetation under different hydrological conditions by establishing empirical formulas and dynamically adjusting thresholds. The technical effect is to improve the monitoring accuracy and reliability of hydrological and ecological processes.

[0202] Further, if Fig.14 As shown, the process of obtaining the final vegetation configuration scheme in step S33 specifically includes the following steps:

[0203] Step S331: analyzing the influence mechanism of vegetation on surge waves, and obtaining a real-time vegetation configuration plan according to the analysis results;

[0204] Step S332: retrieve the data of the historical vegetation configuration plan and compare it with the real-time vegetation configuration plan to obtain the parameters that need to be changed;

[0205] Step S333: setting a comparison threshold. If the comparison result is greater than the comparison threshold, readjust the vegetation configuration model parameters and re-simulate.

[0206] Preferably, step S331 of this embodiment involves analyzing the mechanism of how vegetation affects the propagation of surge waves, and formulating a real-time vegetation configuration scheme based on the results of this analysis. This usually requires the use of numerical simulation and experimental data to evaluate the effect of different vegetation configurations on wave attenuation. By analyzing the impact of vegetation on surge waves, the vegetation configuration can be optimized to achieve the best wave elimination effect, thereby reducing the erosion and damage of surge waves to the coastline. Step S332 involves comparing the historical vegetation configuration scheme data with the current real-time scheme. This may include comparing the density and type of vegetation at different time points and their impact on wave propagation. Through comparative analysis, it is possible to identify which vegetation configuration schemes perform better in practical applications, thereby providing a reference for future vegetation configurations. Step S333 sets a comparison threshold for determining whether the difference between historical data and real-time data exceeds an acceptable range. If exceeded, the model parameters need to be adjusted and the simulation is repeated. By setting a threshold and making dynamic adjustments, it can be ensured that the vegetation configuration scheme is always in the best state to cope with changing environmental conditions and surge conditions. This embodiment uses numerical simulation and comparative analysis to optimize the vegetation configuration scheme, and its technical effect is reflected in improving wave dissipation efficiency, reducing coastal erosion, and enhancing the stability of the overall ecosystem.

[0207] Further, if Fig.15 As shown, this embodiment further provides a landslide surge attenuation system based on biological vegetation configuration. In this embodiment, the landslide surge attenuation system based on biological vegetation configuration is applied to the landslide surge attenuation method based on biological vegetation configuration in the above-mentioned embodiment. The landslide surge attenuation system based on biological vegetation configuration includes a collection module 1, a processing module 2, and a vegetation configuration module 3 which are electrically connected in sequence;

[0208] Among them, the acquisition module 1 is used to construct a landslide surge physical model, collect the basic characteristics of the landslide area, and determine the vegetation configuration range based on the basic characteristics; calculate the flood position and the normal water level based on the vegetation configuration range to determine the elevation area of ​​the vegetation configuration; based on the main function of the landslide area, determine the vegetation type of each elevation area of ​​the vegetation configuration; among them, the basic characteristics of the landslide area include soil type, slope, water flow discharge pattern, etc.; the processing module 2 is used to pre-process the data obtained from the landslide surge physical model, clean the obtained data, normalize the data, and fill in missing values ​​to form a data set; perform data standardization on the data set, calculate the mean and standard deviation of the data set to obtain a standard data set; the vegetation configuration module 3 is used to construct a vegetation configuration model, input the standard data set into the vegetation configuration model, simulate the landslide surge, and obtain the influence mechanism of vegetation on surge; analyze the influence mechanism of vegetation on surge, and obtain a variety of vegetation configuration schemes.

[0209] Preferably, the acquisition module 1 of this embodiment simulates the process of landslide surge through a physical model, and collects the basic characteristics of the landslide area, such as soil type, slope, water discharge mode, etc. The range of vegetation configuration is determined according to the basic characteristics, the flood position and the normal water level are calculated, and the elevation area of ​​the vegetation configuration is determined. Based on the main function of the landslide area, the vegetation type of each elevation area is determined. Through physical model simulation, the morphology and propagation process of landslide surge can be predicted more accurately, and the scientificity and accuracy of the simulation can be improved. According to the basic characteristics and main functions, the vegetation configuration is optimized, the damage of landslide surge to vegetation is reduced, and the adaptability and stability of vegetation are improved. The processing module 2 cleans, normalizes and fills the missing values ​​of the data obtained by the landslide surge physical model to form a data set. The data set is standardized, and the mean and standard deviation of the data set are calculated to obtain a standard data set. Through data cleaning and normalization, the quality and consistency of the data are improved, and the error is reduced; the standardized data set can improve the stability and prediction accuracy of the model and reduce the fluctuation in the model training process. The vegetation configuration module 3 inputs the standard data set into the vegetation configuration model, simulates the landslide surge, and obtains the mechanism of the influence of vegetation on the surge. The influence mechanism of vegetation on the surge is analyzed to obtain a variety of vegetation configuration schemes. Through simulation and analysis, the vegetation configuration strategy is optimized, the protective effect of vegetation on landslide surge is improved, and the disaster loss is reduced. By analyzing the influence mechanism of vegetation on surge, the adaptability and stability of vegetation are improved, and the damage to vegetation caused by landslide surge is reduced. This embodiment realizes the accurate simulation of landslide surge and the optimization of vegetation configuration through physical model simulation, data preprocessing, standardization processing and vegetation configuration model construction. These technical features and technical effects not only improve the scientificity and accuracy of landslide surge simulation, but also optimize the vegetation configuration strategy, improve the adaptability and stability of vegetation, and reduce the damage to vegetation and environment caused by landslide surge.

[0210] This embodiment provides an application example of a landslide surge attenuation method based on ecological vegetation configuration, as shown below:

[0211] The experimental model layout and on-site photos of vegetation mitigation of landslide surge are shown in Fig.16 and Fig.17 The wave tank is a flat-bottomed straight rectangular tank made of plexiglass, 4.75m long, 0.4m wide (b), and 0.44m deep. A plexiglass slide with an inclination angle α of 45° is provided at the upstream end of the tank, and a guide rail matching the groove at the bottom of the landslide block is attached to its upper surface to ensure that the center of mass of the block is always located on the center line of the tank during the sliding process. The landslide block is a plexiglass box filled with concrete, with a total mass of m s is 10.026 kg; the leading edge inclination angle θ of the block is 45°, and the length l s is 0.2m, thickness s is 0.1m, width bs is 0.396m, and the corresponding blocking ratio is B=b s / b is 0.99, so the wave-generating process of the landslide entering the water under experimental conditions can be regarded as a two-dimensional problem. A slope with an inclination angle β of 45° is set at the downstream end of the flume to study the climbing height of waves after passing through the vegetated water area.

[0212] In the experiment, holes were drilled in the bottom plate of the water tank and the height of the insertion was 0.2m (i.e. the vegetation height h v =0.2m) of rigid polyvinyl chloride (PVC) rod to simulate vegetation. Length of simulated vegetation area L v The diameter of the tree trunk is 0.96m and the width is 0.32m. The trunks are evenly distributed on the plane. v ), which are 0.01m, 0.02m and 0.025m respectively. Four tree stem densities (N) were set by inserting the corresponding number of rods into the pre-drilled holes in the bottom plate of the water tank, namely 78, 157, 313 and 625 stems / m 2 These configurations form four different vegetation layout patterns, such as Fig.18 They are marked as a, b, c and d respectively. In addition, this embodiment also sets a special vegetation layout mode S0, which does not contain any simulated stems in the flume. The S0 mode was used to conduct experiments to study the evolution characteristics of landslide-generated waves. The coordinate origin (x; z) is defined at the intersection of the flume floor and the chute.

[0213] In this experiment, 13 experimental series were conducted, each of which was named by combining the tree stem diameter and the vegetation layout pattern. In each experimental series, a total of 9 different still water depth (d0) levels were tested, ranging from 0.12m to 0.36m. For each still water depth level, the landslide release height (h c ), which is defined as the vertical distance between the centroid of the landslide and the still water surface, and its range is set to 0.05m to 0.50m. The main purpose of selecting these parameters is to generate wave amplitudes and wave heights with a wide bandwidth and to evaluate the effects of still water depth and vegetation submergence on wave attenuation.

[0214] The sliding process of the block is recorded by a high-definition camera to calculate the entry speed of the block into the water. The shooting frequency is set to 240fps. A total of 9 capacitive wave height meters (CWG1-9) are arranged along the x-axis direction to record the generation, evolution and climbing process of waves. The accuracy is ±0.1mm and the acquisition frequency is set to 500Hz. Among them, CWG1 and CWG2 are installed at x=0.6m and x=1.2m respectively to obtain the characteristic parameters of the primary surge; CWG3-7 are arranged at equal intervals in the vegetated water area to measure the changes of surge in the vegetated area; CWG8 and CWG9 respectively obtain the wave parameters of the incident surge and its climbing process on the slope.

[0215] This embodiment focuses on analyzing the influence of vegetation on the leading wave of landslide surge. The amplitude a of the leading wave of the surge is defined as the water level difference between the first wave crest and the still water surface, while the height H of the leading wave refers to the water level difference between the first wave crest and the first wave trough. The period T and wavelength L of the leading wave refer to the time interval and horizontal distance between the starting point of the wave and the next upper zero point, respectively. According to the experimental results of this embodiment, the relative wavelength L / d0 of the surge is between 7.66 and 16.17, which indicates that the surge is in the category of medium water depth waves. The following three indicators can usually be used to judge the nonlinearity of the wave, namely the relative amplitude a / d0, the wave steepness H / L and the Ursell number Ur. The experimental results show. The relative amplitude and steepness of the surge fall within the range of 0.07≤a / d0≤0.50 and 0.012≤H / L≤0.073, respectively. These values ​​far exceed the applicable conditions of linear waves, i.e. a / d0<0.03, H / L≤0.006. In the category of medium water waves, the Ursell number is the most core parameter for judging nonlinear waves. The Ursell number is defined as Ur=HL / d0 3 After calculation, the Ursell value of the surge falls within the range of 14.91≤Ur≤68.18, which obviously exceeds the condition for the linear wave theory to be applicable, that is, Ur<1. Therefore, the surge in this embodiment has significant nonlinear characteristics.

[0216] In order to facilitate subsequent analysis, the reference amplitude a is defined 1cref is the amplitude of the leading wave before the surge reaches the vegetation area (approximated by the measured amplitude at CWG2), and the amplitude of the incident surge a in is the amplitude of the incident surge leader wave (approximated by the measured amplitude at CWG8), and the maximum surge height a rp It is the maximum vertical height of the surge leader wave crest climbing along the slope. v =0.02m, the maximum relative height a of the surge under different vegetation coverage densities rp / d0 with relative reference amplitude a 1cref / d0 changes can be seen in Fig.19 It can be clearly seen from the figure that the attenuation effect of vegetation on wave run-up is closely related to the relative amplitude of the surge before it reaches the vegetation area. It is worth noting that under non-flooded conditions, the effect of vegetation on reducing the maximum wave run-up is significantly greater than that under flooded conditions.

[0217] There is a significant correlation between the wave run-up height on the slope and the wave momentum flux parameter M, which depends only on the relative amplitude a of the incident wave. in / d0. Fig. 20 Shows the entire experimental data set, a in / d0 and M. For comparison purposes, the empirical relationship for unbroken solitary waves is also depicted in the figure. The results show that the maximum wave climbing height on the slope is only related to the wave momentum flux parameter and has nothing to do with the vegetation layout pattern. This finding shows that the reduction in the wave climbing height caused by vegetation mainly depends on the attenuation rate of the wave amplitude or wave height when the wave passes through the vegetated water area. In addition, the solitary wave theory can be used to estimate the wave climbing height caused by the landslide. The results of this embodiment also confirm this conclusion, which is also applicable even in the presence of vegetated water areas.

[0218] In order to quantitatively analyze the overall attenuation of wave amplitude and wave height when the wave passes through the vegetation area, this embodiment refers to the existing research results and calculates the amplitude attenuation coefficient Δa. v and wave height attenuation coefficient ΔH v Define as follows:

[0219] Δa v =(a iv -a ov ) / L v

[0220] ΔH v =(H iv -H ov ) / L v

[0221] In the formula, a iv and H iv are the amplitude and height of the wave when it reaches the beginning of the vegetation area; a ov and H ov They represent the amplitude and height of the wave when it reaches the end of the vegetation area; L v is the length of the vegetation zone. For series BV10S4, Δa v and ΔH v Typical measured values ​​are as follows: Fig.21 (a) and Fig.21 As shown in (b). When the still water depth remains unchanged, Δa v and ΔH v They all show a power-law relationship with the relative amplitude and wave height at the vegetation edge. Fig.21 (b) shows that when d0 is less than or greater than h v Under the condition of v It is worth noting that when d0=h v When Δa v On the contrary, Fig.21 (b) shows that, regardless of the still water depth, an increase in water depth always leads to a ΔH v Increase.

[0222] The geometry of a vegetation area is determined by the size of its components (stems, leaves, branches) and their spacing. Nepf emphasized that canopy density (C v ) is a key structural feature that can be quantified as the frontal area of ​​the canopy volume, i.e., C v =N*b v When studying the hydrodynamic behavior of vegetated waters, the frontal area index (λ f ) and the plane area index (λ p ) is a crucial parameter. f The canopy frontal area per unit area of ​​the bed surface (λ f =C v d0), and λ p reflects the plane area occupied by the canopy element (for a cylinder, λ p =(π / 4)C v b v ). In the experimental setting of this embodiment, C v The range is 0.78 to 15.63m -1 ,λ f The range of λ is 0.09 to 5.63, p The range is 0.01 to 0.31.

[0223] In order to systematically analyze the changes in wave attenuation, this embodiment defines the following dimensionless parameters: vegetation submergence (λ sub ) is defined as the ratio of the still water depth to the height of the vegetation submerged. fr ) is defined as the headway area index (λ f ) multiplied by the flooding degree, i.e., λ fr =λ f *λ sub Dimensionless canopy density (c v ) is numerically equivalent to the canopy density (C v It is worth noting that when the still water depth d0 is 0.18 m, under certain experimental conditions, the trough of the leading wave will be higher than the vegetation height h v When the still water depth d0 is 0.20 m, the trough of the leading wave will be lower than the vegetation height h v Therefore, in the subsequent analysis of the amplitude attenuation characteristics, the non-submerged state of vegetation corresponds to a still water depth d0 less than or equal to 0.16 m, while the submerged state of vegetation corresponds to a still water depth d0 greater than or equal to 0.20 m. Similarly, in the analysis of the wave height attenuation characteristics, the non-submerged state of vegetation corresponds to a still water depth d0 less than or equal to 0.16 m, while the prepared submerged state corresponds to a still water depth d0 greater than or equal to 0.24 m.

[0224] Fig. 22 (a) and Fig. 22(b) shows the relative amplitude attenuation coefficient (△a v / λ fr ) with the relative initial amplitude (a iv / d0) changes. The analysis shows that in the vegetation layout type (b v , N) and submerged state (λ sub =1 or λ sub >1) Under constant conditions, △a v / λ fr with a iv There is a clear power function relationship between the power function model parameters and the dimensionless canopy density of vegetation (c v ), this finding lays the foundation for building an amplitude attenuation prediction model suitable for different vegetation coverage and wave conditions. Based on the above analysis, the empirical formula for the relative amplitude attenuation coefficient under non-flooded and flooded vegetation can be derived:

[0225]

[0226] Fig.23 It shows that the model prediction values ​​are highly consistent with the measured values, and the error range is basically kept within 20%.

[0227] Compared with the amplitude attenuation, Fig.24 (a) and (b) of 24 also reveal the relative wave height attenuation coefficient (△H) under non-flooded and flooded vegetation conditions. v / λ fr ) with the relative initial wave height (H iv / d0) changes. v , N) and submerged state (λ sub =1 or λ sub >1)Under the premise that △H v / λ fr With H iv / d0 also presents a power function relationship, and this relationship is also not affected by the change of static water depth. In-depth research found that the parameters in the power function model are related to the dimensionless vegetation canopy density (c v ) have a significant power function correlation, and this finding provides a scientific basis for predicting the attenuation of wave height under vegetation coverage. Based on the above research results, this embodiment constructs an empirical formula for the relative wave height attenuation coefficient under non-flooded and flooded vegetation states:

[0228]

[0229] The relative wave height attenuation coefficient calculated by the empirical model is compared with the measured value. Fig.25 As can be seen from the figure, the predicted value is in good agreement with the measured value, and the error range is basically reduced to within 20%, which verifies the reliability of the established model.

[0230] This example systematically analyzes the experimental data under vegetation inundation conditions, and clearly observes that under different vegetation coverage densities and static water depths, the amplitude attenuation coefficient and the initial relative amplitude show a positive correlation. However, the pattern of this correlation changing with static water depth is significantly regulated by vegetation coverage density. Further detailed analysis reveals that as the vegetation coverage density gradually increases, the relationship between the amplitude attenuation coefficient and the initial relative amplitude has evolved through three clear stages:

[0231] (1) Low vegetation coverage density stage: In this stage, as the flooding depth increases, the rate of change of the amplitude attenuation coefficient to the initial relative amplitude (i.e., the amplitude attenuation speed) increases significantly. This shows that in the case of sparse vegetation, water depth is an important factor in regulating the amplitude attenuation speed.

[0232] (2) Medium vegetation coverage density stage: When the vegetation coverage density reaches a certain critical value, the amplitude attenuation rate under different flooding depths tends to be consistent, indicating that the influence of water depth is balanced by the density of vegetation. However, under the same initial relative amplitude, a deeper flooding depth still leads to a higher amplitude attenuation coefficient.

[0233] (3) High vegetation coverage density stage: As the vegetation coverage density increases further, the amplitude attenuation coefficient is almost completely dependent on the initial relative amplitude, while the change in still water depth has little effect on it. This shows that under high-density vegetation coverage, the shading and dissipation effects of vegetation become the key factors that dominate the amplitude attenuation.

[0234] Based on the results of this experiment, the critical vegetation canopy density C of the above three stages of evolution v The range can be defined as 3.14m -1 Up to 3.925m -1 and 7.825m -1 Up to 12.5m -1 This discovery provides an important basis for a deeper understanding of the impact of vegetation cover on the attenuation mechanism of water flow fluctuations.

[0235] like Fig.26 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.

[0236] The memory 42 stores program instructions for implementing the landslide surge attenuation method based on biological vegetation configuration according to any of the above embodiments.

[0237] The processor 41 is used to execute program instructions stored in the memory 42 to perform landslide surge attenuation based on biological vegetation configuration.

[0238] 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 devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0239] Further, Fig. 27 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 5 of 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 several instructions for enabling 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 methods 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, a server, a mobile phone, and a tablet.

[0240] 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 only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0241] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A landslide surge attenuation method based on biological vegetation configuration, characterized in that: The landslide surge attenuation method configured with biological vegetation includes: Construct a landslide surge physical model, collect the basic characteristics of the landslide area, and determine the vegetation configuration range based on the basic characteristics; calculate the flood position and the normal water level based on the vegetation configuration range to determine the elevation area of ​​the vegetation configuration; determine the vegetation types in each elevation area of ​​the vegetation configuration based on the main function of the landslide area; Among them, the basic characteristics of the landslide area include soil type, slope, and water discharge pattern; Preprocess the data obtained from the landslide surge physical model, clean the obtained data, normalize it, and fill in missing values ​​to form a data set; standardize the data set, calculate the mean value and standard deviation of the data set to obtain a standard data set; A vegetation configuration model was constructed, and the standard data set was input into the vegetation configuration model to simulate landslide surges and derive the mechanism of the influence of vegetation on surges. The mechanism of the influence of vegetation on surges was analyzed to derive a variety of vegetation configuration schemes.

2. The landslide surge attenuation method using biological vegetation configuration according to claim 1 is characterized in that: The process of determining the type of vegetation for each elevation zone of the vegetation configuration includes: Construct a physical model of landslide surge, collect soil types in the landslide area of ​​the physical landslide surge model, calculate the slope distribution of the landslide area, analyze the distribution of rivers, gullies and drainage equipment, and determine the water discharge pattern; The acquired data is normalized and preprocessed, and the vegetation coverage of the landslide area is evaluated according to the normalized preprocessing results; the vegetation types in different areas are determined according to the vegetation coverage and soil type and the growth habits of the vegetation; The flood location in the landslide area is predicted through historical flood data and terrain models. The normal water level in the landslide area is determined based on historical hydrological data and terrain characteristics. The landslide area is divided into different elevation areas based on the flood and normal water levels. Each elevation area corresponds to different vegetation configuration requirements.

3. The landslide surge attenuation method using biological vegetation configuration according to claim 1 is characterized in that: The process of obtaining a standard data set includes: Fill missing values, outliers and duplicate values ​​in the data obtained from the landslide surge physical model; delete outliers and duplicate values; and clean the data; Scale the cleaned data to a specific range; for numerical data, fill it with the average value of the class to which the missing value belongs; for non-numerical data, fill it with the mode of the class to which the missing value belongs; the filled data form a data set; The data set is normalized, the average value of the data set is calculated, and the data set is normalized according to the calculated average value and standard deviation. The standardized data set satisfies the mean value of 0 and the standard deviation of 1.

4. The landslide surge attenuation method using biological vegetation configuration according to claim 1 is characterized in that: The process of arriving at multiple vegetation configurations includes: According to the historical vegetation type data, the initial parameters of the vegetation configuration model are set; according to the elevation area parameters of the landslide surge physical model, the terrain factor weights of the vegetation configuration model are set; the vegetation configuration model is trained using a standard data set; The standard data set is integrated with the historical data and preprocessed; the standard data is input into the vegetation configuration model for simulation calculation to derive the mechanism of the influence of vegetation on surge; The mechanism of vegetation's impact on surge is analyzed to derive different vegetation configuration schemes; historical vegetation configuration schemes are retrieved and compared with real-time vegetation configuration schemes; a comparison threshold is set, and if it is greater than the threshold, the vegetation configuration model is adjusted and iterated in sequence until the final vegetation configuration scheme is obtained.

5. The landslide surge attenuation method using biological vegetation configuration according to claim 4 is characterized in that: The process of training a vegetation configuration model using a standard dataset includes: The elevation area parameters of the landslide surge physical model are divided into target layer, factor layer and index layer; the influence of each terrain factor on different vegetation types and populations is analyzed; and the contribution of each terrain factor to the vegetation type is calculated; The criterion layer includes vegetation type and population distribution characteristics; Construct a judgment matrix to quantify the contribution of each terrain factor to vegetation distribution and obtain the relative importance of each factor; set an impact standard and select terrain factors whose contribution to vegetation distribution is greater than the impact standard; Weights are assigned according to the actual contribution of each terrain factor to the distribution; terrain factors that are greater than the impact standard are given the highest authority.

6. The method for attenuating landslide surges using biological vegetation configuration according to claim 5, characterized in that: The process of selecting terrain factors whose contribution to vegetation distribution is greater than the influence standard includes: Construct a judgment matrix, extract comparative terrain factors, set the influence standard of each terrain factor on vegetation distribution, construct the first judgment matrix, the second judgment matrix and the third judgment matrix; standardize the first judgment matrix, the second judgment matrix and the third judgment matrix; Among them, the first judgment matrix represents the proportion of the importance of the criterion layer to the target layer, and the second judgment matrix and the third judgment matrix represent the proportion of the importance of the factor layer to the criterion layer; Calculate the consistency index and evaluate the consistency of the judgment matrix; set the consistency index threshold, and when the consistency index is greater than the consistency index, adjust the judgment matrix; extract the principal components of the factors affecting vegetation distribution and calculate the contribution values ​​of various terrain factors; Set the impact standard of each terrain factor on vegetation distribution, select terrain factors whose contribution to vegetation distribution is greater than the impact standard, and grant them the highest authority; optimize the weight distribution of each terrain factor through multiple iterations and adjustments.

7. The landslide surge attenuation method using biological vegetation configuration according to claim 4 is characterized in that: The process of deriving the mechanism of vegetation influence on surge includes: The real-time collected landslide surge physical model data is integrated with historical data to remove noise and outliers in the real-time data, normalize data of different scales, and fill them with interpolation; extract key features from the pre-processed data and input the extracted key features into the vegetation configuration model; The vegetation configuration model is simulated, three indicators are used to determine the nonlinearity of the waves, and the surge types are divided according to the nonlinearity; Among them, the three indicators include relative volatility a / d0, steepness H / L and Ursell number Ur; If the relative wavelength L / d0 of the swell is between 2 and 20, the swell is in the category of medium-depth waves; if the relative amplitude a / d0 is greater than or equal to 0.03 and the wave steepness H / L is greater than 0.006, and the Ursell number Ur is greater than or equal to 1, it is judged to be a nonlinear swell; The height of the surge on the slope is calculated according to the classified surge types, and the attenuation characteristics of the surge in the vegetation area are obtained; based on the attenuation characteristics of the surge in the vegetation area, the surge amplitude and wave height attenuation amplitude in the vegetation area are predicted.

8. The landslide surge attenuation method using biological vegetation configuration according to claim 7 is characterized in that: The process of predicting the wave amplitude and wave height attenuation amplitude of the vegetated area according to the attenuation characteristics of the vegetated area swell includes: According to the classification of swell types, the amplitude a is defined 1cref , the amplitude of the incident surge a in and the maximum height of the surge rp , define the amplitude a 1cref , the amplitude of the incident surge a in and the maximum height of the surge rp Calculations are made to form a relationship diagram between the wave run-up height on the slope and the wave momentum flux parameter M; Analyze the relationship diagram and calculate the amplitude attenuation coefficient Δa v and wave height attenuation coefficient ΔH v Define it; calculate the defined amplitude attenuation coefficient and wave height attenuation coefficient to obtain the overall attenuation change diagram of the amplitude and wave height from the time when the wave passes through the vegetation area; The attenuation of the surge wave amplitude and wave height in the vegetation area is predicted based on the overall attenuation change diagram of the wave amplitude and wave height from the time when the wave passes through the vegetation area; the predicted results are compared with the measured results, and a comparison threshold is set. If the comparison threshold is higher than the threshold, the vegetation allocation model is readjusted; Define the amplitude a 1cref is the amplitude of the leading wave before the surge reaches the vegetation area, and the amplitude of the incident surge a in is the amplitude of the incident surge leader wave, and the highest surge height a rp It is the maximum vertical height of the leader wave crest climbing along the slope; The amplitude of the leader wave before the surge reaches the vegetation area, the amplitude of the incident surge leader wave, and the maximum vertical height of the wave leader wave crest climbing along the slope are calculated to obtain the maximum relative climbing height a of the surge at different vegetation coverage densities. rp / d0 with relative parameter amplitude a 1cref / D0 change diagram; According to the relative amplitude a of the incident wave in / d0 to obtain the wave climbing height on the slope and the wave momentum flux parameter M, and analyze the change diagram with the wave climbing height on the slope and the wave momentum flux parameter M, a in / d0 and M get the relationship diagram; Analyze the relationship diagram and calculate the amplitude attenuation coefficient Δa v To define: Δa v =(a iv -a ov ) / L v Attenuation coefficient of wave height ΔH v To define: ΔH v =(H iv -H ov ) / L v Among them, a iv is the amplitude of the wave when it reaches the beginning of the vegetation area, H iv is the wave height when the wave reaches the beginning of the vegetation area, a ov is the amplitude of the wave when it reaches the end of the vegetation area, H ov is the wave height when the wave reaches the end of the vegetation area, L v is the length of the vegetation area; According to the calculation of Δa v and ΔH v The measured value of Δa v and ΔH v The relative amplitude of the vegetation edge and the relative amplitude of the edge and the wave height are in a power law relationship, and Δa is obtained v and ΔH v Measured value diagram of According to Δa v and ΔH v The measured value diagram shows that when d0 is less than or greater than h v Under the condition of water depth increasing, Δa v Increase; when d0 equals h v When Δa v decreases; on the contrary, if the still water depth increases, ΔH v Increasing; get the overall attenuation change diagram of wave amplitude and wave height when the wave passes through the vegetation area.

9. The landslide surge attenuation method using biological vegetation configuration according to claim 8, characterized in that: The process of predicting the surge amplitude and wave height attenuation amplitude in vegetation areas includes: The geometric form of the vegetation area is obtained, and the canopy density in the geometric form of the vegetation area is quantified to obtain the frontal area of ​​the canopy volume; the plane unit area is obtained by calculating according to the frontal area of ​​the canopy; the plane unit area is analyzed to obtain the plane area occupied by the canopy elements; The frontal area that quantifies the canopy volume is: C v =N*b v Among them, C v is the canopy density; The frontal area index is: λ f =C v d0 Among them, λ f is the frontal area index; The plane area occupied by the plane canopy element is: l p =(π / 4)C v d0 Among them, λ p is the planar area occupied by the planar canopy element; The overall wave attenuation change diagram was analyzed, and the vegetation submergence was defined as the ratio of the still water depth to the vegetation submergence height. The modified frontal value index was defined as the product of the frontal value index and the submergence. The dimensionless canopy density c v In numerical terms, it is the canopy density; Calculate the defined data to obtain the change process of the relative amplitude attenuation coefficient with the relative initial amplitude; make predictions based on the change process, compare the prediction results with the actual measurements, set a comparison threshold, and recalculate if it is greater than the comparison threshold; The wave attenuation variation diagram was analyzed, and the vegetation submergence was defined as the ratio of the still water depth to the vegetation submergence height, the modified headward face value index was defined as the product of the headward face value index and the submergence, and the dimensionless canopy density was numerically defined as the canopy density; The modified head-on value index is defined as the product of the head-on value index and the flooding degree: l fr =λ f *l sub Among them, λ fr is the modified frontal area index, λ f is the frontal area index, λ sub is the vegetation submergence degree; The amplitude attenuation characteristics are analyzed. The non-flooded state of vegetation corresponds to a still water depth of less than or equal to 0.16 meters, while the non-flooded state of vegetation corresponds to a still water depth of greater than or equal to 0.2 meters. The wave height attenuation characteristics are analyzed. The non-flooded state of vegetation corresponds to a still water depth of less than or equal to 0.16 meters, while the non-flooded state of vegetation corresponds to a still water depth of greater than or equal to 0.24 meters. Based on the analysis of the wave amplitude attenuation characteristics and wave height attenuation characteristics, the non-flooded and flooded states of vegetation are obtained; Analyze the non-flooded and flooded states of vegetation, and the empirical formula for the relative amplitude attenuation coefficient under the non-flooded and flooded states of vegetation is: Among them, Δa v / λ fr is the relative amplitude attenuation coefficient, a iv / d0 is the relative initial amplitude; Make predictions based on empirical formulas and get results; set a comparison threshold, compare the predicted value with the measured value, and if it is greater than the threshold, readjust the threshold; if it is less than the threshold, analyze the comparison value; According to the analysis results, the variation trajectory of the relative wave height attenuation coefficient under the non-flooded and flooded states of vegetation with the relative initial wave height is obtained; the empirical formula for calculating the relative wave height attenuation coefficient under the non-flooded and flooded states of vegetation is: Where ΔH v / λ fr is the relative wave height attenuation coefficient of vegetation in non-flooded and flooded states, H iv / d0 is the initial wave height.

10. A landslide surge attenuation system configured with biological vegetation, which is applied to the landslide surge attenuation method configured with biological vegetation as claimed in any one of claims 1 to 9, characterized in that: The landslide surge attenuation system configured with biological vegetation includes: Acquisition module: construct a landslide surge physical model, collect the basic characteristics of the landslide area, and determine the vegetation configuration range based on the basic characteristics; calculate the flood position and the normal water level based on the vegetation configuration range to determine the elevation area of ​​the vegetation configuration; based on the main function of the landslide area, determine the vegetation type of each elevation area of ​​the vegetation configuration; Among them, the basic characteristics of the landslide area include soil type, slope, and water discharge pattern; Processing module: pre-process the data obtained from the landslide surge physical model, perform data cleaning, normalization and missing value filling on the obtained data to form a data set; perform data standardization on the data set, calculate the mean value and standard deviation of the data set to obtain a standard data set; Vegetation configuration module: Construct a vegetation configuration model, input the standard data set into the vegetation configuration model, simulate the landslide surge, and derive the mechanism of vegetation's influence on surge; analyze the mechanism of vegetation's influence on surge and derive a variety of vegetation configuration schemes.

Images