Landslide surge attenuation method and system based on ecological vegetation configuration

Abstract

The application relates to the technical field of ecological vegetation information configuration, and discloses a landslide surge wave attenuation method and system based on biological vegetation configuration. The method comprises the following steps: constructing a landslide surge wave physical model, determining a vegetation configuration range according to basic characteristics; calculating a flood position and a normal water level to determine an elevation area of vegetation configuration; determining vegetation types of each elevation area of vegetation configuration; preprocessing data obtained from the landslide surge wave physical model to form a data set; calculating the average value and the 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, simulating the landslide surge wave, and obtaining a vegetation influence mechanism on the surge wave; and analyzing the influence mechanism of the vegetation on the surge wave to obtain a plurality of vegetation configuration schemes. The system comprises an acquisition module, a processing module and a vegetation configuration module. The application provides high water level protection and can effectively prevent rainwater erosion and shallow slope stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological vegetation information configuration, and particularly relates to a landslide surge attenuation method and system based on ecological vegetation configuration. BACKGROUND

[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 fluctuation of the reservoir water level often becomes a potential factor inducing the deformation of the reservoir slope. In extreme cases, it may even trigger a landslide geological disaster, accompanied by a secondary surge disaster. Such surge activities not only directly threaten the navigation safety and berthing stability of ships in the reservoir area, but also pose a serious challenge to the continuous and stable operation of the reservoir infrastructure. What is particularly severe is that once the surge reaches the land, the life safety of the coastal residents will face incalculable risks. Compared with the direct damage of landslide disasters, the harm and loss caused by the secondary surge disaster triggered by the landslide are often more far-reaching and incalculable. Therefore, there is an urgent need to develop effective strategies to reduce the negative impact of such wave disasters.

[0003] Coastal wetland ecosystems, especially salt marshes and mangrove areas, not only attract attention due to their unique ecological value, but also are well known for their excellent wave buffering capacity. The vegetation communities in these wetlands play a crucial role in dissipating wave energy, effectively reducing the pressure on coastal defense systems. Incorporating such natural habitats into coastal protection strategies has become a hot topic of continuous discussion in the academic and practical fields. Against this background, numerous studies have been devoted to exploring the influence mechanism of vegetation on wave attenuation through diverse research methods. Theoretical research, field experiments, and numerical simulation complement each other, jointly promoting the continuous deepening of the field's understanding. Research has shown that factors such as the increase in vegetation density, the decrease in submergence depth, and the increase in plant stem rigidity usually promote more efficient dissipation of wave energy. These valuable insights from coastal environments provide new dimensions of thought for 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 influenced by the complex interaction between crown layer characteristics and incident wave conditions. Therefore, the attenuation characteristics of landslide-induced waves will be significantly different from the results reported in other environments. Despite this, as of now, research on the influence of vegetation on landslide surges in river-type reservoir environments is relatively scarce, further highlighting the urgency and importance of deepening research in this field to elucidate the complex dynamics and provide information for developing strong ecological protection strategies. SUMMARY

[0004] The main purpose of the present application is to provide a landslide surge attenuation method and system based on biological vegetation configuration, so as to solve the problem that the influence of vegetation on the wave caused by landslide in the river reservoir is very limited in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

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

[0007] A landslide surge physical model is constructed, the basic characteristics of the landslide area are collected, and the vegetation configuration range is determined according to the basic characteristics; the flood position and the normal water level are determined according to the vegetation configuration range to determine the elevation area of the vegetation configuration; based on the main function of the landslide area, the vegetation types of each elevation area of the vegetation configuration are determined;

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

[0009] The data obtained by the landslide surge physical model is preprocessed, the obtained data is subjected to data cleaning, normalization processing and missing value filling to form a data set; the data set is subjected to data standardization processing, and the average value and standard deviation of the data set are calculated to obtain a standard data set;

[0010] A vegetation configuration model is constructed, the standard data set is input into the vegetation configuration model, the landslide surge is simulated, and the influence mechanism of vegetation on the surge is obtained; the influence mechanism of vegetation on the surge is analyzed, and a plurality of vegetation configuration schemes are obtained.

[0011] As a further improvement of the present application, the vegetation types of each elevation area of the vegetation configuration are determined, including:

[0012] A landslide surge physical model is constructed, the soil type of the physical landslide surge model landslide area is collected, the slope distribution of the landslide area is calculated, the distribution of rivers, gullies and drainage equipment is analyzed, and the water flow discharge mode is determined;

[0013] The obtained data is subjected to normalization preprocessing, and the vegetation coverage of the landslide area is evaluated according to the normalization preprocessing result; according to the vegetation coverage, the soil type and the growth habit of the vegetation, the vegetation types of different regions are determined;

[0014] The flood position of the landslide area is predicted through historical flood data and terrain model; and the normal water level of the landslide area is determined according to historical hydrological data and terrain characteristics; the landslide area is divided into different elevation areas in combination with the flood and the normal water level; wherein each elevation area corresponds to different vegetation configuration requirements.

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

[0016] The missing values, abnormal values and repeated values in the data obtained by the landslide surge physical model are filled, the abnormal values are deleted, the repeated values are deleted, and the data is cleaned;

[0017] The cleaned data is scaled to a specific range, the missing values are filled with the average value of the class in which the missing value is located, and the non-numeric data is filled with the mode of the data in which the missing value is located, and the filled data forms a data set;

[0018] The data set is standardized, the average value of the data set is calculated, and the data set is standardized according to the calculated average value and standard deviation, and the standardized data set satisfies that the average value is 0 and the standard deviation is 1;

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

[0020] According to 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 weight of the vegetation configuration model is set, and the vegetation configuration model is trained using the standard data set;

[0021] The standard data set is fused with the historical data and preprocessed, the standard data is input into the vegetation configuration model, simulated calculation is carried out, and the influence mechanism of vegetation on surge is obtained;

[0022] The influence mechanism of vegetation on surge is analyzed, different vegetation configuration schemes are obtained, historical vegetation configuration schemes are called, compared with real-time vegetation configuration schemes, a comparison threshold is set, if greater than the threshold, the vegetation configuration model is adjusted, and the iteration is carried out in sequence until the final vegetation configuration scheme is obtained.

[0023] As a further improvement of the present application, the standard data set is used to train the vegetation configuration model, including:

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

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

[0026] A judgment matrix is constructed to quantify the contribution value of each terrain factor to vegetation distribution, and the relative importance between factors is obtained, an influence standard is set, and terrain factors with contribution degree greater than the influence standard are selected;

[0027] Weights are assigned based on 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 topographic 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 standards 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 value of each terrain factor;

[0032] Set the impact standard of each terrain factor on vegetation distribution, select the terrain factor whose contribution to vegetation distribution is greater than the impact standard, and give it 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 mechanism of the influence 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. Data of different scales are normalized and filled using interpolation. Key features are extracted from the pre-processed data and input 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 vegetated area surge are predicted based on the attenuation characteristics of the vegetated area surge, including:

[0038] According to the classification of surge types, the amplitude a is defined 1cref , the amplitude of the incident surge ain and the highest height of the surge a rp , define the amplitude a 1cref , the amplitude of the incident surge a in and the highest height of the surge a rp Calculations are performed 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 the wave passes through the vegetation area;

[0040] The attenuation of 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 the wave passes through the vegetation area to the time 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 exceeded, the vegetation allocation model is readjusted.

[0041] 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;

[0042] The amplitude of the leading wave before the surge reaches the vegetation area, the amplitude of the incident surge leading wave, and the maximum vertical height of the wave leading 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 surge amplitude and wave height attenuation amplitude in vegetation areas includes:

[0045] Obtaining the geometric form of the vegetation area, quantifying the canopy density in the geometric form of the vegetation area to obtain the frontal area of ​​the canopy volume; calculating based on 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;

[0046] The wave overall attenuation change graph is analyzed, the vegetation submergence degree is defined as the ratio of the still water depth and the vegetation submergence height, the modified frontal face value index is defined as the product of the frontal face value index and the submergence degree, and the dimensionless crown layer density is the crown layer density in the numerical value;

[0047] The defined data is calculated to obtain the change process of the relative wave amplitude attenuation coefficient with the relative initial wave amplitude, the prediction is carried out according to the change process, the prediction result is compared with the measured value, a comparison threshold is set, and if the comparison threshold is greater than the comparison threshold, the calculation is re-performed;

[0048] The wave overall attenuation change graph is analyzed, the vegetation submergence degree is defined as the ratio of the still water depth and the vegetation submergence height, the modified frontal face value index is defined as the product of the frontal face value index and the submergence degree, and the dimensionless crown layer density is the crown layer density in the numerical value;

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

[0050] According to the analysis of the wave amplitude attenuation characteristics and the wave height attenuation characteristics, the non-submerged and submerged states of the vegetation are obtained;

[0051] The prediction is carried out according to the empirical formula, the result is obtained, the comparison threshold is set, the prediction value is compared with the measured value, if the comparison threshold is greater than the comparison threshold, the threshold is re-adjusted, and if the comparison threshold is less than the comparison threshold, the comparison value is analyzed;

[0052] According to the analysis result, the relative wave height attenuation coefficient change trajectory with the relative initial wave height under the non-submerged and submerged states of the vegetation is obtained.

[0053] To achieve the above object, the application also provides the following technical scheme:

[0054] A landslide surge wave attenuation system of biological vegetation configuration:

[0055] The acquisition module constructs a landslide surge physical model, acquires the basic characteristics of the landslide area, and determines the vegetation configuration range according to the basic characteristics; the flood position and the normal water level are determined according to the vegetation configuration range to determine the elevation area of the vegetation configuration; the vegetation types of each elevation area of the vegetation configuration are determined based on the main function of the landslide area;

[0056] The basic characteristics of the landslide area include soil type, slope, water flow discharge mode and the like;

[0057] The processing module: the data obtained by the landslide surge physical model is preprocessed, the obtained data is subjected to data cleaning, normalization processing and missing value filling to form a data set; the data set is subjected to data standardization processing, the average value and standard deviation of the data set are calculated to obtain a standard data set;

[0058] The vegetation configuration module: a vegetation configuration model is constructed, the standard data set is input into the vegetation configuration model, the landslide surge is simulated, and the influence mechanism of vegetation on the surge is obtained; the influence mechanism of vegetation on the surge is analyzed, and a plurality of vegetation configuration schemes are obtained.

[0059] The present application simulates the motion of landslide and the formation process of surge by physical simulation method, such as simplified model test and prototype model test. According to the basic characteristics of the landslide area, combined with the influencing factors of landslide surge, the range and elevation area of vegetation configuration are determined. Through physical model test, the surge phenomenon can be intuitively and reliably studied, and the simulation accuracy is improved. Based on the analysis of basic characteristics and influencing factors, the vegetation configuration is optimized, and the influence of landslide surge on environment and infrastructure is reduced. Data cleaning, normalization processing and missing value filling ensure the integrity and consistency of the data set. Through calculating the average value and standard deviation of the data set, data standardization processing is carried out, and the accuracy of data analysis is improved. Through data preprocessing and standardization processing, the quality and usability 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. Based on the standard data set, a vegetation configuration model is constructed 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 plurality of vegetation configuration schemes are proposed. Through model analysis, the specific influence mechanism of vegetation on landslide surge is clarified, which provides a scientific basis for vegetation configuration. The present application aims to clarify the characteristics of impact wave caused by landslide in rigid vegetation environment, especially focusing on the shallow water scene where nonlinearity becomes prominent in the wave evolution process. To achieve this goal, a series of rigid simulation vegetation experiments were carried out in a water wave tank. By changing the initial landslide position and still water depth, a plurality of impact wave conditions with different degrees of nonlinearity were generated. Under the conditions of exposure to water and submergence, four different vegetation layout modes were tested, each mode containing three different diameters. For the waves generated by landslide, the prediction of maximum wave amplitude and wave height was studied, and the nonlinear dispersion relationship controlling wave evolution was established. In addition, a comprehensive analysis and discussion were carried out to systematically investigate the influence of vegetation on wave attenuation, considering the influence of wave amplitude and wave height on wave climbing. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 It is a step flow schematic diagram of an embodiment of the landslide surge method based on biological vegetation configuration of the present application;

[0061] Figure 2A flowchart of a process for determining vegetation species for each elevation zone of a vegetation configuration for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0062] Figure 3 A flowchart of a process for obtaining a standard dataset for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0063] Figure 4 A flowchart of a process for deriving multiple vegetation configuration scenarios for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0064] Figure 5 A flowchart of a process for training a vegetation configuration model using a standard dataset for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0065] Figure 6 A flowchart of a process for selecting terrain factors that have a large contribution to vegetation distribution for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0066] Figure 7 A flowchart of a process for deriving vegetation influence mechanisms for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0067] Figure 8 A flowchart of a process for predicting vegetation zone surge wave amplitude and wave height decay amplitude based on vegetation zone surge decay characteristics for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0068] Figure 9 A flowchart of a process for deriving a plot of wave run-up height versus wave momentum flux parameter M for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0069] Figure 10 A flowchart of a process for deriving a plot of overall wave amplitude and wave height decay through vegetation zones for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0070] Figure 11 A flowchart of a process for predicting vegetation zone surge wave amplitude and wave height decay amplitude for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0071] Figure 12 A flowchart of a process for deriving a dimensionless canopy density for an embodiment of the present invention for a method of landslide surge based on biological vegetation configuration;

[0072] Figure 13The flow chart of the step of deriving the variation of the relative wave amplitude attenuation coefficient with the relative initial wave amplitude for an embodiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0073] Figure 14 The flow chart of the step of deriving the final vegetation configuration scheme for an embodiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0074] Figure 15 The block diagram of an embodiment of the landslide surge system based on the biological vegetation configuration of the present application;

[0075] Figure 16 The experimental model layout diagram for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0076] Figure 17 The experimental model diagram for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0077] Figure 18 The vegetation layout diagram for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0078] Figure 19 The diagram of the variation of the maximum run-up of the surge slope with the wave amplitude in front of the vegetation area for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0079] Figure 20 The diagram of the variation of the maximum run-up of the surge slope with the wave momentum flux for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0080] Figure 21 The diagram of the variation of the wave amplitude and the wave height attenuation coefficient of the BV20S2 series for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0081] Figure 22 The error range diagram for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0082] Figure 23 The comparison diagram of the measured value and the predicted value for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0083] Figure 24 The variation diagram for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0084] Figure 25 The comparison diagram for the experiment of the landslide surge method based on the biological vegetation configuration of the present application;

[0085] Figure 26 The structural diagram of an embodiment of the electronic device of the present application;

[0086] Figure 27 Structure diagram of one embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0087] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0088] The terms “first”, “second”, “third” in the present application are only for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with “first”, “second”, “third” can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of “plurality” is at least two, for example, two, three, etc., unless otherwise explicitly and specifically limited. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0089] In the present embodiment, the phrase “in an embodiment” means that the specific features, structures or properties described in combination with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase at various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment to other embodiments. It is explicitly and implicitly understood by a person of ordinary skill in the art that the embodiments described in the present embodiment can be combined with other embodiments.

[0090] As Figure 1 shown, the present embodiment provides one embodiment of a landslide surge attenuation method based on ecological vegetation configuration, which specifically includes the following steps in the present embodiment:

[0091] Step S1: constructing a landslide surge physical model, collecting the basic characteristics of the landslide area, and determining the vegetation configuration range according to the basic characteristics; determining the elevation area of vegetation configuration according to the flood position and the normal water level calculated based on the vegetation configuration range; determining the vegetation species of each elevation area of vegetation configuration based on the main function of the landslide area;

[0092] The basic characteristics of the landslide area include soil type, slope, water flow discharge mode, etc.

[0093] Step S2: pre-processing the data obtained from the landslide surge physical model, data cleaning, normalization processing and missing value filling are performed on the obtained data to form a data set; the data set is standardized, and the average value and standard deviation of the data set are calculated to obtain a standard data set;

[0094] Step S3: constructing a vegetation configuration model, inputting the standard data set into the vegetation configuration model, simulating the landslide surge, and obtaining the influence mechanism of vegetation on the surge; analyzing the influence mechanism of vegetation on the surge to obtain multiple vegetation configuration schemes.

[0095] Preferably, step S1 of the embodiment is simulated by a physical simulation method, such as a simplified model test and a prototype model test, to simulate the movement of the landslide body and the formation process of the surge. According to the basic characteristics of the landslide area and the influencing factors of the landslide surge, the range and elevation area of vegetation configuration are determined. Through physical model test, the surge phenomenon can be intuitively and reliably studied, and the simulation accuracy is improved. Based on the analysis of the basic characteristics and influencing factors, the vegetation configuration is optimized, and the influence of the landslide surge on the environment and infrastructure is reduced. Data cleaning, normalization processing and missing value filling in step S2 ensure the integrity and consistency of the data set. Through calculation of the average 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 usability of the data are improved. The standardized data set can more accurately reflect the actual situation of the landslide surge, and improve the accuracy of subsequent analysis. Based on the standard data set, a vegetation configuration model is constructed in step S3 to simulate the influence mechanism of vegetation on the landslide surge. Through model analysis, the specific influence mechanism of vegetation on the surge is obtained, and multiple vegetation configuration schemes are proposed. Through model analysis, the specific influence mechanism of vegetation on the landslide surge is determined, which provides a scientific basis for vegetation configuration. Based on the influence mechanism analysis, multiple vegetation configuration schemes are proposed, which provides a variety of choices for practical application.

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

[0097] Step S11: constructing a landslide surge physical model, collecting soil types in a landslide area of the physical landslide surge model, calculating slope distribution of the landslide area, analyzing distribution of rivers, gullies and drainage equipment, and determining water flow discharge mode;

[0098] The soil types include, but are not limited to, humus soil, soil soil, and calcareous soil, etc.

[0099] Step S12: performing normalization preprocessing on the obtained data, evaluating vegetation coverage of the landslide area according to the normalization preprocessing result, and determining vegetation types in different regions according to the vegetation coverage, the soil types and the growth habit of the vegetation;

[0100] The growth habit of the vegetation includes drought resistance and poor soil resistance, etc.

[0101]

[0102] In the formula, V c represents vegetation coverage (Vegetation Coverage), S i represents the vegetation coverage of the landslide area, S 2 represents the surface area (Surface Area) of the i-th vegetation type, in square meters (m i represents the growth factor (Growth Factor) of the i-th vegetation type, which represents the growth potential of the vegetation type, and the value range is 0 to 1, D i represents the drought resistance factor (Drought Resistance Factor) of the i-th vegetation type, which represents the survival ability of the vegetation type under drought conditions, and the value range is 0 to 1, P i represents the poor soil resistance factor (Poor Soil Resistance Factor) of the i-th vegetation type, which represents the survival ability of the vegetation type in poor soil conditions, and the value range is 0 to 1, E i represents the ecological niche factor (Ecological Niche Factor) of the i-th vegetation type, which represents the adaptability and competitiveness of the vegetation type in the ecological system, and the value range is 0 to 1, T i represents the temperature adaptation factor (Temperature Adaptation Factor) of the i-th vegetation type, which represents the adaptability of the vegetation type to temperature changes, and the value range is 0 to 1, R i represents the root depth factor (Root Depth Factor) of the i-th vegetation type, which represents the root depth of the vegetation type, and affects its absorption ability of soil water and nutrients, and the value range is 0 to 1, and n represents the total number of different vegetation types in the landslide area.

[0103] Step S13: predicting the flood position of the landslide area by historical flood data and terrain model; and determining the normal water level of the landslide area according to historical hydrological data and terrain characteristics; and dividing the landslide area into different elevation regions in combination with the flood and the normal water level; wherein each elevation region corresponds to different vegetation configuration requirements.

[0104] Preferably, the step S11 of the embodiment collects soil types of the landslide area, including humus soil, soil soil and calcareous soil, etc., which have important influence on the stability of landslide. The distribution of rivers, gullies and drainage equipment is analyzed to determine the water flow discharge mode, which is crucial for predicting the flood position and the normal water level. Through physical model and soil type analysis, the formation and propagation path of landslide surge can be more accurately predicted, thereby improving the accuracy of early warning. By analyzing the distribution of rivers and drainage equipment, the design of drainage system can be optimized to reduce the flood disaster caused by landslide. The collected data is normalized and pretreated in step S12 to facilitate subsequent analysis; the normalized difference vegetation index is used to evaluate the vegetation coverage of the landslide area, and it is divided into different grades. According to the vegetation coverage, soil type and vegetation growth habit (such as drought tolerance and barren tolerance), the vegetation type of different regions is determined. By evaluating the vegetation coverage and determining the vegetation type of different regions, vegetation management and restoration work can be more effectively carried out, soil fixation can be enhanced, and re-landslide can be prevented. According to the vegetation growth habit, the appropriate vegetation type is selected to improve the effect of ecological restoration and enhance the ecological stability of the region. Step S13 uses historical flood data and terrain model to predict the flood position of the landslide area. According to historical hydrological data and terrain characteristics, the normal water level of the landslide area is determined. In combination with the flood and the normal water level, the landslide area is divided into different elevation regions, and different vegetation types are configured for each elevation region. Through the division of elevation regions, the high-risk areas within the landslide area can be accurately identified, providing scientific basis for subsequent prevention and control measures. According to the different characteristics of the elevation regions, appropriate vegetation types are configured to effectively prevent the occurrence of landslide again and improve the ecological stability of the region.

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

[0106] Step S21: filling the missing values, deleting the abnormal values and deleting the repeated values in the data obtained by the landslide surge physical model; filling the missing values, deleting the abnormal values and deleting the repeated values; and cleaning the data to remove noise, etc.

[0107] Step S22: Scale the cleaned data to a specific range; for numerical data, fill in the missing values with the average value of the class in which the missing value is located; for non-numerical data, fill in the missing values with the mode of the data in the class in which the missing value is located; the completed data forms a data set;

[0108] Step S23: Perform data standardization processing on the data set, calculate the average value of the data set, and perform standardization processing on 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;

[0109] wherein the expression of the standardization processing is:

[0110]

[0111] wherein Z i represents the standardized value, X j represents the original data value, μ represents the average value of the data, σ represents the standard deviation of the data, S k represents the skewness of the data, K u represents the kurtosis of the data, α represents the skewness adjustment factor, usually takes the value range of 0 to 1, β represents the kurtosis adjustment factor, usually takes the value range of 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 the embodiment fills in the missing values in the data, which can be filled in using the average value of the class in which the missing value is located (for numerical data) or the mode (for non-numerical data). By filling in the missing values, the integrity of the data set is ensured, and the analysis bias or model training failure caused by missing values is avoided. The outliers in the data are deleted or replaced, which can use Z-score method, quantile method or autoencoder method to identify and process outliers. By deleting or replacing outliers, the influence of outliers on data analysis and modeling results is reduced, and the accuracy and reliability of the model are improved. The repeated values in the data are deleted, which improves the accuracy and reliability of the data set and avoids the influence of repeated data on the analysis results. In step S22, the data after cleaning is scaled to a specific range. By scaling the data, the data of different features are comparable, which facilitates subsequent data analysis and modeling. In step S23, the data set is standardized, the mean and standard deviation of the data set are calculated, and then the data set is standardized according to the calculated mean and standard deviation, so that the standardized data set satisfies the mean of 0 and the standard deviation of 1. Through data standardization, the unit and scale difference of the data set is eliminated, the data distribution is more concentrated, which is convenient for comparison and weighting, and the accuracy and interpretability of data analysis are improved. By calculating the mean and standard deviation, the basic statistics are provided for subsequent data standardization, which ensures the consistency and accuracy of data processing.

[0113] Further, the process of filling in missing values, deleting outliers, and deleting repeated values in step S21 includes the following steps:

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

[0115] Step S212: calculate the distance between the missing value and all data points, select the K nearest data points, and use the features of the K data points to calculate the missing value by weighted average;

[0116] Step S213: Constructing multiple isolated trees, calculating the average path length of each data point, and evaluating the abnormality degree of the data point according to the path length; calculating the K-neighbor density of each data point, calculating the local anomaly factor of each data point, and evaluating the abnormality degree of the data point according to the local anomaly factor; calculating the mean and standard deviation of the data set, calculating the Z-score of each data point: Z-score=(data point-mean) / standard deviation, and evaluating the abnormality degree of the data point according to the Z-score; according to the abnormality detection result, marking the outliers and recording the position and characteristics of the outliers; mapping high-dimensional data to low-dimensional space through a hash function, and efficiently identifying duplicate data.

[0117] Preferably, the step S211 of the embodiment fills in the missing values, ensures the continuity of the time series data at the missing values, avoids data interruption; the filled data remains smooth, reduces data fluctuation; simple and efficient, suitable for large-scale data processing. Backfilling ensures the continuity of the time series data at the missing values, avoids data interruption; the filled data remains smooth, reduces data fluctuation; simple and efficient, suitable for large-scale data processing; linear interpolation method ensures the continuity of the time series data at the missing values, avoids data interruption, the filled data remains smooth, reduces data fluctuation, is relatively efficient, and is suitable for data with obvious linear trend. Step S212 adaptive filling, the filled data maintains the consistency of the characteristics, avoids feature mutation, fills according to the characteristics of the surrounding data points, improves the accuracy of filling; relatively efficient, suitable for data with obvious feature similarity. Step S213 outlier processing and duplicate value processing, efficiently identifying abnormal points in high-dimensional data, avoiding the influence of abnormal data on the analysis result; suitable for large-scale data processing, with high computational efficiency; efficiently identifying local abnormal points, avoiding the influence of local abnormal data on the analysis result; suitable for data with obvious local abnormality, with high computational efficiency. Z-score method efficiently identifies abnormal points in normally distributed data, avoiding the influence of abnormal data on the analysis result; suitable for normally distributed data, with high computational efficiency. Outlier marking outliers, ensuring the traceability and verifiability of data; marking rather than directly deleting, retaining the possibility of further processing or verification. Locality-sensitive hashing efficiently identifies duplicate data in high-dimensional data, ensuring the uniqueness of the data set; suitable for high-dimensional data, with high computational efficiency.

[0118] In summary, the data set of the embodiment will be comprehensively and high-quality processed to ensure the accuracy and reliability of data analysis. Each step of processing has hierarchy, and the duplication checking rate is more than 40%, which ensures the quality and consistency of the data set. Specifically, it includes: ensuring the continuity of time series data to avoid data interruption; keeping the filled data smooth to reduce data fluctuation; keeping the consistency of the characteristics of the filled data to avoid feature mutation; efficiently identifying abnormal points to avoid the influence of abnormal data on the analysis results; marking abnormal values to ensure the traceability and verifiability of the data; efficiently identifying repeated data in high-dimensional data to ensure the uniqueness of the data set. Through these technical effects, the data set will be comprehensively and high-quality processed to ensure the accuracy and reliability of data analysis.

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

[0120] Step S2131: In the process of constructing each isolated tree, a feature and a feature value are randomly selected to divide the data set into two subsets; each subset is continuously 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, representing the isolation depth of the data point in the isolated 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 isolation tree is performed by randomly selecting features and feature values ​​to ensure that the construction process of each isolation tree is random and avoids data bias; through recursive partitioning, the data points are gradually isolated to form a tree structure; the shallower the depth of the tree, the easier it is for the data point to be isolated, indicating that it may be an outlier; the structure of each isolation tree reflects the isolation path of the data point, and the shallower the depth of the tree, the easier it is for the data point 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 facilitates the subsequent calculation of path length; the shallower the depth of the tree, the easier it is for the data point to be isolated, which helps to identify potential outliers. Step S2132 calculates the path length, which 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; by calculating the path length, the degree of isolation of the data point in the isolation tree is quantified; the shorter the path length, the easier it is for the data point to be isolated, indicating that it may be an outlier; the path length provides a quantitative basis for subsequent anomaly assessment. 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 isolation trees, randomly partitions the dataset, and recursively builds a tree structure. The tree structure then calculates the average path length of each data point to assess its degree of anomaly. The random and recursive partitioning creates a tree structure that facilitates data isolation and anomaly detection. Path length calculation quantifies the degree of isolation of data points, providing a basis for anomaly assessment. The average path length calculation comprehensively assesses the degree of isolation of data points and improves the accuracy of anomaly detection. This entire process, through its hierarchical and random nature, ensures comprehensive and accurate data processing, providing reliable technical support for anomaly detection.

[0125] Further, 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 based on historical vegetation type data; setting terrain factor weights of the vegetation configuration model based on 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 deriving the mechanism of vegetation's impact on surge;

[0128] The preprocessing includes data cleaning, normalization processing, and missing value filling, etc.

[0129] Step S33: Analyzing the influence mechanism of vegetation on the surge, obtaining different vegetation configuration schemes; calling historical vegetation configuration schemes, comparing with real-time vegetation configuration schemes; setting a comparison threshold, if greater than the threshold, adjusting the vegetation configuration model, iterating in turn until the final vegetation configuration scheme is obtained.

[0130] Preferably, step S31 of the embodiment sets the initial parameters of the vegetation configuration model according to historical vegetation species data, and sets the terrain factor weight according to the elevation area parameters of the landslide surge physical model, and trains the vegetation configuration model using a standard data set. It can be initialized based on actual historical data, and through the adjustment of the terrain factor weight, the model can better reflect the influence of different terrains on 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 them (including data cleaning, normalization processing, and missing value filling, etc.), and then inputs the standard data into the vegetation configuration model for simulation calculation, to obtain the influence mechanism of vegetation on the surge. Data fusion and preprocessing improve the quality and consistency of the data, so that the model can more accurately simulate the influence of vegetation on the surge. In addition, through simulation calculation, the mechanism of vegetation under different conditions can be understood in depth, providing a basis for subsequent optimization. Step S33 analyzes the influence mechanism of vegetation on the surge, obtains different vegetation configuration schemes; calls historical vegetation configuration schemes, compares with real-time vegetation configuration schemes; sets a comparison threshold, if greater than the threshold, adjusts the vegetation configuration model, iterates in turn 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 influence of the surge, while ensuring the dynamic adaptability and flexibility of the model. Through continuous adjustment and optimization, the reliability and effectiveness of the model in actual application can be improved.

[0131] Further, as shown in Figure 5 the process of training the vegetation configuration model using a standard data set in step S31 specifically includes the following steps:

[0132] Step S311: Dividing the elevation area parameters of the landslide surge physical model into target layers, factor layers, and index layers; analyzing the influence degree of each terrain factor on different vegetation types and populations; and calculating the contribution of each terrain factor to the vegetation type;

[0133] The criterion layer includes the distribution characteristics of vegetation types and populations.

[0134] Step S312: Constructing a judgment matrix to quantify the contribution of each terrain factor to the distribution of vegetation, and obtaining the relative importance between factors; setting an influence standard to select terrain factors with a contribution to the distribution of vegetation greater than the influence standard;

[0135] Step S313: Assigning weights according to the actual contribution of each terrain factor to the distribution; assigning the highest authority to terrain factors greater than the influence standard.

[0136] Preferably, step S311 of the present embodiment divides the elevation area parameters of the landslide surge physical model into target layers, factor layers, and index layers, constructing a hierarchical analysis framework. This hierarchical structure model helps systematically analyze and quantify the influence 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 of each terrain factor to the distribution of vegetation, and setting an influence standard to select important terrain factors. This method can clearly determine the relative importance between factors, and weight distribution according to the actual contribution. Step S313 assigns the highest authority to terrain factors greater than the influence standard, indicating that the model considers the synergistic effect of multiple factors to ensure that factors with a greater impact on vegetation distribution are given sufficient attention. This method helps improve the prediction accuracy and reliability of the model. The judgment matrix and weight distribution method provides a scientific basis for decision-making, helping to develop effective vegetation management and landslide prevention 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] Further, as shown in Figure 6 Step S312, the process of selecting terrain factors with a contribution to the distribution of vegetation greater than the influence standard includes the following steps:

[0138] Step S3121: Construct a judgment matrix, extract the compared terrain factors, set the influence standard of each terrain factor on the distribution of vegetation, construct the first, second, and third judgment matrices; standardize the first, second, and third judgment matrices;

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

[0140] Step S3122: Calculate the consistency index to evaluate the consistency of the judgment matrix; set a consistency index threshold, adjust the judgment matrix when the consistency index is greater than the consistency index; perform principal component extraction on the factors affecting the distribution of vegetation, and calculate the contribution value of each terrain factor;

[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 give 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 defining the target layer, criterion layer, and factor layer. This hierarchical model facilitates systematic analysis and evaluation of the impact of factors at different levels on the ultimate goal. A first judgment matrix, a second judgment matrix, and a third judgment matrix are constructed, representing the importance of the criterion layer to the target layer and the importance of the factor layer to the criterion layer, respectively. These judgment matrices are standardized to ensure that the weight distribution of factors at each level is reasonable. Standardization and consistency testing of the judgment matrices ensures that the weight distribution of factors at each level is reasonable, avoiding decision-making bias caused by unreasonable weight distribution. Using 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 consistency ratio (CR) to assess the consistency of the judgment matrix. When the CR is greater than 0.1, the judgment matrix needs to be adjusted to improve consistency. Principal component extraction is performed on the factors affecting vegetation distribution, and the contribution value of each terrain factor is calculated to determine the degree of influence of each factor on vegetation distribution. By extracting the principal components and calculating the contribution values, 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. By extracting the principal components and calculating the contribution values, 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. By extracting the principal components and calculating the contribution values, 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 of factors affecting vegetation distribution and scientific decision-making through technical features such as hierarchical model, construction and standardization of judgment matrix, consistency verification 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 mechanism of the influence of vegetation on surge in step S32 specifically includes the following steps:

[0144] Step S321: Fuse the real-time collected landslide surge physical model data with the historical data, remove the noise and abnormal values in the real-time data, normalize the data of different scales, and use the interpolation method for filling; extract the key features from the preprocessed data, and input the extracted key features into the vegetation configuration model;

[0145] Step S322: The vegetation configuration model performs simulation, uses three indexes to judge the nonlinearity degree of the wave, and divides the surge type according to the nonlinearity degree;

[0146] The three indexes include the relative wave amplitude a / d0, the wave steepness H / L, and the Ursell number Ur;

[0147] If the relative wave length L / d0 of the surge is between 2 and 20, the surge is in the category of moderate water depth wave; if the relative wave amplitude a / d0 is greater than or equal to 0.03, 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 as a nonlinear surge;

[0148] Step S323: According to the divided surge type, the climbing height of the surge on the slope is calculated, and the attenuation characteristics of the surge in the vegetation area are obtained; according to the attenuation characteristics of the surge in the vegetation area, the wave amplitude and the wave height attenuation amplitude of the surge in the vegetation area are predicted.

[0149] Preferably, in step S321 of the present 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 more accurately extract key features. Normalizing different scales of data makes different variables comparable. Normalization helps to eliminate the influence of dimension, making the model training more stable and efficient; key features are extracted from the preprocessed data. Step S322 uses the configured model for simulation, judges the nonlinearity of the wave through 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 nonlinearity judgment index, the nonlinearity 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 climbing calculation, the impact of landslide surge on the slope can be evaluated, providing important data support for disaster prevention and mitigation. According to the attenuation characteristics of the surge in the vegetation area, the amplitude and height attenuation of the surge in the vegetation area are predicted. Through attenuation characteristic analysis, the attenuation of landslide surge in the vegetation area can be predicted, providing a scientific basis for vegetation protection engineering. The maximum climbing of the surge on the slope after passing through the vegetation area is mainly controlled by the wave momentum flux of the incident surge in front of the slope, and has no direct correlation with the vegetation coverage density, and the maximum climbing can be effectively estimated by the non-breaking solitary wave theory. When the surge passes through the vegetation, the wave amplitude attenuation coefficient (Δa v ) and the wave height attenuation coefficient (ΔH v ) are in power function relationship with the relative wave amplitude (a iv / d0) and the relative wave height (H iv / d0), respectively. Under the condition of fixed vegetation arrangement pattern and non-submerged state, the wave amplitude attenuation speed increases with the increase of still water depth, and the wave height attenuation speed is positively correlated with the still water depth in the entire water depth range. Under the premise of constant vegetation arrangement pattern and submerged state, considering the effects of surge dispersion and wall dissipation, there is a power function relationship between the relative wave amplitude attenuation coefficient (Δa v / λ fr ) and the relative initial wave amplitude, and the relative wave height attenuation coefficient (ΔH v / λ fr ) and the relative initial wave height, which is independent of the change of still water depth, and the power function model parameters are in power function correlation with the non-dimensional canopy density (c v ) of the vegetation. Under the submerged state of vegetation, the response mode between the wave amplitude attenuation coefficient and the initial relative wave amplitude is significantly affected by the vegetation coverage density, showing three-stage evolution characteristics, and the critical vegetation canopy density c v for the sequential transition of each stage is 3.14m -1 to 3.925m -1 and 7.825m -1to 12.5 m -1 .

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

[0151] Step S3231: According to the divided surge wave type, define the wave amplitude a 1cref , the wave amplitude a in of the incident surge wave, and the highest climbing height a rp of the surge wave, calculate the defined wave amplitude a 1cref , the wave amplitude a in of the incident surge wave, and the highest climbing height a rp of the surge wave, and form a relationship diagram with the wave climbing height and wave momentum flux parameter M on the slope;

[0152] Step S3232: Analyze the relationship diagram, define the wave amplitude attenuation coefficient Δa v and the wave height attenuation coefficient ΔH v ; calculate the defined wave amplitude attenuation coefficient and wave height attenuation coefficient to obtain the overall attenuation change diagram of the wave passing through the vegetation area to the wave amplitude and wave height;

[0153] Step S3233: Predict the wave amplitude and wave height attenuation amplitude of the vegetation area surge wave according to the overall attenuation change diagram of the wave passing through the vegetation area to the wave amplitude and wave height; compare the prediction results with the measured results, set a comparison threshold, and if it is higher than the threshold, adjust the vegetation distribution model again.

[0154] Preferably, step S3231 of the embodiment can better understand the propagation characteristics of waves on the slope by defining and calculating the wave amplitude and other parameters, and provide basic data for the analysis of wave dissipation effect. Through the formed relationship diagram, the attenuation of waves in the vegetation area can be intuitively analyzed, and the basis for evaluating the wave reduction effect of vegetation is provided. Step S3232 accurately predicts the attenuation of waves in the vegetation area by defining and calculating the attenuation coefficient, and provides a scientific basis for the design and optimization of vegetation protection structure. Through the formed overall attenuation change diagram, the wave reduction effect of vegetation can be verified, and reference for actual engineering application is provided. Step S3233 predicts the wave amplitude and wave height attenuation amplitude of the vegetation area according to the overall attenuation change diagram of the wave amplitude and wave height of the wave passing through the vegetation area. The prediction result is compared with the measured result, a comparison threshold is set, and if it is higher than the threshold, the vegetation distribution model is adjusted again. Through the comparison between the measured result and the predicted result, the shortcomings in the model can be found in time, and the model can be adjusted and optimized to improve the accuracy and reliability of the model. Through the readjustment of the vegetation distribution model, the vegetation can be more reasonably configured to achieve the best wave dissipation effect and reduce the engineering cost and maintenance difficulty.

[0155] Further, as shown in Figure 9 Step S3231, the process of forming a relationship diagram of the wave climbing height on the slope and the wave momentum flux parameter M includes the following steps:

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

[0157] Step S32312: calculate the wave amplitude of the leading wave before the surge wave reaches the vegetation area, the wave amplitude of the leading wave of the incident surge wave, and the maximum vertical height of the wave peak of the leading wave climbing along the slope, and obtain the maximum relative climbing height a rp / d0of the surge wave with different vegetation coverage densities; 1cref / d0;

[0158] Step S32313: obtain the wave climbing height on the slope and the wave momentum flux parameter M according to the relative wave amplitude a in / d0of the incident wave, analyze the change condition diagram and the wave climbing height on the slope and the wave momentum flux parameter M, and obtain a relationship diagram of a in / d0and M.

[0159] Preferably, step S32311 of the embodiment defines the wave amplitude a 1cref ​a is the wave amplitude of the leading wave of the incident surge before the surge reaches the vegetation area in a is the wave amplitude of the incident surge before the surge reaches the vegetation area rp a is the wave amplitude of the leading wave of the incident surge before the surge reaches the vegetation area rp a is the wave amplitude of the leading wave of the incident surge before the surge reaches the vegetation area 1cref a is the wave amplitude of the leading wave of the incident surge before the surge reaches the vegetation area in a is the wave amplitude of the leading wave of the incident surge before the surge reaches the vegetation area in a is the wave amplitude of the leading wave of the incident surge before the surge reaches the vegetation area

[0160] Further, as shown in Figure 10 the process of obtaining the overall attenuation change graph of the wave passing through the vegetation area to the wave amplitude and wave height in step S3232 specifically includes the following steps:

[0161] Step S32321: Analyze the relationship graph and define the wave amplitude attenuation coefficient Δa v :

[0162] Δa v = (a iv -a ov ) / L v

[0163] Define the wave height attenuation coefficient ΔH v :

[0164] ΔH v = (H iv -H ov ) / L v

[0165] where a iv is the wave amplitude when the wave 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 wave amplitude when the wave reaches the end of the vegetation area, H ov is the wave height when the wave reaches the end of the vegetation area, and Lv is the length of the vegetation area;

[0166] Step S32322: According to the calculated measurement of Δa v and ΔH v , and analyzing the power-law relationship between Δa v and ΔH v and the relative amplitude of the vegetation edge and the relative amplitude and wave height of the edge, the measurement graph of Δa v and ΔH v is obtained;

[0167] Step S32323: According to the measurement graph of Δa v and ΔH v , it is analyzed that under the condition that d0 is less than or greater than h v , the increase of water depth will increase Δa v ; when d0 is equal to h v , Δa v decreases; on the contrary, if the water is still, the increase of water depth will increase ΔH v ; the overall attenuation variation graph of the wave amplitude and wave height through the vegetation area is obtained.

[0168] Preferably, the step S32321 of the embodiment can quantify the attenuation of the wave propagating in the vegetation area by defining the wave amplitude attenuation coefficient Δa v and the wave height attenuation coefficient ΔH v . It reflects the degree of energy loss of the wave propagating in the vegetation area and is an important parameter for evaluating the attenuation effect of the vegetation on the wave. In step S32322, by calculating the measurement and analyzing the relationship between the relative amplitude and wave height and the edge of the vegetation, it is found that there is a power-law relationship between them. This relationship helps to understand the energy dissipation mechanism of the wave propagating in the vegetation area, so as to better predict the attenuation of the wave under different conditions. In step S32323, by analyzing the variation of the wave attenuation under different water depths, it is concluded that when d0 is less than or greater than h v , the increase of water depth will increase the attenuation of the wave amplitude and wave height; and when d0 is equal to h v , the increase of water depth will decrease the attenuation. 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. Through precise mathematical modeling and experimental data analysis, the embodiment quantifies the influence of vegetation on wave attenuation and reveals the specific influence mechanism of water depth change on wave attenuation. These technical effects not only improve the understanding of the propagation law of the wave in the vegetation area, but also provide important guidance for practical engineering applications, such as reasonable use of vegetation for wave control in the fields of coastal protection, ecological restoration, etc.

[0169] Further, as Figure 11As shown, the process of vegetation area surge wave amplitude and height attenuation amplitude prediction in step S3233 specifically includes the following steps:

[0170] Step S32331: Obtain the geometric shape of the vegetation area, quantify the canopy density in the geometric shape of the vegetation area to obtain the frontal area of the canopy volume; calculate the planar unit area according to the canopy frontal area; analyze the planar unit area to obtain the planar area occupied by the canopy element;

[0171] The frontal area of the canopy volume is quantified as:

[0172] C v = N * b v

[0173] Wherein, C v is the canopy density;

[0174] The frontal area index is:

[0175] λ f = C v d0

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

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

[0178] λ p = (π / 4) C v d0

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

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

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

[0182] Preferably, step S32331 of the embodiment obtains the geometric morphology of the vegetation area, quantifies the canopy density in the geometric morphology of the vegetation area to obtain the frontal area of the canopy volume, calculates the planar unit area according to the frontal area of the canopy, and analyzes the planar unit area to obtain the planar area occupied by the canopy element. 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 the data. Step S32332 analyzes the overall wave attenuation variation diagram, defines the vegetation tree degree as the ratio of the still water depth to the vegetation basin degree, defines the modified frontal area index as the product of the frontal area index and the modification degree, and the dimensionless canopy density is numerically the canopy density. By defining the vegetation tree degree and the modified frontal area index, the influence 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 variation process of the relative wave amplitude attenuation coefficient with the relative initial wave amplitude, predicts the variation process, compares the prediction result with the measured result, sets a comparison threshold, and recalculates if the comparison threshold is greater than the comparison threshold. By calculating and predicting the relative wave amplitude attenuation coefficient, the influence of vegetation on wave attenuation can be more accurately evaluated, and the management strategy can be adjusted in a timely manner. This method can improve the stability and function of the vegetation ecosystem while reducing prediction errors. The embodiment quantifies and calculates the canopy structure and density of the vegetation area, and evaluates the influence of vegetation on wave attenuation, thereby providing scientific basis and technical support for vegetation management and ecological research. These technical features and effects help to improve the stability and function of the vegetation ecosystem, and optimize vegetation management and ecological restoration strategies.

[0183] Further, as shown in Figure 12 Step S32332, the dimensionless canopy density is numerically the canopy density, and the process specifically includes the following steps:

[0184] Step S323321: Analyze the wave attenuation variation diagram, define the vegetation submergence degree as the ratio of the still water depth to the vegetation submergence height, define the modified frontal area index as the product of the frontal area index and the submergence degree, 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 submergence degree, which is:

[0186] λ fr =λ f *λ sub

[0187] Wherein, λ fris the modified headway area index, λ f is the headway 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 of less than or equal to 0.16 meters, while the non-submerged state of vegetation corresponds to a still water depth of 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 of less than or equal to 0.16 meters, while the non-submerged state of vegetation corresponds to a still water depth of 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-submerged and submerged 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 "corrected frontal area index," which is obtained by multiplying the frontal area index by the submergence. Furthermore, the concept of dimensionless canopy density, i.e., a numerical representation of canopy density, is mentioned. By introducing vegetation submergence and the corrected frontal area index, the impact of vegetation on wave attenuation can be more accurately described. This quantitative approach helps understand the absorption and dissipation mechanisms of wave energy by vegetation under different submergence conditions, thereby improving the accuracy and applicability of wave attenuation models. Step S323322 analyzes the amplitude attenuation characteristics, defining the non-submerged state of vegetation as a still water depth less than or equal to 0.16 meters, and the submerged state as a still water depth greater than or equal to 0.2 meters. Simultaneously, the wave height attenuation characteristics are analyzed, defining the non-submerged state as a still water depth less than or equal to 0.16 meters, and the submerged state as a still water depth greater than or equal to 0.24 meters. By clearly distinguishing between 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, enabling it to 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 Figure 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: Analyzing the non-submerged and submerged state of vegetation, and the empirical formula of the relative wave amplitude attenuation coefficient under the non-submerged and submerged state of vegetation:

[0193]

[0194]

[0195] wherein, Δa v / λ fr is the relative wave amplitude attenuation coefficient, a iv / d0is the relative initial wave amplitude.

[0196] Step S323332: According to the empirical formula, the result is obtained; set the comparison threshold, compare the predicted value with the measured value, if greater than the threshold, then re-adjust the threshold; if less than the threshold, analyze the comparison value;

[0197] Step S323333: According to the analysis result, the trajectory of the relative wave height attenuation coefficient changing with the relative initial wave height under the non-submerged and submerged state of vegetation is obtained; the empirical formula of the relative wave height attenuation coefficient under the non-submerged and submerged state of vegetation is calculated:

[0198]

[0199]

[0200] wherein, ΔH v / λ fr is the relative wave height attenuation coefficient under the non-submerged and submerged state of vegetation, H iv / d0is the initial wave height.

[0201] Preferably, step S323331 of this embodiment establishes an empirical formula to describe the relative amplitude attenuation coefficient of vegetation in both non-submerged and submerged states. This empirical formula allows for more accurate prediction and analysis of vegetation changes under different hydrological conditions, providing a scientific basis for research on the coupling mechanisms of hydrological and ecological processes. Step S323332 sets a comparison threshold, compares the predicted value with the measured value, and adjusts the threshold based on the comparison result. Utilizing historical information from dynamic variables to adjust the trigger threshold further conserves limited communication resources. Dynamic threshold adjustment improves prediction accuracy and reliability, ensuring consistency between predicted results and actual observed data, thereby enhancing the monitoring accuracy of hydrological and ecological processes. Step S323333 analyzes the relative wave height attenuation coefficient of vegetation at different initial wave heights, deriving its variation trajectory and establishing a corresponding empirical formula. This method utilizes initial wave height as a variable, enabling quantification of vegetation changes at different initial wave heights. Establishing an empirical formula allows for more accurate prediction and analysis of vegetation changes at different initial wave heights, providing a scientific basis for research on the coupling mechanisms of hydrological and ecological processes. This embodiment quantifies and predicts vegetation changes 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 Figure 14 As shown, the process of obtaining the final vegetation configuration scheme in step S33 specifically includes the following steps:

[0203] Step S331: Analyze the impact mechanism of vegetation on surge waves, and derive a real-time vegetation configuration plan based on the analysis results;

[0204] Step S332: Retrieve data from historical vegetation configuration plans and compare them with the real-time vegetation configuration plan to determine 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 the present embodiment involves analyzing the mechanism of how vegetation affects the propagation of surges, and formulating a real-time vegetation configuration scheme based on the results of this analysis. This typically requires the use of numerical simulations and experimental data to evaluate the effects of different vegetation configurations on wave attenuation. By analyzing the impact of vegetation on surges, the vegetation configuration can be optimized to achieve the best wave dissipation effect, thereby reducing the erosion and damage of the coastline by surges. Step S332 involves comparing historical vegetation configuration scheme data with the current real-time scheme. This can include comparing vegetation density, type, and its impact on wave propagation at different time points. Through comparative analysis, it can be identified which vegetation configuration scheme performs better in practical application, thereby providing a reference for future vegetation configuration. Step S333 sets a comparison threshold to determine whether the difference between historical data and real-time data exceeds an acceptable range. If it does, the model parameters need to be adjusted and the simulation needs to be performed again. By setting a threshold and dynamically adjusting it, it can be ensured that the vegetation configuration scheme is always in the best state to cope with changing environmental conditions and surges. The present embodiment uses numerical simulation and comparative analysis to optimize the vegetation configuration scheme, and its technical effect lies in improving wave dissipation efficiency, reducing coastal erosion, and improving the stability of the overall ecological system.

[0207] Further, as shown in Figure 15 the present embodiment also provides a landslide surge attenuation system based on biological vegetation configuration. In the present 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 embodiments. The landslide surge attenuation system based on biological vegetation configuration comprises a collection module 1, a processing module 2, and a vegetation configuration module 3 connected in sequence.

[0208] The collection 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 according to the basic characteristics; the flood position and the normal water level are calculated according to the vegetation configuration range to determine the elevation area of the vegetation configuration; the vegetation types of each elevation area of the vegetation configuration are determined based on the main function of the landslide area; wherein the basic characteristics of the landslide area include soil type, slope, water flow discharge mode, etc. The processing module 2 is used to preprocess the data obtained from the landslide surge physical model, and to perform data cleaning, normalization processing, and missing value filling on the obtained data to form a data set; the data set is subjected to data standardization processing, and the average value and standard deviation of the data set are calculated 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 vegetation impact mechanism on the surge; the vegetation impact mechanism on the surge is analyzed to obtain a plurality of vegetation configuration schemes.

[0209] Preferably, the collection module 1 of the present embodiment simulates the process of landslide surge through a physical model, collects the basic characteristics of the landslide area, such as soil type, slope, water flow discharge mode, etc. According to the basic characteristics, the range of vegetation configuration is determined, the flood position and the normal water level are calculated, and the elevation area of vegetation configuration is determined. Based on the main function of the landslide area, the vegetation species of each elevation area is determined. Through physical model simulation, the shape and propagation process of landslide surge can be more accurately predicted, improving the scientificity and accuracy of simulation. According to the basic characteristics and main function, the vegetation configuration is optimized to reduce the damage of landslide surge to vegetation and improve the adaptability and stability of vegetation. The data obtained from the physical model of landslide surge are cleaned, normalized and missing value filled in the processing module 2 to form a data set. The data set is standardized to calculate the mean and standard deviation of the data set to obtain a standard data set. Through data cleaning and normalization, the quality and consistency of the data are improved, and errors are reduced; the standardized data set can improve the stability and prediction accuracy of the model, and reduce the fluctuations in the model training process. The vegetation configuration module 3 inputs the standard data set into the vegetation configuration model to simulate the landslide surge and obtain the influence mechanism of vegetation on the surge. The influence mechanism of vegetation on the surge is analyzed to obtain multiple vegetation configuration schemes. Through simulation and analysis, the vegetation configuration strategy is optimized to improve the protection effect of vegetation on landslide surge and reduce disaster loss. Through the analysis of the influence mechanism of vegetation on the surge, the adaptability and stability of vegetation are improved, and the damage of landslide surge to vegetation is reduced. The present embodiment realizes accurate simulation of landslide surge and optimization of vegetation configuration through physical model simulation, data preprocessing, standardization 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 of landslide surge to vegetation and the environment.

[0210] The present embodiment provides application examples of the landslide surge attenuation method based on ecological vegetation configuration, as shown below:

[0211] The experimental model layout and field photos of vegetation reducing landslide surge are shown in Figure 16 and Figure 17 respectively. The wave tank is a flat bottom straight rectangular tank made of organic glass, 4.75 m long, 0.4 m wide (b), and 0.44 m deep. The upstream end of the tank is provided with an organic glass chute with an inclination angle α of 45°, and a guide rail is attached to the upper surface to match the groove at the bottom of the landslide block, so that the centroid of the block always lies on the center line of the tank during sliding. The landslide block is an organic glass box filled with concrete, with a total mass m s of 10.026 kg; the front edge of the block has an inclination angle θ of 45°, a length l s of 0.2 m, a thickness s of 0.1 m, and a 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 considered a two-dimensional problem. A slope with an inclination angle β of 45° was installed at the downstream end of the flume to study the run-up 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 inserted holes was 0.2m (i.e. the height of the vegetation h v =0.2m) rigid polyvinyl chloride (PVC) rods are used to simulate vegetation. The length of the simulated vegetation area is L v The diameter of the trunk is 0.96m, the width is 0.32m, and the trunk is 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 trees / m 2 These configurations form four different vegetation layout patterns, such as Figure 18 Labeled as a, b, c, and d, respectively. Furthermore, this example also incorporates a special vegetation layout pattern, S0, which contains no simulated stems within the flume. Experiments using the S0 pattern were conducted to investigate the evolution of landslide-generated waves. The coordinate origin (x, z) was defined at the intersection of the flume floor and the chute.

[0213] In this experiment, 13 experimental series were conducted. Each series 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 ), defined as the vertical distance between the landslide's centroid and the still water surface, with a range of 0.05 to 0.50 m. These parameters were selected primarily to generate wave amplitudes and heights with a wide bandwidth and to evaluate the effects of still water depth and vegetation submergence on wave attenuation.

[0214] The block's descent was recorded by a high-definition camera at a frequency of 240 fps to calculate the block's entry velocity. Nine capacitive wave height gauges (CWG1-9) were deployed along the x-axis to record the generation, evolution, and rise of waves, with an accuracy of ±0.1 mm and an acquisition frequency of 500 Hz. CWG1 and CWG2 were installed at positions x = 0.6 m and x = 1.2 m, respectively, to obtain the characteristic parameters of the primary surge. CWG3-7 were evenly spaced in vegetated waters to measure the changes in surge within the vegetated area. CWG8 and CWG9 were used to obtain the wave parameters of the incident surge and its rise on the slope, respectively.

[0215] The present embodiment focuses on the influence of vegetation on the leading wave of the surge. The amplitude a of the leading wave is defined as the water level difference between the first wave crest and the calm water surface, while the wave height H refers to the water level difference between the first wave crest and the first wave trough. The period T and the wavelength L of the leading wave refer to the time interval and the horizontal distance between the wave origin and the next up-crossing zero point, respectively. According to the experimental results of the present 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 intermediate water depth waves. The nonlinearity of the wave can be judged by the following three indicators: the relative amplitude a / d0, the wave steepness H / L, and the Ursell number Ur. The experimental results show that the relative amplitude and the wave steepness of the surge fall within the range of 0.07≤a / d0≤0.50 and 0.012≤H / L≤0.073, respectively, which are far beyond the applicable conditions of linear waves, i.e., a / d0<0.03 and H / L≤0.006. In the category of intermediate water depth 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 number of the surge falls within the range of 14.91≤Ur≤68.18, which is obviously beyond the applicable conditions of linear wave theory, i.e., Ur<1. Therefore, the surge in the present embodiment has significant nonlinear characteristics.

[0216] For subsequent analysis, the reference amplitude a 1cref is defined as the amplitude of the leading wave before the surge reaches the vegetation area (approximated by the measured amplitude at CWG2), the amplitude a in of the incident surge is the amplitude of the leading wave of the incident surge (approximated by the measured amplitude at CWG8), and the maximum climb a rp of the surge is the maximum vertical height of the leading wave crest of the surge along the slope. When the vegetation area tree stem b v =0.02m, the maximum relative climb a rp / d0 of the surge under different vegetation cover densities varies with the relative reference amplitude a 1cref / d0, as shown in Figure 19 From the figure, it can be clearly seen that the attenuation effect of vegetation on the wave climb is closely related to the relative amplitude of the surge before reaching the vegetation area. It is worth noting that under the non-submerged condition, the reduction effect of vegetation on the maximum climb of the wave is significantly higher than that under the submerged condition.

[0217] The wave climb height on the slope has a significant correlation with the wave momentum flux parameter M, which only depends on the relative amplitude a in / d0 of the incident wave. Figure 20 shows the a in / d0 versus M. For comparison purposes, the figure also depicts the empirical relationship derived for unbroken solitary waves. The results show that the maximum wave rise height on a slope is only related to the wave momentum flux parameter and is independent of the vegetation layout. This finding suggests that the reduction in surge rise height caused by vegetation depends primarily on the attenuation rate of the wave amplitude or wave height as the wave passes through vegetated water areas. In addition, solitary wave theory can be used to estimate the wave rise height generated by landslides. The results of this example 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] Where 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 area. For series BV10S4, Δa v and ΔH v Typical measured values ​​are as follows: Figure 21 (a) and Figure 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. Figure 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, Figure 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 headway area index (λ f ) and the plane area index (λ p ) is a crucial parameter. f Indicates the canopy frontal area per unit area of ​​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 vegetation submergence. fr ) is defined as the headway area index (λ f ) multiplied by the submergence 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 0.18m, 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 0.20m, the trough of the leading wave will be lower than the vegetation height h v Therefore, when analyzing the amplitude attenuation characteristics later, 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, when analyzing 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] Figure 22 (a) and Figure 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 ) and this discovery lays the foundation for constructing an amplitude attenuation prediction model applicable to different vegetation cover and wave conditions. Based on the above analysis, an empirical formula for the relative amplitude attenuation coefficient under non-flooded and flooded vegetation conditions can be derived:

[0225]

[0226] Figure 23 The results show that the model prediction values ​​are highly consistent with the measured values, and the error range is basically kept within 20%.

[0227] Compared with amplitude attenuation, Figure 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 shows 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, which provides a scientific basis for predicting the attenuation of wave height under vegetation cover. Based on the above research results, this example constructs an empirical formula for the relative wave height attenuation coefficient under non-submerged and submerged vegetation states:

[0228]

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

[0230] This example systematically analyzes experimental data under vegetation inundation conditions and clearly observes a positive correlation between the amplitude attenuation coefficient and the initial relative amplitude under varying vegetation cover densities and still water depths. However, the pattern of this correlation with still water depth is significantly modulated by vegetation cover density. Further detailed analysis reveals that the relationship between the amplitude attenuation coefficient and the initial relative amplitude evolves through three distinct stages as vegetation cover density gradually increases:

[0231] (1) Low vegetation cover density stage: In this stage, as the submerged water depth increases, the rate of change of the amplitude attenuation coefficient with respect to the initial relative amplitude (i.e., the amplitude attenuation rate) increases significantly. This indicates that under sparse vegetation conditions, water depth is an important factor in regulating the amplitude attenuation rate.

[0232] (2) Medium vegetation cover density stage: When the vegetation cover density reaches a certain critical value, the amplitude attenuation rate at different submerged water depths tends to be consistent, indicating that the effect of water depth is balanced by the density of vegetation. However, under the same initial relative amplitude, deeper submerged water depths still result in higher amplitude attenuation coefficients.

[0233] (3) High vegetation cover density stage: As the vegetation cover 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 indicates that under high-density vegetation cover, 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 is v The range can be defined as 3.14m -1 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 Figure 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 configured to execute program instructions stored in the memory 42 to perform the landslide surge attenuation based on the biological vegetation configuration.

[0238] The processor 41 can also be referred to as a CPU (Central Processing Unit). The processor 41 can be an integrated circuit chip including a processing unit. The processor 41 can also be a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component. The general purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0239] Further, Figure 27 For a structural diagram of the storage medium of an embodiment of the present application, the storage medium 5 of the embodiment of the present application stores program instructions 51 capable of implementing all the methods described above. The program instructions 51 can be stored in the storage medium in the form of a software product, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk or an optical disk, and various media capable of storing program codes, or a terminal device such as a computer, a server, a mobile phone, a tablet, etc.

[0240] In several embodiments of the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of units is only a logical function division. In actual implementation, another division manner can be used, for example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0241] The specific embodiments of the application are described in detail above, but they are only examples. The application is not limited to the specific embodiments described above. Any equivalent modification or replacement made by those skilled in the art to the application is also within the scope of the application. Therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principle range of the application should be covered in the scope of the application.

Claims

1. A method for landslide surge attenuation based on bio-vegetation configuration, characterized in that, The landslide surge wave attenuation method of the biological vegetation configuration comprises: A landslide surge physical model is constructed, basic characteristics of a landslide area are collected, and a vegetation configuration range is determined according to the basic characteristics; a flood position and a normal water level are determined according to the vegetation configuration range to determine an elevation area of the vegetation configuration; and vegetation types of each elevation area of the vegetation configuration are determined based on a main function of the landslide area; The basic characteristics of the landslide area include soil types, slope and water flow discharge mode; The data obtained from the landslide surge physical model are preprocessed, the obtained data are subjected to data cleaning, normalization processing and missing value filling to form a data set; the data set is subjected to data standardization processing, and the average value and standard deviation of the data set are calculated to obtain a standard data set; A vegetation configuration model is constructed, the standard data set is input into the vegetation configuration model, the landslide surge is simulated, and the influence mechanism of the vegetation on the surge is obtained; the influence mechanism of the vegetation on the surge is analyzed, and a plurality of vegetation configuration schemes are obtained; The real-time collected landslide surge physical model data are fused with historical data, noise and abnormal values in the real-time data are removed, data of different scales are subjected to normalization processing, and interpolation method is used for filling; the preprocessed data are subjected to key feature extraction, and the extracted key features are input into the vegetation configuration model; The vegetation configuration model is simulated, three indexes are used to judge the nonlinearity degree of the wave, and the surge type is divided according to the nonlinearity degree; The three indexes include relative wave amplitude a / d0, wave steepness H / L and Ursell number Ur; If the relative wave length L / d0 of the surge is between 7.66 and 16.17, the surge is in the category of moderate water depth wave; if the relative wave amplitude a / d0 falls within the range of 0.07 to 0.50 and the wave steepness H / L falls within the range of 0.012 to 0.073, and the Ursell number Ur is less than 1, it is judged as a nonlinear feature; The climbing height of the surge on the slope is calculated according to the divided surge type, and the attenuation characteristics of the surge in the vegetation area are obtained; the wave amplitude and wave height attenuation amplitude of the surge in the vegetation area are predicted according to the attenuation characteristics of the surge in the vegetation area; According to the divided types of the swells, define the wave amplitude a 1cref , the wave amplitude a of the incident swells in , and the highest climbing height a of the swells rp , the defined wave amplitude a 1cref , the wave amplitude a of the incident swells in , and the highest climbing height a of the swells rp are calculated, and a relationship diagram is formed with the wave climbing height on the slope and the wave momentum flux parameter M; The relationship graph is analyzed, the amplitude attenuation coefficient Δa v and the wave height attenuation coefficient ΔH v are defined; the defined amplitude attenuation coefficient and the wave height attenuation coefficient are calculated to obtain a graph of overall attenuation changes of the wave from the wave amplitude and the wave height to the vegetation area. The wave amplitude and wave height attenuation amplitude of the surge in the vegetation area are predicted according to the overall attenuation change graph of the wave amplitude and wave height of the wave penetrating the vegetation area; the prediction result is compared with the measured result, a comparison threshold is set, and if the threshold is higher, the vegetation distribution model is adjusted again; Definition of wave height a 1cref wave height of the leading wave of a surf, wave height a of the incident surf in wave height of the leading wave of an incident surf, maximum run-up a of the surf rp maximum vertical height of the wave crest of the leading wave up the slope The wave amplitude of the leading wave before the surge reaches the vegetation area, the wave amplitude of the incident surge leading wave and the maximum vertical height of the wave leading wave crest climbing along the slope are calculated to obtain the maximum relative climbing height a of the surge with different vegetation coverage densities rp / d0 with the relative parameter wave amplitude a 1cref / d0 variation chart According to the relative wave amplitude a of the incident wave in The wave run-up height on the slope and the wave momentum flux parameter M are obtained from a / d0, and the variation diagram is analyzed with the wave run-up height on the slope and the wave momentum flux parameter M to obtain the relationship between a / d0 and M in The relationship diagram of a / d0 and M; The relationship graph is analyzed to define the amplitude decay coefficient Δa v ​ The wave height attenuation coefficient ΔH v is defined as: wherein, Hs is the wave height at the start of the vegetation zone, Hs is the wave height at the start of the vegetation zone, Hs is the wave height at the start of the vegetation zone, Hs is the wave height at the start of the vegetation zone, L is the length of the vegetation zone; According to the calculation and measurement values, and analyzing and the relative amplitude and wave height of the vegetation edge, the relative amplitude and wave height of the vegetation edge are in a power-law relationship, obtaining and measurement value graphs; According to and the measured values, it is analyzed that when d0 is less than or greater than h v , the water depth increases, and increases; when d0 is equal to h v , then decreases; on the contrary, if the water is still, the water depth increases, and increases; the overall attenuation change graph of the wave amplitude and wave height when the wave passes through the vegetation area is obtained.

2. The bio-vegetation configured landslide runout wave attenuation method of claim 1, wherein, The process of determining the vegetation types of each elevation area of the vegetation configuration comprises: A landslide surge physical model is constructed, the soil types of the physical landslide surge model landslide area are collected, the slope distribution of the landslide area is calculated, the distribution of rivers, gullies and drainage equipment is analyzed, and the water flow discharge mode is determined; The obtained data are subjected to normalization preprocessing, the vegetation coverage of the landslide area is evaluated according to the normalization preprocessing result; and the vegetation types of different areas are determined according to the vegetation coverage, soil types and growth habits of the vegetation. The flood position of the landslide area is predicted through historical flood data and a terrain model; and the normal water level of the landslide area is determined according to historical hydrological data and terrain characteristics; and the landslide area is divided into different elevation areas in combination of the flood and the normal water level; wherein each elevation area corresponds to different vegetation configuration requirements.

3. The bio-vegetation configured landslide runout wave attenuation method of claim 1, wherein, The process of obtaining the standard data set comprises: The missing values, abnormal values and repeated values in the data obtained by the landslide surge physical model are processed accordingly; the missing values are filled, the abnormal values are deleted, and the repeated values are deleted; and the data is cleaned; The cleaned data is scaled to a specific range; for numerical data, the missing values are filled with the average value of the class in which the missing values are located; for non-numerical data, the missing values are filled with the mode of the data in the class in which the missing values are located; and the filled data forms a data set; The data set is standardized, the average value of the data set is calculated, and the data set is standardized according to the calculated average value and standard deviation, so that the standardized data set satisfies the conditions of mean value 0 and standard deviation 1.

4. The bio-vegetation configured landslide runout wave attenuation method of claim 1, wherein, The process of obtaining a plurality of vegetation configuration schemes comprises: The initial parameters of the vegetation configuration model are set according to historical vegetation type data; the terrain factor weight of the vegetation configuration model is set according to the elevation area parameters of the landslide surge physical model; and the vegetation configuration model is trained using the standard data set; The standard data set is fused with historical data and preprocessed; the standard data is input into the vegetation configuration model for simulation calculation to obtain the influence mechanism of vegetation on surge; The influence mechanism of vegetation on surge is analyzed to obtain different vegetation configuration schemes; historical vegetation configuration schemes are called 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 the iteration is performed in sequence until the final vegetation configuration scheme is obtained.

5. The bio-vegetation configured landslide runout wave attenuation method of claim 4, wherein, The process of training the vegetation configuration model using the standard data set comprises: The elevation area parameters of the landslide surge physical model are divided into target layers, factor layers and index layers; the influence degree of each terrain factor on different vegetation types and populations is analyzed; and the contribution degree of each terrain factor to vegetation types is calculated; The criterion layer includes vegetation type and population distribution characteristics; A judgment matrix is constructed to quantify the contribution value of each terrain factor to vegetation distribution, and the relative importance between factors is obtained; an influence standard is set, and terrain factors with a contribution degree greater than the influence standard are selected; The actual contribution value of each terrain factor to distribution is allocated a weight; and the terrain factors with a contribution degree greater than the influence standard are given the highest authority.

6. The bio-vegetation configured landslide runout wave attenuation method of claim 5, wherein, The process of selecting terrain factors with a contribution degree greater than the influence standard comprises: A judgment matrix is constructed, the compared terrain factors are extracted, the influence standard of each terrain factor on vegetation distribution is set, and judgment matrix 1, judgment matrix 2 and judgment matrix 3 are constructed; and judgment matrix 1, judgment matrix 2 and judgment matrix 3 are standardized; Wherein, judgment matrix 1 represents the proportion of the importance degree of the criterion layer to the target layer, and judgment matrix 2 and judgment matrix 3 represent the proportion of the importance degree of the factor layer to the criterion layer. A consistency index is calculated to evaluate the consistency of the judgment matrix; a consistency index threshold is set, and the judgment matrix is adjusted when the consistency index is greater than the consistency index threshold; the factors affecting the vegetation distribution are subjected to principal component extraction, and the contribution values of the terrain factors are calculated; Set the influence standard of each terrain factor on the vegetation distribution, select the terrain factor whose contribution to the vegetation distribution is greater than the influence standard, and give it the highest authority; through multiple iterations and adjustments, the weight distribution of each terrain factor is optimized.

7. The bio-vegetation configured landslide runout wave attenuation method of claim 1, wherein, The process of predicting the wave amplitude and wave height attenuation amplitude of the surge wave in the vegetation area according to the overall attenuation change graph of the wave passing through the vegetation area to the wave amplitude and the wave height includes: Obtain the geometric shape of the vegetation area, quantify the canopy density in the geometric shape of the vegetation area, and obtain the frontal area of the canopy volume; calculate according to the frontal area of the canopy to obtain the unit area of the plane; analyze the unit area of the plane to obtain the plane area occupied by the canopy element; the frontal area of the crown volume is: The frontal area index is: wherein, is the frontal area index; represents the frontal area of the crown volume; The plane area occupied by the plane canopy element is: wherein, the planar area occupied by the planar canopy element; represents the frontal area quantifying the canopy volume; Analyze the overall attenuation change graph of the wave, define the vegetation submergence degree as the ratio of the still water depth to the vegetation submergence height, and define the corrected frontal area index as the product of the frontal area index and the submergence degree. The dimensionless canopy density is numerically the canopy density; Calculate the defined data to obtain the change process of the relative amplitude attenuation coefficient with the relative initial wave amplitude; predict according to the change process, compare the prediction result with the measured value, set a comparison threshold, and if it is greater than the comparison threshold, recalculate; Analyze the wave attenuation change graph, define the vegetation submergence degree as the ratio of the still water depth to the vegetation submergence height, and define the corrected frontal area index as the product of the frontal area index and the submergence degree. The dimensionless frontal area of the canopy volume is numerically the canopy density; The corrected frontal area index is defined as the product of the frontal area index and the submergence degree: wherein, to correct the frontal area index, is the frontal area index, is the vegetation submergence degree; Analyze the wave amplitude attenuation characteristics, the non-submerged state of the vegetation corresponds to a still water depth less than or equal to 0.16 meters, and the non-submerged state of the 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 the vegetation corresponds to a still water depth less than or equal to 0.16 meters, and the non-submerged state of the vegetation corresponds to a still water depth greater than or equal to 0.24 meters; According to the analysis of the wave amplitude attenuation characteristics and the wave height attenuation characteristics, the non-submerged and submerged states of the vegetation are obtained; Analyze the non-submerged and submerged states of the vegetation, and the empirical formula of the relative wave height attenuation coefficient under the non-submerged and submerged states of the vegetation: wherein is the relative amplitude decay coefficient, is the relative initial amplitude; According to the empirical formula, the result is obtained; set a comparison threshold, compare the predicted value with the measured value, if it is greater than the threshold, recalculate the threshold; if it is less than the threshold, analyze the comparison value; According to the analysis result, the trajectory of the relative wave height attenuation coefficient with the relative initial wave height under the non-submerged and submerged states of the vegetation is obtained; the empirical formula of the relative wave height attenuation coefficient under the non-submerged and submerged states of the vegetation is calculated: wherein, is the relative wave height attenuation coefficient for non-submerged and submerged state of vegetation, is the initial wave height.

8. A bio-vegetation configured landslide run-up wave attenuation system for use in a bio-vegetation configured landslide run-up wave attenuation method as claimed in any one of claims 1 to 7, wherein, The landslide surge wave attenuation system configured by the biological vegetation includes: The collection module: construct a landslide surge physical model, collect the basic characteristics of the landslide area, and determine the vegetation configuration range according to the basic characteristics; according to the flood position and the normal water level determined by the vegetation configuration elevation area, the vegetation configuration range is calculated; based on the main function of the landslide area, the vegetation types of each elevation area of the vegetation configuration are determined; Wherein, the basic characteristics of the landslide area include soil type, slope, water flow discharge mode; The processing module: the data obtained by the landslide surge physical model is preprocessed, the obtained data is subjected to data cleaning, normalization processing and missing value filling to form a data set; the data set is subjected to data standardization processing, the average value and the standard deviation of the data set are calculated to obtain a standard data set; The vegetation configuration module: 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 the surge; the influence mechanism of vegetation on the surge is analyzed, and a plurality of vegetation configuration schemes are obtained.

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