Water and soil loss risk assessment method and system for long-distance linear engineering

Abstract

The invention discloses a water and soil loss risk assessment method and system for a long-distance linear project, and relates to the technical field of water and soil loss risk assessment, and the method comprises the steps: presetting a sample plot grid rule, and positioning K project sample plots; sample plot ecological element determination is carried out on the first engineering sample plot; constructing a first risk judgment matrix; comprehensively evaluating the risk grade membership degree set by adopting a first risk judgment matrix, and outputting a first sample place risk grade; and after K sample plot risk levels of K engineering sample plots are calculated and output one by one, verification migration of a low-risk sample plot associated vegetation configuration strategy is carried out. The technical problems that in the prior art, long-distance linear engineering water and soil loss risk assessment is not accurate enough, and the effectiveness of migration from a low-risk vegetation configuration strategy to a high-risk area is insufficient are solved, and accurate assessment and grading of long-distance linear engineering water and soil loss risks are achieved; and the effectiveness of migration of the low-risk vegetation configuration strategy to the high-risk area is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and water loss risk assessment, and in particular to a soil and water loss risk assessment method and system for long-distance linear engineering. Background Art

[0002] The construction of long-distance linear projects, due to their large spatial spans and complex terrain, can easily lead to soil erosion during construction, negatively impacting the ecological environment. Existing soil erosion risk assessment methods for long-distance linear projects suffer from inaccurate assessments, failing to fully consider multiple factors along the project route, such as topography, vegetation, and soil. Furthermore, they are unable to effectively transfer vegetation configuration strategies from low-risk areas to high-risk areas, resulting in a lack of precise guidance for soil erosion prevention and control in long-distance linear projects.

[0003] Existing technologies have technical problems such as inaccurate risk assessment of soil erosion in long-distance linear projects and insufficient effectiveness of migrating low-risk vegetation configuration strategies to high-risk areas. Summary of the Invention

[0004] This application provides a method and system for soil and water loss risk assessment of long-distance linear projects, which is used to solve the technical problems in the existing technology of inaccurate soil and water loss risk assessment of long-distance linear projects and insufficient effectiveness of migrating low-risk vegetation configuration strategies to high-risk areas.

[0005] In view of the above problems, this application provides a method and system for soil and water loss risk assessment of long-distance linear projects.

[0006] In a first aspect, the present application provides a method for assessing soil and water loss risk in long-distance linear projects, the method comprising: A sample plot grid rule is preset according to the spatial span of the long-distance linear project, and K engineering sample plots are located along the embankments on both sides of the long-distance linear project based on the sample plot grid rule; the sample plot ecological elements of the first engineering sample plot are measured, and the vegetation characteristics and hierarchical soil characteristic data of the first sample plot are output; a first risk judgment matrix is ​​constructed according to the first terrain parameters of the first engineering sample plot; after mapping the first sample plot vegetation characteristics, the first terrain parameters and the hierarchical soil characteristic data into a risk level membership set based on the membership function, the risk level membership set is comprehensively evaluated using the first risk judgment matrix, and the risk level of the first sample plot is output; after calculating and outputting the K sample plot risk levels of the K engineering sample plots one by one, the low-risk sample plot associated vegetation configuration strategy is verified and migrated according to the attribute clustering characteristics of the K engineering sample plots.

[0007] A second aspect of the present application provides a soil and water loss risk assessment system for long-distance linear projects, the system comprising: An engineering sample site positioning module is used to preset the sample site grid rules according to the spatial span of the long-distance linear project, and locate K engineering sample sites along the embankments on both sides of the long-distance linear project based on the sample site grid rules; an ecological factor determination module is used to determine the sample site ecological factors of the first engineering sample site, and output the first sample site vegetation characteristics and hierarchical soil characteristic data; a first risk judgment matrix construction module is used to construct a first risk judgment matrix according to the first terrain parameters of the first engineering sample site; a first sample site risk level output module is used to map the first sample site vegetation characteristics, first terrain parameters and hierarchical soil characteristic data into a risk level membership set based on a membership function, and then use the first risk judgment matrix to comprehensively evaluate the risk level membership set and output the first sample site risk level; a verification and migration module is used to calculate and output the K sample site risk levels of the K engineering sample sites one by one, and then verify and migrate the low-risk sample site associated vegetation configuration strategy according to the attribute clustering characteristics of the K engineering sample sites.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Based on the spatial span of a long-distance linear project, a sample grid rule is pre-set to locate K project sample plots. The first project sample plot is measured for ecological elements, outputting the first sample plot's vegetation characteristics and hierarchical soil characteristic data. A first risk judgment matrix is ​​constructed. The first risk judgment matrix is ​​used to comprehensively evaluate the risk level membership set and output the first sample plot's risk level. After calculating and outputting the K sample plot risk levels for each of the K project sample plots, a validation and migration strategy for vegetation allocation associated with low-risk sample plots is performed based on the attribute clustering characteristics of the K project sample plots. This method achieves the technical effect of accurately assessing and grading soil and water loss risks for long-distance linear projects, and improving the effectiveness of migrating low-risk vegetation allocation strategies to high-risk areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 A schematic flow chart of a method for assessing soil and water loss risk in long-distance linear projects provided in an embodiment of the present application; Figure 2 Schematic diagram of the structure of the soil and water loss risk assessment system for long-distance linear projects provided in an embodiment of the present application.

[0011] Explanation of the accompanying reference numerals: engineering sample site positioning module 10 , ecological factor determination module 20 , first risk judgment matrix construction module 30 , first sample site risk level output module 40 , verification and migration module 50 . DETAILED DESCRIPTION

[0012] This application provides a method and system for soil and water loss risk assessment of long-distance linear projects, which is used to solve the technical problems in the existing technology that the soil and water loss risk assessment of long-distance linear projects is not accurate enough and the migration effectiveness of low-risk vegetation configuration strategies to high-risk areas is insufficient.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0014] Example 1, as Figure 1 As shown, the present application provides a method for assessing soil and water loss risks in long-distance linear projects, the method comprising: Step S100: Preset a sample plot grid rule according to the spatial span of the long-distance linear project, and locate K engineering sample plots along the dikes on both sides of the long-distance linear project based on the sample plot grid rule.

[0015] Specifically, a grid rule based on fixed distance intervals (e.g., every 5 kilometers) or geomorphic units (e.g., different soil type areas, slope sections) is established, taking into account the project's geographic extension, terrain complexity, and the spatial variability of factors affecting soil erosion. Subsequently, based on this preset sample plot grid rule, project sample plots are located along the levees on both sides of the long-distance linear project, and K project sample plots are identified. During the positioning process, it is necessary to ensure that the sample plots evenly cover the typical terrain, soil, and vegetation distribution areas along the project, such as different geological sections such as hard soil sections, soft soil sections, and sandy soil sections, as well as key disturbance areas such as levee slopes and spoil areas. This ensures that the K project sample plots are spatially representative and provide a scientific sample basis for subsequent soil erosion risk assessment.

[0016] Step S200: measuring the ecological elements of the first engineering sample plot, and outputting the vegetation characteristics and hierarchical soil characteristic data of the first sample plot.

[0017] Specifically, firstly, the vegetation sample layout rules and soil stratification sampling rules are preset, for example, 3M 2m×2m shrub sample plots and 3M 1m×1m herb sample plots are laid out along the diagonal of the sample plot (M is a positive integer), and soil samples are collected at three layers of 0~20cm, 20~40cm, and 40~60cm; then, according to the vegetation sample layout rules, the first vegetation sample area distribution is delineated within the projection range of the first engineering sample plot, and the area is traversed to extract and record the plant species, number of plants, and fresh weight. and dry weight and other vegetation characteristics of the first sample; then, according to the soil stratified sampling rules, the hierarchical sampling point matrix was located in the first engineering sample site, and the original soil samples were taken vertically with a ring knife to obtain the hierarchical soil sample matrix, and the physical indicators such as soil bulk density and porosity were measured. After mixing the soil samples of the same level, the biochemical indicators such as organic carbon, nitrogen, phosphorus and potassium content, microbial biomass and enzyme activity were measured, and then the stratified soil physical property matrix and the hierarchical biochemical characteristic data were spatially discretized, hierarchically spliced ​​and normalized, and finally the hierarchical soil characteristic data were output.

[0018] Step S300: constructing a first risk judgment matrix according to the first terrain parameters of the first engineering site.

[0019] Specifically, a terrain risk classification table is first predefined, and the first terrain parameters (slope, slope length, and terrain relief) are substituted into the table for standardization, converting them into the first slope risk level, the first slope length risk level, and the first relief risk level. Then, a terrain factor comparison matrix is ​​constructed based on the indicator composition of the first terrain parameter and the importance of each indicator factor. The three risk levels are then input into the terrain factor comparison matrix, and pairwise comparisons of terrain indicators are performed to obtain an initial risk judgment matrix. Finally, the initial risk judgment matrix is ​​processed by weight normalization to obtain the first risk judgment matrix.

[0020] Step S400: After mapping the first quadrat vegetation characteristics, first terrain parameters and hierarchical soil characteristic data into a risk level membership set based on a membership function, the risk level membership set is comprehensively evaluated using the first risk judgment matrix to output the first quadrat risk level.

[0021] Specifically, first, the interval normalization processing of the vegetation characteristics of the first quadratic sample is performed using the range method, and then the first normalized vegetation membership is generated by combining the vegetation factor weight calculation; secondly, the first slope risk level, the first slope length risk level and the first undulation risk level corresponding to the first terrain parameter are converted into risk membership values ​​to obtain the first terrain membership array containing the first slope membership, the first slope length membership and the first undulation membership; then, the hierarchical soil risk quantification of the hierarchical soil characteristic data is performed in combination with the soil layer depth risk setting, and then the risk membership value is converted to obtain the hierarchical soil membership; finally, the first normalized vegetation membership, the first terrain membership array and the hierarchical soil membership are used as the risk level membership set and input into the first risk judgment matrix. The first risk comprehensive evaluation value is calculated through matrix operation, and the first plot risk level is located and output based on the matching relationship between the evaluation value and the preset risk level threshold.

[0022] Step S500: After calculating and outputting the risk levels of the K engineering plots one by one, verifying and migrating the vegetation configuration strategy associated with the low-risk plots according to the attribute clustering characteristics of the K engineering plots.

[0023] Specifically, after calculating and outputting the risk levels of K engineering sites, a terrain similarity evaluation is first carried out based on the terrain parameters of these K engineering sites, and the K engineering sites are aggregated into W groups through this evaluation; then, the W groups of engineering sites are sorted within the group according to the risk level of each site, so as to determine W low-risk sites; finally, the vegetation configuration strategy associated with these W low-risk sites is called, and vegetation configuration optimization is implemented for the high-risk sites in the group, so as to realize the verification and migration application of the vegetation configuration strategy of low-risk sites in high-risk areas.

[0024] In one possible implementation, step S200 further includes: Step S210: Preset vegetation quadrat layout rules and soil stratification sampling rules.

[0025] Step S220: Based on the vegetation quadrat layout rule, the first vegetation quadrat area distribution is projected and delineated on the first engineering plot.

[0026] Step S230: traverse the regional distribution of the first vegetation quadrat, extract and record the vegetation features of the first quadrat.

[0027] Step S240: After collecting a first-level mixed soil sample from the first engineering sample site according to the soil stratified sampling rule, measure and output stratified soil characteristic data.

[0028] Specifically, the preset vegetation plot layout rules are as follows: 3M 2m×2m shrub plots and 3M 1m×1m herb plots (M is a positive integer) are set along the diagonal line within the engineering plot to record vegetation characteristics such as plant species, number of plants, biomass (fresh weight and dry weight); the preset soil stratified sampling rules are as follows: 3 mixed soil samples are taken along the diagonal line within the engineering plot, the soil at a depth of 0~60cm is divided into 3 layers, and the original soil samples are taken vertically with a ring knife to determine the soil bulk density, organic carbon, nitrogen, phosphorus and potassium content, pH, microbial activity and other physical, chemical and biological indicators.

[0029] According to the preset vegetation plot layout rules, the first vegetation plot area distribution is delineated along the diagonal direction of the projection plane of the first engineering plot. The distribution includes 3M 2m×2m shrub plots and 3M 1m×1m herb plots arranged along the diagonal (M is a positive integer), forming a regular plot array to ensure the systematic sampling and measurement of vegetation characteristics in the engineering plot.

[0030] According to the designated distribution of the first vegetation sample plot area, 3M shrub sample plots (2m×2m) and 3M herb sample plots (1m×1m) arranged diagonally were traversed in turn. In each sample plot, vegetation characteristic data such as plant species, number of plants, biomass (including fresh weight and dry weight) were extracted and recorded in detail. Through field measurements, accurate basic data of vegetation ecological elements were provided for subsequent soil and water loss risk assessment.

[0031] According to the preset soil stratification sampling rules, three mixed soil sampling points were located along the diagonal line in the first engineering plot. The soil at a depth of 0-60 cm was divided into three sampling layers. Root-soil samples were collected vertically using a circular cutter to form a layered soil sample matrix. Physical properties such as soil bulk density and porosity of soil samples in each layer were measured to output a layered soil physical property matrix. Soil samples from the same layer were mixed, and soil chemical indicators such as total and effective amounts of organic carbon, nitrogen, phosphorus and potassium, as well as biological indicators such as microbial biomass and enzyme activity were measured to output layered biochemical characteristic data. The layered biochemical characteristic data were then spatially discretized into a matrix form, spatially aligned and hierarchically spliced ​​with the layered soil physical property matrix. Finally, the spliced ​​characteristic matrix was normalized with reference to the soil erosion classification standard to output layered soil characteristic data containing soil physical and biochemical properties.

[0032] In one possible implementation, step S220 further includes: Step S221: The first vegetation quadrat area distribution includes 3M shrub quadrat and 3M herb quadrat set along the diagonal, where M is a positive integer.

[0033] Specifically, the first vegetation plot area distribution is to set 3M shrub plots and 3M herb plots along the diagonal direction within the projection range of the first project plot, where M is a positive integer. This layout helps to form a regular sampling layout within the plot, ensuring the uniformity and representativeness of vegetation characteristic data collection, and facilitating the subsequent systematic recording and analysis of indicators such as plant species, plant numbers, and biomass.

[0034] In one possible implementation, step S240 further includes: Step S241: Locate a hierarchical sampling point matrix in the first engineering sample site according to the soil stratified sampling rule.

[0035] Step S242: vertically sampling undisturbed soil samples using a circular cutter at the hierarchical sampling point matrix to obtain a hierarchical soil sample matrix.

[0036] Step S243: Outputting a layered soil physical property matrix by measuring the soil bulk density and soil porosity of the layered soil sample matrix.

[0037] Step S244: After mixing the soil sample matrices at the same level, perform preset biochemical index determination and output biochemical characteristic data of the level.

[0038] Step S245: normalizing the hierarchical soil physical property matrix and hierarchical biochemical characteristic data, and outputting the hierarchical soil characteristic data.

[0039] Specifically, according to the pre-set soil stratification sampling rules, within the scope of the first engineering sample site, three soil layers (such as 0~20cm, 20~40cm, and 40~60cm) were divided according to the depth range of 0~60cm, and several sampling points were evenly distributed in each soil layer, thus forming a multi-level sampling point matrix. This matrix is ​​used to guide subsequent soil sampling work to ensure that the collected soil samples can comprehensively and evenly reflect the characteristics of different soil layers.

[0040] In the located hierarchical sampling point matrix, for each sampling point, a ring knife tool was used to vertically insert into the soil, keeping the ring knife perpendicular to the ground to ensure that undisturbed original soil samples were obtained. Soil samples were collected at sampling points in each soil layer, such as 0~20cm, 20~40cm, and 40~60cm. The soil samples of each soil layer were arranged in order according to the location of the sampling point, thus forming a hierarchical soil sample matrix containing soil samples at different depths, providing original samples for subsequent determination of soil physical and biochemical properties.

[0041] For each soil layer in the hierarchical soil sample matrix, the bulk density of the soil was measured using the knife ring method. The bulk density value was calculated by calculating the ratio of the fresh weight of the soil within the knife ring to the volume. The porosity of the soil was measured using a pressure membrane instrument or porosimeter to obtain the ratio of the pore volume to the total volume of the soil. The soil bulk density and porosity data of each sampling point were organized by soil layer depth and spatial location to form a stratified soil physical property matrix containing the physical properties of soil layers at different depths. This matrix intuitively reflects the compaction level and air and water permeability of each soil layer, providing basic physical property data for subsequent soil risk assessment.

[0042] Multiple soil samples from the same soil layer within the hierarchical soil sample matrix were thoroughly mixed to obtain composite samples from each soil layer. These composite samples were then used to determine pre-set biochemical indicators. The soil chemical indicator set included organic carbon content, total and effective amounts of nitrogen, phosphorus, and potassium. Organic carbon was determined using the potassium dichromate oxidation method, nitrogen content was determined using the Kjeldahl method, phosphorus content was determined using the molybdenum antimony colorimetric method, and potassium content was determined using flame photometry. The soil biological indicator set included microbial biomass and enzyme activity. Microbial biomass was determined using chloroform fumigation extraction, while enzyme activity was determined using spectrophotometry. The biochemical indicator data for each soil layer were collated and output as hierarchical biochemical characteristic data, providing a basis for analyzing soil biochemical properties and soil erosion risks.

[0043] Referring to the spatial distribution of the hierarchical sampling point matrix, the hierarchical biochemical characteristic data are spatially discretized to correspond to the spatial position of the hierarchical soil physical property matrix, and the hierarchical biochemical characteristic matrix is ​​output. Then, the hierarchical biochemical characteristic matrix and the hierarchical soil physical property matrix are hierarchically spliced ​​through spatial alignment to obtain a hierarchical characteristic matrix containing soil physical and biochemical characteristics. Finally, the soil erosion classification standard is used to normalize the various indicators in the hierarchical characteristic matrix, and the indicators of different dimensions are uniformly mapped to the [0, 1] interval. Finally, standardized hierarchical soil characteristic data are output, providing unified and standardized soil characteristic input for subsequent soil and water loss risk assessment.

[0044] In one possible implementation, step S245 further includes: Step S2451: referring to the hierarchical sampling point matrix, spatially discretizing the hierarchical biochemical feature data, and outputting a hierarchical biochemical feature matrix.

[0045] Step S2452: performing hierarchical data splicing by spatially aligning the hierarchical biochemical characteristic matrix and the layered soil physical property matrix to obtain a hierarchical characteristic matrix.

[0046] Step S2453: using the soil erosion classification standard to perform index normalization processing on the hierarchical feature matrix, and outputting the hierarchical soil feature data.

[0047] Specifically, with the spatial distribution of the hierarchical sampling point matrix as a reference, the hierarchical biochemical characteristic data (such as organic carbon content, total and effective amounts of nitrogen, phosphorus and potassium, microbial biomass, enzyme activity, etc.) are spatially discretized according to the location of the sampling points, so that each biochemical indicator data corresponds to a specific coordinate position in the matrix, thereby forming a hierarchical biochemical characteristic matrix consistent with the structure of the hierarchical sampling point matrix, realizing the spatial structured expression of the biochemical characteristic data, and providing a unified spatial reference framework for the subsequent integration with soil physical property data.

[0048] The output hierarchical biochemical characteristic matrix and the stratified soil physical property matrix are aligned based on the soil layer depth and spatial position to ensure that the data of the same soil layer depth and spatial coordinate points correspond one to one. Then, the soil physical property data (such as bulk density and porosity) and the biochemical characteristic data (such as organic carbon, nitrogen, phosphorus and potassium content, microbial biomass, etc.) are spliced ​​to form a hierarchical characteristic matrix containing multi-dimensional soil characteristics, realizing the spatial integration of soil physical and biochemical data, and providing a comprehensive data basis for subsequent unified normalization processing and risk assessment.

[0049] By determining the threshold range of each indicator in the soil erosion classification standard, for example, the erosion classification threshold of soil bulk density is ≤1.3g / cm³ for low erosion risk, 1.3-1.5g / cm³ for medium erosion risk, and organic carbon content ≥20g / kg for low erosion risk. Then, each indicator in the hierarchical feature matrix, such as soil bulk density, organic carbon content, nitrogen, phosphorus, potassium content, microbial biomass, etc., is normalized using the range method, and the formula is: , where x is the original index value, and The normalized values ​​of each indicator are combined into a new matrix, which ultimately outputs hierarchical soil characteristic data. This converts indicators of different dimensions into dimensionless data within the [0, 1] interval, facilitating subsequent risk assessment calculations.

[0050] In one possible implementation, step S244 further includes: Step S2441: The preset biochemical indicators include a soil chemical indicator set and a soil biological indicator set. The soil chemical indicator set consists of organic carbon, total nitrogen, phosphorus and potassium, and effective nitrogen, phosphorus and potassium. The soil biological indicator set consists of microbial biomass and enzyme activity.

[0051] Specifically, the preset biochemical indicators are divided into two categories: soil chemical indicator set and soil biological indicator set. The soil chemical indicator set mainly includes indicators such as organic carbon, total nitrogen, phosphorus, and potassium, and effective nitrogen, phosphorus, and potassium. These indicators can reflect the chemical composition and nutrient status of the soil. For example, organic carbon content can be determined by potassium dichromate oxidation, while total nitrogen, phosphorus, and potassium and effective nitrogen and phosphorus can be determined by Kjeldahl nitrogen determination, molybdenum antimony colorimetry, and flame photometry, respectively. The soil biological indicator set consists of microbial biomass and enzyme activity. Microbial biomass can be determined by chloroform fumigation extraction, while enzyme activity is usually detected by spectrophotometry. These biological indicators help us understand the intensity of biological activity in the soil and the biological functions of the soil.

[0052] In one possible implementation, step S300 further includes: Step S310: pre-define a terrain risk classification table, and use the first terrain parameter to traverse the terrain risk classification table to perform terrain risk classification standardization, and convert and output a first slope risk level, a first slope length risk level, and a first undulation risk level.

[0053] Step S320: constructing a terrain factor comparison matrix based on the index composition of the first terrain parameter and the assignment of index factor importance.

[0054] Step S330: inputting the first slope risk level, the first slope length risk level, and the first undulation risk level into the terrain factor comparison matrix to perform pairwise comparison of terrain indicators and obtain an initial risk judgment matrix.

[0055] Step S340: Processing the initial risk judgment matrix by weight normalization to obtain the first risk judgment matrix.

[0056] Specifically, a terrain risk grading table containing risk level classification standards corresponding to terrain parameters such as slope, slope length, and terrain undulation is predefined. In the table, corresponding risk levels are set for matching terrain parameter values ​​in different ranges. Then, the first terrain parameters of the first engineering sample site, namely the specific values ​​of slope, slope length and terrain undulation, are substituted into the terrain risk grading table for traversal one by one. By comparing each parameter value with the threshold range of each risk level in the table, the standardized processing of terrain risk grading is completed, and finally the first slope risk level, the first slope length risk level and the first undulation risk level are converted and output, providing standardized terrain risk level data for the subsequent construction of the risk judgment matrix.

[0057] Based on the composition of the primary topographic parameters (slope, slope length, and terrain relief) of the first project site and the importance assigned to each factor, a terrain factor comparison matrix was constructed using the 1-9 scaling method of the analytic hierarchy process (AHP). In this method, factors of equal importance are assigned a value of 1. If a factor is more important than another, it is assigned a value of 3-9 in ascending order of importance, and vice versa. Here, based on the relative importance order of slope > terrain relief > slope length, in the matrix, slope is assigned an importance of 1 relative to itself, 5 relative to terrain relief, and 7 relative to slope length. The inverse of terrain relief compared to slope is 1 / 5, 1 relative to itself, and 3 relative to slope length. The inverses of slope length compared to slope and terrain relief are 1 / 7 and 1 / 3, respectively, and 1 relative to itself. This constructs a comparison matrix reflecting the relative importance of terrain factors.

[0058] First, obtain the obtained first slope risk level, first slope length risk level, and first relief risk level. These risk level values ​​are then substituted into the previously constructed terrain factor comparison matrix. This matrix, constructed using the 1-9 scaling method of the Analytic Hierarchy Process (AHP), reflects the relative importance of slope, slope length, and relief. For example, the matrix might assign a value of 7 to slope length and 5 to relief. Then, based on the relative importance scale of each terrain factor in the matrix, a pairwise comparison operation is performed on the three terrain risk levels. This is done by multiplying each terrain risk level by the corresponding elements of the other two risk levels and summing the results. This takes into account the importance weights of each terrain factor and the risk level values. This ultimately yields an initial risk judgment matrix, which preliminarily quantifies the combined impact of different terrain indicators in risk assessment.

[0059] When performing weight normalization on the initial risk judgment matrix, first calculate the column sum of each column of the matrix, then divide each element in the matrix by the column sum of its column to obtain the normalized matrix element, and then calculate the average value of the elements in each row to obtain the weight vector of each terrain factor. Finally, the first risk judgment matrix is ​​constructed through this weight vector, which can accurately reflect the weight ratio of terrain factors such as slope, slope length, and terrain undulation in soil and water loss risk assessment, and provide a scientific quantitative basis for the subsequent comprehensive evaluation of risk levels based on membership functions.

[0060] In one possible implementation, step S400 further includes: Step S410: performing interval normalization processing on the vegetation characteristics of the first quadrat using the range method, and then calculating and generating a first normalized vegetation membership by combining the vegetation factor weight.

[0061] Step S420: Convert the first slope risk level, the first slope length risk level, and the first undulation risk level corresponding to the first terrain parameter into risk membership values, and output a first terrain membership array, wherein the first terrain membership array includes a first slope membership, a first slope length membership, and a first undulation membership.

[0062] Step S430: After quantifying the hierarchical soil risk of the hierarchical soil characteristic data in combination with the soil depth risk setting, convert the risk membership value and output the hierarchical soil membership.

[0063] Step S440: taking the first normalized vegetation membership, the first terrain membership array and the hierarchical soil membership as a risk level membership set, inputting them into the first risk judgment matrix, and calculating and outputting a first comprehensive risk evaluation value.

[0064] Step S450: using the first comprehensive risk evaluation value to match and locate the first sample site risk level.

[0065] Specifically, the range method was first used to normalize the data on plant species, plant number, and biomass (fresh and dry weight) included in the first quadratic vegetation characteristics. Specifically, the formula normalized value = (original value - minimum value of the indicator) / (maximum value of the indicator - minimum value of the indicator) was used to map the numerical range of each vegetation characteristic indicator to the interval [0, 1], thereby eliminating the impact of dimensional differences between different indicators. Subsequently, the normalized vegetation characteristic values ​​were weighted and calculated based on the pre-set vegetation factor weights, namely the importance weights of different vegetation types in soil and water conservation functions, ultimately generating the first normalized vegetation membership. This membership can quantitatively represent the contribution of vegetation elements to soil and water loss risk assessment.

[0066] Based on the pre-defined correspondence between risk levels and membership, the first slope risk level, first slope length risk level, and first relief risk level in the first terrain parameter are converted separately. For the first slope risk level, a slope less than 5° corresponds to low risk, with a membership of 0.8; a slope between 5° and 15° corresponds to medium risk, with a membership of 0.5; and a slope greater than 15° corresponds to high risk, with a membership of 0.2. For the first slope length risk level, a slope less than 50m corresponds to low risk, with a membership of 0.7; a slope between 50m and 100m corresponds to medium risk, with a membership of 0.4; and a slope greater than 100m corresponds to high risk, with a membership of 0.1. For the first relief risk level, a terrain relief less than 3m corresponds to low risk, with a membership of 0.9; a terrain relief between 3m and 5m corresponds to medium risk, with a membership of 0.6; and a terrain relief greater than 5m corresponds to high risk, with a membership of 0.3. Through the above conversion, the first slope membership, the first slope length membership, and the first undulation membership are obtained respectively, which are integrated and output as the first terrain membership array to complete the quantitative characterization of terrain risk and provide data support for subsequent comprehensive risk assessment.

[0067] Combining pre-defined soil depth risk criteria, the system quantifies the risk of each soil layer, including bulk density, organic carbon, and nitrogen, phosphorus, and potassium content, based on the bulk density-organic carbon thresholds of each layer (e.g., a bulk density greater than 1.3 g / cm³ and an organic carbon content less than 10 g / kg in the 0-20 cm layer). The risk level is then converted to a risk membership value based on the corresponding relationship between risk level and membership degree (e.g., high risk corresponds to a membership degree of 0.2, medium risk corresponds to a membership degree of 0.5, and low risk corresponds to a membership degree of 0.8). The final output is a hierarchical soil membership degree containing the risk membership values ​​of each soil layer, which quantifies the degree of soil and water loss risk at different depths.

[0068] The first normalized vegetation membership, the first terrain membership array (including the first slope membership, the first slope length membership, and the first relief membership), and the hierarchical soil membership obtained through preliminary processing are combined to form a risk level membership set. This set is then substituted into the constructed first risk judgment matrix as input data. Through matrix multiplication, that is, the weighted summation of each membership value and the corresponding weight coefficient in the matrix, the first comprehensive risk assessment value is calculated. This value comprehensively reflects the impact of multiple factors such as vegetation, terrain, and soil on soil and water loss risk.

[0069] The calculated first comprehensive risk evaluation value is compared and matched with the pre-set risk level threshold range. For example, the comprehensive risk evaluation value is set to 0~0.3 as low risk, 0.3~0.7 as medium risk, and 0.7~1 as high risk. By judging the interval where the first comprehensive risk evaluation value is located, the risk level of the first sample site is located and determined, and the final judgment of the soil and water loss risk level of the sample site is completed.

[0070] In one possible implementation, step S500 further includes: Step S510: performing terrain similarity evaluation based on K terrain parameters to aggregate the K engineering sites into W groups of engineering sites.

[0071] Step S520: Sort the W groups of engineering sample plots according to the risk levels of the K sample plots to locate W low-risk sample plots.

[0072] Step S530: calling the W associated vegetation configuration strategies of the W low-risk plots to optimize the vegetation configuration of the high-risk plots in the group.

[0073] Specifically, the Euclidean distance is used to calculate the difference in terrain parameters (slope, slope length, and terrain relief) between any two plots in K engineering plots. The formula is: ,in 、 、 where are the slope, slope length, and terrain relief parameter values ​​for the i-th plot, respectively. Then, using the K-means clustering algorithm, with the Euclidean distance of terrain parameters as the similarity metric, the K engineering plots are divided into W clusters. The plots within each cluster have similar terrain characteristics, resulting in W groups of engineering plots, thus grouping the engineering plots based on their terrain similarity.

[0074] After clustering K engineering plots into W groups based on terrain similarity, for each group of engineering plots, the engineering plots within the group are sorted in ascending order based on the calculated risk level values ​​of each sample, thereby determining the engineering plot with the lowest risk level in each group. Ultimately, W low-risk plots are located from the W groups, providing a basis for the subsequent migration of vegetation configuration strategies.

[0075] After locating W low-risk plots, the team extracted the vegetation configuration strategies associated with each low-risk plot. These strategies included information such as plant species, planting density, and matching methods appropriate for the terrain and risk level. These strategies were then applied to the high-risk plots within each group, with adjustments and optimizations made based on the specific conditions of the high-risk plots. For example, in high-risk plots with steeper slopes, the same mixed planting pattern of drought-tolerant shrubs and herbs as in the low-risk plots in the same group could be adopted to enhance soil retention and reduce the risk of soil erosion, thereby optimizing the vegetation configuration for the high-risk plots within the group.

[0076] Example 2, based on the same inventive concept as the method for assessing soil and water loss risk of long-distance linear projects in the above-mentioned embodiment, Figure 2 As shown, this application provides a soil and water loss risk assessment system for long-distance linear projects. The system and method embodiments in this application are based on the same inventive concept. The system includes: The engineering sample plot positioning module 10 is used to preset a sample plot grid rule according to the spatial span of the long-distance linear project, and locate K engineering sample plots along the dikes on both sides of the long-distance linear project based on the sample plot grid rule.

[0077] The ecological factor determination module 20 is used to measure the ecological factors of the first engineering sample plot and output the vegetation characteristics and hierarchical soil characteristic data of the first sample plot.

[0078] The first risk judgment matrix construction module 30 is used to construct a first risk judgment matrix according to the first terrain parameters of the first engineering site.

[0079] The first plot risk level output module 40 is used to map the first plot vegetation characteristics, first terrain parameters and hierarchical soil characteristic data into a risk level membership set based on a membership function, and then use the first risk judgment matrix to comprehensively evaluate the risk level membership set to output the first plot risk level.

[0080] The verification and migration module 50 is used to perform verification and migration of the vegetation configuration strategy associated with low-risk plots according to the attribute clustering characteristics of the K engineering plots after calculating and outputting the risk levels of the K plots one by one.

[0081] Furthermore, the system is also used to implement the following functions: Preset vegetation quadrat layout rules and soil stratification sampling rules; project and delineate the first vegetation quadrat area distribution on the first engineering sample site based on the vegetation quadrat layout rules; traverse the first vegetation quadrat area distribution, extract and record the first quadrat vegetation characteristics; after collecting the first layer of mixed soil samples from the first engineering sample site based on the soil stratification sampling rules, measure and output stratified soil characteristic data.

[0082] Furthermore, the system is also used to implement the following functions: A terrain risk classification table is predefined, and the first terrain parameter is used to traverse the terrain risk classification table to perform terrain risk classification standardization, converting and outputting a first slope risk level, a first slope length risk level, and a first undulation risk level; a terrain factor comparison matrix is ​​constructed based on the indicator composition and indicator factor importance assignment of the first terrain parameter; the first slope risk level, the first slope length risk level, and the first undulation risk level are input into the terrain factor comparison matrix to perform pairwise comparison of terrain indicators to obtain an initial risk judgment matrix; the initial risk judgment matrix is ​​processed by weight normalization to obtain the first risk judgment matrix.

[0083] Furthermore, the system is also used to implement the following functions: After the first sample plot vegetation characteristics are interval-normalized using the range method, a first normalized vegetation membership is generated by combining the vegetation factor weight calculation; the first slope risk level, the first slope length risk level, and the first undulation risk level corresponding to the first terrain parameter are converted into risk membership values, and a first terrain membership array is output, wherein the first terrain membership array includes the first slope membership, the first slope length membership, and the first undulation membership; after the hierarchical soil risk is quantified based on the soil layer depth risk setting, the risk membership value is converted and a hierarchical soil membership is output; the first normalized vegetation membership, the first terrain membership array, and the hierarchical soil membership are used as a risk level membership set, input into the first risk judgment matrix, and a first comprehensive risk evaluation value is calculated and output; the first comprehensive risk evaluation value is used to match and locate the first sample plot risk level.

[0084] Furthermore, the system is also used to implement the following functions: A terrain similarity evaluation is performed based on K terrain parameters to aggregate the K engineering plots into W groups of engineering plots. The W groups of engineering plots are sorted within the group according to the risk levels of the K plots to locate W low-risk plots. The W associated vegetation configuration strategies of the W low-risk plots are called to optimize the vegetation configuration of the high-risk plots within the group.

[0085] Furthermore, the system is also used to implement the following functions: The first vegetation quadrat area distribution includes 3M shrub quadrat areas and 3M herb quadrat areas set along a diagonal line, where M is a positive integer.

[0086] Furthermore, the system is also used to implement the following functions: According to the soil stratified sampling rule, a hierarchical sampling point matrix is ​​positioned in the first engineering sample site; a circular knife is used to vertically sample undisturbed soil samples in the hierarchical sampling point matrix to obtain a hierarchical soil sample matrix; the soil bulk density and soil porosity of the hierarchical soil sample matrix are measured to output a hierarchical soil physical property matrix; after mixing the hierarchical soil sample matrices at the same level, preset biochemical indicators are measured to output hierarchical biochemical characteristic data; the hierarchical soil physical property matrix and the hierarchical biochemical characteristic data are normalized to output the hierarchical soil characteristic data.

[0087] Furthermore, the system is also used to implement the following functions: Referring to the hierarchical sampling point matrix, the hierarchical biochemical characteristic data are spatially discretized to output a hierarchical biochemical characteristic matrix; hierarchical data splicing is performed by spatially aligning the hierarchical biochemical characteristic matrix and the stratified soil physical property matrix to obtain a hierarchical characteristic matrix; the hierarchical characteristic matrix is ​​normalized using the soil erosion classification standard to output the hierarchical soil characteristic data.

[0088] Furthermore, the system is also used to implement the following functions: The preset biochemical indicators include a soil chemical indicator set and a soil biological indicator set. The soil chemical indicator set consists of organic carbon, total nitrogen, phosphorus and potassium, and effective nitrogen, phosphorus and potassium. The soil biological indicator set consists of microbial biomass and enzyme activity.

[0089] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0091] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. Soil and water loss risk assessment method for long-distance linear projects, characterized by: The method comprises: Presetting a sample plot grid rule according to the spatial span of the long-distance linear project, and locating K engineering sample plots along the dikes on both sides of the long-distance linear project based on the sample plot grid rule; Conduct ecological factor measurements on the first project sample plot and output the first sample plot vegetation characteristics and hierarchical soil characteristic data; constructing a first risk judgment matrix according to the first terrain parameter of the first engineering sample site; After mapping the first quadrat vegetation characteristics, the first terrain parameters, and the hierarchical soil characteristic data into a risk level membership set based on a membership function, the risk level membership set is comprehensively evaluated using the first risk judgment matrix to output a first quadrat risk level; After calculating and outputting the K risk levels of the K engineering plots one by one, verification and migration of vegetation configuration strategies associated with low-risk plots are performed according to attribute clustering characteristics of the K engineering plots.

2. The soil and water loss risk assessment method for long-distance linear engineering according to claim 1, characterized in that: The method includes measuring ecological factors of a first project sample plot and outputting vegetation characteristics and hierarchical soil characteristic data of the first sample plot. Preset vegetation plot layout rules and soil stratification sampling rules; According to the vegetation quadrat layout rule, projecting and demarcating the first vegetation quadrat area distribution on the first engineering sample site; Traversing the regional distribution of the first vegetation quadrat, extracting and recording vegetation characteristics of the first quadrat; After collecting a first-level mixed soil sample from the first engineering sample site according to the soil stratified sampling rule, the stratified soil characteristic data is measured and output.

3. The soil and water loss risk assessment method for long-distance linear engineering according to claim 1, characterized in that: Constructing a first risk judgment matrix based on first terrain parameters of the first engineering site, the method includes: Predefine a terrain risk classification table, and use the first terrain parameter to traverse the terrain risk classification table to perform terrain risk classification standardization, and convert and output a first slope risk level, a first slope length risk level, and a first undulation risk level; Constructing a terrain factor comparison matrix based on the index composition of the first terrain parameter and the assignment of index factor importance; Inputting the first slope risk level, the first slope length risk level, and the first undulation risk level into the terrain factor comparison matrix, performing pairwise comparison of terrain indicators, and obtaining an initial risk judgment matrix; The initial risk judgment matrix is ​​processed by weight normalization to obtain the first risk judgment matrix.

4. The soil and water loss risk assessment method for long-distance linear engineering according to claim 3, characterized in that: After mapping the first quadrat vegetation characteristics, the first terrain parameters, and the hierarchical soil characteristic data into a risk level membership set based on a membership function, comprehensively evaluating the risk level membership set using the first risk judgment matrix to output a first quadrat risk level, the method includes: After the vegetation characteristics of the first quadrat are normalized by using the range method, the first normalized vegetation membership is generated by combining the vegetation factor weight calculation; performing risk membership conversion on the first slope risk level, the first slope length risk level, and the first relief risk level corresponding to the first terrain parameter, and outputting a first terrain membership array, wherein the first terrain membership array includes a first slope membership, a first slope length membership, and a first relief membership; After quantifying the hierarchical soil risk based on the hierarchical soil characteristic data in combination with the soil depth risk setting, the risk membership value is converted and the hierarchical soil membership is output; Taking the first normalized vegetation membership, the first terrain membership array, and the hierarchical soil membership as a risk level membership set, inputting the first risk judgment matrix, and calculating and outputting a first comprehensive risk evaluation value; The first comprehensive risk evaluation value is used to match and locate the risk level of the first sample site.

5. The soil and water loss risk assessment method for long-distance linear engineering according to claim 1, characterized in that: After calculating and outputting the risk levels of the K engineering plots one by one, verifying and migrating the vegetation configuration strategy associated with the low-risk plots according to the attribute clustering characteristics of the K engineering plots, the method includes: Performing terrain similarity evaluation based on K terrain parameters to aggregate the K engineering plots into W groups of engineering plots; Sorting the W groups of engineering sample plots within the group according to the risk levels of the K sample plots to locate W low-risk sample plots; The W associated vegetation configuration strategies of the W low-risk plots are called to optimize the vegetation configuration of the high-risk plots in the group.

6. The method for soil and water loss risk assessment of a long-distance linear project according to claim 2, wherein: The first vegetation quadrat area distribution includes 3M shrub quadrat areas and 3M herb quadrat areas set along a diagonal line, where M is a positive integer.

7. The soil and water loss risk assessment method for long-distance linear engineering according to claim 2, characterized in that: After collecting a first-level mixed soil sample from the first engineering sample site according to the soil stratification sampling rule, measuring and outputting stratified soil characteristic data, the method includes: Locating a matrix of hierarchical sampling points in the first engineering sample site according to the soil stratified sampling rule; In the matrix of hierarchical sampling points, a circular knife is used to vertically take undisturbed soil samples to obtain a hierarchical soil sample matrix; Outputting a layered soil physical property matrix by measuring the soil bulk density and soil porosity of the layered soil sample matrix; After mixing the matrix of soil samples at the same level, performing a predetermined biochemical index determination and outputting biochemical characteristic data of the level; The hierarchical soil physical property matrix and the hierarchical biochemical characteristic data are normalized to output the hierarchical soil characteristic data.

8. The method for soil and water loss risk assessment of a long-distance linear project according to claim 7, wherein: Normalizing the hierarchical soil physical property matrix and hierarchical biochemical characteristic data and outputting the hierarchical soil characteristic data, the method includes: Referring to the hierarchical sampling point matrix, spatially discretizing the hierarchical biochemical feature data, and outputting a hierarchical biochemical feature matrix; hierarchical data splicing is performed by spatially aligning the hierarchical biochemical characteristic matrix and the stratified soil physical property matrix to obtain a hierarchical characteristic matrix; The soil erosion classification standard is used to perform index normalization processing on the hierarchical feature matrix, and the hierarchical soil feature data is output.

9. The method for soil and water loss risk assessment of a long-distance linear project according to claim 7, wherein: The preset biochemical indicators include a soil chemical indicator set and a soil biological indicator set. The soil chemical indicator set consists of organic carbon, total nitrogen, phosphorus and potassium, and effective nitrogen, phosphorus and potassium. The soil biological indicator set consists of microbial biomass and enzyme activity.

10. A method and system for assessing soil and water loss risk in long-distance linear projects, characterized in that: The system is used to implement the soil and water loss risk assessment method for a long-distance linear project according to any one of claims 1 to 9, and the system comprises: The engineering sample site positioning module is used to preset the sample site grid rules according to the spatial span of the long-distance linear project, and locate K engineering sample sites along the embankments on both sides of the long-distance linear project based on the sample site grid rules; The ecological factor determination module is used to measure the ecological factors of the first project sample plot and output the vegetation characteristics and hierarchical soil characteristic data of the first sample plot; A first risk judgment matrix construction module, configured to construct a first risk judgment matrix according to a first terrain parameter of the first engineering site; a first plot risk level output module, configured to map the first plot vegetation characteristics, the first terrain parameters, and the hierarchical soil characteristic data into a risk level membership set based on a membership function, comprehensively evaluate the risk level membership set using the first risk judgment matrix, and output the first plot risk level; The verification and migration module is used to calculate and output the risk levels of the K engineering plots one by one, and then verify and migrate the vegetation configuration strategy associated with the low-risk plots according to the attribute clustering characteristics of the K engineering plots.

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