A soil erosion degree assessment method and system based on hydrological data

Abstract

The present invention relates to the technical field of soil erosion assessment, and specifically to a method and system for assessing soil erosion based on hydrological data. The method comprises obtaining first data, the first data including a pre-determined assessment factor; obtaining sample data using an erosion needle method, the sample data including a set of associated data measured within a preset aggregation time interval; and calculating a rainfall erosivity function, a terrain function, and a vegetation cover function based on the associated data using a particle swarm optimization algorithm to construct a soil erosion modulus assessment function. The present invention utilizes a particle swarm optimization algorithm to construct the rainfall erosivity function, the terrain function, and the vegetation cover function by introducing total rainfall, rainfall duration, maximum rainfall density, and the duration during which the rainfall density is not less than a preset rainfall density threshold, thereby obtaining a more realistic soil erosion modulus assessment function.

Description

Technical Field

[0001] The present invention relates to the technical field of soil erosion assessment, and in particular to a soil erosion degree assessment method and system based on hydrological data. Background Art

[0002] In the field of environmental science, the soil erosion modulus is usually used to measure the degree of soil erosion. In the usual sense, the soil erosion modulus refers to the amount of soil lost per unit area per unit time. Existing technologies often use the soil loss equation to quantitatively calculate the soil erosion modulus, among which the most classic soil loss equation is the RUSLE equation. The RUSLE equation can be expressed as follows: the soil erosion modulus is equal to the product of the rainfall erosivity factor, the soil erodibility factor, the topography factor, the vegetation cover factor, and the soil and water conservation measures factor. At present, it is generally believed in the industry that the rainfall erosivity factor reflects the potential ability of rainfall to strip and transport soil, and is related to the amount of rainfall; the soil erodibility factor reflects the sensitivity of the soil itself to erosion, and depends on the physical and chemical properties of the soil; the topography factor characterizes the combined effect of slope length and slope on erosion, and reflects the cumulative effect of runoff and gravity; the vegetation cover factor characterizes the degree of protection of the soil by vegetation or crop cover, and the higher the coverage, the weaker the erosion; the soil and water conservation measures factor reflects the inhibitory effect of engineering or agronomic measures on erosion.

[0003] However, existing RUSLE equations present several technical challenges. First, the various factors in the RUSLE equation are often derived through empirical or theoretical simplifications, lacking a quantitative basis for calculation. Second, RUSLE assumes that the factors are independent and linearly multiplicative, ignoring the complex interactions between them. This assumption may be acceptable for some regions with stable geomorphic environments, but can lead to significant errors when applied to extreme geomorphic environments. For example, in agricultural-natural areas with frequent alternations between droughts and heavy rainfall and vegetation cover, the rainfall erosivity factor, topographic factor, and vegetation cover factor are strongly coupled. Drought can cause soil to clump, reducing its adhesion and making it more susceptible to heavy rainfall, thus altering the initial topography and, consequently, the topographic factor. Vegetation cover is also dependent on rainfall: heavier rainfall results in lower vegetation cover. However, existing techniques fail to consider the dynamic impact of rainfall on topographic and vegetation cover factors. Therefore, in these extreme geomorphic environments, it is difficult to accurately characterize the soil erosion modulus using existing techniques. Third, the soil erosion modulus considered by existing technologies usually refers to the amount of soil loss per unit area per unit time, such as the amount of soil loss per hectare of land in one year. However, this measurement method is too simplified, and the factors used to calculate the soil erosion modulus are not decoupled from time. Therefore, it cannot be used to analyze soil loss under flexible time scales and is difficult to use to predict soil loss at any time in the future.

[0004] With the rapid development of big data technology, the use of big data algorithms for assessment and calculation has been increasingly applied in various fields. How to use big data algorithms to quantitatively characterize the interactive coupling relationships between some factors in the soil erosion modulus calculation formula, and thus derive a more realistic soil erosion modulus assessment function, is a technical issue that needs to be studied. Summary of the Invention

[0005] (1) Technical problems to be solved

[0006] The purpose of the present invention is to provide a soil erosion degree assessment method and system based on hydrological data, so as to obtain a rainfall erosion force function, a terrain function, and a vegetation cover function that can reflect the impact of rainfall conditions on rainfall erosion force, terrain factors, and vegetation cover factors, and thus obtain a soil erosion modulus assessment value function that is more in line with reality.

[0007] (2) Technical solution

[0008] To achieve the above object, the present invention provides a method for assessing soil erosion degree based on hydrological data, the method comprising the following steps:

[0009] S1, obtaining first data; the first data includes pre-obtained evaluation factors; the evaluation factors include soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measures factor of the area to be evaluated.

[0010] S2. Sample data is obtained through the erosion needle method, where the sample data includes a set of associated data measured within a preset aggregation time interval; the data in the associated data group includes a soil erosion modulus and hydrological data; the hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and duration during which the rainfall density is not less than a rainfall density threshold; the soil erosion modulus represents the weight of soil lost per hectare due to rainfall; and the rainfall density threshold is preset.

[0011] S3, using the particle swarm optimization algorithm to calculate the rainfall erosivity function, terrain function, and vegetation cover function based on the associated data group; the rainfall erosivity function reflects the functional relationship between rainfall erosivity and hydrological data; the terrain function reflects the disturbance of the hydrological data on the initial terrain factor; the vegetation cover function reflects the disturbance of the hydrological data on the initial vegetation cover factor.

[0012] S4, construct the soil erosion modulus evaluation value function based on the rainfall erosion function, terrain function, vegetation cover function and evaluation factors.

[0013] Furthermore, the method of obtaining sample data by the erosion needle method, wherein the sample data includes a group of associated data measured within a preset aggregation time interval, includes:

[0014] Construct sample scenes based on the pre-acquired geomorphological features of the area to be evaluated; the number of sample scenes is recorded as ,Will The sample scenes are recorded as the first sample scene to the Sample scene; for each sample scene, measure the rainfall according to the first time interval set in advance, and obtain the first rainfall data sequence to the Rainfall data sequence; according to the first rainfall data sequence to the The total rainfall, rainfall duration, maximum rainfall density, and duration of rainfall density not less than the rainfall density threshold within the aggregation time interval are calculated from the rainfall data series and recorded as the first total rainfall to the Total rainfall, duration of the first rainfall to the Duration of rainfall, first maximum rainfall density to the Maximum rainfall density, duration from the first threshold to the Reach threshold duration.

[0015] Measured by erosion needle method The soil erosion modulus of the sample scene is recorded as the first soil erosion modulus to the Soil erosion modulus; the first soil erosion modulus to the Soil erosion modulus and total rainfall from the first to the Total rainfall, duration of the first rainfall to the Duration of rainfall, first maximum rainfall density to the Maximum rainfall density, duration from the first threshold to the The first association data is obtained by associating the first association data to the second association data. Associated data; associate the first associated data to the The associated data are combined to obtain an associated data group.

[0016] Furthermore, the maximum rainfall density is the maximum value in the rainfall data sequence.

[0017] Furthermore, the duration during which the rainfall density is not lower than the rainfall density threshold is the product of the data values ​​in the rainfall data sequence being within a preset proportional coefficient multiplied by the number of data between the maximum rainfall density and the maximum rainfall density and the first time interval.

[0018] Furthermore, the method of calculating the rainfall erosivity function, the terrain function, and the vegetation cover function using the particle swarm optimization algorithm according to the associated data group includes:

[0019] The soil erosion modulus equation is constructed based on the soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measure factor. The soil erosion modulus equation is expressed as:

[0020] ;

[0021] in, Indicates the Soil erosion modulus, Indicates the Total rainfall, represents the soil erodibility factor, Indicates the calculated Terrain factors, Indicates the calculated Vegetation cover factor, represents the soil and water conservation measures factor, Indicates the predefined Deviation modulus; represents the predefined first coefficient, The value range is 1 to Integers between Topographic factors reflect the Terrain characteristics of sample scenes, Vegetation cover factor reflects the The degree of vegetation shelter in the sample scene, Deviation modulus reflection and 、 、 、 、 、 The error term between products.

[0022] The calculation formula is:

[0023] ;

[0024] in, represents the initial terrain factor, Indicates the duration of rainfall, Indicates the Maximum rainfall density, Indicates the preset limit rainfall density, which is greater than the first maximum rainfall density to the The maximum value of the maximum rainfall density, Indicates a preset limit duration, which is greater than the aggregation time interval. represents the predefined second coefficient, represents the predefined third coefficient, Represents the predefined fourth coefficient.

[0025] The calculation formula is:

[0026] ;

[0027] in, represents the initial vegetation cover factor, Indicates is the unit step function of the independent variable, Indicates the duration of reaching the threshold; represents the predefined fifth coefficient, Indicates the predefined sixth coefficient.

[0028] A first objective function is constructed based on the soil erosion modulus equation and the associated data set; a first constraint condition is constructed based on the soil erosion modulus equation and the associated data set; with the minimum value of the first objective function as the goal and the first constraint condition as the constraint condition, the particle swarm optimization algorithm is used to calculate the optimal values ​​of the first coefficient to the sixth coefficient, which are respectively denoted as to .

[0029] according to to The rainfall erosion function, terrain function and vegetation cover function are calculated.

[0030] Furthermore, the first objective function is:

[0031] ;

[0032] in, represents the first objective function.

[0033] Furthermore, the first constraint condition is:

[0034] ;

[0035] in, Indicates the preset lower limit of the terrain factor, The preset upper limit of the terrain factor.

[0036] Furthermore, the basis to Methods for calculating rainfall erosivity function, terrain function, and vegetation cover function include:

[0037] according to to The rainfall erosion function, terrain function, and vegetation cover function are calculated, among which the rainfall erosion function for:

[0038] ;

[0039] in, Represents the predicted total rainfall, obtained through forecasting.

[0040] Terrain Function for:

[0041] ;

[0042] in, It indicates the predicted rainfall duration, obtained through prediction; Indicates the predicted maximum rainfall density, obtained through prediction.

[0043] Vegetation cover function for:

[0044] ;

[0045] in, Indicates the duration of time during which the predicted rainfall density is not less than the rainfall density threshold, obtained through prediction.

[0046] Furthermore, the method for constructing a soil erosion modulus evaluation value function based on the rainfall erosivity function, the terrain function, the vegetation cover function, and the evaluation factor includes:

[0047] Based on the rainfall erosivity function, terrain function, vegetation cover function and assessment factors, the soil erosion modulus assessment value function is constructed using a factor multiplication formula; the factor multiplication formula is:

[0048] ;

[0049] in, Represents the soil erosion modulus assessment value function.

[0050] Based on the same inventive concept, on the other hand, the present invention also provides a soil erosion degree assessment system based on hydrological data, which includes: a first data acquisition module, a sample collection module, an optimization calculation module and a function construction module connected in sequence.

[0051] The first data acquisition module is used to acquire first data; the first data includes pre-obtained evaluation factors; the evaluation factors include the soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measures factor of the area to be evaluated.

[0052] The sample collection module is used to obtain sample data through the erosion needle method, and the sample data includes a related data group measured within a preset aggregation time interval; the data in the related data group includes a soil erosion modulus and hydrological data; the hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and duration during which the rainfall density is not less than a rainfall density threshold; the soil erosion modulus represents the weight of soil lost per hectare due to rainfall; and the rainfall density threshold is obtained by presetting.

[0053] The optimization calculation module is used to calculate the rainfall erosivity function, terrain function, and vegetation cover function based on the associated data group using the particle swarm optimization algorithm; the rainfall erosivity function reflects the functional relationship between rainfall erosivity and hydrological data; the terrain function reflects the disturbance of the hydrological data on the initial terrain factor; and the vegetation cover function reflects the disturbance of the hydrological data on the initial vegetation cover factor.

[0054] The function construction module is used to construct a soil erosion modulus evaluation value function based on a rainfall erosion function, a terrain function, a vegetation cover function and an evaluation factor.

[0055] (3) Beneficial effects

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The particle swarm optimization algorithm is used to calculate the relationship between rainfall, rainfall duration, maximum rainfall density, the duration when the rainfall density is not less than the rainfall density threshold and rainfall erosivity, terrain factors, and vegetation cover factors, so as to obtain rainfall erosivity function, terrain function, and vegetation cover function that can reflect the impact of rainfall conditions on rainfall erosivity, terrain factors, and vegetation cover factors, and then obtain a soil erosion modulus assessment value function that is more in line with reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flowchart of a soil erosion degree assessment method based on hydrological data according to Example 1 of the present invention;

[0059] Figure 2 This is a schematic diagram of the module composition of a soil erosion degree assessment system based on hydrological data according to Example 2 of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] Before giving examples, it is necessary to explain the application scenarios of the present invention. The present invention is applied to the calculation of the soil erosion modulus assessment function in extreme landforms. In such extreme landforms, the topographic factors and vegetation cover factors in the RUSLE equation change dynamically with rainfall. The soil erosion modulus, unlike the traditional soil erosion modulus, represents the weight of soil lost per hectare due to rainfall. The measurement time of the soil erosion modulus can be pre-specified based on actual conditions.

[0062] Example 1: Figure 1 As shown, this embodiment provides a soil erosion degree assessment method based on hydrological data, the method comprising the following steps:

[0063] S1, obtaining first data; the first data includes pre-obtained evaluation factors; the evaluation factors include soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measures factor of the area to be evaluated.

[0064] S2. Sample data is obtained through the erosion needle method, where the sample data includes a set of associated data measured within a preset aggregation time interval; the data in the associated data group includes a soil erosion modulus and hydrological data; the hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and duration during which the rainfall density is not less than a rainfall density threshold; the soil erosion modulus represents the weight of soil lost per hectare due to rainfall; and the rainfall density threshold is preset.

[0065] S3, using the particle swarm optimization algorithm to calculate the rainfall erosivity function, terrain function, and vegetation cover function based on the associated data group; the rainfall erosivity function reflects the functional relationship between rainfall erosivity and hydrological data; the terrain function reflects the disturbance of the hydrological data on the initial terrain factor; the vegetation cover function reflects the disturbance of the hydrological data on the initial vegetation cover factor.

[0066] S4, construct the soil erosion modulus evaluation value function based on the rainfall erosion function, terrain function, vegetation cover function and evaluation factors.

[0067] Exemplarily, pre-acquired soil erodibility factors, initial terrain factors, initial vegetation cover factors, and soil and water conservation measures factors are used as static basic assessment parameters, namely, assessment factors. The soil erodibility factor is retrieved from the database of the National Earth System Science Data Center, while the initial terrain factors, initial vegetation cover factors, and soil and water conservation measures factors are retrieved from the geospatial data cloud. The area to be assessed is a 30-by-30-meter area. The slope, slope length, slope aspect, and terrain curvature of the area to be assessed are measured and statistically obtained and recorded as first characteristic data. The soil texture (i.e., the ratio of sand, silt, and clay), organic matter content, permeability, soil layer thickness, and shear strength of the area to be assessed are measured and statistically obtained and recorded as second characteristic data. The vegetation type (i.e., trees, shrubs, and grasslands), coverage percentage, and vertical height of the vegetation in the area to be assessed are measured and statistically obtained and recorded as third characteristic data. The first, second, and third characteristic data are combined to form the geomorphic characteristics of the area to be assessed, and a sample scenario is constructed based on the geomorphic characteristics of the area to be assessed. The key parameters of the sample scene are consistent with the geomorphological characteristics of the area to be evaluated within the error range. The error range is set to -10%~10%. The key parameters include slope, slope length, slope aspect, terrain curvature, soil texture (i.e., sand, silt, clay ratio), organic matter content, permeability, soil layer thickness, shear strength, vegetation type (i.e., trees, shrubs, grassland), coverage percentage, and vegetation vertical height. The number of sample scenes is recorded as . The sample scenes are placed in natural environments respectively, and the erosion needle method is used to collect actual sample data within a preset aggregation time interval, specifically including the measured soil erosion modulus, and the corresponding hydrological data are recorded synchronously. The hydrological data include total rainfall, rainfall duration, maximum rainfall density, and the duration when the rainfall density is not less than the rainfall density threshold. The steps of the erosion needle method are to insert 100 pre-selected erosion needles into the soil in the sample scene with a preset insertion depth and insertion interval, measure the change in the erosion needle insertion depth at the aggregation time interval, and then obtain the soil erosion modulus within the aggregation time interval. The aggregation time interval is 360 minutes, that is, 6 hours. The measured soil erosion modulus is associated with the hydrological data one by one according to the scene to form an associated data group containing multiple groups of dynamic observation data. Based on a correlation dataset, a particle swarm optimization algorithm was used to iteratively optimize the coefficients in the pre-set soil erosion modulus equation. The equation dynamically modifies the terrain and vegetation cover factors by incorporating total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not falling below a rainfall density threshold. This quantitatively characterizes the nonlinear coupled effects of total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not falling below a rainfall density threshold on vegetation cover attenuation, as well as their impact on the terrain factor. The optimized coefficients were used to generate rainfall erosivity, terrain, and vegetation cover functions. The optimized rainfall erosivity, terrain, and vegetation cover functions were then multiplied with the static soil erodibility factor and soil and water conservation measures factor to generate a soil erosion modulus assessment function that comprehensively reflects the dynamic interactions under extreme landforms. This allows for refined modeling of soil erosion severity in complex environments, such as alternating rainstorms and droughts.

[0068] Furthermore, the method of obtaining sample data by the erosion needle method, wherein the sample data includes a group of associated data measured within a preset aggregation time interval, includes:

[0069] Construct sample scenes based on the pre-acquired geomorphological features of the area to be evaluated; the number of sample scenes is recorded as ,Will The sample scenes are recorded as the first sample scene to the Sample scene; for each sample scene, measure the rainfall according to the first time interval set in advance, and obtain the first rainfall data sequence to the Rainfall data sequence; according to the first rainfall data sequence to the The total rainfall, rainfall duration, maximum rainfall density, and duration of rainfall density not less than the rainfall density threshold within the aggregation time interval are calculated from the rainfall data series and recorded as the first total rainfall to the Total rainfall, duration of the first rainfall to the Duration of rainfall, first maximum rainfall density to the Maximum rainfall density, duration from the first threshold to the Reach threshold duration.

[0070] Measured by erosion needle method The soil erosion modulus of the sample scene is recorded as the first soil erosion modulus to the Soil erosion modulus; the first soil erosion modulus to the Soil erosion modulus and total rainfall from the first to the Total rainfall, duration of the first rainfall to the Duration of rainfall, first maximum rainfall density to the Maximum rainfall density, duration from the first threshold to the The first association data is obtained by associating the first association data to the second association data. Associated data; associate the first associated data to the The associated data are combined to obtain an associated data group.

[0071] For example, five sample areas are divided from a 5,000 square meter area obtained in advance. The length and width of each sample area are both 30 meters, and the area of ​​each sample area is 900 square meters, so the total area of ​​the five sample areas is 4,500 square meters. The remaining 500 square meters of the 5,000 square meters of land are isolation zones between the five sample areas. The purpose of setting isolation zones is to avoid mutual interference between the five sample areas. The geomorphological features of the five sample areas are set as the geomorphological features of the area to be evaluated through artificial construction. Then, the 24 hours of each day are evenly divided into four time intervals, each of which is 6 hours long, which is equal to the aggregation time interval. The four time intervals are numbered in sequence and marked as the first interval, the second interval, the third interval, and the fourth interval respectively. 720 random numbers are randomly generated, and the random number is one of 1, 2, 3, and 4, and any two consecutive random numbers must meet the value selection rules. The value selection rule is: if the previous random number is 4, the next random number cannot be 1 or 2; if the previous random number is 3, the next random number cannot be 1. Random numbers 1, 2, 3, and 4 are associated with the first, second, third, and fourth intervals, respectively. The 720 measurement times are then determined based on these 720 random numbers. For example, if the first random number among the 720 random numbers is 2, measurements are taken during the second interval (6:00 AM to 12:00 PM) on the first day. If the second random number among the 720 random numbers is 1, measurements are taken during the first interval (0:00 AM to 6:00 AM) on the second day, and so on until the end of the 720th day. This value selection rule ensures that the interval between any two adjacent measurement times is at least 12 hours. Within any interval between any two adjacent measurement times, the geomorphic features of the five sample areas are manually restored to the geomorphic features of the area to be assessed. This method generates 3,600 sample scenarios.

[0072] For each sample scenario, rainfall was measured using a weighing rain gauge at a pre-set first time interval, yielding a rainfall data sequence from the first to the 3600th rainfall data sequence in millimeters. The first time interval was 1 minute, so the length of each of the first to 3600th rainfall data sequences was 360. Based on the first to 3600th rainfall data sequences, the total rainfall within six hours, rainfall duration, maximum rainfall density, and duration during which the rainfall density was no less than the rainfall density threshold were calculated. These were recorded as the first total rainfall to the 3600th total rainfall, the first rainfall duration to the 3600th rainfall duration, the first maximum rainfall density to the 3600th maximum rainfall density, and the first threshold duration to the 3600th threshold duration, respectively. The soil erosion modulus (SEM) for each of the 3600 sample scenarios was measured using the erosion needle method. The SEM represents the weight of soil lost per hectare due to rainfall, measured in tons per hectare. The SEM was calculated by converting the measured soil loss weight for each sample scenario based on the area ratio. In other words, the soil erosion modulus for the 3,600 sample scenarios measured here actually refers to the weight of soil lost per hectare due to rainfall within 6 hours. The first to 3,600th soil erosion modulus are associated with the first to 3,600th total rainfall, the first to 3,600th rainfall duration, the first to 3,600th maximum rainfall density, and the first to 3,600th threshold duration, respectively, to obtain the first to 3,600th associated data. The first to 3,600th associated data are combined to form an associated data set. For example, during the first 6 hours of measurement, there was no rainfall. Therefore, the first total rainfall was 0 mm, the first rainfall duration was 0 minutes, the first maximum rainfall density was 0 mm, the first to 3,600th threshold duration was 0 minutes, and the first soil erosion modulus was 0 tons / hectare. The first total rainfall, the first rainfall duration, the first maximum rainfall density, the first to 3,600th threshold duration, and the first soil erosion modulus are recorded as the first associated data. During the second 6-hour measurement, the second total rainfall was 12 mm, the second rainfall duration was 325 minutes, the second maximum rainfall density was 0.11 mm, the second duration at the threshold was 12 minutes, and the second soil erosion modulus was 0.288 tons / hectare. The second total rainfall, second rainfall duration, second maximum rainfall density, second duration at the threshold, and second soil erosion modulus are recorded as the second associated data. This process continues in this way, yielding the first to 3600th associated data.

[0073] Furthermore, the maximum rainfall density is the maximum value in the rainfall data sequence.

[0074] For example, the maximum rainfall density is the maximum value in the rainfall data sequence. Since the rainfall data sequence is the rainfall measured every minute, the maximum rainfall density is the maximum rainfall in 1 minute, in millimeters.

[0075] Furthermore, the duration during which the rainfall density is not lower than the rainfall density threshold is the product of the data values ​​in the rainfall data sequence being within a preset proportional coefficient multiplied by the number of data between the maximum rainfall density and the maximum rainfall density and the first time interval.

[0076] For example, let's take the second rainfall data sequence. The preset scaling factor is 0.95. Since the maximum rainfall density in the second rainfall data sequence, i.e., the second maximum rainfall density, is 0.11 mm, the preset scaling factor multiplied by the maximum rainfall density equals 0.1045 mm. A search within the second rainfall data sequence reveals 12 data points between 0.1045 mm and 0.11 mm (inclusive). Since the first time interval is 1 minute, the duration during which the rainfall density is no less than the rainfall density threshold is 12 minutes, i.e., the second threshold duration is 12 minutes.

[0077] Furthermore, the method of calculating the rainfall erosivity function, the terrain function, and the vegetation cover function using the particle swarm optimization algorithm according to the associated data group includes:

[0078] The soil erosion modulus equation is constructed based on the soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measure factor. The soil erosion modulus equation is expressed as:

[0079] ;

[0080] in, Indicates the Soil erosion modulus, Indicates the Total rainfall, represents the soil erodibility factor, Indicates the calculated Terrain factors, Indicates the calculated Vegetation cover factor, represents the soil and water conservation measures factor, Indicates the predefined Deviation modulus; represents the predefined first coefficient, The value range is 1 to Integers between Topographic factors reflect the Terrain characteristics of sample scenes, Vegetation cover factor reflects the The degree of vegetation shelter in the sample scene, Deviation modulus reflection and 、 、 、 、 、 The error term between products.

[0081] The calculation formula is:

[0082] ;

[0083] in, represents the initial terrain factor, Indicates the duration of rainfall, Indicates the Maximum rainfall density, Indicates the preset limit rainfall density, which is greater than the first maximum rainfall density to the The maximum value of the maximum rainfall density, Indicates a preset limit duration, which is greater than the aggregation time interval. represents the predefined second coefficient, represents the predefined third coefficient, Represents the predefined fourth coefficient.

[0084] The calculation formula is:

[0085] ;

[0086] in, represents the initial vegetation cover factor, Indicates is the unit step function of the independent variable, Indicates the duration of reaching the threshold; represents the predefined fifth coefficient, Indicates the predefined sixth coefficient.

[0087] A first objective function is constructed based on the soil erosion modulus equation and the associated data set; a first constraint condition is constructed based on the soil erosion modulus equation and the associated data set; with the minimum value of the first objective function as the goal and the first constraint condition as the constraint condition, the particle swarm optimization algorithm is used to calculate the optimal values ​​of the first coefficient to the sixth coefficient, which are respectively denoted as to .

[0088] according to to The rainfall erosion function, terrain function and vegetation cover function are calculated.

[0089] For example, the soil erosion modulus in the soil erosion modulus equation represents the weight of soil lost per hectare due to rainfall, with the unit being tons / hectare. Indicates the Total rainfall in millimeters. It represents the soil erodibility factor, reflecting the sensitivity of the soil itself to erosion, and its unit is (ton·hour) / (MJ·mm). Indicates the Topographic factor reflects the impact of topography on soil erosion and is dimensionless. Indicates the Vegetation cover factor reflects the impact of vegetation cover on soil erosion and is dimensionless. It represents the soil and water conservation measures factor, reflecting the impact of soil and water conservation measures on soil erosion. It represents the first coefficient, reflecting the conversion relationship between total rainfall and rainfall energy, with the unit being MJ / (hectare·hour).

[0090] The calculation formula consists of four parts. The first part is the initial terrain factor, which reflects the initial conditions of the terrain. The second part reflects the interference of rainfall on the terrain factor. Greater rainfall accelerates the increase of the terrain factor. This is because in agricultural-natural areas with frequent alternations of drought and heavy rain and covered by vegetation, when rainfall increases, the slope and slope length of the terrain will change accordingly, and the changes will develop in a direction that is more likely to cause soil erosion. The third part reflects the interference of rainfall duration on the terrain factor. The duration of rainfall will make the soil softer, making it more likely to cause soil erosion. The fourth part reflects the interference of maximum rainfall density on the terrain factor. The increase in maximum rainfall density will impact the adhesion relationship between soils, thereby causing changes in the terrain. It is important to note that there is a mutual coupling relationship between the interference of rainfall duration and maximum rainfall density on the terrain factor. In the calculation formula, dimensionless, The unit is minutes, The unit is millimeter, The unit is millimeter, The unit is minutes, The unit is 1 / mm, The unit is 1 / minute, The unit is 1 / mm.

[0091] The calculation formula consists of two parts. The first part is the initial vegetation cover factor, which reflects the initial condition of vegetation cover. The second part reflects the impact of rainfall on vegetation cover. When the maximum rainfall density is less than When the maximum rainfall density is greater than or equal to When the rainfall impacts the vegetation itself, the vegetation coverage of the soil will decrease, and the degree of reduction is similar to This, in turn, leads to increased soil erosion. In the calculation formula, The unit is minutes, The unit is 1 / minute, The unit is millimeters.

[0092] Furthermore, the first objective function is:

[0093] ;

[0094] in, represents the first objective function.

[0095] Furthermore, the first constraint condition is:

[0096] ;

[0097] in, Indicates the preset lower limit of the terrain factor, The preset upper limit of the terrain factor.

[0098] Furthermore, the basis to Methods for calculating rainfall erosivity function, terrain function, and vegetation cover function include:

[0099] according to to The rainfall erosion function, terrain function, and vegetation cover function are calculated, among which the rainfall erosion function for:

[0100] ;

[0101] in, Represents the predicted total rainfall, obtained through forecasting.

[0102] Terrain Function for:

[0103] ;

[0104] in, It indicates the predicted rainfall duration, obtained through prediction; Indicates the predicted maximum rainfall density, obtained through prediction.

[0105] Vegetation cover function for:

[0106] ;

[0107] in, Indicates the duration of time during which the predicted rainfall density is not less than the rainfall density threshold, obtained through prediction.

[0108] Furthermore, the method for constructing a soil erosion modulus evaluation value function based on the rainfall erosivity function, the terrain function, the vegetation cover function, and the evaluation factor includes:

[0109] Based on the rainfall erosivity function, terrain function, vegetation cover function and assessment factors, the soil erosion modulus assessment value function is constructed using a factor multiplication formula; the factor multiplication formula is:

[0110] ;

[0111] in, Represents the soil erosion modulus assessment value function.

[0112] For example, based on the meteorological data, the rainfall per minute in the next 6 hours can be predicted, and then the 、 、 、 ,Will 、 、 、 Substituting the soil erosion modulus assessment value function, we can obtain the weight of soil loss per hectare caused by rainfall in the next 6 hours in the area to be assessed.

[0113] Example 2: Based on the same inventive concept, Figure 2 As shown, this embodiment also provides a soil erosion degree assessment system based on hydrological data, which includes: a first data acquisition module, a sample collection module, an optimization calculation module, and a function construction module connected in sequence.

[0114] The first data acquisition module is used to acquire first data; the first data includes pre-obtained evaluation factors; the evaluation factors include the soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measures factor of the area to be evaluated.

[0115] The sample collection module is used to obtain sample data through the erosion needle method, and the sample data includes a related data group measured within a preset aggregation time interval; the data in the related data group includes a soil erosion modulus and hydrological data; the hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and duration during which the rainfall density is not less than a rainfall density threshold; the soil erosion modulus represents the weight of soil lost per hectare due to rainfall; and the rainfall density threshold is obtained by presetting.

[0116] The optimization calculation module is used to calculate the rainfall erosivity function, terrain function, and vegetation cover function based on the associated data group using the particle swarm optimization algorithm; the rainfall erosivity function reflects the functional relationship between rainfall erosivity and hydrological data; the terrain function reflects the disturbance of the hydrological data on the initial terrain factor; and the vegetation cover function reflects the disturbance of the hydrological data on the initial vegetation cover factor.

[0117] The function construction module is used to construct a soil erosion modulus evaluation value function based on a rainfall erosion function, a terrain function, a vegetation cover function and an evaluation factor.

[0118] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0119] Finally, it should be noted that although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A soil erosion degree assessment method based on hydrological data, characterized in that: The method comprises the following steps: S1, obtaining first data; the first data includes pre-obtained evaluation factors; the evaluation factors include soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measures factor of the area to be evaluated; S2, obtaining sample data using the erosion needle method, the sample data comprising a set of associated data measured within a preset aggregation time interval; the data in the associated data set comprising a soil erosion modulus and hydrological data; the hydrological data comprising total rainfall, rainfall duration, maximum rainfall density, and duration during which rainfall density is not less than a rainfall density threshold; the soil erosion modulus represents the weight of soil lost per hectare due to rainfall; the rainfall density threshold is preset; S3, using a particle swarm optimization algorithm to calculate a rainfall erosivity function, a terrain function, and a vegetation cover function based on the associated data set; the rainfall erosivity function reflects the functional relationship between rainfall erosivity and hydrological data; the terrain function reflects the perturbation of the hydrological data on the initial terrain factor; and the vegetation cover function reflects the perturbation of the hydrological data on the initial vegetation cover factor; S4, constructing the soil erosion modulus evaluation value function based on the rainfall erosion function, terrain function, vegetation cover function and evaluation factors; The method of calculating the rainfall erosivity function, the terrain function, and the vegetation coverage function using the particle swarm optimization algorithm according to the associated data group includes: A soil erosion modulus equation is constructed based on the soil erodibility factor, the initial terrain factor, the initial vegetation cover factor, and the soil and water conservation measure factor; a first objective function is constructed based on the soil erosion modulus equation and the associated data set; a first constraint condition is constructed based on the soil erosion modulus equation and the associated data set; with the minimum value of the first objective function as the goal and the first constraint condition as the constraint condition, the particle swarm optimization algorithm is used to calculate the optimal values ​​of the first to sixth coefficients, which are respectively denoted as to ; according to to The rainfall erosion function, terrain function and vegetation cover function are calculated.

2. The soil erosion degree assessment method based on hydrological data according to claim 1, characterized in that: The method of obtaining sample data by the erosion needle method, wherein the sample data includes a set of associated data measured within a preset aggregation time interval, includes: Construct sample scenes based on the pre-acquired geomorphological features of the area to be evaluated; the number of sample scenes is recorded as ,Will The sample scenes are recorded as the first sample scene to the Sample scene; for each sample scene, measure the rainfall according to the first time interval set in advance, and obtain the first rainfall data sequence to the Rainfall data sequence; according to the first rainfall data sequence to the The total rainfall, rainfall duration, maximum rainfall density, and duration of rainfall density not less than the rainfall density threshold within the aggregation time interval are calculated from the rainfall data series and recorded as the first total rainfall to the Total rainfall, duration of the first rainfall to the Duration of rainfall, first maximum rainfall density to the Maximum rainfall density, duration from the first threshold to the duration of reaching the threshold; Measured by erosion needle method The soil erosion modulus of the sample scene is recorded as the first soil erosion modulus to the Soil erosion modulus; the first soil erosion modulus to the Soil erosion modulus and total rainfall from the first to the Total rainfall, duration of the first rainfall to the Duration of rainfall, first maximum rainfall density to the Maximum rainfall density, duration from the first threshold to the The first association data is obtained by associating the first association data to the second association data. Associated data; associate the first associated data to the The associated data are combined to obtain an associated data group.

3. The soil erosion degree assessment method based on hydrological data according to claim 2, characterized in that: The maximum rainfall density is the maximum value in the rainfall data sequence.

4. The soil erosion degree assessment method based on hydrological data according to claim 3, characterized in that: The duration during which the rainfall density is not lower than the rainfall density threshold is the product of the data values ​​in the rainfall data sequence being within a preset proportional coefficient multiplied by the number of data between the maximum rainfall density and the maximum rainfall density and the first time interval.

5. The soil erosion degree assessment method based on hydrological data according to claim 4, characterized in that: The soil erosion modulus equation is expressed as: ； in, Indicates the Soil erosion modulus, Indicates the Total rainfall, represents the soil erodibility factor, Indicates the calculated Terrain factors, Indicates the calculated Vegetation cover factor, represents the soil and water conservation measures factor, Indicates the predefined Deviation modulus; represents the predefined first coefficient, The value range is 1 to Integers between Topographic factors reflect the Terrain characteristics of sample scenes, Vegetation cover factor reflects the The degree of vegetation shelter in the sample scene, Deviation modulus reflection and the error term between products; The calculation formula is: ； in, represents the initial terrain factor, Indicates the duration of rainfall, Indicates the Maximum rainfall density, Indicates the preset limit rainfall density, which is greater than the first maximum rainfall density to the The maximum value of the maximum rainfall density, Indicates a preset limit duration, which is greater than the aggregation time interval. represents the predefined second coefficient, represents the predefined third coefficient, represents a predefined fourth coefficient; The calculation formula is: ； in, represents the initial vegetation cover factor, Indicates is the unit step function of the independent variable, Indicates the duration of reaching the threshold; represents the predefined fifth coefficient, Indicates the predefined sixth coefficient.

6. The soil erosion degree assessment method based on hydrological data according to claim 5, characterized in that: The first objective function is: ； in, represents the first objective function.

7. The soil erosion degree assessment method based on hydrological data according to claim 6, characterized in that: The first constraint condition is: ； in, Indicates the preset lower limit of the terrain factor, The preset upper limit of the terrain factor.

8. The soil erosion degree assessment method based on hydrological data according to claim 7, characterized in that: The basis to Methods for calculating rainfall erosivity function, terrain function, and vegetation cover function include: according to to The rainfall erosion function, terrain function, and vegetation cover function are calculated, among which the rainfall erosion function for: ； in, It represents the predicted total rainfall, obtained through prediction; Terrain Function for: ； in, It indicates the predicted rainfall duration, obtained through prediction; It represents the predicted maximum rainfall density, obtained through prediction; Vegetation cover function for: ； in, Indicates the duration of time during which the predicted rainfall density is not less than the rainfall density threshold, obtained through prediction.

9. The soil erosion degree assessment method based on hydrological data according to claim 8, characterized in that: The method for constructing a soil erosion modulus evaluation value function based on a rainfall erosivity function, a terrain function, a vegetation cover function, and an evaluation factor includes: Based on the rainfall erosivity function, terrain function, vegetation cover function and assessment factors, the soil erosion modulus assessment value function is constructed using a factor multiplication formula; the factor multiplication formula is: ； in, Represents the soil erosion modulus assessment value function.

10. A soil erosion degree assessment system based on hydrological data, used to execute the method according to any one of claims 1 to 9, characterized in that: The system comprises: a first data acquisition module, a sample collection module, an optimization calculation module and a function construction module connected in sequence; The first data acquisition module is used to acquire first data; the first data includes pre-obtained evaluation factors; the evaluation factors include soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measures factor of the area to be evaluated; The sample collection module is configured to obtain sample data using an erosion needle method, the sample data comprising a set of associated data measured within a preset aggregation time interval; the data in the associated data set comprises a soil erosion modulus and hydrological data; the hydrological data comprises total rainfall, rainfall duration, maximum rainfall density, and duration during which rainfall density is not less than a rainfall density threshold; the soil erosion modulus represents the weight of soil lost per hectare due to rainfall; the rainfall density threshold is preset; The optimization calculation module is used to calculate the rainfall erosivity function, the terrain function, and the vegetation cover function based on the associated data group using the particle swarm optimization algorithm; the rainfall erosivity function reflects the functional relationship between rainfall erosivity and hydrological data; the terrain function reflects the disturbance of the hydrological data on the initial terrain factor; and the vegetation cover function reflects the disturbance of the hydrological data on the initial vegetation cover factor; The function construction module is used to construct a soil erosion modulus evaluation value function based on the rainfall erosion function, the terrain function, the vegetation cover function and the evaluation factor; The method of calculating the rainfall erosivity function, the terrain function, and the vegetation coverage function using the particle swarm optimization algorithm according to the associated data group includes: A soil erosion modulus equation is constructed based on the soil erodibility factor, the initial terrain factor, the initial vegetation cover factor, and the soil and water conservation measure factor; a first objective function is constructed based on the soil erosion modulus equation and the associated data set; a first constraint condition is constructed based on the soil erosion modulus equation and the associated data set; with the minimum value of the first objective function as the goal and the first constraint condition as the constraint condition, the particle swarm optimization algorithm is used to calculate the optimal values ​​of the first to sixth coefficients, which are respectively denoted as to ; according to to The rainfall erosion function, terrain function and vegetation cover function are calculated.

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