Soil erosion degree evaluation method and system based on hydrological data

Abstract

The invention relates to the technical field of soil erosion assessment, in particular to a hydrological data-based soil erosion degree assessment method and system, and the method comprises the steps: obtaining first data which comprises an assessment factor obtained in advance; obtaining sample data through an erosion needle method, wherein the sample data comprises an associated data group obtained by measurement in a preset aggregation time interval; according to the associated data set, a rainfall erosion force function, a terrain function and a vegetation coverage function are obtained through calculation by adopting a particle swarm optimization algorithm, and then a soil erosion modulus evaluation value function is obtained through construction. According to the method, the total rainfall capacity, the rainfall duration, the maximum rainfall density and the duration when the rainfall density is not lower than a preset rainfall density threshold value are introduced, and a rainfall erosion force function, a terrain function and a vegetation coverage function are constructed through a particle swarm optimization algorithm, so that a soil erosion modulus evaluation value function which is more practical is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of soil erosion assessment, and specifically to a method and system for assessing the degree of soil erosion 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. Generally speaking, the soil erosion modulus refers to the amount of soil loss per unit area per unit time. In the prior art, the soil loss equation is often used to quantitatively calculate the soil erosion modulus, and the most classic soil loss equation is the RUSLE equation. The RUSLE equation can be expressed as: the soil erosion modulus is equal to the product of the rainfall erosivity factor, soil erodibility factor, topographic factor, vegetation cover factor, and soil and water conservation measure factor. Currently, the industry generally believes that the rainfall erosivity factor reflects the potential ability of rainfall to strip and transport soil and is related to the rainfall amount; 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 topographic factor characterizes the combined influence of slope length and slope on erosion, reflecting the runoff accumulation effect and gravity; the vegetation cover factor characterizes the degree of protection of the soil by vegetation or crop cover, and the higher the cover, the weaker the erosion; the soil and water conservation measure factor reflects the inhibitory effect of engineering or agronomic measures on erosion.

[0003] However, the existing RUSLE equation has the following technical problems: First, each factor in the RUSLE equation is often obtained through empirical or theoretical simplified calculations, lacking a quantitative calculation basis. Second, the RUSLE assumes that the factors are independent and linearly multiplicative, ignoring the complex interaction between the factors. This assumption may be considered valid for some regions with stable geomorphic environments, but it will produce a large error if applied to extreme geomorphology. For example, in the agricultural-natural transition area with frequent alternation of drought and heavy rain and vegetation cover, there is a strong internal coupling relationship among the rainfall erosivity factor, topographic factor, and vegetation cover factor. Drought will cause the soil to cake, and the adhesion between the caked soils will decrease, making it easier to be washed away by heavy rain, thus changing the initial terrain and then changing the topographic factor; and the vegetation cover factor is also related to the rainfall amount, and the stronger the rainfall, the lower the vegetation coverage of the soil. The prior art does not consider the dynamic influence of rainfall conditions on the topographic factor and vegetation cover factor. Therefore, it is difficult to accurately describe the soil erosion modulus using the prior art in these extreme geomorphic environments. Third, the soil erosion modulus considered in the prior art 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 the soil loss situation on a flexible time scale and is difficult to predict the soil loss situation at any future time.

[0004] With the rapid development of big data technology, the use of big data algorithms for evaluation and calculation has been increasingly applied in various fields. How to quantitatively characterize the interaction and coupling relationships between some factors in the soil erosion modulus calculation formula using big data algorithms, and then obtain a soil erosion modulus evaluation value function that can better fit the actual situation is a technical problem that needs to be studied. Summary of the Invention

[0005] (1) Technical Problems to be Solved The purpose of the present invention is to provide a method and system for evaluating the degree of soil erosion based on hydrological data, so as to obtain a rainfall erosivity function, a terrain function, and a vegetation cover function that can reflect the influence of rainfall conditions on rainfall erosivity, terrain factors, and vegetation cover factors, and then obtain a soil erosion modulus evaluation value function that better fits the actual situation.

[0006] (2) Technical Solutions To achieve the above purpose, the present invention provides a method for evaluating the degree of soil erosion based on hydrological data, and the method includes the following steps: S1, obtaining first data; the first data includes pre-obtained evaluation factors; the evaluation factors include the soil erodibility factor, the initial terrain factor, the initial vegetation cover factor, and the soil and water conservation measure factor of the area to be evaluated.

[0007] S2, obtaining sample data through the erosion needle method, the sample data includes an associated data group measured within a pre-set aggregation time interval; the data in the associated data group includes the soil erosion modulus and hydrological data; the hydrological data includes the total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not lower than the rainfall density threshold; the soil erosion modulus represents the weight of soil loss per hectare caused by rainfall; the rainfall density threshold is obtained through pre-setting.

[0008] S3, calculating the rainfall erosivity function, the terrain function, and the vegetation cover function according to 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 perturbation of hydrological data on the initial terrain factor; the vegetation cover function reflects the perturbation of hydrological data on the initial vegetation cover factor.

[0009] S4, constructing a soil erosion modulus evaluation value function according to the rainfall erosivity function, the terrain function, the vegetation cover function, and the evaluation factors.

[0010] Further, the method for obtaining sample data through the erosion needle method, the sample data includes an associated data group measured within a pre-set aggregation time interval includes: Construct a sample scenario according to the pre-acquired geomorphic features of the area to be evaluated; the number of the sample scenarios is denoted as , and sample scenarios are respectively denoted as the first sample scenario to the sample scenario; for each sample scenario, measure the rainfall according to the preset first time interval to obtain the first rainfall data sequence to the rainfall data sequence; calculate the total rainfall, rainfall duration, maximum rainfall density, and the duration with rainfall density not lower than the rainfall density threshold within the aggregation time interval according to the first rainfall data sequence to the rainfall data sequence, and denote them as the first total rainfall to the total rainfall, the first rainfall duration to the rainfall duration, the first maximum rainfall density to the maximum rainfall density, the first duration reaching the threshold to the duration reaching the threshold.

[0011] Measure the soil erosion modulus of sample scenarios by using the erosion needle method, and denote them as the first soil erosion modulus to the soil erosion modulus; respectively associate the first soil erosion modulus to the soil erosion modulus with the first total rainfall to the total rainfall, the first rainfall duration to the rainfall duration, the first maximum rainfall density to the maximum rainfall density, the first duration reaching the threshold to the duration reaching the threshold to obtain the first associated data to the associated data; combine the first associated data to the associated data to obtain an associated data group.

[0012] Further, the maximum rainfall density is the maximum value in the rainfall data sequence.

[0013] Further, the duration with rainfall density not lower than the rainfall density threshold is the product of the number of data values in the rainfall data sequence that are between the preset proportionality coefficient multiplied by the maximum rainfall density and the maximum rainfall density and the first time interval.

[0014] Further, the method for calculating the rainfall erosion force function, terrain function, and vegetation cover function according to the associated data group by using the particle swarm optimization algorithm includes: Construct a soil erosion modulus equation according to 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: ; Among them, represents the soil erosion modulus, represents the total rainfall, represents the soil erodibility factor, represents the calculated topographic factor, represents the calculated vegetation cover factor, represents the soil and water conservation measure factor, represents the predefined deviation modulus; represents the predefined first coefficient, is an integer between 1 and ; Among them, the topographic factor reflects the topographic characteristics of the sample scenario, and the vegetation cover factor reflects the degree of rain shelter of the vegetation in the sample scenario, and the , , , , , error term between the products.

[0015] The calculation formula of ; Among them, represents the initial topographic factor, represents the rainfall duration, represents the maximum rainfall density, represents the preset limit rainfall density, and the limit rainfall density is greater than the maximum value among the first maximum rainfall density to the maximum rainfall density, represents the preset limit duration, and the limit duration is greater than the aggregation time interval, represents the predefined second coefficient, represents the predefined third coefficient, represents the predefined fourth coefficient.

[0016] The calculation formula of ; Among them, represents the initial vegetation coverage factor, represents as the independent variable unit step function, represents the duration of reaching the threshold; represents the predefined fifth coefficient, represents the predefined sixth coefficient.

[0017] Construct a first objective function according to the soil erosion modulus equation and the associated data set; construct a first constraint condition according to 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, use the particle swarm optimization algorithm to calculate the optimal values of the first to sixth coefficients, denoted as to .

[0018] According to to calculate the rainfall erosivity function, terrain function, and vegetation cover function.

[0019] Furthermore, the first objective function is: ; Among them, represents the first objective function.

[0020] Furthermore, the first constraint condition is: ; Among them, represents the predefined lower limit of the terrain factor, is the predefined upper limit of the terrain factor.

[0021] Furthermore, the method for calculating the rainfall erosivity function, terrain function, and vegetation cover function according to to includes: According to to calculate the rainfall erosivity function, terrain function, and vegetation cover function, where the rainfall erosivity function is: ; Among them, represents the predicted total rainfall, obtained through prediction.

[0022] The terrain function is: ; Among them, Indicates the predicted rainfall duration, obtained through prediction; Indicates the predicted maximum rainfall density, obtained through prediction.

[0023] Vegetation cover function Is: ; Wherein, Indicates the duration during which the predicted rainfall density is not lower than the rainfall density threshold, obtained through prediction.

[0024] Furthermore, the method for constructing the soil erosion modulus evaluation value function based on the rainfall erosivity function, terrain function, vegetation cover function, and evaluation factors includes: Based on the rainfall erosivity function, terrain function, vegetation cover function, and evaluation factors, a soil erosion modulus evaluation value function is constructed using the factor multiplication formula; the factor multiplication formula is: ; Wherein, Indicates the soil erosion modulus evaluation value function.

[0025] Based on the same inventive concept, on the other hand, the present invention also provides a soil erosion degree evaluation system based on hydrological data, the system includes, connected in sequence: a first data acquisition module, a sample collection module, an optimization calculation module, and a function construction module.

[0026] 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 measure factor of the area to be evaluated.

[0027] The sample collection module is used to obtain sample data through the erosion needle method, the sample data includes an associated data group measured within a preset aggregation time interval; the data in the associated data group includes soil erosion modulus, hydrological data; the hydrological data includes total rainfall, rainfall duration, maximum rainfall density, duration during which the rainfall density is not lower than the rainfall density threshold; the soil erosion modulus represents the weight of soil loss per hectare caused by rainfall; the rainfall density threshold is obtained through presetting.

[0028] The optimization calculation module is used to calculate the rainfall erosivity function, terrain function, and vegetation cover function according to 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 hydrological data to the initial terrain factor; the vegetation cover function reflects the disturbance of hydrological data to the initial vegetation cover factor.

[0029] The function construction module is used to construct a soil erosion modulus evaluation value function according to the rainfall erosivity function, topographic function, vegetation cover function and evaluation factors.

[0030] (3) Beneficial effects Compared with the prior art, the beneficial effects of the present invention are as follows: The particle swarm optimization algorithm is used to calculate the relationships between rainfall, rainfall duration, maximum rainfall density, duration with rainfall density not lower than the rainfall density threshold and rainfall erosivity, topographic factor, vegetation cover factor, so as to obtain a rainfall erosivity function, topographic function, and vegetation cover function that can reflect the influence of rainfall conditions on rainfall erosivity, topographic factor, and vegetation cover factor, and further obtain a soil erosion modulus evaluation value function that is more in line with the actual situation. Description of the drawings

[0031] Figure 1 It is a flowchart of a method for evaluating soil erosion degree based on hydrological data according to Embodiment 1 of the present invention; Figure 2 It is a schematic diagram of the module composition of a system for evaluating soil erosion degree based on hydrological data according to Embodiment 2 of the present invention. Detailed implementation manners

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] Before giving examples, it is necessary to elaborate on the application scenarios of the concept of the present invention. The present invention is applied to the calculation of the soil erosion modulus evaluation value function in extreme geomorphic environments. In the extreme geomorphic environment, the topographic factor and vegetation cover factor in the RUSLE equation will change dynamically with the rainfall process. The soil erosion modulus is different from the traditional soil erosion modulus, indicating the weight of soil loss per hectare caused by rainfall. The measurement time of the soil erosion modulus can be specified in advance according to the actual situation.

[0034] Embodiment 1: As Figure 1 shown, this embodiment provides a method for evaluating soil erosion degree based on hydrological data, and the method includes the following steps: S1, obtaining first data; the first data includes pre-obtained evaluation factors; the evaluation factors include the soil erodibility factor, initial topographic factor, initial vegetation cover factor, and soil and water conservation measure factor of the area to be evaluated.

[0035] S2. Obtain sample data through the erosion needle method. The sample data includes an associated data group measured within a preset aggregation time interval. The data in the associated data group includes soil erosion modulus and hydrological data. The hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and the duration when the rainfall density is not lower than the rainfall density threshold. The soil erosion modulus represents the weight of soil loss per hectare caused by rainfall. The rainfall density threshold is obtained through presetting.

[0036] S3. Calculate the rainfall erosivity function, terrain function, and vegetation cover function according to the associated data group by 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 perturbation of hydrological data on the initial terrain factor. The vegetation cover function reflects the perturbation of hydrological data on the initial vegetation cover factor.

[0037] S4. Construct a soil erosion modulus evaluation value function according to the rainfall erosivity function, terrain function, vegetation cover function, and evaluation factors.

[0038] Exemplarily, the soil erodibility factor, initial terrain factor, initial vegetation cover factor, and soil and water conservation measure factor obtained in advance are used as static basic evaluation parameters, that is, evaluation factors. Among them, the soil erodibility factor is read from the database of the National Earth System Science Data Center, and the initial terrain factor, initial vegetation cover factor, and soil and water conservation measure factor are read from the Geospatial Data Cloud. The area to be evaluated is a 30 - meter - by - 30 - meter area. The slope, slope length, slope aspect, and terrain curvature of the area to be evaluated are measured and statistically obtained, denoted as the first characteristic data. The soil texture (i.e., the proportion of sand, silt, and clay), organic matter content, permeability, soil layer thickness, and shear strength of the area to be evaluated are measured and statistically obtained, denoted as the second characteristic data. The vegetation type (i.e., trees, shrubs, grassland), coverage percentage, and vegetation vertical height of the area to be evaluated are measured and statistically obtained, denoted as the third characteristic data. The first characteristic data, second characteristic data, and third characteristic data are combined into the geomorphic characteristics of the area to be evaluated, and a sample scenario is constructed according to the geomorphic characteristics of the area to be evaluated. The key parameters of the sample scenario are consistent with the geomorphic 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., the proportion of sand, silt, and clay), 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 the sample scenarios is denoted as Place the sample scenarios in the natural environment respectively, and use the erosion needle method to collect actual sample data within a preset aggregation time interval, specifically including the measured soil erosion modulus, and synchronously record the corresponding hydrological data. The hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not lower 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 scenario at a preset insertion depth and insertion interval, measure the change in the insertion depth of the erosion needles at the aggregation time interval, and then statistically obtain the soil erosion modulus within the aggregation time interval. The aggregation time interval is 360 minutes, that is, 6 hours. Correlate the measured soil erosion modulus with the hydrological data scene by scene to form an associated data group containing multiple sets of dynamic observation data. Based on the associated data group, use the particle swarm optimization algorithm to iteratively optimize the coefficients in the preset soil erosion modulus equation. The soil erosion modulus equation dynamically corrects the terrain factor and vegetation cover factor by introducing total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not lower than the rainfall density threshold, so as to quantitatively describe the non-linear coupling effect of total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not lower than the rainfall density threshold on the attenuation of vegetation cover, as well as the impact on the terrain factor. Generate rainfall erosivity function, terrain function, and vegetation cover function through the optimized coefficients. Multiply and combine the optimized rainfall erosivity function, terrain function, and vegetation cover function with the static soil erodibility factor and soil and water conservation measure factor to generate a soil erosion modulus evaluation value function that comprehensively reflects the dynamic interaction under extreme landforms, so as to realize the refined modeling of soil erosion degree in complex environments such as alternation of rainstorms and droughts.

[0039] Further, the method for obtaining sample data through the erosion needle method, where the sample data includes the associated data group measured within a preset aggregation time interval, includes: Construct a sample scenario according to the geomorphic features of the area to be evaluated obtained in advance; the number of the sample scenarios is denoted as , and sample scenarios are respectively denoted as the first sample scenario to the sample scenario; for each sample scenario, measure the rainfall at a preset first time interval to obtain the first rainfall data sequence to the rainfall data sequence; calculate the total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not lower than the rainfall density threshold within the aggregation time interval according to the first rainfall data sequence to the rainfall data sequence, and denote them as the first total rainfall to the total rainfall, the first rainfall duration to the Rainfall duration, the first maximum rainfall density to the maximum rainfall density, the first duration until the threshold is reached to the duration until the threshold is reached.

[0040] The soil erosion modulus of sample scenarios measured by the erosion needle method is denoted as the first soil erosion modulus to the soil erosion modulus; respectively correlate the first soil erosion modulus to the soil erosion modulus with the first total rainfall to the total rainfall, the first rainfall duration to the rainfall duration, the first maximum rainfall density to the maximum rainfall density, the first duration until the threshold is reached to the duration until the threshold is reached to obtain the first correlation data to the correlation data; combine the first correlation data to the correlation data to obtain a correlation data set.

[0041] Exemplarily, five sample areas are demarcated from the pre-acquired 5000-square-meter land. Each sample area is 30 meters in length and width, and the area of each sample area is 900 square meters. Therefore, the total area of the five sample areas is 4500 square meters. The remaining 500 square meters in the 5000-square-meter land is the isolation belt between the five sample areas. The purpose of setting the isolation belt is to avoid mutual interference between the five sample areas. The geomorphic features of the five sample areas are set to those of the area to be evaluated through artificial construction. Then, the 24 hours of each day are evenly divided into four time intervals, each with a duration of 6 hours, which is equal to the aggregation time interval. The four time intervals are numbered in sequence and are respectively labeled as the first interval, the second interval, the third interval, and the fourth interval. 720 random numbers are randomly generated, and the random number is one of 1, 2, 3, and 4, and any two consecutive random numbers need to satisfy the value-taking rule. The value-taking rule is: if the previous random number is 4, then the next random number cannot be 1 or 2; if the previous random number is 3, then the next random number cannot be 1. The random numbers 1, 2, 3, and 4 are respectively associated with the first interval, the second interval, the third interval, and the fourth interval, and the measurement times for 720 days are determined according to the 720 random numbers. For example: if the first random number among the 720 random numbers is 2, then the measurement is carried out within the second interval (i.e., 6:00 - 12:00) on the first day; if the second random number among the 720 random numbers is 1, then the measurement is carried out within the first interval (i.e., 0:00 - 6:00) on the second day, and so on until the 720th day ends. Through the value-taking rule, the interval between any two adjacent measurement times can be at least 12 hours. Within the interval between any two adjacent measurement times, the geomorphic features of the five sample areas are restored to those of the area to be evaluated through artificial restoration. Through this method, 3600 sample scenarios can be constructed.

[0042] For each sample scenario, the rainfall is measured by a weighing rain gauge at a preset first time interval, obtaining the first rainfall data sequence to the 3600th rainfall data sequence, with the unit being millimeters. The first time interval is 1 minute, so the lengths of the first rainfall data sequence to the 3600th rainfall data sequence are all 360. Calculate the total rainfall, rainfall duration, maximum rainfall intensity, and the duration when the rainfall intensity is not lower than the rainfall intensity threshold within 6 hours according to the first rainfall data sequence to the 3600th rainfall data sequence, denoted as the first total rainfall to the 3600th total rainfall, the first rainfall duration to the 3600th rainfall duration, the first maximum rainfall intensity to the 3600th maximum rainfall intensity, and the first duration reaching the threshold to the 3600th duration reaching the threshold respectively. Measure the soil erosion modulus of 3600 sample scenarios by the erosion pin method. The soil erosion modulus represents the weight of soil loss per hectare caused by rainfall, with the unit being tons per hectare, and is obtained by converting the weight of soil loss in the sample scenario measured actually according to the area ratio. That is to say, the soil erosion modulus of the 3600 sample scenarios measured here actually refers to the weight of soil loss per hectare caused by rainfall within 6 hours. Associate the first soil erosion modulus to the 3600th soil erosion modulus with the first total rainfall to the 3600th total rainfall, the first rainfall duration to the 3600th rainfall duration, the first maximum rainfall intensity to the 3600th maximum rainfall intensity, and the first duration reaching the threshold to the 3600th duration reaching the threshold respectively, obtaining the first associated data to the 3600th associated data. Combine the first associated data to the 3600th associated data to get an associated data group. For example, within 6 hours of the first measurement, there is no rainfall, so the first total rainfall is 0 mm, the first rainfall duration is 0 minutes, the first maximum rainfall intensity is 0 mm, the first duration reaching the threshold is 0 minutes, and the first soil erosion modulus is 0 tons per hectare. Denote the first total rainfall, the first rainfall duration, the first maximum rainfall intensity, the first duration reaching the threshold, and the first soil erosion modulus as the first associated data. Within 6 hours of the second measurement, the second total rainfall is 12 mm, the second rainfall duration is 325 minutes, the second maximum rainfall intensity is 0.11 mm, the second duration reaching the threshold is 12 minutes, and the second soil erosion modulus is 0.288 tons per hectare. Denote the second total rainfall, the second rainfall duration, the second maximum rainfall intensity, the second duration reaching the threshold, and the second soil erosion modulus as the second associated data. And so on, obtaining the first associated data to the 3600th associated data.

[0043] Furthermore, the maximum rainfall intensity is the maximum value in the rainfall data sequence.

[0044] Exemplarily, the maximum rainfall density is the maximum value in the rainfall data sequence. Since the rainfall data sequence is the rainfall measured per minute, the maximum rainfall density is the maximum rainfall within 1 minute, with the unit of millimeter.

[0045] Further, the duration during which the rainfall density is not lower than the rainfall density threshold is the product of the number of data values in the rainfall data sequence that fall between the product of a preset proportionality coefficient and the maximum rainfall density and the maximum rainfall density, and the first time interval.

[0046] Exemplarily, taking the second rainfall data sequence as an example. The preset proportionality coefficient is 0.95. Since the maximum rainfall density in the second rainfall data sequence, that is, the second maximum rainfall density, is 0.11 millimeter, the product of the preset proportionality coefficient and the maximum rainfall density is 0.1045 millimeter. Searching in the second rainfall data sequence, it is found that there are 12 data values between 0.1045 millimeter and 0.11 millimeter (including the data endpoints), and the first time interval is 1 minute. Therefore, the duration during which the rainfall density is not lower than the rainfall density threshold is 12 minutes, that is, the second threshold - reaching duration is 12 minutes.

[0047] Further, the method for calculating the rainfall erosivity function, terrain function, and vegetation cover function using the particle swarm optimization algorithm based on the associated data group includes: Construct a soil erosion modulus equation 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: ; Among them, represents the soil erosion modulus, represents the total rainfall, represents the soil erodibility factor, represents the calculated terrain factor, represents the calculated vegetation cover factor, represents the soil and water conservation measure factor, represents the predefined deviation modulus; represents the predefined first coefficient, is an integer with values from 1 to ; among them, the terrain factor reflects the terrain characteristics of the sample scenario, and the vegetation cover factor reflects the rain - shielding degree of the vegetation in the The deviation modulus reflects the error terms between and and and and and the product terms.

[0048] The calculation formula of is: wherein, represents the initial terrain factor, represents the rainfall duration, represents the maximum rainfall density, represents the preset limit rainfall density, and the limit rainfall density is greater than the maximum value among the first maximum rainfall density to the maximum rainfall density, represents the preset limit duration, and the limit duration is greater than the aggregation time interval, represents the predefined second coefficient, represents the predefined third coefficient, represents the predefined fourth coefficient.

[0049] The calculation formula of is: wherein, represents the initial vegetation coverage factor, represents the unit step function with as the independent variable, represents the threshold duration; represents the predefined fifth coefficient,

[0050] Construct a first objective function according to the soil erosion modulus equation and the associated data set; construct a first constraint condition according to 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, use the particle swarm optimization algorithm to calculate the optimal values of the first to sixth coefficients, which are respectively denoted as to .

[0051] According to to calculate the rainfall erosivity function, terrain function, and vegetation coverage function.

[0052] Exemplarily, the soil erosion modulus in the soil erosion modulus equation represents the weight of soil loss per hectare caused by rainfall, with the unit of ton / hectare. represents the total rainfall, with the unit of millimeter. represents the soil erodibility factor, reflecting the sensitivity of the soil itself to erosion, with the unit of (ton·hour) / (MJ·mm). represents the topographic factor, reflecting the influence of topography on soil erosion, dimensionless. represents the vegetation cover factor, reflecting the influence of vegetation cover on soil erosion, dimensionless. represents the soil and water conservation measure factor, reflecting the influence of soil and water conservation measures on soil erosion. represents the first coefficient, reflecting the conversion relationship from total rainfall to rainfall energy, with the unit of MJ / (hectare·hour).

[0053] The calculation formula of in the calculation formula of is dimensionless, the unit of is minute, the unit of is minute, the unit of is 1 / mm, the unit of

[0054] The calculation formula of When the impact force of rainfall on the vegetation body is not sufficient to affect the coverage of the vegetation on the soil. When the maximum rainfall density is greater than or equal to the impact force of rainfall on the vegetation body will cause the coverage of the vegetation on the soil to decrease, and the degree of decrease is related to which will further lead to the intensification of soil erosion. In the calculation formula of the unit of is minutes, the unit of

[0055] is millimeters. ; wherein, represents the first objective function.

[0056] Further, the first constraint condition is: ; wherein, represents the lower limit of the terrain factor set in advance, is the upper limit of the terrain factor set in advance.

[0057] Further, the method for calculating the rainfall erosion force function, terrain function, and vegetation coverage function according to to includes: According to to calculate the rainfall erosion force function, terrain function, and vegetation coverage function, where the rainfall erosion force function is: ; wherein, represents the predicted total rainfall, obtained through prediction.

[0058] The terrain function is: ; wherein, represents the predicted rainfall duration, obtained through prediction; represents the predicted maximum rainfall density, obtained through prediction.

[0059] The vegetation coverage function is: ; wherein, represents the duration during which the predicted rainfall density is not lower than the rainfall density threshold, obtained through prediction.

[0060] Furthermore, the method for constructing the soil erosion modulus evaluation value function based on the rainfall erosivity function, terrain function, vegetation cover function, and evaluation factors includes: Based on the rainfall erosivity function, terrain function, vegetation cover function, and evaluation factors, a soil erosion modulus evaluation value function is constructed using the factor multiplication formula; the factor multiplication formula is: ; where represents the soil erosion modulus evaluation value function.

[0061] Exemplarily, based on meteorological data, the rainfall per minute in the next 6 hours is predicted, and then , , , can be obtained. Substituting , , , into the soil erosion modulus evaluation value function, the weight of soil loss per hectare caused by rainfall in the area to be evaluated in the next 6 hours can be obtained.

[0062] Embodiment 2: Based on the same inventive concept, as Figure 2 shown, this embodiment further provides a soil erosion degree evaluation system based on hydrological data. The system includes, connected in sequence: a first data acquisition module, a sample collection module, an optimization calculation module, and a function construction module.

[0063] 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 measure factor of the area to be evaluated.

[0064] The sample collection module is used to obtain sample data through the erosion needle method. The sample data includes an associated data group measured within a preset aggregation time interval; the data in the associated data group includes soil erosion modulus and hydrological data; the hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and the duration of rainfall density not lower than the rainfall density threshold; the soil erosion modulus represents the weight of soil loss per hectare caused by rainfall; the rainfall density threshold is preset.

[0065] The optimization calculation module is used to calculate the rainfall erosivity function, terrain function, and vegetation cover function according to the associated data group by 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 perturbation of the initial terrain factor by hydrological data. The vegetation cover function reflects the perturbation of the initial vegetation cover factor by hydrological data.

[0066] The function construction module is used to construct the soil erosion modulus evaluation value function according to the rainfall erosivity function, terrain function, vegetation cover function, and evaluation factors.

[0067] It should be noted that regarding the system in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0068] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating the degree of soil erosion based on hydrological data, characterized in that, The method includes the following steps: S1. Obtain 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 measure factor of the area to be evaluated; S2. Obtain sample data through the erosion needle method, where the sample data includes an associated data set measured within a pre-set aggregation time interval; the data in the associated data set includes soil erosion modulus and hydrological data; the hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and the duration during which the rainfall density is not lower than the rainfall density threshold; the soil erosion modulus represents the weight of soil loss per hectare caused by rainfall; the rainfall density threshold is obtained through pre-setting; S3. Calculate a rainfall erosivity function, a terrain function, and a vegetation cover function according to the associated data set 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 perturbation of hydrological data on the initial terrain factor; the vegetation cover function reflects the perturbation of hydrological data on the initial vegetation cover factor; S4. Construct a soil erosion modulus evaluation value function according to the rainfall erosivity function, the terrain function, the vegetation cover function, and the evaluation factors.

2. The method for evaluating the degree of soil erosion based on hydrological data according to claim 1, wherein, The method of obtaining sample data through the erosion needle method, where the sample data includes an associated data set measured within a pre-set aggregation time interval, includes: Construct sample scenarios according to the pre-acquired geomorphic features of the area to be evaluated; the number of the sample scenarios is denoted as , and sample scenarios are respectively denoted as the first sample scenario to the sample scenario; for each sample scenario, measure the rainfall at a pre-set first time interval to obtain the first rainfall data sequence to the rainfall data sequence; calculate the total rainfall, rainfall duration, maximum rainfall density, and duration with rainfall density not lower than the rainfall density threshold within the aggregation time interval according to the first rainfall data sequence to the rainfall data sequence, and denote them as the first total rainfall to the total rainfall, the first rainfall duration to the rainfall duration, the first maximum rainfall density to the maximum rainfall density, the first duration reaching the threshold to the duration reaching the threshold; The soil erosion modulus of sample scenarios measured by the erosion needle method is denoted as the first soil erosion modulus to the soil erosion modulus; respectively, the first soil erosion modulus to the soil erosion modulus is correlated with the first total rainfall amount to the total rainfall amount, the first rainfall duration to the rainfall duration, the first maximum rainfall density to the maximum rainfall density, the first duration to reach the threshold to the duration to reach the threshold, and the first set of correlation data to the correlation data; the first set of correlation data to the correlation data are combined to obtain a correlation data set.

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

4. The soil erosion degree evaluation method based on hydrological data according to claim 3, wherein The duration during which the rainfall density is not lower than the rainfall density threshold is the product of the number of data values in the rainfall amount data sequence that are between the pre-set proportionality coefficient multiplied by the maximum rainfall density and the maximum rainfall density and the first time interval.

5. The method for evaluating the degree of soil erosion based on hydrological data according to claim 4, characterized in that, The method of calculating a rainfall erosivity function, a terrain function, and a vegetation cover function according to the associated data set using the particle swarm optimization algorithm includes: Construct a soil erosion modulus equation according to 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: ； Among them, represents the soil erosion modulus, represents the total rainfall, represents the soil erodibility factor, represents the calculated topographic factor, represents the calculated vegetation cover factor, represents the soil and water conservation measure factor, represents the predefined deviation modulus; represents the predefined first coefficient, is an integer ranging from 1 to ; among them, the topographic factor reflects the topographic characteristics of the sample scenario, the vegetation cover factor reflects the degree of vegetation rain shelter of the sample scenario, the deviation modulus reflects the , , , , , error term between the products; The calculation formula is as follows: ； Among them, represents the initial terrain factor, represents the rainfall duration, represents the maximum rainfall density, represents a preset limit rainfall density, and the limit rainfall density is greater than the maximum value among the first maximum rainfall density to the maximum rainfall density, represents a preset limit duration, and the limit duration is greater than the aggregation time interval, represents a predefined second coefficient, represents a predefined third coefficient, represents a predefined fourth coefficient; The calculation formula is as follows: ； Among them, represents the initial vegetation coverage factor, represents the unit step function with as the independent variable, represents the duration of reaching the threshold; represents the predefined fifth coefficient, represents the predefined sixth coefficient; Construct a first objective function based on the soil erosion modulus equation and the associated data set; construct a first constraint condition based on the soil erosion modulus equation and the associated data set; with the goal of minimizing the value of the first objective function and using the first constraint condition as the constraint, use the particle swarm optimization algorithm to calculate the optimal values of the first coefficient to the sixth coefficient, which are respectively denoted as to ; According to to the rainfall erosivity function, terrain function, and vegetation cover function are calculated.

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

7. The method for evaluating the degree of soil erosion based on hydrological data according to claim 6, wherein The first constraint condition is: ； Among them, represents the lower limit of the terrain factor set in advance, is the upper limit of the terrain factor set in advance.

8. The method for evaluating the degree of soil erosion based on hydrological data according to claim 7, wherein The method for calculating the rainfall erosivity function, terrain function, and vegetation cover function according to to includes: According to to the rainfall erosivity function, topographic function, and vegetation cover function are calculated. Among them, the rainfall erosivity function is as follows: ； Among them, represents the total predicted rainfall, obtained through prediction; Terrain function is as follows: ； Among them, represents the predicted rainfall duration, obtained through prediction; represents the predicted maximum rainfall density, obtained through prediction; Vegetation cover function is as follows: ； Among them, represents the duration during which the predicted rainfall density is not lower than the rainfall density threshold, and is obtained through prediction.

9. The soil erosion degree evaluation method based on hydrological data according to claim 8, characterized in that, The method of constructing a soil erosion modulus evaluation value function according to the rainfall erosivity function, the terrain function, the vegetation cover function, and the evaluation factors includes: Construct a soil erosion modulus evaluation value function according to the rainfall erosivity function, the terrain function, the vegetation cover function, and the evaluation factors using a factor multiplication formula; the factor multiplication formula is: ； Among them, represents the function of the soil erosion modulus evaluation value.

10. A soil erosion degree assessment system based on hydrological data for performing the method according to any one of claims 1-9, characterized in that, The system includes, connected in sequence: a first data acquisition module, a sample collection module, an optimization calculation module, and a function construction module; The first data acquisition module is used to obtain 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 measure factor of the area to be evaluated; The sample collection module is used to obtain sample data through the erosion needle method. The sample data includes an associated data set measured within a preset aggregation time interval. The data in the associated data set includes the soil erosion modulus and hydrological data. The hydrological data includes total rainfall, rainfall duration, maximum rainfall density, and the duration when the rainfall density is not lower than the rainfall density threshold. The soil erosion modulus represents the weight of soil loss per hectare caused by rainfall. The rainfall density threshold is obtained through presetting. The optimization calculation module is used to calculate the rainfall erosivity function, terrain function, and vegetation cover function according to the associated data set by 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 perturbation of hydrological data on the initial terrain factor. The vegetation cover function reflects the perturbation of hydrological data on the initial vegetation cover factor. The function construction module is used to construct a soil erosion modulus evaluation value function according to the rainfall erosivity function, terrain function, vegetation cover function, and evaluation factors.

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