Agricultural and forestry compound system control method and system for preventing water and soil loss of black land

Abstract

The invention provides an agriculture and forestry composite system control method and system for preventing water and soil loss of black land, and relates to the technical field of agriculture ecological protection and intelligent control, and the method comprises the steps: arranging a sensor array in an agriculture and forestry composite region, so as to collect key element information of the black land; meteorological data and topographic data of the black land are collected through an environment sensor; constructing a water and soil loss prediction model, and performing risk assessment on water and soil loss according to the key element information, the meteorological data and the topographic data to obtain a risk level; performing irrigation compensation analysis according to the key element information to obtain an irrigation compensation amount; and based on the risk level, the irrigation compensation amount and the key element information, a control instruction is obtained through a multi-objective optimization algorithm. According to the method, closed-loop control is realized through sensor array real-time monitoring, water and soil loss dynamic risk assessment, element migration compensation analysis and multi-target optimization control, and the prevention and control accuracy of water and soil loss of the black land and the soil fertility cooperative maintenance capability are improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural ecological protection and intelligent control technology, and in particular to a control method and system for an agroforestry complex system for preventing soil erosion in black soil. Background Art

[0002] Black soil, a scarce soil resource, is crucial for ensuring food security due to its high organic matter content and fertility. However, due to long-term high-intensity farming, natural erosion, and a lack of effective conservation measures, the black soil region in Northeast my country is facing severe soil erosion.

[0003] Currently, traditional land monitoring methods rely on manual sampling, which cannot capture the dynamic changes of key elements such as nitrogen, phosphorus, potassium, and organic matter in real time. Furthermore, soil erosion risk assessments often use static models that fail to incorporate erosion mechanisms unique to black soil regions, such as freeze-thaw cycles and snowmelt runoff. Therefore, it is crucial to design a control method and system for agroforestry systems to prevent soil erosion in black soil regions. Summary of the Invention

[0004] The purpose of the present invention is to provide an agroforestry system control method and system for preventing soil erosion in black soil, which improves the accuracy of soil erosion prevention and control in black soil through real-time monitoring by sensors and dynamic risk assessment technology.

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

[0006] A method for controlling agroforestry systems to prevent soil erosion in black soil comprises the following steps:

[0007] Deploy sensor arrays in agroforestry areas to collect key element information about black soil, including nitrogen, phosphorus, potassium, organic matter, pH, water content, and salt concentration.

[0008] Environmental sensors are used to collect meteorological and topographic data of the black soil. Meteorological data include rainfall intensity, evaporation, wind erosion index, and light intensity. Topographic data include slope, aspect, surface runoff coefficient, and elevation fluctuation.

[0009] Construct a soil erosion prediction model to assess soil erosion risk based on key element information, meteorological data, and topographic data to obtain a risk level;

[0010] Conduct irrigation compensation analysis based on key element information to obtain irrigation compensation amount;

[0011] Based on the risk level, irrigation compensation amount and key element information, control instructions are obtained through a multi-objective optimization algorithm.

[0012] Optionally, a sensor array can be deployed in agroforestry areas to collect key elements of black soil, including:

[0013] The agroforestry area is divided into multiple grids of 50×50m, and a master node is set at the center of each grid;

[0014] Element information is collected at the main node through an ion selective electrode-near infrared spectroscopy composite probe to obtain nitrogen content, phosphorus content, potassium content, organic matter content and pH value;

[0015] Auxiliary nodes are set at the ridge lines and valley lines where the slope change rate is greater than 3% / 10m in the grid;

[0016] Soil information is collected at the auxiliary node through capacitive moisture sensors and conductivity sensors to obtain moisture content and salt concentration.

[0017] Optionally, the meteorological and topographic data of the black soil are collected by environmental sensors, including:

[0018] Collect three-dimensional wind speed components and calculate the wind erosion index based on the three-dimensional wind speed components;

[0019] Dynamic correction of rainfall intensity based on the relationship between radar reflectivity and rainfall rate;

[0020] A digital elevation model was constructed based on the contour lines of the black soil, and the slope and aspect were calculated using the digital elevation model.

[0021] Optionally, a soil erosion prediction model is constructed to assess soil erosion risk based on key element information, meteorological data, and topographic data to obtain a risk level, including:

[0022] Construct soil parameter regression equations based on key element information and terrain data, and solve the soil parameter regression equations to obtain soil parameter factors;

[0023] Determine soil influencing factors based on key element information and meteorological data;

[0024] Determine the multi-factor coupling erosion amount based on soil influencing factors;

[0025] Comprehensively calculate soil parameter factors and multi-factor coupled erosion to obtain risk indicators;

[0026] A risk assessment of soil and water loss in black soil is conducted based on risk indicators to obtain the risk level.

[0027] Optionally, a risk assessment of soil erosion in black soil is conducted based on risk indicators to obtain a risk level, including:

[0028] When the risk index is ≤1.5, the risk level is 1;

[0029] When 1.5<risk index≤2.5, the risk level is level 2;

[0030] When 2.5<risk index≤4.0, the risk level is level 3;

[0031] When 4.0<risk index≤6.0, the risk level is 4;

[0032] When the risk index is >6.0, the risk level is level 5.

[0033] Optionally, a soil erosion prediction model is constructed to assess soil erosion risk based on key element information, meteorological data, and topographic data to obtain a risk level, and also includes:

[0034] Temperature monitoring of black soil was conducted to obtain soil temperature;

[0035] When the soil temperature crosses 0°C three or more times, the target day is regarded as an effective freeze-thaw cycle day;

[0036] The freeze-thaw erosion enhancement coefficient is calculated by the effective freeze-thaw cycle days;

[0037] The soil shear strength attenuation rate is obtained based on the freeze-thaw erosion enhancement coefficient and water content;

[0038] Based on the soil shear strength attenuation rate and the freeze-thaw erosion calculated according to the slope and water content, the multi-factor coupled erosion amount is dynamically corrected.

[0039] The risk level is adjusted according to the difference in the multi-factor coupling erosion amount before and after dynamic correction.

[0040] Optionally, irrigation compensation analysis is performed based on key element information to obtain irrigation compensation amounts, including:

[0041] Construct element migration state equation based on key element information;

[0042] The element migration state equation is solved by the finite difference method to obtain the predicted distribution of element concentration;

[0043] The irrigation compensation amount is determined based on the predicted distribution of element concentrations.

[0044] Optionally, performing irrigation compensation analysis based on key element information to obtain irrigation compensation amounts also includes:

[0045] When the element concentration predicted distribution is less than a preset compensation threshold, a theoretical compensation amount is determined based on the element concentration predicted distribution;

[0046] The theoretical compensation amount is constrained and corrected according to the soil salt concentration to obtain the corrected compensation amount;

[0047] Based on the key element information, the correction compensation amount is mapped to the execution parameter, and the irrigation compensation amount is replaced with the execution parameter.

[0048] Optionally, based on the risk level, irrigation compensation amount, and key element information, a multi-objective optimization algorithm is used to obtain control instructions, including:

[0049] Determine the optimization objective function based on key element information;

[0050] Determine optimization constraints based on irrigation compensation amount and key element information;

[0051] Based on the optimization constraints, the optimization objective function is iteratively solved by the non-dominated sorting genetic algorithm to obtain the optimization solution;

[0052] Dynamically match the optimization plan with the risk level and generate control instructions.

[0053] An agroforestry system control system for preventing soil erosion in black soil, comprising:

[0054] Soil information collection module, used to collect key element information of black soil; key element information includes: nitrogen content, phosphorus content, potassium content, organic matter content, pH value, water content and salt concentration;

[0055] Environmental information collection module, used to collect meteorological data and topographic data of black soil; meteorological data includes: rainfall intensity, evaporation, wind erosion index and light intensity; topographic data includes: slope, slope direction, surface runoff coefficient and elevation fluctuation;

[0056] The risk assessment module is used to build a soil erosion prediction model to conduct a risk assessment of soil erosion based on key element information, meteorological data, and terrain data to obtain a risk level;

[0057] The compensation module is used to perform irrigation compensation analysis based on key element information and obtain the irrigation compensation amount;

[0058] The control module is used to obtain control instructions through a multi-objective optimization algorithm based on risk level, irrigation compensation amount and key element information.

[0059] The present invention discloses the following technical effects: a control method for an agroforestry system for preventing soil erosion in black soil, comprising: deploying a sensor array in an agroforestry area to collect key element information of the black soil; the key element information includes nitrogen content, phosphorus content, potassium content, organic matter content, pH value, water content, and salt concentration; collecting meteorological and topographic data of the black soil through environmental sensors; the meteorological data includes rainfall intensity, evaporation, wind erosion index, and light intensity; and the topographic data includes slope, aspect, surface runoff coefficient, and elevation fluctuation; constructing a soil erosion prediction model to assess the risk of soil erosion based on the key element information, meteorological data, and topographic data to obtain a risk level; performing irrigation compensation analysis based on the key element information to obtain an irrigation compensation amount; and obtaining control instructions based on the risk level, irrigation compensation amount, and key element information using a multi-objective optimization algorithm. The method achieves closed-loop control through real-time monitoring by the sensor array, dynamic soil erosion risk assessment, element migration compensation analysis, and multi-objective optimization control, thereby improving the accuracy of soil erosion prevention and control and the ability to collaboratively maintain soil fertility in black soil. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 This is a flow chart of the agroforestry system control method of the present invention;

[0062] Figure 2 This is a flow chart of soil and water loss risk assessment of the present invention;

[0063] Figure 3 This is a flow chart of the multi-objective optimization algorithm of the present invention. DETAILED DESCRIPTION

[0064] 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.

[0065] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] like Figure 1 As shown, the present invention provides an agroforestry system control method for preventing soil erosion in black soil, comprising the following steps:

[0067] Step 100: Deploy a sensor array in the agroforestry area to collect key element information of the black soil; the key element information includes: nitrogen content, phosphorus content, potassium content, organic matter content, pH value, water content, and salt concentration;

[0068] Specifically, this embodiment divides the agroforestry area into multiple grids of 50×50m, uses the Beidou RTK positioning system to calibrate the grid vertex coordinates on-site, and buries a main node base at the center point of each grid. The base is made of 304 stainless steel and extends 40cm below the surface to avoid damage from agricultural machinery operations.

[0069] On the main node base, an ion-selective electrode-near-infrared spectroscopy composite probe is vertically inserted into the soil to a depth of 40 cm. After the ion-selective electrode module is started, the ammonium ion electrode measures ammonium nitrogen with the assistance of pH buffer, the phosphate electrode eliminates the influence of soil thermal disturbance through a temperature compensation algorithm, and the potassium ion electrode directly measures the exchangeable potassium concentration; the near-infrared spectroscopy module emits a 900-1700nm band light source in pulse mode, and analyzes the organic matter content through the built-in PLS regression model. Data is collected every 2 hours to avoid the influence of soil warming, thereby obtaining the soil nitrogen content, phosphorus content, potassium content, organic matter content and pH value.

[0070] Auxiliary nodes are set up at ridge lines and valley lines in the grid where the slope change rate is greater than 3% / 10m. A two-layer sensing architecture is adopted, and capacitive moisture sensors are installed in the surface layer of 0-20cm. The dielectric constant is converted into volumetric moisture content based on the Topp formula to obtain the soil moisture content. Four-electrode conductivity sensors are deployed at a depth of 20-40cm in the soil. 1kHz alternating current is applied to avoid polarization effects to obtain the soil salt concentration.

[0071] Step 200: Collecting meteorological data and topographic data of the black soil through environmental sensors; the meteorological data includes: rainfall intensity, evaporation, wind erosion index, and light intensity; the topographic data includes: slope, aspect, surface runoff coefficient, and elevation fluctuation;

[0072] Specifically, in this embodiment, an ultrasonic anemometer array is deployed at the top of the shelterbelt 6m above the ground to collect three-dimensional wind speed components at a frequency of 10Hz. The wind erosion index F is calculated after eliminating the vibration noise of the shelterbelt through Kalman filtering. w , the calculation formula is:

[0073]

[0074] Among them, u tis the starting wind speed, N is the number of sampling points in 10 minutes, which is 6000 in the embodiment, u i is the instantaneous wind speed at the i-th sampling point. When the instantaneous wind speed u i >u t The reflectivity factor Z1 and rainfall rate R are obtained by scanning an X-band dual-polarization weather radar with a radius of 30 km and a resolution of 250 m. g At the same time, the rainfall intensity is calculated by the precipitation collected by the ground tipping bucket rain gauge, and the ZR relationship dynamic calibration equation is established to calculate the rainfall intensity R f To make corrections, the expression of the dynamic calibration equation is:

[0075]

[0076] Among them, α is the weight, which is 0.7 in this embodiment. Finally, the Beidou RTK unmanned vehicle is used to collect elevation points along the contour lines of the black soil, and a 0.5m digital elevation model DEM is generated by the Kriging interpolation method. The slope S is then calculated by the second-order central difference algorithm. d and slope A x , the calculation formulas are:

[0077]

[0078] Step 300: Construct a soil erosion prediction model to assess the risk of soil erosion based on key element information, meteorological data and terrain data to obtain a risk level; the specific steps are as follows: Figure 2 Shown, including:

[0079] Step 301: constructing a soil parameter regression equation based on key element information and terrain data, and solving the soil parameter regression equation to obtain soil parameter factors;

[0080] Specifically, soil parameter factors include the erodibility factor K k and terrain factor LS, the regression equations of the two are:

[0081] K k =0.2·e -0.3OM +0.03·Cl;

[0082]

[0083] Among them, OM is organic matter content, Cl is soil clay content, L c is the slope length, C u The curvature term is newly added and obtained from the DEM model to characterize the slope runoff effect. The regression equations are all solved using the least squares method.

[0084] Step 302: Determine soil impact factors based on key element information and meteorological data;

[0085] Specifically, this embodiment extracts NDVI from multispectral images of drones, and then uses the relationship C y =e -0.035·NDVI Converted to soil impact factor C y .

[0086] Step 303: determining the multi-factor coupled erosion amount according to the soil influencing factors;

[0087] Specifically, the multi-factor coupled erosion amount E d The calculation formula is:

[0088] E d =K k ×LS×C m ×P f ×R q ;

[0089] R q =∑(8.5+0.35·I i )·I i ;

[0090] Among them, R q is the meteorological factor, I i is the single rainfall intensity, C m is the vegetation coverage and management factor, obtained through UAV multispectral monitoring, P f is the soil and water conservation measures factor, which is 1 when there are no engineering measures on the black soil, 0.6 when the black soil is cultivated at contour level, and 0.2 when the black soil has a protective forest belt.

[0091] Step 304: Comprehensively calculate the soil parameter factors and the multi-factor coupled erosion amount to obtain a risk index;

[0092] Specifically, the calculation formula of the risk indicator RI is:

[0093]

[0094] Wherein, E0 is the permissible erosion amount of black soil, which is 200t / km in this embodiment. 2 ,OM t is the real-time organic matter content, and OM0 is the organic matter baseline value, which is 4.0% in this embodiment.

[0095] Step 305: Conduct risk assessment on soil erosion of black soil according to the risk indicators to obtain a risk level.

[0096] Specifically, when the risk index is ≤1.5, the risk level is 1;

[0097] When 1.5<risk index≤2.5, the risk level is level 2;

[0098] When 2.5<risk index≤4.0, the risk level is level 3;

[0099] When 4.0<risk index≤6.0, the risk level is 4;

[0100] When the risk index is >6.0, the risk level is level 5.

[0101] Furthermore, a soil erosion prediction model is constructed to assess the risk of soil erosion based on key element information, meteorological data, and topographic data to obtain the risk level, including:

[0102] The temperature of black soil is monitored by temperature sensors to obtain soil temperature. When the soil temperature crosses 0°C for more than or equal to 3 times, the target day is counted as an effective freeze-thaw cycle day. The freeze-thaw erosion enhancement coefficient K is calculated based on the effective freeze-thaw cycle day. f , the calculation formula is: K f =1+0.18·ln(n+1), where n is the number of effective freeze-thaw cycle days; then the soil shear strength attenuation rate τ is obtained based on the freeze-thaw erosion enhancement coefficient and water content, and the calculation formula is: τ=e -0.05θ ×(0.8+0.2K f ), where θ is the water content; then based on the soil shear strength attenuation rate, combined with the freeze-thaw erosion ΔE calculated based on the slope and water content f The dynamic correction of multi-factor coupled erosion is calculated as follows: The calculation formula of the corrected multi-factor coupled erosion is: E'=E d +K f ·ΔE f ; When E' is compared with the original multi-factor coupling erosion E d When the increase is ≥40%, the risk level will be increased by 1 level.

[0103] It is important to note that the deep fusion mechanism that couples multiple parameters, including meteorological, soil, and topographical, has enhanced the predictive accuracy of risk assessments. A dynamic correlation model between freeze-thaw cycles and soil shear strength has enabled the quantification of freeze-thaw erosion, significantly improving the efficiency and accuracy of black soil erosion predictions and providing reliable data for subsequent protective measures.

[0104] Step 400: performing irrigation compensation analysis based on key element information to obtain irrigation compensation amount;

[0105] Specifically, firstly, the element migration state equation is constructed based on the key element information Among them, C i is the i-th key element information, D i is the diffusion coefficient, which is determined by looking up the soil texture table, v is the infiltration rate, which is calculated by the capacitive water content sensor, and k c is the absorption rate, which is determined according to different actual crops, for example, corn is 0.05h -1 , which is not specifically limited in this embodiment, and z is the vertical direction; the element migration state equation is then converted into an explicit difference format discrete equation by the finite difference method, and the element concentration prediction distribution is obtained after solving it; when the nitrogen concentration in the element concentration prediction distribution is less than 96 mg / kg or the phosphorus concentration is less than 30 mg / kg, according to the formula Determine the theoretical compensation amount Q b , where C ti is the concentration threshold of the i-th element, C ii is the concentration of the i-th element, V r The root layer volume is determined according to different actual crops, for example, 3000m for corn 3 / ha, which is not specifically limited in this embodiment, η is the irrigation coefficient, which is 0.9 when drip irrigation is used and 0.75 when sprinkler irrigation is used, k l is the leaching loss coefficient, which is 1.2 when the slope is greater than 5° and 1 otherwise. Based on real-time conductivity data, an exponential decay model is used to correct the theoretical irrigation volume. When salinity exceeds the standard, the water volume is automatically reduced and additional leaching irrigation is added. The corrected water volume is then converted into executable duration, pulse mode, and fertilizer ratio parameters for the drip irrigation system.

[0106] Step 500: Based on the risk level, irrigation compensation amount and key element information, a control instruction is obtained through a multi-objective optimization algorithm. Figure 3 Shown, including:

[0107] Step 501: Determine the optimization objective function based on key element information;

[0108] Specifically, the risk level is multiplied by the risk index as the weight to obtain a negative weighted value; the deviation between the predicted element concentration distribution and the concentration threshold is minimized to obtain the nutrient balance index; the salt concentration is multiplied by the irrigation compensation amount as the weight to obtain the resource efficiency function; finally, the negative weighted value, nutrient balance index and resource efficiency function are integrated into the optimization objective function.

[0109] Step 502: Determine optimization constraints based on the irrigation compensation amount and key element information;

[0110] Specifically, the optimization constraints include physical constraints and soil safety constraints. The physical constraint is that the irrigation compensation amount must not exceed the maximum water supply capacity of the irrigation system, and the soil safety constraint is that the upper limit of salt concentration must not exceed 3.5mS / cm.

[0111] Step 503: Based on the optimization constraints, the optimization objective function is iteratively solved by a non-dominated sorting genetic algorithm to obtain an optimization solution;

[0112] Specifically, an initial solution is generated based on sensor data in a 50×50m grid. Each solution includes decision variables such as irrigation parameters and protective measures. The optimization objective function is then calculated for each solution. Pareto rankings are assigned using fast non-dominated sorting, and the crowding degree is calculated to ensure the diversity of the solution set. A genetic algorithm with a crossover probability of 0.9 and a mutation probability of 0.1 is then applied. After 100 generations of iteration, the Pareto-optimal solution set is output, resulting in the optimal solution.

[0113] Step 504: Dynamically match the optimization plan with the risk level and generate control instructions.

[0114] Specifically, for risk level 5, the optimal solution of the negative weighted value in the Pareto solution set is selected to generate the shelterbelt encryption instruction; for risk levels 3 and 4, the optimal solution of the nutrient balance index is selected to generate the precision drip irrigation instruction; for risk levels 1 and 2, the optimal solution of the resource efficiency function is selected to generate the salt precision control instruction.

[0115] The present invention also provides an agroforestry system control system for preventing soil erosion in black soil, comprising:

[0116] Soil information collection module, used to collect key element information of black soil; key element information includes: nitrogen content, phosphorus content, potassium content, organic matter content, pH value, water content and salt concentration;

[0117] Environmental information collection module, used to collect meteorological data and topographic data of black soil; meteorological data includes: rainfall intensity, evaporation, wind erosion index and light intensity; topographic data includes: slope, slope direction, surface runoff coefficient and elevation fluctuation;

[0118] The risk assessment module is used to build a soil erosion prediction model to conduct a risk assessment of soil erosion based on key element information, meteorological data, and terrain data to obtain a risk level;

[0119] The compensation module is used to perform irrigation compensation analysis based on key element information and obtain the irrigation compensation amount;

[0120] The control module is used to obtain control instructions through a multi-objective optimization algorithm based on risk level, irrigation compensation amount and key element information.

[0121] The beneficial effects of the present invention are as follows:

[0122] 1) The grid-based sensor deployment solves the data blind spot problem in complex terrain;

[0123] 2) The dynamic correction mechanism of freeze-thaw erosion coupled with soil parameters, meteorological factors, and vegetation cover factors enables the quantification of soil and water loss risk and improves the accuracy of prediction;

[0124] 3) Accurate matching of control schemes was achieved through element migration state equations and constraint conditions, reducing resource waste, improving irrigation efficiency, and avoiding fertility loss caused by over-irrigation;

[0125] 4) It specifically addresses the unique erosion mechanisms of the Northeast black soil, such as freeze-thaw cycles and slope runoff, filling the gaps in traditional static models.

[0126] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0127] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for controlling agroforestry systems to prevent soil erosion in black soil, characterized in that: The steps include: Deploy sensor arrays in agroforestry areas to collect key element information about black soil, including nitrogen, phosphorus, potassium, organic matter, pH, water content, and salt concentration. The meteorological data and topographic data of the black soil are collected by environmental sensors; the meteorological data include: rainfall intensity, evaporation, wind erosion index and light intensity; the topographic data include: slope, slope direction, surface runoff coefficient and elevation fluctuation; Constructing a soil erosion prediction model to perform a risk assessment on soil erosion based on the key element information, the meteorological data, and the terrain data to obtain a risk level; Performing irrigation compensation analysis based on the key element information to obtain an irrigation compensation amount; Based on the risk level, the irrigation compensation amount and the key element information, a control instruction is obtained through a multi-objective optimization algorithm.

2. The agroforestry system control method for preventing soil erosion in black soil according to claim 1, characterized in that: Deploy sensor arrays in agroforestry areas to collect key elements of black soil, including: Divide the agroforestry area into multiple grids of 50×50 m, and set a master node at the center point of each grid; Collecting element information at the master node using an ion selective electrode-near infrared spectroscopy composite probe to obtain the nitrogen content, the phosphorus content, the potassium content, the organic matter content, and the pH value; Auxiliary nodes are set at the ridge lines and valley lines of the grid where the slope change rate is greater than 3% / 10m; Soil information is collected at the auxiliary node through a capacitive moisture sensor and a conductivity sensor to obtain the moisture content and the salt concentration.

3. The agroforestry system control method for preventing soil erosion in black soil according to claim 1, characterized in that: The meteorological data and topographic data of the black soil are collected by environmental sensors, including: Collecting three-dimensional wind speed components and calculating a wind erosion index based on the three-dimensional wind speed components; Dynamically correcting the rainfall intensity based on the relationship between radar reflectivity and rainfall rate; A digital elevation model is constructed according to the contour lines of the black soil, and the slope and the slope direction are calculated using the digital elevation model.

4. The agroforestry system control method for preventing soil erosion in black soil according to claim 1, characterized in that: Constructing a soil and water loss prediction model to perform a risk assessment on soil and water loss based on the key element information, the meteorological data, and the terrain data to obtain a risk level, including: Constructing a soil parameter regression equation based on the key element information and the terrain data, and solving the soil parameter regression equation to obtain a soil parameter factor; Determining soil impact factors based on the key element information and the meteorological data; determining the multi-factor coupling erosion amount according to the soil influencing factors; Comprehensively calculating the soil parameter factors and the multi-factor coupled erosion amount to obtain a risk index; A risk assessment of soil and water loss on the black soil is performed based on the risk indicators to obtain the risk level.

5. The agroforestry system control method for preventing soil erosion in black soil according to claim 4, characterized in that: The risk level of soil erosion of the black soil is obtained by conducting a risk assessment based on the risk indicators, including: When the risk index is ≤1.5, the risk level is level 1; When 1.5<the risk index≤2.5, the risk level is level 2; When 2.5<the risk index≤4.0, the risk level is level 3; When 4.0<the risk index≤6.0, the risk level is level 4; When the risk index is greater than 6.0, the risk level is level 5.

6. The agroforestry system control method for preventing soil erosion in black soil according to claim 5, characterized in that: Constructing a soil and water loss prediction model to perform a risk assessment on soil and water loss based on the key element information, the meteorological data, and the terrain data to obtain a risk level, further comprising: Performing temperature monitoring on the black soil to obtain soil temperature; When the soil temperature crosses 0°C for 3 or more times, the target day is regarded as an effective freeze-thaw cycle day; The freeze-thaw erosion enhancement coefficient is obtained by calculating the effective freeze-thaw cycle days; Obtaining a soil shear strength attenuation rate according to the freeze-thaw erosion enhancement coefficient and the water content; Dynamically correcting the multi-factor coupled erosion amount based on the soil shear strength attenuation rate and the freeze-thaw erosion amount calculated according to the slope and the water content; The risk level is adjusted according to the difference in the multi-factor coupling erosion amount before and after the dynamic correction.

7. The agroforestry system control method for preventing soil erosion in black soil according to claim 1, characterized in that: An irrigation compensation analysis is performed based on the key element information to obtain an irrigation compensation amount, including: Constructing an element migration state equation according to the key element information; Solving the element migration state equation by finite difference method to obtain element concentration prediction distribution; The irrigation compensation amount is determined according to the predicted element concentration distribution.

8. The agroforestry system control method for preventing soil erosion in black soil according to claim 7, characterized in that: Performing irrigation compensation analysis based on the key element information to obtain irrigation compensation amount also includes: When the element concentration predicted distribution is less than a preset compensation threshold, determining a theoretical compensation amount according to the element concentration predicted distribution; Performing constraint correction on the theoretical compensation amount according to the salt concentration of the soil to obtain a corrected compensation amount; Based on the key element information, the correction compensation amount is mapped to an execution parameter, and the irrigation compensation amount is replaced with the execution parameter.

9. The agroforestry system control method for preventing soil erosion in black soil according to claim 1, characterized in that: Based on the risk level, the irrigation compensation amount, and the key element information, a control instruction is obtained through a multi-objective optimization algorithm, including: Determining an optimization objective function based on the key element information; Determining optimization constraints according to the irrigation compensation amount and the key element information; Based on the optimization constraints, the optimization objective function is iteratively solved by a non-dominated sorting genetic algorithm to obtain an optimization solution; Dynamically matching the optimization plan with the risk level to generate the control instruction.

10. An agroforestry system control system for preventing soil erosion in black soil, characterized in that: include: Soil information collection module, used to collect key element information of black soil; The key element information includes: nitrogen content, phosphorus content, potassium content, organic matter content, pH value, water content and salt concentration; An environmental information collection module is used to collect meteorological data and topographic data of the black soil; the meteorological data includes: rainfall intensity, evaporation, wind erosion index and light intensity; the topographic data includes: slope, slope direction, surface runoff coefficient and elevation fluctuation; a risk assessment module for constructing a soil and water loss prediction model to perform a risk assessment on soil and water loss based on the key element information, the meteorological data, and the terrain data to obtain a risk level; A compensation module, configured to perform irrigation compensation analysis based on the key element information to obtain an irrigation compensation amount; A control module is used to obtain control instructions through a multi-objective optimization algorithm based on the risk level, the irrigation compensation amount and the key element information.

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