Method and system for efficiently regulating and controlling water resources in cold region under soil erosion

Abstract

The invention discloses an efficient regulation and control method and system for water resources in a cold region under soil erosion, and the method comprises the steps: collecting regional weather, soil and crop data, and dividing a space grid unit; constructing a freeze-thaw period water balance model, and simulating day-by-day soil water content and snow melting water dynamics; an irrigation-yield correlation model and an improved soil erosion model are established, and the driving effect of irrigation water quantity on crop growth and soil loss is quantified; designing a yield, water saving amount and erosion amount three-objective optimization function, generating a Pareto optimal solution set by adopting a third-generation non-dominated sorting genetic algorithm (NSGA-III), and optimizing an irrigation scheme in combination with a decision algorithm. The method has the advantages that the limitation of traditional single target regulation and control is broken through multi-model coupling, and accurate quantification of the contribution rate of the irrigation water amount in soil erosion is achieved; the water and soil loss risk of the high-gradient area is effectively reduced based on the differential regulation and control strategy of the space grid, and meanwhile the water demand of crops is guaranteed; the black soil area water resource utilization efficiency is improved, and cultivated land degradation is inhibited.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural water management, and particularly to a method and system for efficient regulation of water resources in cold regions under soil erosion. Background Art

[0002] The Northeast Black Soil Region is the most important commercial grain production base in China. Its soil has a high organic matter content and stable structure, bearing the strategic function of ensuring national food security. However, this region is located in the seasonal frozen soil zone, and the unique freeze-thaw water cycle process complicates the hydrological conditions: during the spring snowmelt period every year, the water content of the surface soil surges, exacerbating runoff formation; while the traditional irrigation system only focuses on the water supply during the crop growth period and ignores the dynamic migration law of soil water during the freeze-thaw period, resulting in a mismatch between the irrigation water allocation and the soil water storage capacity, and the long-term regional water resource utilization efficiency is lower than 50%.

[0003] At the same time, large-scale flood irrigation operations further induce soil erosion. When calculating hydraulic erosion, existing soil erosion models (such as USLE, RUSLE) generally regard rainfall as the only erosion driving force and do not quantitatively analyze the erosion contribution of irrigation water volume. Research shows that the proportion of soil erosion caused by irrigation in the Northeast irrigation area can reach 18%-32% of the total annual erosion volume. However, due to the lack of parameterization methods for irrigation erosion factors in existing models, the erosion prediction values are systematically 30% lower than the measured values. This deviation directly causes the average annual loss thickness of the black soil layer to reach 0.3-1 cm, and the soil erosion area in this region has expanded to 276,000 hectares in the past 10 years, threatening the stability of grain production capacity.

[0004] In addition, there are multiple contradictions in the water resource allocation in cold regions: 1) The conversion mechanism between soil water and groundwater during the freeze-thaw period is unclear, resulting in an excessive amount of drought-resistant irrigation water during the spring sowing period (generally exceeding 1.5 times the crop water requirement); 2) The irrigation system is disconnected from the soil erosion prevention and control goal. Although existing water-saving irrigation technologies reduce water consumption, they do not synchronously optimize erosion control parameters; 3) The multi-objective collaborative regulation model is missing, and it is impossible to achieve a balance among grain yield, water-saving benefits, and black soil protection. Therefore, there is an urgent need to develop a water resource regulation technology that integrates freeze-thaw water cycle analysis, quantitative assessment of irrigation erosion, and multi-objective optimization to break through the bottleneck problem of sustainable agricultural development in cold regions. Summary of the Invention

[0005] In view of the defects of the existing technology, the present invention provides a method and system for efficient regulation of water resources in cold regions under soil erosion.

[0006] In order to achieve the above invention objectives, the technical solutions adopted by the present invention are as follows:

[0007] A method for efficient regulation of water resources in cold regions under soil erosion, characterized by comprising the following steps:

[0008] S1: Obtain the meteorological and hydrological data, topographic data, soil type data, crop planting data, and irrigation water use coefficient of the target area;

[0009] S2: Divide the research area into spatial grid cells and identify the crop planting types of each grid;

[0010] S3: Construct a freeze-thaw cycle hydrodynamic model based on the system dynamics model and perform daily hydrological cycle simulations through the water balance equation and the snow cover mass conservation equation;

[0011] S4: Establish an irrigation water volume - crop yield relationship model, a water balance model for the crop growth period, and a soil erosion amount calculation model, where the irrigation water volume, snowmelt water volume, and evapotranspiration are used as erosion driving factors;

[0012] S5: Construct a multi-objective optimization function that includes maximizing yield, minimizing irrigation water volume, and minimizing soil erosion amount;

[0013] S6: Use the NSGA-Ⅲ algorithm for multi-objective optimization and select the optimal irrigation plan from the Pareto solution set through the TOPSIS algorithm based on the entropy weight method.

[0014] Further, in step S3:

[0015] The water balance equation during the freeze-thaw period is:

[0016] DW t = DW t-1 + P t + DM t + ΔW t - R t - E s,t - E q,t - ΔI t

[0017] where DW t is the soil water content on the t-th day; DW t-1 is the soil water content on the (t - 1)-th day; P t is the rainfall on the t-th day; DM t is the snowmelt water equivalent on the t-th day; ΔW t is the soil solid-liquid conversion amount caused by the soil temperature change on the t-th day; R t is the surface runoff on the t-th day; E s,t is the sublimation amount of snow cover and the evaporation amount of snowmelt water on the t-th day; E q,t is the evaporation amount of shallow soil on the t-th day; ΔI t is the infiltration amount between soil layers on the t-th day; t is the day ordinal number.

[0018] The snow cover mass conservation equation is:

[0019] S t = S0 + P x,t + P t - E x,t - E z,t - DM t

[0020] Wherein, S t is the snow water equivalent on the t-th day; S0 is the initial snow water equivalent; P x,t is the snowfall water equivalent on the t-th day; E x,t is the sublimation amount of snow on the t-th day; E z,t is the evaporation amount of snow water on the t-th day.

[0021] Furthermore, in the step S4:

[0022] The crop yield model adopts the Jensen model, as shown in the following formula:

[0023]

[0024] Y a is the actual crop yield; Y m is the maximum crop yield; ET ai is the actual water consumption during the i-th growth stage of the growth period; ET mi is the maximum water consumption during the i-th growth stage of the growth period; λ i is the water shortage sensitivity index during the i-th growth stage;

[0025] The soil erosion model adopts the improved RUSLE model, and the rainfall erosion factor R calculation includes the irrigation water volume parameter P i and calculates the net rainfall P n as shown in the following formula:

[0026]

[0027] P n = P + M + DM - E

[0028] In the formula, R i is the half-month rainfall erosion rate; P d12 is the average daily rainfall when the rainfall exceeds 12 mm per day; P y12 is the average annual rainfall when the daily rainfall exceeds 12 mm per day; P j is the daily rainfall exceeding 12 mm on the j-th day of the i-th half-month; P n is the net rainfall; P is the rainfall; M is the irrigation volume; DM is the snowmelt water volume; E is the evapotranspiration.

[0029] Furthermore, in the RUSLE model:

[0030] The calculation of the vegetation coverage factor C adopts the NDVI dynamic classification method, and the spatial heterogeneity expression of the vegetation coverage rate is realized through the following formula:

[0031]

[0032] In the formula, F is the vegetation coverage rate; NDVI max 、NDVI min are the NDVI values corresponding to the cumulative probabilities of 95% and 5% pixels in the study area;

[0033] The soil and water conservation factor P is differentially assigned according to the land use type, with a value of 0.01 for paddy fields and 0.4 for dry fields.

[0034] Furthermore, the multi-objective optimized irrigation scheme in step S6 includes the following steps:

[0035] A. Population initialization: Set the population size and the number of iterations of the NSGA-Ⅲ algorithm. Each individual represents a global irrigation scheme, including the daily irrigation water setting values of all grid cells;

[0036] B. Individual fitness calculation:

[0037] Middle loop: For each individual, that is, the irrigation scheme, perform iterative calculations of the water balance equation day by day according to the growth period to calculate the actual water consumption, yield, and total irrigation volume of each grid;

[0038] Erosion amount calculation: Input the total irrigation volume into the improved RUSLE model to calculate the soil erosion amount of each grid;

[0039] C. Multi-objective optimization: The outer loop performs the crossover, mutation, and selection operations of NSGA-Ⅲ, aiming at maximizing the global total yield, minimizing the total irrigation volume, and minimizing the total erosion amount, to generate the Pareto optimal solution set.

[0040] The present invention also discloses a high-efficiency water resource regulation system in cold regions under soil erosion, which can be used to implement the above-mentioned high-efficiency water resource regulation method in cold regions under soil erosion. Specifically, it includes:

[0041] Data acquisition module, used to obtain the meteorological and hydrological data, topographic data, soil type data, crop planting data, and irrigation water use coefficient of the target area;

[0042] Spatial grid division module, connected to the data acquisition module, used to divide the study area into spatial grid cells and identify the crop planting types of each grid;

[0043] Freeze-thaw cycle simulation engine, constructed based on the system dynamics model, connected to the spatial grid division module, and performing daily hydrological cycle simulation through the water balance equation and the snow mass conservation equation;

[0044] The relational model construction module includes:

[0045] The irrigation water volume - crop yield relational model is used to quantify the association between irrigation and crop yield;

[0046] The water balance model for the crop growth period is used to calculate the dynamic balance between irrigation water volume and crop water consumption;

[0047] The soil erosion amount calculation model is connected to the freeze - thaw cycle simulation engine, and uses the irrigation water volume, snowmelt water volume, and evapotranspiration as erosion driving factors to predict the soil loss amount;

[0048] The multi - objective optimization module is connected to the relational model construction module, and internally contains a multi - objective optimization function including maximizing yield, minimizing irrigation water volume, and minimizing soil erosion amount;

[0049] The optimization decision - making module configures the NSGA - Ⅲ algorithm and the TOPSIS algorithm based on the entropy weight method, and is used to perform multi - objective optimization and output the optimal irrigation plan from the Pareto solution set;

[0050] The irrigation plan output interface is connected to the optimization decision - making module, and is used to generate control instructions including irrigation time, irrigation water volume, and erosion risk level.

[0051] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above - mentioned method for efficient regulation of water resources in cold regions under soil erosion is realized.

[0052] The present invention also discloses a computer - readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above - mentioned method for efficient regulation of water resources in cold regions under soil erosion is realized.

[0053] Compared with the prior art, the advantages of the present invention are as follows:

[0054] 1. By integrating the water cycle in cold regions, crop water demand response, and soil erosion dynamics models, it breaks through the limitation of the traditional method of analyzing a single process in isolation, and realizes the coordinated optimization of water resource allocation and soil protection. This method can comprehensively consider the hydrological dynamics under freeze - thaw action, the disturbance of irrigation to soil structure, and the water demand of crop growth, and significantly improve the scientificity and systematicness of the regulation plan.

[0055] 2. Incorporating human activities such as irrigation and snowmelt and natural factors into the soil erosion assessment system together makes up for the deficiency of the prior art in quantifying soil and water loss caused by irrigation. Through multi - factor coupling analysis, high - risk areas are accurately identified, providing a theoretical basis for formulating targeted prevention and control strategies.

[0056] 3. Based on the multi-objective optimization algorithm, while ensuring food production capacity, it effectively balances water-saving benefits and soil protection needs, and solves the contradiction between resource waste and ecological damage in traditional irrigation systems. This method can automatically generate an optimal solution set that takes into account yield, water conservation, and environmental protection, and supports decision-makers to flexibly select implementation plans according to actual needs.

[0057] 4. Through high-resolution spatial grid simulation and visualization technology, it realizes the refined management of regional water resources and soil erosion. According to the soil characteristics, slopes, and crop types of different geographical units, irrigation parameters are configured differentially to avoid resource misallocation problems caused by "one-size-fits-all" regulation.

[0058] 5. By inhibiting irrigation-driven soil erosion, it slows down the degradation rate of the black soil layer and improves the sustainable production capacity of cultivated land. Long-term application can maintain the stability of soil structure and organic matter content, laying a foundation for enhancing the resilience of the agricultural ecosystem in cold regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flowchart of the method for efficiently regulating water resources in cold regions under soil erosion according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples.

[0061] As Figure 1 shown, the present invention provides a method for efficiently regulating water resources in cold regions under soil erosion, including the following steps:

[0062] S1: Collection of basic data: Collect meteorological and hydrological data (including daily rainfall, temperature, wind speed, evapotranspiration) for more than 10 years in the target area, DEM terrain data, spatial distribution data of soil types (including sand, silt, clay content, and organic carbon content), crop planting structure data (rice / corn / soybean planting area and growth period), and irrigation water use coefficient. The data sources include meteorological station observation data, MODIS remote sensing images, and soil census databases, and the station data is spatially interpolated to a 1 km grid using the Kriging interpolation method.

[0063] S2: Use ArcGIS software to divide the study area into 1 km × 1 km spatial grids, and based on the NDVI time series data of Sentinel-2 remote sensing images, use the random forest classification algorithm to identify the crop types in each grid and output the spatial distribution raster of paddy fields and dry fields.

[0064] S3: Grid point freeze-thaw cycle simulation: Distinguish the soil types within the grid, and through the water balance equation and mass conservation equation during the melting period, use relevant system dynamics models to conduct hydrological cycle simulation. The specific formula is as follows:

[0065] DM t = DM t-1 + P t + DM t + ΔW t - R t - E s,t - E q,t - ΔI t (1)

[0066] Among them, DW t is the soil water content on the t-th day, in mm; DW t-1 is the soil water content on the (t - 1)-th day, in mm; P t is the rainfall on the t-th day, in mm; DM t is the snowmelt water equivalent on the t-th day, in mm; ΔW t is the amount of soil solid-liquid conversion caused by soil temperature change on the t-th day, in mm; R t is the surface runoff on the t-th day, in mm; E s,t is the sublimation amount of snow cover and evaporation amount of snowmelt water on the t-th day, in mm; E q,t is the evaporation amount of shallow soil on the t-th day, in mm; ΔI t is the infiltration amount between soil layers on the t-th day, in mm; t is the day sequence number.

[0067] During the freeze-thaw period, the water cycle considers the mass conservation among snowfall, snow cover, snowmelt, evaporation and sublimation:

[0068] S t = S0 + P x,t + P t - E x,t - E z,t - DM t (2)

[0069] Among them, S t is the snow cover water equivalent on the t-th day, in mm; S0 is the initial snow cover water equivalent, in mm; P x,t is the snowfall water equivalent on the t-th day, in mm; E x,t is the sublimation amount of snow cover on the t-th day, in mm; E z,t is the evaporation amount of snowmelt water on the t-th day, in mm.

[0070] S4: Quantify the relationships between irrigation water volume and yield, and between irrigation water volume and soil erosion:

[0071] (1) Use the Jensen model to quantify the relationship between irrigation water volume and crop yield.

[0072]

[0073] Y a is the actual crop yield, kg / hm 2 ; Y m is the maximum crop yield, kg / hm 2 ; ET ai is the actual water consumption during the i-th growth stage of the growth period, mm; ET mi is the maximum water consumption during the i-th growth stage of the growth period, mm; λ i is the water shortage sensitivity index during the i-th growth stage.

[0074] (2) Use the water balance equation to determine the irrigation quota during the crop growth period, and calculate the actual irrigation water consumption through the irrigation water use coefficient to ensure that the irrigation water supply does not exceed the regional available water resources.

[0075] ① Water balance equation for paddy fields during the growth period:

[0076] h t = h t-1 + P + M - ET a - S - d (4)

[0077] h t The water depth on the t-th day, mm; h t-1 The water depth on the (t - 1)-th day, mm; P is the rainfall on the t-th day, mm; M is the irrigation quota on the t-th day, mm; ET ai is the actual water consumption on the t-th day, mm; S is the seepage on the t-th day, mm; d is the drainage on the t-th day, mm.

[0078] The deep seepage of paddy field irrigation is obtained through the following formula:

[0079]

[0080] In the formula, τ is the drainage characteristic parameter; θ SAT is the soil saturation water content, m 2 / m 3 ; θ FC is the soil field water holding capacity, m 2 / m 3 ; θ i is the actual soil water content on the day, m 2 / m 3 ; Δz is the soil profile thickness, mm.

[0081] ② Water balance equation for dryland crops during the growth period:

[0082] W t= W t-1 + W T + P + K + M - ET a (6)

[0083] W t The planned water storage in the wetting layer on the t-th day, mm; W t-1 The planned water storage in the wetting layer on the (t - 1)-th day, mm; P is the rainfall on the t-th day, mm; K is the groundwater utilization on the t-th day, mm; M is the irrigation amount on the t-th day, mm; ET a is the crop water consumption on the t-th day, mm.

[0084] ③ The reference crop water requirement is obtained through the Penman-Monteith formula. The specific calculation formula is as follows:

[0085]

[0086] In the formula, Δ is the slope of the saturated vapor pressure curve, kPa / °C; R n is the net radiation at the crop surface, MJ; G is the soil heat flux, MJ / (m 2 ·day); γ is the humidity calculation constant, kPa / °C; T is the air temperature at a height of 1.5 - 2.5 m, °C; u2 is the wind speed at a height of 2 m, m / s; e s is the saturated vapor pressure, kPa; e a is the actual vapor pressure, kPa.

[0087] (3) The soil erosion amount is obtained using the RUSLE model:

[0088] A = R·K·L·S·C·P (8)

[0089] In the formula, A is the soil erosion modulus, t / (hm 2 ·a); R is the rainfall erosion factor, MJ·mm / (hm 2 ·h·a); K is the soil erodibility factor, t·h / (MJ·mm); L, S are the slope length and slope factors; C is the vegetation cover and management factor; P is the soil and water conservation factor. The acquisition of each parameter is as follows:

[0090] ① Rainfall erosion factor R:

[0091]

[0092] P n = P + M + DM - E (10)

[0093] In the formula, R i is the half-month rainfall erosion rate; P d12 is the average daily rainfall when the rainfall exceeds 12 mm per day, mm; P y12Annual average rainfall on days with rainfall exceeding 12 mm per day, mm; P j Daily rainfall exceeding 12 mm on the j-th day of the i-th half-month, mm; P n Net rainfall, mm; P is rainfall, mm; M is irrigation volume, mm; DM is snowmelt water volume, mm; E is evapotranspiration volume, mm.

[0094] ② Soil erodibility factor K:

[0095]

[0096] S a 、S i 、C i and C a are the percentages of sand, silt, clay, and organic carbon content in the topsoil, %; S n =1 - S a / 100.

[0097] ③ Slope length and slope factors L, S:

[0098]

[0099] L=(λ / 22.13) m (13)

[0100]

[0101] θ is the extracted slope value data, with the unit of °; λ is the horizontal projected slope length, m.

[0102] ④ Vegetation cover and management factor C:

[0103]

[0104] F is the vegetation coverage rate, %; NDVI max 、NDVI min are the NDVI values corresponding to the cumulative probabilities of 95% and 5% pixels in the study area.

[0105] ⑤ Soil and water conservation factor P: Determined according to land use types, where forest land, bare land, saline land, and sandy land are assigned a value of 1; construction land and water bodies are assigned a value of 0; dry farmland is 0.4; paddy field is 0.01.

[0106] S5: Construct an optimization objective function: Based on the freeze-thaw simulation, construct an optimization model with the maximization of yield (max Y), the minimization of water volume (min M), and the minimization of soil erosion amount (min A).

[0107] S6: Embed the objective function into the NSGA-Ⅲ optimization algorithm for multi-objective optimization: Take the irrigation water volume interval of a single irrigation as the decision-making variable, perform daily water volume cycling iteration during the crop growth period, and optimize the irrigation time and water volume.

[0108] The process of multi-objective optimization includes the following steps:

[0109] A. Population initialization: Set the population size (200 - 500 individuals) and the number of iterations (300 - 1000 times) of the NSGA-Ⅲ algorithm. Each individual represents a global irrigation plan, including the daily irrigation water volume setting values of all grid cells.

[0110] B. Calculation of individual fitness:

[0111] Middle loop: For each individual (irrigation plan), perform iterative calculation of the water balance equation day by day during the growth period, and calculate the actual water consumption ET a , yield Y a and total irrigation volume M of each grid;

[0112] Calculation of erosion amount: Input the irrigation volume M into the improved RUSLE model to calculate the soil erosion amount A of each grid;

[0113] C. Multi-objective optimization:

[0114] The outer loop performs the crossover, mutation, and selection operations of NSGA-Ⅲ, aiming to maximize the global total yield ∑Y a , minimize the total irrigation volume ∑M, and minimize the total erosion amount ∑A, and generate a Pareto optimal solution set;

[0115] Determine the index weights of yield, irrigation volume, and erosion amount based on the entropy weight method, and use the TOPSIS algorithm to find the optimal solution.

[0116] In another embodiment of the present invention, a high-efficiency water resource regulation system in cold regions under soil erosion is provided. This system can be used to implement the above-mentioned high-efficiency water resource regulation method in cold regions under soil erosion. Specifically, it includes:

[0117] A data acquisition module for obtaining meteorological and hydrological data, topographic data, soil type data, crop planting data, and irrigation water use coefficient of the target area;

[0118] A spatial grid division module, connected to the data acquisition module, for dividing the research area into spatial grid cells and identifying the crop planting types of each grid;

[0119] A freeze-thaw cycle simulation engine, constructed based on the system dynamics model, connected to the spatial grid division module, and performing daily hydrological cycle simulation through the water balance equation and the snow mass conservation equation;

[0120] The relationship model construction module includes:

[0121] An irrigation water volume - crop yield relationship model, which is used to quantify the association between irrigation and crop yield;

[0122] A water balance model for the crop growth period, which is used to calculate the dynamic balance between irrigation water volume and crop water consumption;

[0123] A soil erosion amount calculation model, which is connected to the freeze - thaw cycle simulation engine, and uses irrigation water volume, snowmelt water volume and evapotranspiration as erosion driving factors to predict soil loss amount;

[0124] A multi - objective optimization module, which is connected to the relationship model construction module, and internally contains a multi - objective optimization function including maximizing yield, minimizing irrigation water volume and minimizing soil erosion amount;

[0125] An optimization decision - making module, which configures the NSGA - Ⅲ algorithm and the TOPSIS algorithm based on the entropy weight method, and is used to perform multi - objective optimization and output the optimal irrigation plan from the Pareto solution set;

[0126] An irrigation plan output interface, which is connected to the optimization decision - making module, and is used to generate a control instruction including irrigation time, irrigation water volume and erosion risk level.

[0127] In another embodiment of the present invention, a terminal device is provided. The terminal device includes a processor and a memory. The memory is used to store a computer program. The computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention may be used for the operation of the method for efficient regulation of water resources in cold regions under soil erosion.

[0128] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by the processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0129] One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for efficient regulation of water resources in cold regions under soil erosion in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0130] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0131] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0132] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0134] Those of ordinary skill in the art will appreciate that the embodiments described herein are provided to assist the reader in understanding the implementation of the present invention and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations without departing from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for efficient regulation of water resources in cold regions under soil erosion, characterized in that, It includes the following steps: S1: Obtain the meteorological and hydrological data, topographic data, soil type data, crop planting data, and irrigation water use coefficient of the target area; S2: Divide the research area into spatial grid units and identify the crop planting types of each grid; S3: Build a freeze-thaw cycle hydrodynamic model based on the system dynamics model, and conduct daily hydrological cycle simulations through the water balance equation and the snow cover mass conservation equation; S4: Establish an irrigation water volume - crop yield relationship model, a crop growth period water balance model, and a soil erosion amount calculation model, where the irrigation water volume, snowmelt water volume, and evapotranspiration are used as erosion driving factors; S5: Build a multi-objective optimization function that includes maximizing yield, minimizing irrigation water volume, and minimizing soil erosion amount; S6: Use the NSGA-Ⅲ algorithm for multi-objective optimization, and select the irrigation scheme from the Pareto solution set through the TOPSIS algorithm based on the entropy weight method.

2. The method for efficient regulation of water resources in cold regions under soil erosion according to claim 1, characterized in that: In the said step S3: The water balance equation during the freeze-thaw period is: DW t = DW t-1 + P t + DM t + ΔW t - R t - E s,t - E q,t - ΔI t Among them, DW t is the soil water content on the t-th day; DW t-1 is the soil water content on the (t - 1)-th day; P t is the rainfall on the t-th day; DM t is the snowmelt water equivalent on the t-th day; ΔW t is the amount of soil solid-liquid conversion caused by the soil temperature change on the t-th day; R t is the surface runoff on the t-th day; E s,t is the sublimation amount of snow cover and the evaporation amount of snowmelt water on the t-th day; E q,t is the evaporation amount of shallow soil on the t-th day; ΔI t is the infiltration amount between soil layers on the t-th day; t is the day number; The snow cover mass conservation equation is: S t = S0 + P x,t + P t - E x,t - E z,t - DM t Among them, S t is the snow water equivalent on the t-th day; S0 is the initial snow water equivalent; P x,t is the snowfall water equivalent on the t-th day; E x,t is the snow sublimation amount on the t-th day; E z,t is the snowmelt evaporation amount on the t-th day.

3. The method for efficient regulation of water resources in cold regions under soil erosion according to claim 1, wherein: In the said step S4: The crop yield model adopts the Jensen model, as follows: Y a is the actual crop yield; Y m is the maximum crop yield; ET ai is the actual water consumption during the i-th growth stage of the growth period; ET mi is the maximum water consumption during the i-th growth stage of the growth period; λ i is the water shortage sensitivity index during the i-th growth stage; The soil erosion model uses the improved RUSLE model, and the irrigation water volume parameter P is included in the calculation of the rainfall erosion factor R i , and the net rainfall P is calculated n , as shown in the following formula: P n = P + M + DM - E Wherein, R i is the half-month rainfall erosion rate; P d12 is the average daily rainfall when the rainfall exceeds 12 mm per day; P y12 is the average annual rainfall when the daily rainfall exceeds 12 mm per day; P j is the daily rainfall exceeding 12 mm on the j-th day of the i-th half-month; P n is the net rainfall; P is the rainfall; M is the irrigation amount; DM is the snowmelt water volume; E is the evapotranspiration amount.

4. The method for efficient regulation of water resources in cold regions under soil erosion according to claim 3, characterized in that: In the said RUSLE model: The calculation of the vegetation cover factor C adopts the NDVI dynamic classification method, and the spatial heterogeneity expression of the vegetation coverage rate is realized through the following formula: where F is the vegetation coverage rate; NDVI max , NDVI min are the NDVI values corresponding to the cumulative probabilities of 95% and 5% pixels within the study area; The soil and water conservation factor P is differentially assigned according to the land use type, with a value of 0.01 for paddy fields and 0.4 for dry fields.

5. The method for efficient regulation of water resources in cold regions under soil erosion according to claim 1, characterized in that: The irrigation scheme for multi-objective optimization in the said step S6 includes the following steps: A. Population initialization: Set the population size and iteration times of the NSGA-Ⅲ algorithm. Each individual represents a global irrigation scheme, including the daily irrigation water volume setting values of all grid units; B. Individual fitness calculation: Middle loop: For each individual, that is, the irrigation scheme, perform iterative calculations of the water balance equation day by day according to the growth period, and calculate the actual water consumption, yield, and total irrigation volume of each grid; Erosion amount calculation: Input the total irrigation volume into the improved RUSLE model to calculate the soil erosion amount of each grid; C. Multi-objective optimization: The outer loop performs the crossover, mutation, and selection operations of NSGA-Ⅲ, aiming at maximizing the global total yield, minimizing the total irrigation volume, and minimizing the total erosion amount, and generates the Pareto optimal solution set.

6. An efficient water resource regulation system in cold regions under soil erosion, characterized in that: This system can be used to implement the method for efficient regulation of water resources in cold regions under soil erosion described in any one of claims 1 to 5. Specifically, it includes: A data acquisition module, which is used to obtain the meteorological and hydrological data, topographic data, soil type data, crop planting data, and irrigation water use coefficient of the target area; A spatial grid division module, connected to the data acquisition module, which is used to divide the research area into spatial grid units and identify the crop planting types of each grid; A freeze-thaw cycle simulation engine, built based on the system dynamics model, connected to the spatial grid division module, and conducts daily hydrological cycle simulations through the water balance equation and the snow cover mass conservation equation; A relationship model construction module, including: An irrigation water volume - crop yield relationship model, which is used to quantify the relationship between irrigation and crop yield; A crop growth period water balance model, which is used to calculate the dynamic balance between irrigation water volume and crop water consumption; A soil erosion amount calculation model, connected to the freeze-thaw cycle simulation engine, predicts soil loss by using irrigation water volume, snowmelt water volume, and evapotranspiration as erosion driving factors; A multi-objective optimization module, connected to the relationship model construction module, and internally contains a multi-objective optimization function including maximizing yield, minimizing irrigation water volume, and minimizing soil erosion amount; An optimization decision-making module, configured with the NSGA-Ⅲ algorithm and the TOPSIS algorithm based on the entropy weight method, is used to perform multi-objective optimization and output the optimal irrigation plan from the Pareto solution set; An irrigation plan output interface, connected to the optimization decision-making module, is used to generate a control instruction including irrigation time, irrigation water volume, and erosion risk level.

7. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method for efficient regulation of water resources in cold regions under soil erosion according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by the processor, it implements the method for efficient regulation of water resources in cold regions under soil erosion according to any one of claims 1 to 5.

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