Method and system for evaluating comprehensive benefits of saline-alkali soil improvement based on water-salt balance of a watershed

Abstract

The application provides a salt-alkali soil improvement comprehensive benefit evaluation method and system based on a watershed water salt balance, and belongs to the technical field of agricultural ecological management. The method comprises the following steps: collecting geographical zoning data, soil hydrological data, crop salt tolerance data and irrigation management data, and preprocessing; a salt migration-yield coupling model is constructed, which comprises the following steps: a salt migration dynamic equation is established, and the preprocessed data is substituted into the salt migration dynamic equation to calculate the downstream salt concentration; a yield response model is established, and the downstream salt concentration and the preprocessed data are substituted into the yield response model to calculate the yield increase of the upstream management area and the yield decrease of the downstream; and the net benefit value of the salt-alkali soil improvement is calculated according to the yield increase of the upstream management area and the yield decrease of the downstream. The method not only considers the salt increment of the soil in the management area exceeding the salt tolerance threshold, but also systematically evaluates the yield changes of the upstream and downstream, thereby providing reliable guidance for the selection and optimization of irrigation measures.

Description

Technical Field

[0001] This application relates to the field of agricultural ecological governance technology, and in particular to a method and system for evaluating the comprehensive benefits of saline-alkali land improvement based on watershed water-salt balance. Background Technology

[0002] Traditional methods of salinization involve diverting water from the Yellow River to flush out salt, which reduces salinity in the treated area in the short term. However, surface salt migrates downstream to farmland with tributaries, leading to the risk of secondary salinization.

[0003] Regarding the evaluation of improvement effects, existing technologies mostly focus on assessing the local effects within the treated area itself. For example, Chinese patent application "CN119693797A, a method and system for saline-alkali land improvement based on IoT big data analysis" establishes a comprehensive evaluation model to adjust the saline-alkali land improvement plan by monitoring data such as soil salinity, pH value, and crop growth in the target saline-alkali land. Chinese patent application "CN120198241A, a system and method for optimizing the configuration of shelterbelts in saline-alkali farmland" proposes to find the optimal shelterbelt configuration scheme by optimizing the soil salinity and groundwater level simulation model of the treated area based on deep reinforcement learning. However, these technical solutions only monitor / focus on changes in soil salinity within the treated area itself, failing to establish a quantitative coupling relationship between upstream salt migration and downstream yield loss. This results in an inaccurate assessment of the overall benefits of the treatment project and cannot provide effective guidance for the selection and optimization of irrigation measures.

[0004] Therefore, there is a need to provide an improved technical solution that addresses the shortcomings of the existing technology. Summary of the Invention

[0005] The purpose of this application is to address the lack of comprehensive assessment of upstream and downstream yield changes in existing saline-alkali land management technologies, and to provide a comprehensive benefit evaluation method and system for saline-alkali land improvement based on watershed water-salt balance. By quantifying the dynamic relationship between yield increase in the management area and yield loss downstream, this application provides a scientific basis for saline-alkali land management decisions and irrigation measure optimization.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] Firstly, this application provides a comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance, the method comprising:

[0008] Step S1, Data Acquisition and Preprocessing: Collect geographic zoning data, soil hydrological data, crop salt tolerance data, and irrigation management data, and perform preprocessing.

[0009] Step S2, constructing a salt migration-yield coupling model, including: establishing a salt migration dynamic equation and substituting the preprocessed data into the salt migration dynamic equation to calculate the downstream salt concentration; establishing a yield response model and substituting the downstream salt concentration and the preprocessed data into the yield response model to calculate the increased yield in the upstream treatment area and the decreased yield in the downstream area.

[0010] Step S3: Calculate the net benefit value of saline-alkali land improvement based on the increased production in the upstream treatment area and the decreased production in the downstream area.

[0011] In the above scheme, a dynamic equation for salt migration is established, using geographical zoning data, soil data, crop salt tolerance data, and irrigation management data as inputs. This reflects that salt migration is influenced by multiple factors, enabling the simulation of salt transport paths from the treated area to downstream areas and the calculation of downstream salt concentration. By calculating upstream yield increases (increased yield in the upstream treated area) and downstream yield decreases (reduced yield in the downstream area) through a yield response model, the change in salt concentration is correlated with agricultural output, constructing a relationship between salt migration and crop yield response. This reflects the spatial spillover effect of the treatment measures. Furthermore, the net benefit value is calculated based on upstream yield increases and downstream yield decreases, achieving an integrated expression from local treatment effects to global treatment benefits, supporting scientific decision-making.

[0012] In some possible implementations, the salt migration dynamic equation includes: a residual term, an input accumulation term, and a correction term;

[0013] The remaining term is used to calculate the contribution of the remaining salt in the upstream treatment area to the salt in the downstream area based on the initial salt concentration and salt attenuation coefficient of the treatment area, and is denoted as the attenuation contribution.

[0014] The input accumulation term is used to input the coefficient based on the salinity of the irrigation water. Salt attenuation coefficient, which is used to calculate the cumulative amount of salt transported to downstream farmland during the irrigation process in the upstream treatment area, and is recorded as the newly added salt content;

[0015] The correction term is used to calculate the salinity correction amount based on the groundwater depth;

[0016] The downstream salt concentration is calculated based on the attenuation contribution, the newly added salt content, and the salt correction amount.

[0017] Among them, the salt input coefficient of irrigation water The calculation formula is:

[0018] ;

[0019] The salt decay coefficient was determined through simulation experiments, years of observation data, and expert experience.

[0020] In the above scheme, the attenuation contribution, the added salt content, and the salt correction amount are used as common inputs to calculate the downstream salt concentration, which can be expressed as: In this equation, the attenuation contribution represents the endogenous salt residue, the newly added salt represents the exogenous input, and the salt correction represents environmental regulation. Therefore, the salt migration dynamic equation characterizes the salt migration process from three dimensions: endogenous residue, exogenous input, and environmental regulation. By coupling multiple factors, it enhances the characterization ability of the actual water and salt transport mechanism, while distinguishing the salt contribution from different causes, supporting attribution analysis and precise regulation.

[0021] In some possible implementations, the expression for the dynamic equation of salt migration is as follows:

[0022] ,

[0023] In the formula, For downstream salt concentration, The initial salt concentration in the treatment area. For irrigation cycle, This is the salt attenuation coefficient. The input coefficient for irrigation water salinity is dimensionless. This is the groundwater depth coefficient. The depth of the groundwater.

[0024] The above scheme transforms the complex water-salt transport process into a computable functional relationship through an explicit mathematical expression, supporting computer simulation and batch evaluation, and improving the engineering applicability of the method. By introducing a time dimension (irrigation cycle), the salt migration calculation acquires time-dynamic characteristics, which can be used to predict the impact of salt migration under different irrigation regimes.

[0025] In some possible implementations, the production response model includes a governance area production enhancement model and a downstream production loss model, wherein the expression for the governance area production enhancement model is as follows:

[0026] ,

[0027] The expression for the downstream production loss model is as follows:

[0028] ,

[0029] In the formula, To increase production in the upstream treatment area, As the base output of the governance area, The crop yield increase coefficient, To reduce production for downstream industries, For downstream salt concentration, This represents the salt tolerance threshold for a particular crop. The salt sensitivity index of a certain crop. The threshold for a 50% yield reduction for a particular crop. The increase in salt concentration exceeding the salt tolerance threshold, i.e. .

[0030] In the above scheme, the production response model is divided into two sub-models: the production increase model of the treatment area is used to calculate the upstream production increase caused by treatment; the downstream production loss model is used to calculate the downstream production decrease caused by salt migration. This realizes a two-way evaluation of positive production increase and negative production decrease, reflects a systematic evaluation thinking, breaks through the limitation of traditional methods that only focus on local production increase, and realizes a comprehensive evaluation of the entire basin.

[0031] In some possible implementations, step S3, calculating the net benefit value of saline-alkali land improvement based on the increased yield in the upstream treatment area and the decreased yield in the downstream area, includes:

[0032] Calculate the net increase in output based on the increased output in the upstream treatment area and the decreased output in the downstream area;

[0033] The net increase in output and the ecological benefits are weighted and summed to obtain the net benefit value, which is used to represent the quantitative result of the comprehensive benefits of saline-alkali land improvement.

[0034] In the above scheme, step S3 is divided into two sub-processes: first, calculating the net increase in output to measure the agricultural output benefits brought about by saline-alkali land management; and second, weighted summing of agricultural output benefits and ecological benefits to provide an overall evaluation integrating economic and ecological dimensions. This method not only focuses on agricultural production but also incorporates ecological impacts, achieving multi-dimensional integrated assessment and reflecting the scientific concept of systemic governance.

[0035] In some possible implementations, after step S3, an irrigation optimization triggering step is also included, which specifically includes:

[0036] The irrigation parameter optimization process is triggered when any of the following conditions are met:

[0037] The downstream salt concentration exceeds the crop's salt tolerance threshold;

[0038] The net benefit value is lower than the preset net benefit threshold.

[0039] In the above scheme, the irrigation optimization triggering step constitutes the decision-making link, extending decision support from assessment to regulation. Triggering condition one: downstream salt concentration exceeds the crop salt tolerance threshold, indicating that downstream farmland is in a salt stress risk zone, serving as an early warning signal at the ecological security level. Triggering condition two: net benefit value is lower than a preset threshold, indicating insufficient overall benefit of the governance project, reflecting the need for improvement of the governance scheme. The mechanism of triggering upon fulfilling any condition enhances system sensitivity, ensuring optimization can be initiated when ecological risks or economic inefficiencies occur. Furthermore, all of the above conditions are automatically triggered; once a problem is detected, the optimization process begins, giving the scheme closed-loop feedback capability and improving the applicability of the method in complex real-world environments.

[0040] Some possible implementation methods also include: the specific steps of irrigation optimization implementation are: adjusting one or more of the irrigation volume, irrigation cycle or irrigation water quality, and selecting the scheme that maximizes the overall net benefit by iteratively calculating the downstream salt concentration and net benefit value under different schemes.

[0041] In the above scheme, irrigation problems are identified through irrigation optimization triggering steps, and measures are changed to solve these problems through irrigation optimization implementation steps. Furthermore, an iterative calculation method is used to adjust one or more of the following: irrigation quantity, irrigation cycle, or irrigation water quality, forming a feedback loop of parameter adjustment, model recalculation, and benefit evaluation, thereby achieving automated irrigation optimization based on quantitative results.

[0042] Secondly, this embodiment provides a comprehensive benefit evaluation system for saline-alkali land improvement based on watershed water-salt balance. This system is used to implement the comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance provided in any of the above embodiments, including:

[0043] The data acquisition and preprocessing unit is configured to acquire geographic zoning data, soil hydrological data, crop salt tolerance data, and irrigation management data, and perform preprocessing.

[0044] Salt migration-yield coupling model construction unit: configured to construct a salt migration-yield coupling model, including: establishing a salt migration dynamic equation and substituting the preprocessed data into the salt migration dynamic equation to calculate the downstream salt concentration; establishing a yield response model and substituting the downstream salt concentration and the preprocessed data into the yield response model to calculate the increased yield in the upstream treatment area and the decreased yield in the downstream area.

[0045] The evaluation unit is configured to calculate the net benefit value of saline-alkali land improvement based on the increased production in the upstream treatment area and the decreased production in the downstream area.

[0046] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program / instructions thereon, characterized in that the computer program / instructions, when executed by a processor, implement the steps of the method provided in any of the above embodiments.

[0047] Fourthly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method provided in any of the above embodiments.

[0048] The technical effects of the second, third, and fourth aspects of this application can be referred to the description of the first aspect, and will not be repeated here. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the structure of a computer device provided according to some embodiments of this application.

[0050] Figure 2 This is a flowchart illustrating a comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance, provided according to some embodiments of this application.

[0051] Figure 3 This is a technical flowchart of a comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance. Detailed Implementation

[0052] The embodiments of this application will now be described with reference to the accompanying drawings.

[0053] The embodiments of this application can be applied to Figure 1 The computer devices shown may be, but are not limited to, mobile terminals such as mobile phones, tablets, handheld computers, and personal digital assistants (PDAs), smart home devices such as smart TVs and smart cameras, wearable devices such as smart bracelets, smartwatches, and smart glasses, or other desktop, laptop, notebook, ultra-mobile personal computer (UMPC), netbook, and smart screen computer devices.

[0054] like Figure 1 As shown, the computer device 200 may include one or more of the following components: a processor 201, a memory 203, a communication interface 202, and a communication bus 204. The memory 203 can be connected to the processor 201 via the bus 204. The bus can transfer data between the processor 201 and the memory 203. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0055] Processor 201 may include one or more processing cores. Processor 201 can connect to various parts within the computer device 200 using various interfaces and lines. It performs various functions of the computer device 200 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 203, and by calling data stored in memory 203. For example, processor 201 may include an application processor (AP), a modem processor, a CPU, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA), and / or a neural network processing unit (NPU). Among these, the CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed; the NPU is used to implement artificial intelligence (AI) functions; and the modem is used to handle wireless communication. Different processing units can be independent devices or integrated into one or more processors. For example, the multiple processing units shown above are all integrated into a single SoC, or the AP is a separate semiconductor chip, while other processing units are integrated into a single SoC. This application does not limit this to any particular type.

[0056] The memory 203 may include random access memory (RAM), read-only memory (ROM), or non-transitory computer-readable storage medium. The memory 203 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 203 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, such as a comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance; the data storage area may store data created based on the use of the computer device 200, such as geographic zoning data and soil hydrological data.

[0057] In addition, those skilled in the art will understand that the structure of the computer device 200 shown in the above figures does not constitute a limitation on the computer device 200. The computer device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the computer device 200 also includes components such as a microphone, speaker, radio frequency circuit, sensor, audio circuit, power supply, and Bluetooth module, which will not be described in detail here.

[0058] This embodiment provides a comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance, such as... Figure 2 As shown, the method includes the following steps:

[0059] Step S1, Data Acquisition and Preprocessing: Collect geographic zoning data, soil hydrological data, crop salt tolerance data, and irrigation management data, and perform preprocessing.

[0060] The geographic zoning data includes at least the governance area and downstream area. In addition, it may also include county-level administrative divisions. After zoning modeling, the geographic data further includes various typical geomorphic zoning areas, such as piedmont plains, central plains, and coastal plains.

[0061] Specifically, data collected from the treatment area may include, for example, initial soil salinity concentration, total irrigation water volume, and crop type (salt-tolerant crops such as Suaeda salsa and sweet sorghum). Downstream farmland data may include, for example, soil salinity thresholds, crop salt tolerance, irrigation canal distribution, and tributary water volume. Environmental data may include, for example, irrigation water salinity concentration, regional evaporation, and groundwater depth. In addition, the collected data also include soil hydrological parameters.

[0062] The initial soil salinity concentration reflects the salinity level of saline-alkali land before treatment and serves as a benchmark parameter for evaluating treatment effectiveness. It is used as input to the model to simulate salinity changes. Crop type is used to identify the planting structure of the treated area and, combined with salt tolerance, to assess yield response potential, supporting yield change assessment. Soil salinity threshold defines the upper limit of salinity that downstream farmland can tolerate; exceeding this limit may lead to yield reduction, serving as a critical standard for assessing the risk of secondary salinization. Crop salt tolerance is used to assess the growth risk of downstream crops under the influence of salt migration and is a key biological parameter connecting salt migration and yield loss. Irrigation canal distribution and tributary water volume characterize salt migration paths and hydrodynamic conditions, supporting the simulation of salt transport from the treated area to downstream areas. Irrigation water salinity concentration reflects the salt load of the irrigation water source itself and affects the initial salinity concentration of the flushed water, serving as an important boundary condition for salt input. Regional evaporation mainly affects soil moisture evaporation and salt surface accumulation processes, participating in water-salt balance calculations, especially under conditions without drainage. The depth of groundwater mainly affects the rate of salt uplift caused by capillary rise, and is a key geological and hydrological parameter that determines whether salt is prone to return to salt.

[0063] The ranges and optimal values ​​of the above parameters are based on regional surveys and engineering standards. For example, the range of soil salinity concentration is based on soil survey data; the range of irrigation water volume is based on regional statistical data; and the salinity attenuation coefficient is determined through simulation experiments, long-term monitoring data, and expert experience. The optimal values ​​(preferred values) are derived through typical regional trials and benefit comparisons. For example, the optimal irrigation volume value is derived from years of experimental data in the Yucheng demonstration area, where the net benefit is 30%–40% higher than the range boundary value. The optimal soil salinity threshold value is based on relevant research from wheat salt stress field trials (the yield reduction slope increases sharply when salinity > 0.5%). Providing the ranges of parameter values ​​is beneficial for risk control. For example, when downstream salinity exceeds the threshold, an automatic warning can be triggered to prevent secondary salinization.

[0064] By acquiring geographical zoning data, soil data, crop salt tolerance data, and irrigation management data, the comprehensiveness of the model's input parameters is ensured. This data is then preprocessed to generate the input data required for the salt migration-yield coupling model. Preprocessing improves data quality, reduces model calculation errors, and lays the foundation for the calculation of the salt migration-yield coupling model.

[0065] In this embodiment, multi-source data (such as soil, crops, hydrology, and environment) from the treatment area and downstream farmland are integrated into a single framework to jointly serve the construction of a salinity migration-yield coupling model. This model is used to quantify the migration impact and supports cross-regional, dynamic yield impact assessment, laying a data foundation for subsequent model construction and assessment.

[0066] Step S2, constructing a salt migration-yield coupling model, including: establishing a salt migration dynamic equation and substituting the preprocessed data into the salt migration dynamic equation to calculate the downstream salt concentration; establishing a yield response model (also known as a yield coupling model), substituting the downstream salt concentration and the preprocessed data into the yield response model to calculate the increased yield in the upstream treatment area and the decreased yield in the downstream area.

[0067] In this embodiment, the salinity migration dynamic equation is used to quantify the migration process of salinity from the treated area to downstream farmland. The yield response model includes a treated area yield enhancement model and a downstream yield loss model. The treated area yield enhancement model is used to calculate the yield increase in the upstream treated area based on the treated area data, reflecting the local improvement effect. The downstream yield loss model is used to combine downstream farmland data and the output of the salinity migration dynamic equation to calculate the expected downstream yield, in order to assess the negative impact of secondary salinization on the downstream area.

[0068] In some specific embodiments, the dynamic equation for salt migration, also known as the salt migration model, includes: a residual term, an input accumulation term, and a correction term. The residual term is used to calculate the contribution of the residual salt in the upstream treatment area to the salt in the downstream area based on the initial salt concentration and salt decay coefficient of the treatment area, and is denoted as the decay contribution. The input accumulation term is used to calculate the cumulative amount of salt transported from the upstream treatment area to the downstream farmland during the irrigation process based on the irrigation water salt input coefficient and the salt decay coefficient, and is denoted as the new salt content. The correction term is used to calculate the salt correction based on the groundwater depth. The downstream salt concentration is calculated by multiplying the decay contribution, the new salt content, and the salt correction.

[0069] Among them, the salt input coefficient of irrigation water The calculation formula is:

[0070] (1)

[0071] The salinity attenuation coefficient was determined through simulation experiments, years of regional observation data, and expert experience.

[0072] In this embodiment, the input data required for the dynamic equation of salt migration, such as the initial salt concentration in the treatment area and the amount of irrigation water, are all from the data of the treatment area, the downstream farmland data, or the environmental data.

[0073] The derivation process of the dynamic equation for salt migration is described below:

[0074] Step 11: Establish the physical foundation—the law of conservation of mass.

[0075] The migration of salt in the soil system follows the law of conservation of mass. Considering a control volume (a soil column per unit area), the rate of change of salt content is equal to the input rate minus the output rate:

[0076] (2)

[0077] In the formula, Soil salinity content (dimensionless, i.e., mass fraction). For salt input flux, For salt output flux, For time.

[0078] Step 12: Define the salt input and output fluxes.

[0079] Salt input flux ( The main source of salt is from irrigation water, and its flux is related to the salt concentration of the irrigation water. Related to the irrigation rate, to simplify the model, it is represented here as a time-independent constant input source. This refers to the amount of salt input per unit of time.

[0080] Salt output flux ( Salt is primarily removed through water leaching and drainage systems, assuming its removal rate is related to the current soil salinity. Proportional, that is ,in, The salt attenuation coefficient is ( This comprehensively reflects the influence of physical properties such as soil texture and drainage conditions on the rate of salt migration.

[0081] Step 13: Construct a first-order linear nonhomogeneous differential equation.

[0082] To describe the fundamental dynamics of soil salinity changes over time, the flux expression is substituted into the mass conservation equation, resulting in the following equation:

[0083] (3)

[0084] Step 14: Solve the differential equation.

[0085] Solve the first-order linear nonhomogeneous differential equation constructed in step 13. The standard solution of the equation consists of homogeneous solutions and particular solutions.

[0086] First, find the homogeneous solution: the corresponding equation Solving for: , These are the initial salinity conditions.

[0087] Then, find the particular solution: Assume that when the system reaches equilibrium, the salinity no longer changes. Then the particular solution is: .

[0088] Next, find the general solution: the sum of the homogeneous solution and the particular solution, expressed as:

[0089] (4)

[0090] Step 15: Introduce spatial scale and irrigation practices.

[0091] The general solution obtained by the above process The previous method only described salinity changes at a single point. To apply this to the assessment of migration from upstream to downstream and to correlate it with irrigation events, the following key innovative improvements were made:

[0092] (1) Introducing spatial parameters: Defined as the initial salinity (concentration) in the upstream treatment area. , will general solution Defined as elapsed time Downstream salt concentration after (a complete irrigation cycle) .

[0093] (2) Redefining input terms: Practice shows that constant input sources This is usually difficult to obtain directly through measurement; therefore, this embodiment uses a constant input source. Specifically, it is defined as "the net salt input per unit area during each irrigation cycle," a value determined by the salt concentration of irrigation water, the amount of irrigation water, and the initial salt reserves in the soil. It is dimensionless and is expressed using the irrigation water salt input coefficient. To indicate, that is, to replace Through the above improvements, the absolute flux, which is difficult to measure directly, can be... This is transformed into a relative coefficient form that can be calculated and controlled through irrigation management measures (such as water quantity and water quality). This greatly enhances the practicality of the model.

[0094] With the above improvements, the formula of the basic model evolves as follows:

[0095] (5)

[0096] Step 16: The influence of groundwater depth.

[0097] Since groundwater migrates upwards through capillary action and is a significant source of surface salt accumulation during the dry season, profoundly affecting the salt decay process, this embodiment assumes that groundwater depth has a linear effect on the net salt output rate, and introduces a groundwater depth correction factor ( To regulate salt transport, among which, The proportionality coefficient ( The groundwater depth coefficient (also known as the groundwater depth coefficient) represents the intensity of the influence of groundwater depth. The depth of groundwater (m).

[0098] Using the groundwater depth correction factor as a multiplier, the overall effect of groundwater on the aforementioned salinity migration process (i.e., attenuation coefficient) is comprehensively reflected. The overall enhancement or weakening effect of the process it represents.

[0099] Step 17: Final Model. The groundwater depth correction factor is coupled into the basic model to obtain the final dynamic equation for salinity migration, as shown below:

[0100] (6)

[0101] In the formula, The downstream soil salinity concentration (%) The initial soil salinity concentration (%) in the treatment area. The salinity attenuation coefficient (the range of values ​​is...) Optimal value ); It is the irrigation cycle (days). ); The input coefficient for irrigation water salinity is dimensionless, and its calculation formula is: (irrigation water salinity concentration × total irrigation water volume) / (initial soil salinity content in the treatment area × soil bulk density × total soil volume in the area). This is the groundwater depth coefficient; The depth of the groundwater.

[0102] In this embodiment, a dynamic equation describing the migration of salt from the treated area to downstream farmland was established through the above steps to quantify the salt migration process and its impact on downstream farmland. This equation comprehensively considers the initial salt concentration in the treated area and the salt concentration of the Yellow River water. Key parameters such as groundwater depth can reflect the decay and migration patterns of salinity over time. Among these, downstream salinity concentration... This indicates the final salt concentration in downstream farmland after salt migration, and is a key indicator for assessing the impact of mitigation measures on downstream areas, directly related to crop salt tolerance and yield loss. Initial salt concentration in the treated area. As an initiating condition for salt migration, it reflects the salinity level in the treated area and serves as the source term for salt migration; its magnitude directly affects changes in downstream salt concentration. Salt attenuation coefficient. This describes the rate at which salt decays over time during migration, reflecting the dilution or adsorption effect of salt in water flow. The value of determines the dynamic characteristics of salt migration. The larger the attenuation coefficient, the faster the salt attenuation and the smaller the impact on downstream areas. This coefficient represents the ratio of the total salt content introduced into the irrigation system to the initial total salt content of the soil in the evaluation area. This coefficient quantifies the input intensity of external irrigation water sources relative to the background soil salinity.

[0103] In the dynamic equation for salt migration, the remaining term ( This represents the decay process of the initial salt concentration in the treated area over time. Input the cumulative term ( This represents the portion of salt accumulation that occurs during irrigation using irrigation water. (Correction item) This indicates that an overall correction is made using the groundwater level depth, and this correction term incorporates the groundwater depth coefficient. This is to adjust for the enhanced salinity migration effect caused by changes in groundwater depth, and to avoid the problem that the same treatment scheme may have different benefits in different regions due to the neglect of the impact of groundwater.

[0104] The salt migration dynamic equation provided in this embodiment not only considers the decay process of the initial salt concentration in the treatment area and the cumulative process of downstream input salt, but also introduces a salt decay coefficient ( ) and the depth of groundwater ( This paper achieves a quantitative description of the salt migration process, comprehensively considers the interaction between upstream and downstream and the influence of groundwater depth (1~5m) on salt transport, and all input parameters in the equation are derived from actual monitoring data, which is operable and has application value. It can provide reliable input for subsequent yield coupling models, and thus provide a scientific basis for saline-alkali land management and irrigation decisions.

[0105] It should be noted that the Huang-Huai-Hai Plain is the largest saline-alkali land distribution area in my country, covering approximately 32,000 km². Existing saline-alkali land management benefit assessment models generally employ uniform or empirical parameters, such as a pre-set fixed salt migration rate. When applied to the large-scale saline-alkali land distribution area of ​​the Huang-Huai-Hai Plain, the wide distribution and significant differences in geography, soil, and hydrology within the saline-alkali land lead to substantial variations in the parameters affecting salt migration. For example, the salt migration rate differs by as much as 40% between Dezhou and Binzhou. Using fixed, uniform parameters can easily cause model predictions to deviate from reality. Therefore, this application also includes: constructing a regionalized parameter system to adapt to the spatial heterogeneity problem in large-scale saline-alkali land management. Specifically:

[0106] First, determine the overall parameter range of the large-scale saline-alkali land distribution area. For example, the regional parameter range of the Huang-Huai-Hai Plain is shown in Table 1. Table 1 is as follows:

[0107] Table 1. Range of regional parameters for the Huang-Huai-Hai Plain

[0108]

[0109] Secondly, geographic zoning modeling is performed, that is, the Huang-Huai-Hai Plain is divided into several types (e.g., three types) of typical geomorphic units, namely: Zone I (piedmont plain), Zone II (central plain), and Zone III (coastal plain). Based on the aforementioned overall parameter range, the selection range of spatial parameters is set for different types of geomorphic units, with the following example ranges:

[0110] Zone I (Pierre Plain): =0.06~0.08, =0.05~0.08;

[0111] Zone II (Central Plain): =0.04~0.06, =0.08~0.12;

[0112] Zone III (Coastal Plain): =0.03~0.05, =0.12~0.15.

[0113] By setting the above steps, the parameters can be dynamically adjusted according to changes in regional hydrogeological conditions, avoiding evaluation bias caused by universal parameters. In practice, researchers can quickly select the corresponding parameter range according to their region to ensure that the output of salinity migration conforms to local realities.

[0114] Based on the completion of the dynamic equation for salt migration, the production coupling model can be constructed. This production coupling model consists of two main parts: a production enhancement model for the treated area and a production loss model for the downstream area.

[0115] The yield enhancement model for the treated area includes: constructing a linear response mechanism between the increase in salt content exceeding the salt tolerance threshold and the yield increase based on the crop yield increase coefficient in the treated area; and calculating the yield increase in the upstream treated area based on the linear response mechanism and the baseline yield of the treated area. The specific expression is as follows:

[0116] (7)

[0117] in, Increase production in the upstream treatment area; The baseline output (benchmark output) for the treatment area; The crop yield increase coefficient (range: 0.5~1.2, with an optimal value of 0.8). This refers to the change in salinity upstream, i.e. .

[0118] The response of crop yield to soil salinity is a complex physiological and ecological process. The downstream yield loss model (yield response model) described in this embodiment is not based on a single theoretical formula, but rather on extensive field experimental observation data combined with crop salt tolerance physiological mechanisms. It is an empirical-mechanistic integrated model constructed through mathematical modeling, with its core function being to simulate two key biological effects:

[0119] (1) Threshold Effect: There is a critical value for the tolerance of crops to salt stress. Below this value, the yield is not affected.

[0120] (2) Diminishing Returns Effect: When the salt content exceeds the critical value, the marginal benefit of the production loss caused by the increase of salt content per unit decreases.

[0121] Based on two key biological effects, the downstream yield loss model is a piecewise function structure, using the crop salt tolerance threshold. As the decision point, when The hourly output loss is 0; when In this case, it is necessary to simulate its diminishing effect to assess the production loss, expressed as follows:

[0122] (8)

[0123] in, It is a function to be determined, describing the degree of yield loss after salt content exceeds the standard.

[0124] Furthermore, targeting To simulate the diminishing returns effect, a function needs to be constructed such that its derivative (i.e., the rate of increase in loss) decreases with increasing salinity. In this embodiment, a reciprocal function is used, and its derivation is as follows:

[0125] (1) Define excess salt content (i.e., the salt increment exceeding the crop's salt tolerance threshold): Let .

[0126] (2) Establish basic assumptions: production loss rate With excess salt It is directly proportional to, but inversely proportional to, a term representing resistance. This resistance is determined by the crop's inherent salt tolerance (the 50% yield reduction threshold). ) and the current level of coercion (using The characteristics (representations) jointly determine this.

[0127] Based on this, a differential equation can be established:

[0128] (9)

[0129] In the formula, It is a proportionality coefficient. This is due to the crop's inherent salt tolerance.

[0130] (3) Solving the differential equation: Integrating both sides of the above differential equation, we get:

[0131] (10)

[0132] in, is the integration constant. This logarithmic form is overly sensitive to low salt stress.

[0133] (4) To address the issue of excessive sensitivity under low-salt stress and to simplify the model, this embodiment creatively adopts the instantaneous rate of change as the model itself and assigns coefficients. New physical meaning (i.e., salt sensitivity index) Thus, we obtain:

[0134] (11)

[0135] The function has the following form:

[0136] when (Seamlessly connected with the segmentation point).

[0137] when ( This represents the theoretical maximum production loss rate, which is usually close to 1, or 100%.

[0138] when (therefore, The physical meaning is the excess salinity value that causes production loss to reach half of the maximum loss.

[0139] To simulate the threshold effect, a piecewise response function is used to calculate the loss, and a function of the degree of yield loss is constructed in the form of a reciprocal function. This allows the model to simulate the biological laws of crops growing normally in low-salt environments and gradually reducing yields in high-salt environments, avoiding overestimation or underestimation of losses.

[0140] (5) Introduction of regionalized crop parameters.

[0141] In this embodiment, the production response model has universality, and its core lies in the parameters. , , It is not a fixed constant, but a regionalized parameter that varies with crop type and subregion of the Huang-Huai-Hai Plain (such as the aforementioned Region I, Region II and Region III).

[0142] (Salt tolerance threshold): The critical soil salinity value at which crop yield begins to decline is determined through controlled experiments (such as greenhouse hydroponics) and field observations.

[0143] (Salt sensitivity index) and (50% yield reduction threshold): By setting up field trials with different salinity gradients in representative areas, measuring relative yields, and fitting the model using nonlinear regression methods (such as the Marquardt algorithm), the optimal parameter set for a specific crop in a specific region can be obtained.

[0144] Ultimately, the production enhancement model in the treated area and the production loss model in the downstream area together form the production coupling model, which is expressed mathematically as follows:

[0145]

[0146] (12)

[0147] in, This refers to the increase in salt content exceeding the crop's salt tolerance threshold; To reduce production for downstream industries; This represents the salt tolerance threshold for downstream crops. The salt sensitivity index of a certain crop; A 50% yield reduction threshold for a particular crop is used to represent the crop's salt tolerance characteristics. When the function outputs 0, it indicates no production loss. When this occurs, a production cut is triggered, and the production cut is calculated.

[0148] It should be noted that the baseline yield refers to the potential yield under no salt stress. For downstream salt concentration The difference between the salt tolerance threshold of a certain crop and the salt tolerance threshold of a certain crop. This represents the actual output after the salinity of the treated area decreases, reflecting the change in yield following the implementation of treatment measures. It is a quantitative indicator of the local treatment benefits; the crop yield increase coefficient. A conversion factor used to convert salt increments exceeding the salt tolerance threshold into a yield response, reflecting the relative yield increase resulting from a unit decrease in salt content. This indicates the output losses caused by excessive salt content downstream, which is the core output with a negative impact. The calculation is based on the output of the dynamic equation of salt migration. As an input variable, it reflects the salt input load brought about by upstream governance, embodies the coupling relationship between the output of the upstream governance area and the output of the downstream area, and realizes the systematic integration of the output model.

[0149] To accurately characterize the differences in physiological responses of different crops to salt stress, this embodiment further includes the introduction of regionalized crop parameters: independent salt tolerance threshold parameters are configured for each major crop in different regions to form an adaptive parameter library. Taking winter wheat, maize, and cotton as examples, the adaptive parameter library is shown in Table 2. Table 2 is as follows:

[0150] Table 2 Adaptive Parameter Library

[0151]

[0152] The parameters in Table 2 are derived from crop physiology studies and regional trial data, while in the yield coupling model represented by formula (12), all parameters ( , , All of them have The annotation indicates that the yield response value dynamically changes with crop type in the calculation, enabling the system running this method to automatically retrieve the corresponding parameters from the aforementioned parameter library based on the actual crop type planted in downstream farmland. This achieves a basic adaptive mechanism of matching parameters according to crop type. In other words, by establishing an adaptive parameter library and matching parameters according to crop type, the yield response model possesses crop type adaptive capabilities. It can scientifically quantify the yield loss under salt stress based on the physiological characteristics of different crops, significantly improving the accuracy, flexibility, and agricultural reality matching of the evaluation of saline-alkali land management benefits.

[0153] The salinity migration-yield coupling model provided in this embodiment can realize the dynamic coupling between the salinity migration process and the agricultural output response, forming a complete causal chain, enabling a scientific assessment from local treatment to the benefits of the entire watershed, and providing data support for the comprehensive benefit evaluation of saline-alkali land improvement.

[0154] Step S3: Calculate the net benefit value of saline-alkali land improvement based on the increased production in the upstream treatment area and the decreased production in the downstream area.

[0155] This step involves comprehensively comparing the increased production in the treated area with the decreased production downstream, outputting a net benefit value, quantifying the overall improvement benefits, and achieving a systematic assessment of the upstream and downstream processes.

[0156] Furthermore, in step S3, the calculation of the net benefit value specifically includes the following sub-steps:

[0157] Step S31: Calculate the net increase in output based on the increased output in the upstream treatment area and the decreased output in the downstream area. .

[0158] Step S32: The net increase in output and the ecological benefits are weighted and summed to obtain the net benefit value, which is used to represent the quantitative result of the comprehensive benefits of saline-alkali land improvement.

[0159] The formula for calculating net benefit value is as follows:

[0160] (13)

[0161] in, Net benefit value, This is the net increase in output (i.e., increased output in the upstream treatment area minus decreased output in the downstream area). This indicates the percentage of net increase in output. For ecological benefits (salt balance index). , The weights for net output value-added and ecological benefits are respectively. Preferably, The value is 0.7. The value is 0.3.

[0162] like If the improvement of saline-alkali land is successful, it indicates that the improvement of saline-alkali land is meaningful; otherwise, irrigation strategies need to be adjusted.

[0163] in, Also known as an economic benefit indicator, it is used to reflect the actual grain production increase effect of the governance plan on the whole basin scale, while taking into account the impact of upstream production increase and downstream potential production reduction. Also known as an ecological benefit index, it is used to measure the degree of salt balance in a system. The highest score is given when the salt level is close to the ideal growth value for crops. Penalties are imposed for excessively low or high salt levels, reflecting the concept of water conservation and salt control.

[0164] In some optional embodiments, after step S3, an irrigation optimization triggering step is further included, which specifically includes:

[0165] The irrigation parameter optimization process is triggered when any of the following conditions are met: the downstream salt concentration exceeds the preset salt threshold; or the net benefit value is lower than the preset net benefit threshold.

[0166] In this embodiment, the system automatically triggers the irrigation optimization decision-making process when any of the following conditions are met:

[0167] (1) Downstream salt content exceeds the standard, pseudocode is: The criterion for judgment is that the downstream salt concentration exceeds the crop's salt tolerance threshold. In other words, when the salt concentration in downstream farmland exceeds the range that crops can tolerate, it indicates that the remediation activities have posed a direct threat to downstream agricultural production. At this point, an optimization process is triggered, aiming to adjust irrigation strategies to reduce the risk of salt migration downstream and prevent secondary salinization.

[0168] (2) Net benefit critical warning, pseudocode is: , The criterion for judgment is that the net benefit value is lower than the preset net benefit threshold. The net benefit threshold is the minimum acceptable benefit level set according to the degree of salinization in different regions. For example, the net benefit threshold is 15% for the Huang-Huai-Hai Plain and 8% for severely saline-alkali areas. This trigger condition indicates that when the overall comprehensive benefit of the treatment project is lower than the set standard, it means that the current irrigation scheme has failed to achieve the expected goals in terms of both crop yield and ecology, thus triggering the optimization process.

[0169] The irrigation optimization triggering conditions, by setting two key criteria—downstream salinity exceeding the standard and net benefit below the threshold—achieve a dynamic monitoring and response mechanism for the governance process, realizing the transformation from passive assessment to proactive regulation, supporting intelligent and refined Yellow River irrigation management, and improving the scientific nature and adaptability of saline-alkali land governance.

[0170] In summary, the technical solution provided in this application links the treated area with downstream farmland through a salt migration model (dynamic equation) and couples it with a yield model (boost and loss). It establishes a correlation model between salt migration rate, crop salt tolerance threshold and yield loss to quantify the negative downstream impact of salt migration in the upstream treated area on downstream farmland. This enables a systematic assessment of upstream and downstream yield changes based on the watershed water-salt balance, effectively alleviating the problem of imbalance between local treatment and overall benefits in saline-alkali land improvement.

[0171] The following reference Figure 3 The overall process of the method provided in this embodiment will be explained again.

[0172] like Figure 3 As shown, the data input layer requires the acquisition of data including geographic zoning data, soil hydrological parameters, crop salt tolerance parameters, and irrigation management data. Next, the acquired data is used to perform watershed water and salt dynamic simulations, including obtaining the salt attenuation coefficient through simulation experiments. The benefit calculation layer includes economic benefit calculations, ecological benefit calculations, and comprehensive net benefit calculations to obtain the net benefit value of saline-alkali land improvement. Subsequently, based on the calculated net benefit value, a decision-making process is performed: if preset conditions are met, the process proceeds to the optimization decision layer, triggering the irrigation parameter optimization process; otherwise, a benefit assessment report is directly generated. The optimization decision includes steps such as adjusting management parameters, multi-scenario simulation, and comparing the optimal solution. Through optimization decision-making, the optimal decision recommendation is output.

[0173] The above solution will be illustrated below with a specific example. Taking the saline-alkali area of ​​Yucheng County, Shandong Province as an example, the implementation steps are as follows:

[0174] 1. Data input and parameter setting.

[0175] (1) Data for the treatment area are as follows:

[0176] Initial soil salinity concentration: 1.8%~2.2% (typical value 1.9%);

[0177] Annual irrigation water volume (i.e., total irrigation water volume): 600~1400 m³ / mu (optimal value 900 m³ / mu).

[0178] Crop type: Salt-tolerant cotton (salt tolerance threshold ≤ 0.6%).

[0179] (2) Downstream farmland data are as follows:

[0180] Soil salinity threshold: 0.4%~0.7% (based on wheat planting requirements, the optimal value is 0.5%).

[0181] Irrigation canal system distribution: main and tributary density 2.5 km / km²;

[0182] Tributary water volume: Average annual water diversion 3.2 × 10⁻⁶ 6 m³.

[0183] (3) Environmental data are as follows:

[0184] The salinity of the Yellow River water is 0.12%~0.25% (average 0.18%).

[0185] Regional annual evaporation: 950-1100 mm (average 1050 mm);

[0186] Groundwater level: 1.5-2.5m (average 2.0m).

[0187] 2. Regionalization parameters.

[0188] Based on the location and crop type of Yucheng County, Shandong Province, regionalization parameters were determined, as shown in Table 3. Table 3 is as follows:

[0189] Table 3 Regionalization Parameters of Yucheng County, Shandong Province

[0190]

[0191] 3. Salt migration calculation.

[0192] Substituting the parameters from Table 3 into the salt migration dynamic equation of formula (6), the downstream salt concentration is calculated as follows: 0.005129 (converted to a percentage: 0.5129%). Therefore: the downstream salt concentration is 0.5129%, exceeding the wheat's salt tolerance threshold (i.e., =0.005, which is 0.5% when expressed as a percentage.

[0193] 4. Production Coupling Analysis.

[0194] (1) Calculate the yield increase in the treated area according to formula (12). 0.0104 (converted to a percentage: 1.04%). Based on the above calculations, the output of the treated area increased by 1.04%.

[0195] (2) Due to The production reduction criteria are met, and the downstream production loss is calculated according to formula (12). , 0.01904 (converted to percentage: 1.904%).

[0196] 5. Comprehensive benefit assessment.

[0197] First, calculate the ecological benefits. As can be seen from the foregoing explanation, It is expressed as a salt balance index. Therefore, The calculation formula is as follows:

[0198] (14)

[0199] Substituting the parameters, we get: 0.9742 (converted to percentage: 97.42%).

[0200] Then, according to formula (13), the net benefit value of the Yucheng case is calculated: 28.6212%, this value represents the quantitative result of the comprehensive benefits of saline-alkali land improvement in Yucheng under the current improvement measures.

[0201] 6. Irrigation optimization.

[0202] Based on the aforementioned calculations, the downstream salt concentration The value is 0.005129, while the salt tolerance threshold for wheat is 0.5%. The downstream salt concentration exceeds the crop's salt tolerance threshold, thus triggering the irrigation parameter optimization process.

[0203] Adjust one or more of the following: irrigation volume, irrigation cycle, or irrigation water quality. Iteratively calculate the downstream salinity concentration and net benefit value under different schemes, and select the scheme that maximizes the overall net benefit as the optimal scheme. The optimization process for different irrigation volumes is shown in Table 4.

[0204] Table 4 Irrigation Optimization Process

[0205]

[0206] As shown in Table 4, the downstream salinity concentration decreases with increasing irrigation volume. However, the net benefit does not increase or decrease monotonically with increasing irrigation volume; instead, it first increases and then decreases, reaching a peak at an irrigation volume of 800 m³ / mu. Therefore, while blindly increasing irrigation volume can significantly improve the saline-alkali land improvement effect in the treated area, it leads to excessively high downstream salinity and reduced ecological benefits. Conversely, excessively low irrigation (e.g., 700 m³ / mu) controls downstream salinity concentration, but insufficient upstream treatment results in inadequate yield increase and reduced crop output. Thus, there exists an optimal irrigation range that balances yield and ecological benefits. The method provided in this application can identify the optimal irrigation strategy based on data from the treated area, downstream areas, and the environment, thereby providing a scientific basis for optimizing irrigation strategies and balancing ecological and production goals.

[0207] In summary, the method provided in this embodiment offers beneficial effects in three aspects: economic benefits, ecological benefits, and decision support. Economically, it avoids resource waste caused by indiscriminate treatment, increasing farmland output and overall income. Ecologically, it balances salt migration and water resource utilization, reducing the risk of secondary salinization. In terms of decision support, it provides quantitative indicators to guide the optimization of irrigation water volume and crop layout. This scheme not only focuses on the reduction of soil salinity in the treated area but also quantifies the negative impact of salt migration on downstream farmland by constructing a salt migration-yield coupling model. This results in a systematic evaluation of yield changes in the upstream and downstream of the watershed, enhancing the scientific rigor of the comprehensive benefit evaluation of saline-alkali land improvement and providing a scientific basis for saline-alkali land treatment decisions and irrigation measure optimization.

[0208] Based on the same inventive concept, this embodiment provides a comprehensive benefit evaluation system for saline-alkali land improvement based on watershed water-salt balance. This system is used to execute the comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance provided in any of the above embodiments, including:

[0209] The data acquisition and preprocessing unit is configured to acquire geographic zoning data, soil data, crop salt tolerance data, and irrigation management data, and perform preprocessing.

[0210] Salt migration-yield coupling model construction unit: configured to construct a salt migration-yield coupling model, including: establishing a salt migration dynamic equation and substituting the preprocessed data into the salt migration dynamic equation to calculate the downstream salt concentration; establishing a yield response model and substituting the downstream salt concentration and the preprocessed data into the yield response model to calculate the increased yield in the upstream treatment area and the decreased yield in the downstream area.

[0211] The evaluation unit is configured to calculate the net benefit value of saline-alkali land improvement based on the increased production in the upstream treatment area and the decreased production in the downstream area.

[0212] The comprehensive benefit evaluation system for saline-alkali land improvement based on watershed water-salt balance provided in this embodiment can realize the steps and processes of the comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance provided in any of the above embodiments, and achieve the same technical effect, which will not be described in detail here.

[0213] This application provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0214] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A comprehensive benefit evaluation method for saline-alkali land improvement based on watershed water-salt balance, characterized in that, include: Step S1, Data Acquisition and Preprocessing: Collect geographic zoning data, soil data, crop salt tolerance data, and irrigation management data, and perform preprocessing. Step S2, constructing a salt migration-yield response model, including: establishing a salt migration dynamic equation and substituting the preprocessed data into the salt migration dynamic equation to calculate the downstream salt concentration; establishing a yield response model and substituting the downstream salt concentration and the preprocessed data into the yield response model to calculate the increased yield in the upstream treatment area and the decreased yield in the downstream area; the expression of the salt migration dynamic equation is as follows: ， In the formula, For downstream salt concentration, The initial salt concentration in the treatment area. For irrigation cycle, This is the salt attenuation coefficient. The input coefficient is the salinity of the irrigation water. This is the groundwater depth coefficient. The depth of groundwater; The production response model includes a production enhancement model for the treated area and a downstream production loss model. The expression for the production enhancement model for the treated area is as follows: ， The expression for the downstream production loss model is as follows: ， In the formula, To increase production in the upstream treatment area, As the base output of the governance area, The crop yield increase coefficient, To reduce production for downstream industries, For downstream salt concentration, This represents the salt tolerance threshold for a particular crop. The salt sensitivity index of a certain crop. The threshold for a 50% yield reduction for a particular crop. The increase in salt concentration exceeding the salt tolerance threshold, i.e. ; Step S3, calculate the net benefit value of saline-alkali land improvement based on the increased production in the upstream treatment area and the decreased production in the downstream area, including: calculating the net increase in production based on the increased production in the upstream treatment area and the decreased production in the downstream area; The net increase in output and the ecological benefits are weighted and summed to obtain the net benefit value. The net benefit value is used to represent the quantitative result of the comprehensive benefits of saline-alkali land improvement. Specifically, the ecological benefit is the salt balance index.

2. The method according to claim 1, characterized in that, The dynamic equation for salt migration includes: a residual term, an input cumulative term, and a correction term; The remaining term is used to calculate the contribution of the remaining salt in the upstream treatment area to the salt in the downstream area based on the initial salt concentration and salt attenuation coefficient of the treatment area, and is denoted as the attenuation contribution. The input accumulation term is used to calculate the cumulative amount of salt transported from the upstream treatment area to the downstream farmland during the irrigation process based on the irrigation water salinity input coefficient and the salinity attenuation coefficient, and is recorded as the newly added salinity. The correction term is used to calculate the salinity correction amount based on the groundwater depth; The downstream salt concentration is calculated based on the attenuation contribution, the newly added salt content, and the salt correction amount. Among them, the salt input coefficient of irrigation water The calculation formula is: ； The salinity attenuation coefficient was determined through simulation experiments, years of regional observation data, and expert experience.

3. The method according to claim 1, characterized in that, Following step S3, an irrigation optimization triggering step is also included, which specifically involves: The irrigation parameter optimization process is triggered when any of the following conditions are met: The downstream salt concentration exceeds the crop's salt tolerance threshold; The net benefit value is lower than the preset net benefit threshold.

4. The method according to claim 3, characterized in that, Also includes: The specific steps for implementing irrigation optimization are as follows: adjust one or more of the following: irrigation volume, irrigation cycle, or irrigation water quality; and select the scheme that maximizes the overall net benefit by iteratively calculating the downstream salt concentration and net benefit value under different schemes.

5. A comprehensive benefit evaluation system for saline-alkali land improvement based on watershed water-salt balance, the system being used to execute the method described in any one of claims 1 to 4, comprising: The data acquisition and preprocessing unit is configured to acquire geographic zoning data, soil data, crop salt tolerance data, and irrigation management data, and perform preprocessing. Salt migration-yield response model construction unit: configured to construct a salt migration-yield response model, including: establishing a salt migration dynamic equation and substituting the preprocessed data into the salt migration dynamic equation to calculate the downstream salt concentration; establishing a yield response model and substituting the downstream salt concentration and the preprocessed data into the yield response model to calculate the increased yield in the upstream treatment area and the decreased yield in the downstream area. The expression for the dynamic equation of salt migration is as follows: ， In the formula, For downstream salt concentration, The initial salt concentration in the treatment area. For irrigation cycle, This is the salt attenuation coefficient. The input coefficient is the salinity of the irrigation water. This is the groundwater depth coefficient. The depth of groundwater; The production response model includes a production enhancement model for the treated area and a downstream production loss model. The expression for the production enhancement model for the treated area is as follows: ， The expression for the downstream production loss model is as follows: ， In the formula, To increase production in the upstream treatment area, As the base output of the governance area, The crop yield increase coefficient, To reduce production for downstream industries, For downstream salt concentration, This represents the salt tolerance threshold for a particular crop. The salt sensitivity index of a certain crop. The threshold for a 50% yield reduction for a particular crop. The increase in salt concentration exceeding the salt tolerance threshold, i.e. ; The evaluation unit is configured to calculate the net benefit value of saline-alkali land improvement based on the increased production in the upstream treatment area and the decreased production in the downstream area, including: calculating the net increase in production based on the increased production in the upstream treatment area and the decreased production in the downstream area; The net increase in output and the ecological benefits are weighted and summed to obtain the net benefit value. The net benefit value is used to represent the quantitative result of the comprehensive benefits of saline-alkali land improvement. Specifically, the ecological benefit is the salt balance index.

6. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 4.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

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